Review of China's land surface quantitative remote sensing development in 2019
- Vol. 24, Issue 6, Pages: 618-671(2020)
Published: 07 June 2020
DOI: 10.11834/jrs.20209476
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Published: 07 June 2020 ,
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梁顺林,白瑞,陈晓娜,程洁,范闻捷,何涛,贾坤,江波,蒋玲梅,焦子锑,刘元波, 倪文俭, 邱凤, 宋柳霖, 孙林, 唐伯惠, 闻建光, 吴桂平, 谢东辉, 姚云军, 袁文平, 张永光, 张玉珍, 张云腾, 张晓通, 赵天杰, 赵祥.2020.2019年中国陆表定量遥感发展综述.遥感学报,24(6): 618-671
LIANG Shunlin,BAI Rui,CHEN Xiaona,CHENG Jie,FAN Wenjie,HE Tao,JIA Kun,JIANG Bo,JIANG Lingmei,JIAO Ziti,et al. 2020. Review of China's land surface quantitative remote sensing development in 2019. Journal of Remote Sensing(Chinese),24(6): 618-671
为了更好地了解中国定量遥感的发展态势和加强同行之间的信息交流,根据中国学者2019年发表的SCI检索论文和部分中文论文,对陆表定量遥感的核心进展进行了总结,涉及数据预处理(云及其阴影识别,大气与地形校正)、陆表辐射传输建模、不同变量的反演方法、产品生产评价与精度验证,以及相关应用等内容。陆表变量产品较多,本文概要介绍了反射率、下行太阳辐射、反照率、地表温度、长波辐射、总净辐射、荧光遥感、植被生化参数、叶面积指数、光合有效辐射比、植被覆盖度、森林高度、森林生物量、植被生产力、土壤水分、雪水当量、雪盖、蒸散发、地表与地下水量等最新进展,也一并介绍了2019年与定量遥感相关的科研项目、学术交流会与暑假培训班等内容。
In order to better understand the development of quantitative remote sensing in China and strengthen the exchange of information among peers
this paper summarizes the core parts of quantitative remote sensing over land surface based on the SCI (Scientific Citation Index) indexed papers and some Chinese papers published by Chinese scholars in 2019
including pre-processing methods (cloud and shadow detection
atmospheric correction and terrain correction
etc.); land surface radiative transfer modeling;inversion methods;tproduct production
evaluation,accuracy validation and applications. Surface products include directional reflectance
downward solar radiation
albedo
surface temperature
long wave radiation
net radiation
fluorescence
remote sensing vegetation biochemical parameters
leaf area index
fraction of the absorbed photosynthetic active radiation
vegetation coverage
forest height
forest biomass
vegetation productivity
soil moisture
snow water equivalent
snow cover
evaporation
surface and underground water
etc. The related research projects
professional symposium and summer training courses are also introduced.
定量遥感陆表综述中国
quantitative remote sensinglandreviewChina
Adhikari H, Heiskanen J, Maeda E E and Pellikka P K E. 2016. The effect of topographic normalization on fractional tree cover mapping in tropical mountains: an assessment based on seasonal Landsat time series. International Journal of Applied Earth Observation and Geoinformation, 52: 20-31 [DOI: 10.1016/j.jag.2016.05.008http://dx.doi.org/10.1016/j.jag.2016.05.008]
Bai L L, Long D and Yan L. 2019. Estimation of surface soil moisture with downscaled land surface temperatures using a data fusion approach for heterogeneous agricultural land. Water Resources Research, 55(2): 1105-1128 [DOI: 10.1029/2018WR024162http://dx.doi.org/10.1029/2018WR024162]
Bei X Y, Yao Y J, Zhang L L, Xu T R, Jia K, Zhang X T, Shang K, Xu J and Chen X W. 2019. Long-term spatiotemporal dynamics of terrestrial biophysical variables in the three-river headwaters region of China from satellite and meteorological datasets. Remote Sensing, 11(14): 1633 [DOI: 10.3390/rs11141633http://dx.doi.org/10.3390/rs11141633]
Bento V A, DaCamara C C, Trigo I F, Martins J P A and Duguay-Tetzlaff A. 2017. Improving Land Surface Temperature Retrievals over Mountainous Regions. Remote Sensing, 9(1): 38 [DOI: https://doi.org/10.3390/rs9010038http://dx.doi.org/https://doi.org/10.3390/rs9010038]
Bishop M P and Colby J D. 2011. Topographic Normalization of Multispectral Satellite Imagery//Singh V P, Singh P and Haritashya U K, eds. Encyclopedia of Snow, Ice and Glaciers. Dordrecht: Springer: 1187-1196 [DOI: 10.1007/978-90-481-2642-2_664http://dx.doi.org/10.1007/978-90-481-2642-2_664]
Blair J B, Rabine D L and Hofton M A. 1999. The Laser Vegetation Imaging Sensor: a medium-altitude, digitisation-only, airborne laser altimeter for mapping vegetation and topography. ISPRS Journal of Photogrammetry and Remote Sensing, 54(2/3): 115-122 [DOI: 10.1016/S0924-2716(99)00002-7http://dx.doi.org/10.1016/S0924-2716(99)00002-7]
Brunt D. 1932. Notes on radiation in the atmosphere. I. Quarterly Journal of the Royal Meteorological Society, 58(247): 39-420 [DOI: 10.1002/qj.49705824704http://dx.doi.org/10.1002/qj.49705824704]
Brutsaert W. 1975. On a derivable formula for long‐wave radiation from clear skies. Water Resources Research, 11(5): 742-744 [DOI: 10.1029/WR011i005p00742http://dx.doi.org/10.1029/WR011i005p00742]
Bryant R G and Rainey M P. 2002. Investigation of flood inundation on playas within the Zone of Chotts, using a time-series of AVHRR. Remote Sensing of Environment, 82(2/3): 360-375 [DOI: 10.1016/S0034-4257(02)00053-6http://dx.doi.org/10.1016/S0034-4257(02)00053-6]
Calmant S, Seyler F and Cretaux J F. 2008. Monitoring continental surface waters by satellite altimetry. Surveys in Geophysics, 29(4/5): 247-269 [DOI: 10.1007/s10712-008-9051-1http://dx.doi.org/10.1007/s10712-008-9051-1]
Cao M Q, Chen Y Z, Wang X Q and Ding J C. 2019. Temporal and spatial variation of vegetation coverage in Tarim River Basin//Proceedings of IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium. Yokohama: IEEE: 6614-6617 [DOI: 10.1109/IGARSS.2019.8898201http://dx.doi.org/10.1109/IGARSS.2019.8898201]
Carabajal C C and Harding D J. 2005. ICESat validation of SRTM C-band digital elevation models. Geophysical Research Letters, 32(22): L22S01 [DOI: 10.1029/2005GL023957http://dx.doi.org/10.1029/2005GL023957]
Carmona F, Rivas R and Caselles V. 2014. Estimation of daytime downward longwave radiation under clear and cloudy skies conditions over a sub-humid region. Theoretical and Applied Climatology, 115(1/2): 281-295 [DOI: 10.1007/s00704-013-0891-3http://dx.doi.org/10.1007/s00704-013-0891-3]
Chai D F, Newsam S, Zhang H K, Qiu Y F and Huang J F. 2019. Cloud and cloud shadow detection in Landsat imagery based on deep convolutional neural networks. Remote Sensing of Environment, 225: 307-316 [DOI: 10.1016/j.rse.2019.03.007http://dx.doi.org/10.1016/j.rse.2019.03.007]
Chambers D P, Wahr J and Nerem R S. 2004. Preliminary observations of global ocean mass variations with GRACE. Geophysical Research Letters, 31(13): L13310 [DOI: 10.1029/2004GL020461http://dx.doi.org/10.1029/2004GL020461]
Chang Y X, Jiao Z T, Dong Y D, Zhang X N, He D D, Yin S Y, Cui L and Ding A X. 2019. Parameterization and correction of hotspot parameters of Ross-Li kernel driven models on POLDER dataset. Journal of Remote Sensing, 23(4): 661-672
常雅轩, 焦子锑, 董亚冬, 张小宁, 何丹丹, 尹思阳, 崔磊, 丁安心. 2019. Ross-Li核驱动模型热点参数化及其校正—以POLDER数据为例. 遥感学报, 23(4): 661-672 [DOI: 10.11834/jrs.20198332http://dx.doi.org/10.11834/jrs.20198332]
Che T, Hao X H, Dai L Y, Li H Y, Huang X D and Xiao L. 2019. Snow cover variation and its impacts over the Qinghai-Tibet Plateau. Bulletin of Chinese Academy of Sciences, 34(11): 1247-1253
车涛, 郝晓华, 戴礼云, 李弘毅, 黄晓东, 肖林. 2019. 青藏高原积雪变化及其影响. 中国科学院院刊, 34(11): 1247-1253 [DOI: 10.16418/j.issn.1000-3045.2019.11.007http://dx.doi.org/10.16418/j.issn.1000-3045.2019.11.007]
Che T, Li X, Jin R, Armstrong R and Zhang T J. 2008. Snow depth derived from passive microwave remote-sensing data in China. Annals of Glaciology, 49: 145-154 [DOI: 10.3189/172756408787814690http://dx.doi.org/10.3189/172756408787814690]
Chen B W, Pang Y, Li Z Y, Lu H, Liu L X, North P R J and Rosette J A B. 2019a. Ground and top of canopy extraction from photon-counting LiDAR data using local outlier factor with ellipse searching area. IEEE Geoscience and Remote Sensing Letters, 16(9): 1447-1451 [DOI: 10.1109/LGRS.2019.2899011http://dx.doi.org/10.1109/LGRS.2019.2899011]
Chen H, Zhang W C, Ning N and Guo Y D. 2019b. Long-term groundwater storage variations estimated in the Songhua River Basin by using GRACE products, land surface models, and in-situ observations. Science of the Total Environment, 649: 372-387 [DOI: 10.1016/j.scitotenv.2018.08.352http://dx.doi.org/10.1016/j.scitotenv.2018.08.352]
Chen J, Wang Y F, Zheng J J and Cao L G. 2019. Measurement of cultivated land utilization efficiency: Construction and application of random forest. Journal of Natural Resources, 34(6): 1345-1356
陈军, 汪永丰, 郑佳佳, 曹立国. 2019. 中国阿牙克库木湖水量变化及其驱动机制. 自然资源学报, 34(6): 1345-1356 [DOI: 10.31497/zrzyxb.20190618http://dx.doi.org/10.31497/zrzyxb.20190618]
Chen J J, Zhao X N, Zhang H Z, Qin Y and Yi S H. 2019c. Evaluation of the accuracy of the field quadrat survey of alpine grassland fractional vegetation cover based on the satellite remote sensing pixel scale. ISPRS International Journal of Geo-Information, 8(11): 497 [DOI: 10.3390/ijgi8110497http://dx.doi.org/10.3390/ijgi8110497]
Chen J M and Black T A. 1992. Defining leaf area index for non-flat leaves. Plant, Cell and Environment, 15(4): 421-429 [DOI: 10.1111/j.1365-3040.1992.tb00992.xhttp://dx.doi.org/10.1111/j.1365-3040.1992.tb00992.x]
Chen J Q, Sciusco P, Ouyang Z T, Zhang R, Henebry G M, John R and Roy D P. 2019d. Linear downscaling from MODIS to landsat: Connecting landscape composition with ecosystem functions. Landscape Ecology, 34(12): 2917-2934 [DOI: 10.1007/s10980-019-00928-2http://dx.doi.org/10.1007/s10980-019-00928-2]
Chen L, Wang Y Q, Ren C Y, Zhang B and Wang Z M. 2019e. Assessment of multi-wavelength SAR and multispectral instrument data for forest aboveground biomass mapping using random forest kriging. Forest Ecology and Management, 447: 12-25 [DOI: 10.1016/j.foreco.2019.05.057http://dx.doi.org/10.1016/j.foreco.2019.05.057]
Chen L, Yan G J, Wang T X, Ren H Z, Hu R H, Chen S B and Zhou H M. 2019f. Spatial scale consideration for estimating all-sky surface shortwave radiation with a modified 1-D radiative transfer model. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(3): 821-835 [DOI: 10.1109/jstars.2019.2896644http://dx.doi.org/10.1109/jstars.2019.2896644]
Chen S D, She D X, Zhang L P, Guo M Y and Liu X. 2019i. Spatial downscaling methods of soil moisture based on multisource remote sensing data and its application. Water, 11(7): 1401 [DOI: 10.3390/w11071401http://dx.doi.org/10.3390/w11071401]
Chen S L, Huang Y F, Gao S and Wang G Q. 2019g. Impact of physiological and phenological change on carbon uptake on the Tibetan Plateau revealed through GPP estimation based on spaceborne solar-induced fluorescence. Science of the Total Environment, 663: 45-59 [DOI: 10.1016/j.scitotenv.2019.01.324http://dx.doi.org/10.1016/j.scitotenv.2019.01.324]
Chen S Y, Liu L Y, Zhang X, Liu X J, Chen X D, Qian X J, Xu Y and Xie D H. 2019h. Retrieval of the fraction of radiation absorbed by Photosynthetic Components (FAPARgreen) for forest using a triple-source leaf-wood-soil layer approach. Remote Sensing, 11(21): 2471 [DOI: 10.3390/rs11212471http://dx.doi.org/10.3390/rs11212471]
Chen X J, Mo X G, Zhang Y C, Sun Z G, Liu Y, Hu S and Liu S X. 2019k. Drought detection and assessment with solar-induced chlorophyll fluorescence in summer maize growth period over North China Plain. Ecological Indicators, 104: 347-356 [DOI: 10.1016/j.ecolind.2019.05.017http://dx.doi.org/10.1016/j.ecolind.2019.05.017]
Chen X L, Su Z B, Ma Y M and Middleton E M. 2019j. Optimization of a remote sensing energy balance method over different canopy applied at global scale. Agricultural and Forest Meteorology, 279: 107633 [DOI: 10.1016/j.agrformet.2019.107633http://dx.doi.org/10.1016/j.agrformet.2019.107633]
Chen X L, Su Z B, Ma Y M, Yang K, Wen J and Zhang Y. 2013. An improvement of roughness height parameterization of the Surface Energy Balance System (SEBS) over the Tibetan Plateau. Journal of Applied Meteorology and Climatology, 52(3): 607-622 [DOI: 10.1175/JAMC-D-12-056.1http://dx.doi.org/10.1175/JAMC-D-12-056.1]
Chen X N, Bao A M, Zhang H L and Liu M Y. 2010. A study on methods and accuracy assessment for extracting snow covered areas from MODIS images based on pixel unmixing: A case on the middle of the Tianshan Mountain. Resources Science, 32(9): 1761-1768
陈晓娜, 包安明, 张红利, 柳梅英. 2010. 基于混合像元分解的MODIS积雪面积信息提取及其精度评价-以天山中段为例. 资源科学, 32(9): 1761-1768
Chen Y L, Wang S S, Ren Z G, Huang J F, Wang X Z, Liu S S, Deng H J and Lin W K. 2019l. Increased evapotranspiration from land cover changes intensified water crisis in an arid river basin in northwest China. Journal of Hydrology, 574: 383-397 [DOI: 10.1016/j.jhydrol.2019.04.045http://dx.doi.org/10.1016/j.jhydrol.2019.04.045]
Chen Y N, Gu H F, Wang M N, Gu Q, Ding Z, Ma M G, Liu R Y and Tang X G. 2019m. Contrasting performance of the remotely-derived gpp products over different climate zones across China. Remote Sensing, 11(16): 1855[DOI: 10.3390/rs11161855http://dx.doi.org/10.3390/rs11161855]
Chen Y P, Sun K M, Chen C, Bai T, Park T, Wang W L, Nemani R R and Myneni R B. 2019n. Generation and evaluation of LAI and FPAR products from Himawari-8 Advanced Himawari Imager (AHI) data. Remote Sensing, 11(13): 1517 [DOI: 10.3390/rs11131517http://dx.doi.org/10.3390/rs11131517]
Cheng D Y and Li X D. 2019. Vegetation coverage change in a karst area and effects of terrain and population. Journal of Geo-Information Science, 21(8): 1227-1239
程东亚, 李旭东. 2019. 喀斯特地区植被覆盖度变化及地形与人口效应研究. 地球信息科学学报, 21(8): 1227-1239 [DOI: 10.12082/dqxxkx.2019.180548http://dx.doi.org/10.12082/dqxxkx.2019.180548]
Cheng J and Kustas W P. 2019a. Using very high resolution thermal infrared imagery for more accurate determination of the impact of land cover differences on evapotranspiration in an irrigated agricultural area. Remote Sensing, 11(6): 613 [DOI: 10.3390/rs11060613http://dx.doi.org/10.3390/rs11060613]
Cheng J, Yang F and Guo Y M. 2019b. A Comparative study of bulk parameterization schemes for estimating cloudy-sky surface downward longwave radiation. Remote Sensing, 11(5): 528 [DOI: 10.3390/rs11050528http://dx.doi.org/10.3390/rs11050528]
Crawford T M andDuchon C E. 1999. An improved parameterization for estimating effective atmospheric emissivity for use in calculating daytime downwelling longwave radiation. Journal of Applied Meteorology, 38(4): 474-480 [DOI: 10.1175/1520-0450(1999)038<0474:AIPFEE>2.0.CO;2http://dx.doi.org/10.1175/1520-0450(1999)038<0474:AIPFEE>2.0.CO;2]
Crétaux J F, Abarca-del-Río R, Bergé-Nguyen M, Arsen A, Drolon V, Clos G and Maisongrande P. 2016. Lake volume monitoring from space. Surveys in Geophysics, 37(2): 269-305 [DOI: 10.1007/s10712-016-9362-6http://dx.doi.org/10.1007/s10712-016-9362-6]
Cui B, Zhao Q J, Huang W J, Song X Y, Ye H C and Zhou X F. 2019a. A new integrated vegetation index for the estimation of winter wheat leaf chlorophyll content. Remote Sensing, 11(8): 974 [DOI: 10.3390/rs11080974http://dx.doi.org/10.3390/rs11080974]
Cui L, Jiao ZT, Dong YD, Sun M, Zhang XN, Yin SY, Ding AX, Chang YX, Guo J and Xie R. 2019b. Estimating forest canopy height using MODIS BRDF data emphasizing typical-angle reflectances. Remote Sensing, 11(19): 2239 [DOI: 10.3390/rs11192239http://dx.doi.org/10.3390/rs11192239]
Cui M Y, Wang J B, Wang S Q, Yan H and Li Y N. 2019. Temporal and spatial distribution of evapotranspiration and its influencing factors on Qinghai-Tibet Plateau from 1982 to 2014. Journal of Resources and Ecology, 10(2): 213-224
崔明月, 王军邦, 王绍强, 延昊, 李英年. 2019. 1982-2014年青藏高原地表蒸散量时空分布及其变化影响因子分析(英文). 资源与生态学报, 10(2): 213-224 [DOI: 10.5814/j.issn.1674-764x.2019.02.012http://dx.doi.org/10.5814/j.issn.1674-764x.2019.02.012]
Cui Y R, Xiong C, Lemmetyinen J, Shi J C, Jiang L M, Peng B, Li H X, Zhao T J, Ji D B and Hu T X. 2016. Estimating Snow water equivalent with backscattering at X and Ku band based on absorption loss. Remote Sensing, 8(6): 505 [DOI: 10.3390/rs8060505http://dx.doi.org/10.3390/rs8060505]
Dao P D, He Y H and Lu B. 2019. Maximizing the quantitative utility of airborne hyperspectral imagery for studying plant physiology: An optimal sensor exposure setting procedure and empirical line method for atmospheric correction. International Journal of Applied Earth Observation and Geoinformation, 77: 140-50 [DOI: 10.1016/j.jag.2018.11.010http://dx.doi.org/10.1016/j.jag.2018.11.010]
Deng C L, Zhang B Q, Cheng L Y, Hu L Q and Chen F H. 2019a. Vegetation dynamics and their effects on surface water-energy balance over the Three-North Region of China. Agricultural and Forest Meteorology, 275: 79-90 [DOI: 10.1016/j.agrformet.2019.05.012http://dx.doi.org/10.1016/j.agrformet.2019.05.012]
Deng Y, Jiang W G, Tang Z H, Ling Z Y and Wu Z F. 2019b. Long-term changes of open-surface water bodies in the Yangtze river basin based on the google earth engine cloud platform. Remote Sensing, 11(19): 2213
Ding A X, Jiao Z T, Dong Y D, Zhang X N, He D D, Cui L, Yin S Y and Chang Y X, 2019. Performance assessment of the operational MODIS BRDF model for snow/ice cover type. Journal of Remote Sensing, 23(6): 1147-1158
丁安心, 焦子锑, 董亚冬, 张小宁, 何丹丹, 崔磊, 尹思阳, 常雅轩. 2019. 业务化MODIS BRDF模型对冰雪BRDF/反照率的反演能力评估. 遥感学报, 23(6): 1147-1158) [DOI: 10.11834/jrs.20198037http://dx.doi.org/10.11834/jrs.20198037]
Ding A X, Jiao Z T, Dong Y D, Zhang X N, Peltoniemi J I, Mei L L, Guo J, Yin S Y, Cui L, Chang Y X and Xie R. 2019a. Evaluation of the snow albedo retrieved from the snow kernel improved the ross-roujean BRDF model. Remote Sensing, 11(13): 1611 [DOI: 10.3390/rs11131611http://dx.doi.org/10.3390/rs11131611]
Ding A X, Jiao Z T, Dong Y D, Qu Y, Zhang X N, Xiong C, He D D, Yin S Y, Cui L and Chang Y X. 2019b. An assessment of the performance of two snow kernels in characterizing snow scattering properties. International Journal of Remote Sensing, 40(16): 6315-6335 [DOI: 10.1080/01431161.2019.1590878http://dx.doi.org/10.1080/01431161.2019.1590878]
Disney M I, Lewis P and North P R J. 2000. Monte Carlo ray tracing in optical canopy reflectance modelling. Remote Sensing Reviews, 18(2/4): 163-196 [DOI: 10.1080/02757250009532389http://dx.doi.org/10.1080/02757250009532389]
Dong Y D, Jiao Z T, Cui L, Zhang H, Zhang X N, Yin S Y, Ding A X, Chang Y X, Xie R and Guo J. 2019. Assessment of the hotspot effect for the PROSAIL model with POLDER hotspot observations based on the hotspot-enhanced kernel-driven BRDF model. IEEE Transactions on Geoscience and Remote Sensing, 57(10): 8048-8064 [DOI: 10.1109/TGRS.2019.2917923http://dx.doi.org/10.1109/TGRS.2019.2917923]
Dou J X, Grimmond S, Cheng Z G, Miao S G, Feng D Y and Liao M S. 2019. Summertime surface energy balance fluxes at two Beijing sites. International Journal of Climatology, 39(5): 2793-2810 [DOI: 10.1002/joc.5989http://dx.doi.org/10.1002/joc.5989]
Du S H, Liu L Y, Liu X J, Guo J, Hu J C, Wang S Q and Zhang Y G. 2019. SIFSpec: Measuring solar-induced chlorophyll fluorescence observations for remote sensing of photosynthesis. Sensors, 19(13): 3009 [DOI: 10.3390/s19133009http://dx.doi.org/10.3390/s19133009]
Duan S B, Li Z L, Li H, Goettsche F M, Wu H, Zhao W, Leng P, Zhang X and Coll, C. 2019a. Validation of Collection 6 MODIS land surface temperature product using in situ measurements. Remote Sensing of Environment, 225: 16-29 [DOI: 10.1016/j.rse.2019.02.020http://dx.doi.org/10.1016/j.rse.2019.02.020]
Duan S B, Li Z L, Wang C, Zhang S, Tang B H, Leng P and Gao MF. 2019b. Land-surface temperature retrieval from Landsat 8 single-channel thermal infrared data in combination with NCEP reanalysis data and ASTER GED product. International Journal of Remote Sensing, 40: 1763-1778 [DOI: 10.1080/01431161.2018.1460513http://dx.doi.org/10.1080/01431161.2018.1460513]
España M L, Baret F, Aries F, Chelle M, Andrieu B and Prévot L. 1999. Modeling maize canopy 3D architecture: Application to reflectance simulation. Ecological Modelling, 122(1/2): 25-43 [DOI: 10.1016/S0304-3800(99)00070-8http://dx.doi.org/10.1016/S0304-3800(99)00070-8]
Fan W L, Li J, Liu Q H, Zhang Q, Yin G F, Li A N, Zeng Y L, Xu B D, Xu X J, Zhou G M and Du H Q. 2018. Topographic correction of forest image data based on the canopy reflectance model for sloping terrains in multiple forward mode. Remote Sensing, 10(5): 717 [DOI: 10.3390/rs10050717http://dx.doi.org/10.3390/rs10050717]
Fan X L and Qu Y. 2019. Retrieval of high spatial resolution aerosol optical depth from HJ-1 A/B CCD data. Remote Sensing, 11(7): 832 [DOI: 10.3390/rs11070832http://dx.doi.org/10.3390/rs11070832]
Fang Y, Li H, Wan W, Zhu S Y, Wang Z J, Hong Y and Wang H. 2019. Assessment of water storage change in China’s lakes and reservoirs over the last three decades. Remote Sensing, 11(12): 1467 [DOI: DOI: 10.3390/rs11121467http://dx.doi.org/DOI: 10.3390/rs11121467]
Feng F and Wang K C. 2019. Does the modern-era retrospective analysis for research and applications-2 aerosol reanalysis introduce an improvement in the simulation of surface solar radiation over China? International Journal of Climatology, 39(3): 1305-1318 [DOI: 10.1002/joc.5881http://dx.doi.org/10.1002/joc.5881]
Feng G P, Song Q T and Jiang X W. 2019. Global groundwater storage changes and characteristics observed by satellite gravimetry. Remote Sensing Technology and Application, 34(4): 822-828
冯贵平, 宋清涛, 蒋兴伟. 2019. 卫星重力监测全球地下水储量变化及其特征. 遥感技术与应用, 34(4): 822-828 [DOI: 10.11873/j.issn.1004-0323.2019.4.0822http://dx.doi.org/10.11873/j.issn.1004-0323.2019.4.0822]
Feng L, Hu W Y, Li Y X and Zhang E W. 2019. Dynamic monitoring of multi-year vegetation coverage in Sichuan province based on google earth engines. Forest Resources Management, (4): 124-131
冯李, 胡文英, 李应鑫, 张恩伟. 2019. Google Earth Engine在四川省多年植被覆盖度动态监测中的应用. 林业资源管理, (4): 124-131 [DOI: 10.13466/j.cnki.lyzygl.2019.04.018]
Feng L L, Jia Z Q, Li Q X, Zhao A Z, Zhao Y L and Zhao Z J. 2019. Spatiotemporal change of sparse vegetation coverage in northern China. Journal of the Indian Society of Remote Sensing, 47(2): 359-366 [DOI: 10.1007/s12524-018-0912-xhttp://dx.doi.org/10.1007/s12524-018-0912-x]
Frappart F, Seyler F, Martinez J M, León J G and Cazenave A. 2005. Floodplain water storage in the Negro River basin estimated from microwave remote sensing of inundation area and water levels. Remote Sensing of Environment, 99(4): 387-399 [DOI: 10.1016/j.rse.2005.08.016http://dx.doi.org/10.1016/j.rse.2005.08.016]
Fu D J, Su F Z, Wang J and Sui Y J. 2019. Patterns of arctic tundra greenness based on spatially downscaled solar-induced fluorescence. Remote Sensing, 11(12): 1460 [DOI: 10.3390/rs11121460http://dx.doi.org/10.3390/rs11121460]
Gan G J, Kang T T, Yang S, Bu J Y, Feng Z M and Gao Y C. 2019. An optimized two source energy balance model based on complementary concept and canopy conductance. Remote Sensing of Environment, 223: 243-256 [DOI: 10.1016/j.rse.2019.01.020http://dx.doi.org/10.1016/j.rse.2019.01.020]
Gao H L. 2015. Satellite remote sensing of large lakes and reservoirs: From elevation and area to storage. Wiley Interdisciplinary Reviews: Water, 2(2): 147-157 [DOI: 10.1002/wat2.1065http://dx.doi.org/10.1002/wat2.1065]
Gao H L, Birkett C M and Lettenmaier D P. 2012. Global monitoring of large reservoir storage from satellite remote sensing. Water Resources Research, 48(9): W09504 [DOI: 10.1029/2012WR012063http://dx.doi.org/10.1029/2012WR012063]
Gao L M and Zhang L L. 2019. Spatiotemporal dynamics of the vegetation coverage in Qinghai Lake basin. Journal of Geo-Information Science, 21(9): 1318-1329
高黎明, 张乐乐. 2019. 青海湖流域植被盖度时空变化研究. 地球信息科学学报, 21(9): 1318-1329 [DOI: 10.12082/dqxxkx.2019.180696http://dx.doi.org/10.12082/dqxxkx.2019.180696]
Gao Y, Hao X H, He D C, Huang G H, Wang J, Zhao H Y, Wei Y R, Shao D H and Wang W G. 2019. Snow cover mapping algorithm in the Tibetan Plateau based on NDSI threshold optimization of different land cover types. Journal of Glaciology and Geocryology, 41(5): 1162-1172
高扬, 郝晓华, 和栋材, 黄广辉, 王建, 赵宏宇, 魏亚瑞, 邵东航, 王卫国. 2019. 基于不同土地覆盖类型NDSI阈值优化下的青藏高原积雪判别. 冰川冻土, 41(5): 1162-1172 [DOI: 10.7522/j.issn.1000-0240.2019.1155http://dx.doi.org/10.7522/j.issn.1000-0240.2019.1155]
Gastellu-Etchegorry J P, Demarez V, Pinel V and Zagolski F. 1996. Modeling radiative transfer in heterogeneous 3-D vegetation canopies. Remote Sensing of Environment, 58(2): 131-156 [DOI: https://doi.org/10.1016/0034-4257(95)00253-7http://dx.doi.org/https://doi.org/10.1016/0034-4257(95)00253-7]
GCOS. 2011. Systematic Observation Requirements for Satellite-Based Data Products for Climate. Geneva: WMO: 79-83
Ge X Y, Wang J Z, Ding J L, Cao X Y, Zhang Z P, Liu J and Li X H. 2019. Combining UAV-based hyperspectral imagery and machine learning algorithms for soil moisture content monitoring. PeerJ, 7: e6926 [DOI: 10.7717/peerj.6926http://dx.doi.org/10.7717/peerj.6926]
Goel N S, Rozehnal I and Thompson R L. 1991. A computer graphics based model for scattering from objects of arbitrary shapes in the optical region. Remote Sensing of Environment, 36(2): 73-104 [DOI: 10.1016/0034-4257(91)90032-2http://dx.doi.org/10.1016/0034-4257(91)90032-2]
Govaerts Y M and Verstraete M M. 1998. Raytran: A Monte Carlo ray-tracing model to compute light scattering in three-dimensional heterogeneous media. IEEE Transactions on Geoscience and Remote Sensing, 36(2): 493-505 [DOI: 10.1109/36.662732http://dx.doi.org/10.1109/36.662732]
Gower S T, Kucharik C J and Norman J M. 1999. Direct and indirect estimation of leaf area index, fAPAR, and net primary production of terrestrial ecosystems. Remote Sensing of Environment, 70(1): 29-51 [DOI: 10.1016/S0034-4257(99)00056-5http://dx.doi.org/10.1016/S0034-4257(99)00056-5]
Gu X H, Li J F, Chen Y D, Kong D D and Liu J Y. 2019. Consistency and discrepancy of global surface soil moisture changes from multiple model ‐ based data sets against satellite observations. Journal of Geophysical Research: Atmospheres, 124(3): 1474-1495 [DOI: 10.1029/2018JD029304http://dx.doi.org/10.1029/2018JD029304]
Guo J M, Gao Y F, Li S T, Pema R, Wang Y Y, Zhang Y J and Liu R H. 2019. Estimation model of leaf water content of winter wheat based on multi-angle hyperspectral remote sensing. Journal of Anhui Agricultural University, 46(1): 124-132
郭建茂, 高云峰, 李淑婷, 白玛仁增, 王阳阳, 张一甲, 刘荣花. 2019. 基于多角度高光谱遥感的冬小麦叶片含水率估算模型. 安徽农业大学学报, 46(1): 124-132 [DOI: 10.13610/j.cnki.1672-352x.20190314.011http://dx.doi.org/10.13610/j.cnki.1672-352x.20190314.011]
Guo L J, Liu R M, Men C, Wang Q R, Miao Y X and Zhang Y. 2019a. Quantifying and simulating landscape composition and pattern impacts on land surface temperature: A decadal study of the rapidly urbanizing city of Beijing, China. Science of the Total Environment, 654: 430-440 [DOI: 10.1016/j.scitotenv.2018.11.108http://dx.doi.org/10.1016/j.scitotenv.2018.11.108]
Guo Y M, Cheng J and Liang S L. 2019b. Comprehensive assessment of parameterization methods for estimating clear-sky surface downward longwave radiation. Theoretical and Applied Climatology, 135(3): 1045-1058 [DOI: 10.1007/s00704-018-2423-7http://dx.doi.org/10.1007/s00704-018-2423-7]
Gupta S K and Shukla D P. 2020. Evaluation of topographic correction methods for LULC preparation based on multi-source DEMs and Landsat-8 imagery. Spatial Information Research, 28(1): 113-127 [DOI: 10.1007/s41324-019-00274-0http://dx.doi.org/10.1007/s41324-019-00274-0]
Hall D K, Riggs G A and Salomonson V V. 1995. Development of methods for mapping global snow cover using moderate resolution imaging spectroradiometer data. Remote Sensing of Environment, 54(2): 127-140 [DOI: 10.1016/0034-4257(95)00137-Phttp://dx.doi.org/10.1016/0034-4257(95)00137-P]
Han L, Wu T T, Liu Q and Liu Z H. 2019a. A novel approach for cloud detection in scenes with snow/ice using high resolution sentinel-2 images. Atmosphere, 10(2): 44 [DOI: 10.3390/atmos10020044http://dx.doi.org/10.3390/atmos10020044]
Han S C, Shum C K, Jekeli C and Alsdorf D. 2005. Improved estimation of terrestrial water storage changes from GRACE. Geophysical Research Letters, 32(7): L07302 [DOI: 10.1029/2005GL022382http://dx.doi.org/10.1029/2005GL022382]
Han X J, Duan S B, Huang C and Li Z L. 2019c. Cloudy land surface temperature retrieval from three-channel microwave data. International Journal of Remote Sensing, 40(5/6): 1793-1807 [DOI: 10.1080/01431161.2018.1471552http://dx.doi.org/10.1080/01431161.2018.1471552]
Han X Z, Wang F and Han Y. 2019b. Fengyun-3DMERSI true color imagery developed for environmental applications. Journal of Meteorological Research, 33(5): 914-924 [DOI: 10.1007/s13351-019-9028-7http://dx.doi.org/10.1007/s13351-019-9028-7]
Hancock S, Armston J, Hofton M, Sun X L, Tang H, Duncanson L I, Kellner J R and Dubayah R. 2019. The GEDI simulator: a large-footprint waveform lidar simulator for calibration and validation of spaceborne missions. Earth and Space Science, 6(2): 294-310 [DOI: 10.1029/2018EA000506http://dx.doi.org/10.1029/2018EA000506]
Hao D L, Asrar G R, Zeng Y L, Zhu Q, Wen J G, Xiao Q and Chen M. 2019a. Estimating hourly land surface downward shortwave and photosynthetically active radiation from DSCOVR/EPIC observations. Remote Sensing of Environment, 232: 111320 [DOI: 10.1016/j.rse.2019.111320http://dx.doi.org/10.1016/j.rse.2019.111320]
Hao D L, Wen J G, Xiao Q, Lin X W, You D Q, Tang Y, Liu Q and Zhang S S. 2019b. Sensitivity of coarse-scale snow-free land surface shortwave albedo to topography. Journal of Geophysical Research: Atmospheres, 124(16): 9028-9045 [DOI: 10.1029/2019jd030660http://dx.doi.org/10.1029/2019jd030660]
Hao D L, Wen J G, Xiao Q, Wu S B, Lin X W, You D Q and Tang Y. 2018. Modeling anisotropic reflectance over composite sloping terrain. IEEE Transactions on Geoscience and Remote Sensing, 56(7): 3903-3923 [DOI: 10.1109/TGRS.2018.2816015http://dx.doi.org/10.1109/TGRS.2018.2816015]
Hao H K, Li W Z, Zhao X, Chang Q R and Zhao P X. 2019c. Estimating the aboveground carbon density of coniferous forests by combining airborne LiDAR and allometry models at plot level. Frontiers in Plant Science, 10: 917 [DOI: 10.3389/fpls.2019.00917http://dx.doi.org/10.3389/fpls.2019.00917]
Hao S R, Jiang L M, Shi J C, Wang G X and Liu X J. 2019d. Assessment of MODIS-based fractional snow cover products over the Tibetan Plateau. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(2): 533-548 [DOI: 10.1109/JSTARS.2018.2879666http://dx.doi.org/10.1109/JSTARS.2018.2879666]
He D D, Jiao Z T, Dong Y D, Zhang X N, Ding A X, Yin S Y, Cui L and Chang Y X. 2019b. Preliminary verification of Landsat satellite albedo from airborne WIDAS data. Journal of Remote Sensing, 23(1): 53-61
何丹丹, 焦子锑, 董亚冬, 张小宁, 丁安心, 尹思阳, 崔磊, 常雅轩. 2019b. 机载WIDAS数据的Landsat卫星反照率初步验证. 遥感学报, 23(1): 53-61 [DOI: 10.11834/jrs.20198007http://dx.doi.org/10.11834/jrs.20198007]
He D D, Jiao Z T, Dong Y D, Zhang X N, Zhang H and Ding A X. 2019a. Verification of BRDF archetype inversion algorithm from surface observations of airborne WIDAS. Journal of Remote Sensing, 23(4): 620-629
何丹丹, 焦子锑, 董亚冬, 张小宁, 张虎, 丁安心. 2019a. 机载WIDAS地表观测的BRDF原型反演算法验证. 遥感学报, 23(4): 620-629 [DOI: 10.11834/jrs.20197446http://dx.doi.org/10.11834/jrs.20197446]
He M, Hu Y X, Chen N, Wang D H, Huang J P and Stamnes K. 2019a. High cloud coverage over melted areas dominates the impact of clouds on the albedo feedback in the Arctic. Scientific Reports, 9(1): 9529 [DOI: 10.1038/s41598-019-44155-whttp://dx.doi.org/10.1038/s41598-019-44155-w]
He T, Gao F, Liang S L and Peng Y. 2019b. Mapping Climatological bare soil albedos over the contiguous United States using MODIS data. Remote Sensing, 11(6): 666 [DOI: 10.3390/rs11060666http://dx.doi.org/10.3390/rs11060666]
He T, Zhang Y, Liang S L, Yu Y Y and Wang D D. 2019c. Developing land surface directional reflectance and albedo products from geostationary GOES-R and himawari data: theoretical basis, operational implementation, and validation. Remote Sensing, 11(22): 2655 [DOI: 10.3390/rs11222655http://dx.doi.org/10.3390/rs11222655]
Hou J L, Huang C L, Zhang Y, Guo J F and Gu J. 2019. Gap-filling of MODIS fractional snow cover products via non-local spatio-temporal filtering based on machine learning techniques. Remote Sensing, 11(1): 90 [DOI: 10.3390/rs11010090http://dx.doi.org/10.3390/rs11010090]
Hu T, Li H, Cao B, van Dijk A I J M, Renzullo L J, Xu Z, Zhou J, Du Y and Liu, Q. 2019a. Influence of emissivity angular variation on land surface temperature retrieved using the generalized split-window algorithm. International Journal of Applied Earth Observation and Geoinformation, 82: 101917 [DOI: https://doi.org/10.1016/j.jag.2019.101917http://dx.doi.org/https://doi.org/10.1016/j.jag.2019.101917]
Hu T, Renzullo L J, Cao B, van Dijk A I J M, Du Y M, Li H, Cheng J, Xu Z H, Zhou J and Liu Q H. 2019b. Directional variation in surface emissivity inferred from the MYD21 product and its influence on estimated surface upwelling longwave radiation. Remote Sensing of Environment, 228: 45-60 [DOI: 10.1016/j.rse.2019.04.012http://dx.doi.org/10.1016/j.rse.2019.04.012]
Hu Y H, Hou M T, Zhao C L, Zhen X J, Yao L and Xu Y H. 2019c. Human-induced changes of surface albedo in Northern China from 1992-2012. International Journal of Applied Earth Observation and Geoinformation, 79: 184-191 [DOI: 10.1016/j.jag.2019.03.018http://dx.doi.org/10.1016/j.jag.2019.03.018]
Hu Z Y, Zhou Q M, Chen X, Chen D L, Li J F, Guo M Y, Yin G and Duan Z. 2019d. Groundwater depletion estimated from GRACE: a challenge of sustainable development in an arid region of central Asia. Remote Sensing, 11(16): 1908 [DOI: 10.3390/rs11161908http://dx.doi.org/10.3390/rs11161908]
Huang C, Duan S B, Jiang X G, Han X J, Leng P, Gao M F and Li Z L. 2019a. A physically based algorithm for retrieving land surface temperature under cloudy conditions from AMSR2 passive microwave measurements. International Journal of Remote Sensing, 40(5/6): 1828-1843 [DOI: 10.1080/01431161.2018.1508920http://dx.doi.org/10.1080/01431161.2018.1508920]
Huang C J, Qiao F L, Chen S Y, Xue Y H and Guo J S. 2019b. Observation and parameterization of broadband sea surface albedo. Journal of Geophysical Research: Oceans, 124(7): 4480-4491 [DOI: 10.1029/2018jc014444http://dx.doi.org/10.1029/2018jc014444]
Huang C Y, Wei H L, Rau J Y and Jhan J P. 2019c. Use of principal components of UAV-acquired narrow-band multispectral imagery to map the diverse low stature vegetation fAPAR. GIScience and Remote Sensing, 56(4): 605-623 [DOI: 10.1080/15481603.2018.1550873http://dx.doi.org/10.1080/15481603.2018.1550873]
Huang H B, Liu C X, Wang X Y, Zhou X L and Gong P. 2019d. Integration of multi-resource remotely sensed data and allometric models for forest aboveground biomass estimation in China. Remote Sensing of Environment, 221: 225-234 [DOI: 10.1016/j.rse.2018.11.017http://dx.doi.org/10.1016/j.rse.2018.11.017]
Huang H G and Lian J. 2015. A 3D approach to reconstruct continuous optical images using lidar and MODIS. Forest Ecosystems, 2(1): 20 [DOI: 10.1186/s40663-015-0044-5http://dx.doi.org/10.1186/s40663-015-0044-5]
Huang H G, Qin W H and Liu Q H. 2013. RAPID: a radiosity applicable to porous IndiviDual objects for directional reflectance over complex vegetated scenes. Remote Sensing of Environment, 132: 221-237 [DOI: 10.1016/j.rse.2013.01.013http://dx.doi.org/10.1016/j.rse.2013.01.013]
Huang H G, Zhang Z Y, Ni W J, Chai L N, Qin W H, Liu G, Xie D H, Jiang L M and Liu Q H. 2018. Extending RAPID model to simulate forest microwave backscattering. Remote Sensing of Environment, 217: 272-291 [DOI: 10.1016/j.rse.2018.08.011http://dx.doi.org/10.1016/j.rse.2018.08.011]
Huang H G. 2018. Accelerated RAPID model using heterogeneous porous objects. Remote Sensing, 10(8): 1264 [DOI: 10.3390/rs10081264http://dx.doi.org/10.3390/rs10081264]
Huang H G. 2019. Principles and applications of the three-dimensional remote sensing mechanism model RAPID. Remote Sensing Technology and Application, 34(5): 901-913
黄华国. 2019. 3维遥感机理模型RAPID原理及其应用. 遥感技术与应用, 34(5): 901-913 [DOI: 10.11873/j.issn.1004-0323.2019.5.0901http://dx.doi.org/10.11873/j.issn.1004-0323.2019.5.0901]
Huang L K, Zhou W, Liu L L, Chen J and Wang H Y. 2019. Research on surface snow depth retrieval of new L5 signals from GPS. Bulletin of Surveying and Mapping, (7): 1-5, 11
黄良珂, 周威, 刘立龙, 陈军, 王浩宇. 2019. 基于GPS新型L5信号的地表雪深反演研究. 测绘通报, (7): 1-5, 11 [DOI: 10.13474/j.cnki.11-2246.2019.0208http://dx.doi.org/10.13474/j.cnki.11-2246.2019.0208]
Huang Q, Li X D, Han P F, Li D, Zhao F Y and Hou A Z. 2019e. Validation and application of water levels derived from Sentinel-3A for the Brahmaputra River. Science China Technological Sciences, 62(10): 1760-1772 [DOI: 10.1007/s11431-019-9535-3http://dx.doi.org/10.1007/s11431-019-9535-3]
Huang S, Ding J L, Zou J, Liu B H, Zhang J Y and Chen W Q. 2019f. Soil moisture retrival based on sentinel-1 imagery under sparse vegetation coverage. Sensors, 19(589): 1-18 [DOI: 10.3390/s19030589http://dx.doi.org/10.3390/s19030589]
Huang X D, Liu C Y, Wang Y L, Feng Q S and Liang T G. 2019g. Snow cover variations across China from 1952-2012. The Cryosphere Discussions [DOI: 10.5194/tc-2019-152http://dx.doi.org/10.5194/tc-2019-152]
Huang X J, Xiao J F and Ma M G. 2019h. Evaluating the performance of satellite-derived vegetation indices for estimating gross primary productivity using FLUXNET observations across the Globe. Remote Sensing, 11(15): 1823 [DOI: 10.3390/rs11151823http://dx.doi.org/10.3390/rs11151823]
Huang Z Y, Yeh P J F, Pan Y, Jiao J J, Gong H L, Li X J, Güntner A, Zhu Y Q, Zhang C and Zheng L Q. 2019i. Detection of large-scale groundwater storage variability over the karstic regions in Southwest China. Journal of Hydrology, 569: 409-422 [DOI: 10.1016/j.jhydrol.2018.11.071http://dx.doi.org/10.1016/j.jhydrol.2018.11.071]
Hurni K, Van Den Hoek J and Fox J. 2019. Assessing the spatial, spectral, and temporal consistency of topographically corrected Landsat time series composites across the mountainous forests of Nepal. Remote Sensing of Environment, 231: 111225 [DOI: 10.1016/j.rse.2019.111225http://dx.doi.org/10.1016/j.rse.2019.111225]
Idso S B and Jackson R D. 1969. Thermal radiation from the atmosphere. Journal of Geophysical Research, 74(23): 5397-5403 [DOI: 10.1029/JC074i023p05397http://dx.doi.org/10.1029/JC074i023p05397]
Idso S B. 1981. A set of equations for full spectrum and 8‐to 14‐μm and 10.5‐to 12.5‐μm thermal radiation from cloudless skies. Water Resources Research, 17(2): 295-304 [DOI: 10.1029/WR017i002p00295http://dx.doi.org/10.1029/WR017i002p00295]
Jacobs J D. 1978. Radiation climate of Broughton Island//Barry R G and Jacobs J D, eds. Energy budget studies in relation to fast-ice breakup processes in Davis Strait. Boulder: Institute of Arctic and Alpine Research, University of Colorado: 105-120
Jia K, Yang L Q, Liang S L, Xiao Z Q, Zhao X, Yao Y J, Zhang X T, Jiang B and Liu D Y. 2019b. Long-Term Global Land Surface Satellite (GLASS) fractional vegetation cover product derived from MODIS and AVHRR data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(2): 508-518 [DOI: 10.1109/JSTARS.2018.2854293http://dx.doi.org/10.1109/JSTARS.2018.2854293]
Jia K, Yao Y J, Wei X Q, Gao S, Jiang B and Zhao X. 2013. A review on fractional vegetation cover estimation using remote sensing. Advances in Earth Science, 28(7): 774-782
贾坤, 姚云军, 魏香琴, 高帅, 江波, 赵祥. 2013. 植被覆盖度遥感估算研究进展. 地球科学进展, 28(7): 774-782
Jia L, Ren Z P, Li Z B, Xu G C, Shi P, Zhang Y X and Wang B. 2019. Temporal and spatial evolution of vegetation coverage in Xi’an City from 2000 to 2013. Research of Soil and Water Conservation, 26(6): 274-279
贾路, 任宗萍, 李占斌, 徐国策, 时鹏, 张译心, 王斌. 2019. 2000-2013年西安市植被覆盖度时空演变. 水土保持研究, 26(6): 274-279 [DOI: 10.13869/j.cnki.rswc.2019.06.036http://dx.doi.org/10.13869/j.cnki.rswc.2019.06.036]
Jia Y, Jin S G, Savi P, Gao Y, Tang J, Chen Y X and Li W M. 2019a. GNSS-R soil moisture retrieval based on a XGboost machine learning aided method: performance and validation. Remote Sensing, 11(14): 1655 [DOI: 10.3390/rs11141655http://dx.doi.org/10.3390/rs11141655]
Jiang H, Lu N, Qin J, Tang W J and Yao L. 2019a. A deep learning algorithm to estimate hourly global solar radiation from geostationary satellite data. Renewable and Sustainable Energy Reviews, 114: 109327 [DOI: 10.1016/j.rser.2019.109327http://dx.doi.org/10.1016/j.rser.2019.109327]
Jiang L M, Wang P, Zhang L X, Yang H and Yang J T. 2014. Improvement of snow depth retrieval for FY3B-MWRI in China. Science China Earth Sciences, 57(6): 1278-1292 [DOI: 10.1007/s11430-013-4798-8http://dx.doi.org/10.1007/s11430-013-4798-8]
Jiang Y S, Chen F, Gao Y H, Barlage M and Li J D. 2019b. Using multisource satellite data to assess recent snow-cover variability and uncertainty in the Qinghai–Tibet Plateau. Journal of Hydrometeorology, 20(7): 1293-1306 [DOI: 10.1175/JHM-D-18-0220.1http://dx.doi.org/10.1175/JHM-D-18-0220.1]
Jiang Y Z, Tang R L, Jiang X G and Li Z L. 2019c. Impact of clouds on the estimation of daily evapotranspiration from MODIS-derived instantaneous evapotranspiration using the constant global shortwave radiation ratio method. International Journal of Remote Sensing, 40(5/6): 1930-1944 [DOI: 10.1080/01431161.2018.1482025http://dx.doi.org/10.1080/01431161.2018.1482025]
Jiang Y Z, Tang R L, Jiang X G, Li Z L and Gao C X. 2019d. Estimation of soil evaporation and vegetation transpiration using two trapezoidal models from MODIS data. Journal of Geophysical Research: Atmospheres, 124(14): 7647-7664 [DOI: 10.1029/2019jd030542http://dx.doi.org/10.1029/2019jd030542]
Jiao D D, Ji X B, Jin B W, Zhao L W, Zhang J L and Guo F. 2019. Relationship between land cover type and evapotranspiration on the basis of Landsat 8 and ZY3 data fusion approach for a desert oasis in the middle Hexi corridor area of the arid regions of northwestern China. Acta Ecologica Sinica, 39(19): 7097-7109
焦丹丹, 吉喜斌, 金博文, 赵丽雯, 张靖琳, 郭飞. 2019. 西北干旱区河西走廊荒漠绿洲土地覆盖类型与蒸散的关系研究——基于Landsat 8和ZY3数据融合. 生态学报, 39(19): 7097-7109 [DOI: 10.5846/stxb201807161532http://dx.doi.org/10.5846/stxb201807161532]
Jiao Z H, Ren H Z, Mu X H, Zhao J, Wang T X and Dong J J. 2019b. Evaluation of four sky view factor algorithms using digital surface and elevation model data. Earth and Space Science, 6(2): 222-237 [DOI: 10.1029/2018EA000475http://dx.doi.org/10.1029/2018EA000475]
Jiao Z T, Ding A X, Kokhanovsky A, Schaaf C, Bréon F M, Dong Y D, Wang Z S, Liu Y, Zhang X N, Yin S Y, Cui L, Mei L L and Chang Y X. 2019a. Development of a snow kernel to better model the anisotropic reflectance of pure snow in a kernel-driven BRDF model framework. Remote Sensing of Environment, 221: 198-209 [DOI: 10.1016/j.rse.2018.11.001http://dx.doi.org/10.1016/j.rse.2018.11.001]
Jiao Z T, Schaaf C B, Dong Y D, Román M, Hill M J, Chen J M, Wang Z S, Zhang H, Saenz E, Poudyal R, Gatebe C, Bréon F M, Li X W and Strahler A. 2016. A method for improving hotspot directional signatures in BRDF models used for MODIS. Remote Sensing of Environment, 186: 135-151 [DOI: 10.1016/j.rse.2016.08.007http://dx.doi.org/10.1016/j.rse.2016.08.007]
Jiao Z T, Zhang H, Dong Y D, Liu Q, Xiao Q and Li X W. 2015. An algorithm for retrieval of surface albedo from small view-angle airborne observations through the use of BRDF archetypes as prior knowledge. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(7): 3279-3293 [DOI: 10.1109/JSTARS.2015.2414925http://dx.doi.org/10.1109/JSTARS.2015.2414925]
Jin H A, Li A N, Xu W X, Xiao Z Q, Jiang J Y and Xue H Z. 2019b. Evaluation of topographic effects on multiscale leaf area index estimation using remotely sensed observations from multiple sensors. ISPRS Journal of Photogrammetry and Remote Sensing, 154: 176-188 [DOI: 10.1016/j.isprsjprs.2019.06.008http://dx.doi.org/10.1016/j.isprsjprs.2019.06.008]
Jin H A, Li A N, Yin G F, Xiao Z Q, Bian J H, Nan X and Jing J C. 2019a. A multiscale assimilation approach to improve Fine-Resolution leaf area index dynamics. IEEE Transactions on Geoscience and Remote Sensing, 57(10): 8153-8168 [DOI: 10.1109/TGRS.2019.2918548http://dx.doi.org/10.1109/TGRS.2019.2918548]
Jin S K, Ma Y Y, Zhang M, Gong W, Dubovik O, Liu B M, Shi Y F and Yang C L. 2019c. Retrieval of 500 m aerosol optical depths from MODIS measurements over urban surfaces under heavy aerosol loading conditions in winter. Remote Sensing, 11(19): 2218 [DOI: 10.3390/rs11192218http://dx.doi.org/10.3390/rs11192218]
Kang S C, Cong Z Y, Wang X P, Zhang Q G, Ji Z M, Zhang Y L and Xu B Q. 2019a. The transboundary transport of air pollutants and their environmental impacts on Tibetan Plateau. Chinese Science Bulletin, 64(27): 2876-2884 [DOI: 10.1360/Tb-2019-0135http://dx.doi.org/10.1360/Tb-2019-0135]
Kang S C, Zhang Q G, Qian Y, Ji Z M, Li C L, Cong Z Y, Zhang Y L, Guo J M, Du W T, Huang J, You Q L, Panday A K, Rupakheti M, Chen D L, Gustafsson Ö, Thiemens M H and Qin D H. 2019b. Linking atmospheric pollution to cryospheric change in the Third Pole region: current progress and future prospects. National Science Review, 6(4): 796-809 [DOI: 10.1093/nsr/nwz031http://dx.doi.org/10.1093/nsr/nwz031]
Kong B, Liu N, Lin L N, He Y, Wang Y J and Pan Z D. 2019. Assessment of meteorological variables and heat fluxes from atmospheric reanalysis and objective analysis products over the Bering Sea. International Journal of Climatology, 39(11): 4429-4450 [DOI: 10.1002/joc.6083http://dx.doi.org/10.1002/joc.6083]
Konzelmann T, van de Wal R S W, Greuell W, Bintanja R, Henneken E A C and Abe-Ouchi A. 1994. Parameterization of global and longwave incoming radiation for the Greenland Ice Sheet. Global and Planetary Change, 9(1/2): 143-164 [DOI: 10.1016/0921-8181(94)90013-2http://dx.doi.org/10.1016/0921-8181(94)90013-2]
Kuang W H, Liu A L, Dou Y Y, Li G Y and Lu D S. 2019. Examining the impacts of urbanization on surface radiation using Landsat imagery. GIScience and Remote Sensing, 56(3): 462-484 [DOI: 10.1080/15481603.2018.1508931http://dx.doi.org/10.1080/15481603.2018.1508931]
Kwok R, Kacimi S, Markus T, Kurtz N T, Studinger M, Sonntag J G, Manizade S S, Boisvert L N and Harbeck J P. 2019. ICESat-2 surface height and sea ice freeboard assessed with ATM lidar acquisitions from operation IceBridge. Geophysical Research Letters, 46(20): 11228-11236 [DOI: 10.1029/2019GL084976http://dx.doi.org/10.1029/2019GL084976]
Le T C, Nie S, Pan H and Li L C. 2019. Land Surface Temperature Retrieval and Urban Heat Island Effect Based on Landsat8 Image in Fuzhou City. Journal of Northwest Forestry University, 34(5): 154-160
乐通潮, 聂森, 潘辉, 李丽纯. 2019. 基于Landsat8卫星影像的地表温度反演及福州春季城市热岛效应分析. 西北林学院学报, 34(5):154-160 [DOI: 10.3969/j.issn.1001-7461.2019.05.24http://dx.doi.org/10.3969/j.issn.1001-7461.2019.05.24]
Levizou E, Drilias P, Psaras G K and Manetas Y. 2005. Nondestructive assessment of leaf chemistry and physiology through spectral reflectance measurements may be misleading when changes in trichome density co-occur. New Phytologist, 165(2): 463-472 [DOI: 10.1111/j.1469-8137.2004.01250.xhttp://dx.doi.org/10.1111/j.1469-8137.2004.01250.x]
Lewis P. 1999. Three-dimensional plant modelling for remote sensing simulation studies using the Botanical Plant Modelling System. Agronomie, 19(3/4): 185-210 [DOI: 10.1051/agro:19990302http://dx.doi.org/10.1051/agro:19990302]
Li C, Li Y C and Li M Y. 2019a. Improving Forest Aboveground Biomass (AGB) estimation by incorporating crown density and using landsat 8 OLI images of a subtropical forest in western Hunan in central China. Forests, 10(2): 104 [DOI: 10.3390/f10020104http://dx.doi.org/10.3390/f10020104]
Li C W, Lu H, Leung L R, Yang K, Li H Y, Wang W, Han M L and Chen Y Y. 2019b. Improving land surface temperature simulation in CoLM over the Tibetan plateau through fractional vegetation cover derived from a remotely sensed clumping index and model-simulated leaf area index. Journal of Geophysical Research: Atmospheres, 124(5): 2620-2642 [DOI: 10.1029/2018JD028640http://dx.doi.org/10.1029/2018JD028640]
Li D, Tian L, Wan Z F, Jia M, Yao X, Tian Y C, Zhu Y, Cao W X and Cheng T. 2019c. Assessment of unified models for estimating leaf chlorophyll content across directional-hemispherical reflectance and bidirectional reflectance spectra. Remote Sensing of Environment, 231: 111240 [DOI: 10.1016/j.rse.2019.111240http://dx.doi.org/10.1016/j.rse.2019.111240]
Li J, Ju W M, He W, Wang H M, Zhou Y L and Xu M Z. 2019d. An algorithm differentiating sunlit and shaded leaves for improving canopy conductance and vapotranspiration estimates. Journal of Geophysical Research: Biogeosciences, 124(4): 807-824 [DOI: 10.1029/2018JG004675http://dx.doi.org/10.1029/2018JG004675]
Li L L, Yu T, Zhao L M, Zhan Y L, Zheng F J, Zhang Y Z, Mumtaz F and Wang C M. 2019e. Characteristics and trend analysis of the relationship between land surface temperature and nighttime light intensity levels over China. Infrared Physics and Technology, 97: 381-390 [DOI: 10.1016/j.infrared.2019.01.018http://dx.doi.org/10.1016/j.infrared.2019.01.018]
Li L Y, Mu X H, Macfarlane C, Song W J, Chen J, Yan K and Yan G J. 2018a. A half-Gaussian fitting method for estimating fractional vegetation cover of corn crops using unmanned aerial vehicle images. Agricultural and Forest Meteorology, 262: 379-390 [DOI: 10.1016/j.agrformet.2018.07.028http://dx.doi.org/10.1016/j.agrformet.2018.07.028]
Li P, He Z W, He D, Xue D J, Wang Y and Cao S S. 2020. Fractional vegetation coverage response to climatic factors based on grey relational analysis during the 2000-2017 growing season in Sichuan Province, China. International Journal of Remote Sensing, 41(3): 1170-1190 [DOI: 10.1080/01431161.2019.1657605http://dx.doi.org/10.1080/01431161.2019.1657605]
Li W G, Sun Z Q, Lu S and Omasa K. 2019g. Estimation of the leaf chlorophyll content using multiangular spectral reflectance factor. Plant, Cell and Environment, 42(11): 3152-3165 [DOI: 10.1111/pce.13605http://dx.doi.org/10.1111/pce.13605]
Li W K, Guo Q H, Tao S L and Su Y J. 2018b. VBRT: a novel voxel-based radiative transfer model for heterogeneous three-dimensional forest scenes. Remote Sensing of Environment, 206: 318-335 [DOI: 10.1016/j.rse.2017.12.043http://dx.doi.org/10.1016/j.rse.2017.12.043]
Li W K, Qiu B, Guo W D, Zhu Z W and Hsu P C. 2019f. Intraseasonal variability of Tibetan Plateau snow cover. International Journal of Climatology [DOI: 10.1002/joc.6407http://dx.doi.org/10.1002/joc.6407]
Li X and Che T. 2007. A review on passive microwave remote sensing of snow cover. Journal of Glaciology and Geocryology, 29(3): 487-496
李新, 车涛. 2007. 积雪被动微波遥感研究进展. 冰川冻土, 29(3): 487-496 [DOI: 10.3969/j.issn.1000-0240.2007.03.023http://dx.doi.org/10.3969/j.issn.1000-0240.2007.03.023]
Li X D, Long D, Huang Q, Han P F, Zhao F Y and Wada Y. 2019i. High-temporal-resolution water level and storage change data sets for lakes on the Tibetan Plateau during 2000-2017 using multiple altimetric missions and Landsat-derived lake shoreline positions. Earth System Science Data, 11(4): 1603-1627 [DOI: 10.5194/essd-11-1603-2019http://dx.doi.org/10.5194/essd-11-1603-2019]
Li X H, Jing Y H, Shen H F and Zhang L P. 2019h. The recent developments in cloud removal approaches of MODIS snow cover product. Hydrology and Earth System Sciences, 23(5): 2401-2416 [DOI: 10.5194/hess-23-2401-2019http://dx.doi.org/10.5194/hess-23-2401-2019]
Li X W and Wang J D. 1995. Vegetation Optical Remote Sensing Model and Parameterization of Vegetation Structure. Beijing: Science Press
李小文, 王锦地. 1995. 植被光学遥感模型与植被结构参数化. 北京: 科学出版社
Li Y, Chen Y and Li Z. 2019j. Developing daily cloud-free snow composite products from MODIS and IMS for the tienshan mountains. Earth and Space Science, 6(2): 266-275 [DOI: 10.1029/2018EA000460http://dx.doi.org/10.1029/2018EA000460]
Li Y, Kang S C, Chen J Z, Hu Z F, Wang K, Paudyal R, Liu J S, Wang X X, Qin X and Sillanpää M. 2019k. Black carbon in a glacier and snow cover on the northeastern Tibetan Plateau: Concentrations, radiative forcing and potential source from local topsoil. Science of the Total Environment, 686: 1030-1038 [DOI: 10.1016/j.scitotenv.2019.05.469http://dx.doi.org/10.1016/j.scitotenv.2019.05.469]
Li Y, Kang S C, Yan F P, Chen J Z, Wang K, Paudyal R, Liu J S, Qin X and Sillanpää M. 2019l. Cryoconite on a glacier on the north-eastern Tibetan plateau: light-absorbing impurities, albedo and enhanced melting. Journal of Glaciology, 65(252): 633-644 [DOI: 10.1017/jog.2019.41http://dx.doi.org/10.1017/jog.2019.41]
Li Y Y and Huang J F. 2019. Remote sensing of pigment content at a leaf scale: comparison among some specular removal and specular resistance methods. Remote Sensing, 11(8): 983 [DOI: 10.3390/rs11080983http://dx.doi.org/10.3390/rs11080983]
Li Y Z, Mao D H, Feng A Q and Schillerberg T. 2019m. Will human-induced vegetation regreening continually decrease runoff in the loess plateau of China? Forests, 10(10): 906 [DOI: 10.3390/f10100906http://dx.doi.org/10.3390/f10100906]
Li Z W, Shen H F, Cheng Q, Liu Y H, You S C and He Z Y. 2019n. Deep learning based cloud detection for medium and high resolution remote sensing images of different sensors. ISPRS Journal of Photogrammetry and Remote Sensing, 150: 197-212 [DOI: 10.1016/j.isprsjprs.2019.02.017http://dx.doi.org/10.1016/j.isprsjprs.2019.02.017]
Liang S L. 2003. A direct algorithm for estimating land surface broadband albedos from MODIS imagery. IEEE Transactions on Geoscience and Remote Sensing, 41(1): 136-145 [DOI: 10.1109/TGRS.2002.807751http://dx.doi.org/10.1109/TGRS.2002.807751]
Liang S L, Liu Q H, Yan G J, Shi J C and Kerekes J P. 2019a. Foreword to the special issue on the recent progress in quantitative land remote sensing: modeling and estimation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(2): 391-395 [DOI: 10.1109/JSTARS.2019.2895942http://dx.doi.org/10.1109/JSTARS.2019.2895942]
Liang S L, Wang D D, He T and Yu Y Y. 2019b. Remote sensing of Earth’s energy budget: synthesis and review. International Journal of Digital Earth, 12(7): 737-780 [DOI: 10.1080/17538947.2019.1597189http://dx.doi.org/10.1080/17538947.2019.1597189]
Liao J J, Zhao Y and Chen J M. 2020. A dataset of lake level changes in High Mountain Asia using multi-altimeter data. China Science Data, 51
廖静娟, 赵云, 陈嘉明. 2020. 基于多源雷达高度计数据的高亚洲湖泊水位变化数据集. 中国科学数据, 5(1) [DOI: 10.11922/csdata.2019.0019.zhhttp://dx.doi.org/10.11922/csdata.2019.0019.zh]
Liao Z M, He B B, Bai X J and Quan X W. 2019b. Improving forest height retrieval by reducing the ambiguity of volume-only coherence using multi-baseline PolInSAR data. IEEE Transactions on Geoscience and Remote Sensing, 57(11): 8853-8866 [DOI: 10.1109/TGRS.2019.2923257http://dx.doi.org/10.1109/TGRS.2019.2923257]
Liao Z M, He B B, Quan X W, van Dijk A I J M, Qiu S and Yin C M. 2019a. Biomass estimation in dense tropical forest using multiple information from single-baseline P-band PolInSAR data. Remote Sensing of Environment, 221: 489-507 [DOI: 10.1016/j.rse.2018.11.027http://dx.doi.org/10.1016/j.rse.2018.11.027]
Lin Q N, Huang H G, Chen L and Chen E X. 2017. Topographic correction method for steep mountain terrain images. Journal of Remote Sengsing, 21(5): 776-784
林起楠, 黄华国, 陈玲, 陈尔学. 2017. 陡峭山区影像的半经验地形校正. 遥感学报, 21(5): 776-784 [DOI: 10.11834/jrs.20176384http://dx.doi.org/10.11834/jrs.20176384]
Lin X W, Wen J G, Liu Q H, Xiao Q, You D Q, Wu S B, Hao D L and Wu X D. 2018. A multi-scale validation strategy for albedo products over rugged terrain and preliminary application in Heihe River Basin, China. Remote Sensing, 10(2): 156 [DOI: 10.3390/rs10020156http://dx.doi.org/10.3390/rs10020156]
Lin X W, Wen J G, Wu S B, Hao D L, Xiao Q and Liu Q H . Advances in topographic correction methods for optical remote sensing imageriesJournal of Remote SensingChinese2020,24(6)
林兴稳, 闻建光, 吴胜标, 郝大磊, 肖青, 柳钦火. 2019. 地表反射率地形校正物理模型与效果评价方法研究进展 遥感学报(2020,24(6).[DOI:10.11834jrs.20209167http://dx.doi.org/10.11834jrs.20209167]
Liu C W, Gao Z Q, Li Y B, Gao C Y, Su Z B and Zhang X Y. 2019a. Surface energy budget observed for winter wheat in the north China plain during a fog-haze event. Boundary-Layer Meteorology, 170(3): 489-505 [DOI: 10.1007/s10546-018-0407-xhttp://dx.doi.org/10.1007/s10546-018-0407-x]
Liu D Y, Jia K, Wei X Q, Xia M, Zhang X W, Yao Y J, Zhang X T and Wang B. 2019b. Spatiotemporal comparison and validation of three global-scale fractional vegetation cover products. Remote Sensing, 11(21): 2524 [DOI: 10.3390/rs11212524http://dx.doi.org/10.3390/rs11212524]
Liu E H, Zhou G S and Zhou L. 2019. Fraction of absorbed photosynthetically active radiation over summer maize canopy estimated by hyperspectral remote sensing under different drought conditions. Chinese Journal of Applied Ecology, 30(6): 2021-2029
刘二华, 周广胜, 周莉. 2019. 不同干旱条件下夏玉米全生育期冠层吸收光合有效辐射比的高光谱遥感反演. 应用生态学报, 30(6): 2021-2029 [DOI: 10.13287/j.1001-9332.201906.041http://dx.doi.org/10.13287/j.1001-9332.201906.041]
Liu K, Ren H G, Li S Y and Tan B Y. 2019. Automatic extraction of Tibet Plateau frozen lake based on Tiangong-2 multi-spectral data. Infrared and Laser Engineering, 48(3): 0303004
刘康, 任海根, 李盛阳, 覃帮勇. 2019. 基于天宫二号多光谱数据的青藏高原冻湖自动提取. 红外与激光工程, 48(3): 0303004 [DOI: 10.3788/IRLA201948.0303004http://dx.doi.org/10.3788/IRLA201948.0303004]
Liu K, Wang S D, Li X K and Wu T X. 2019c. Spatially disaggregating satellite land surface temperature with a nonlinear model across agricultural areas. Journal of Geophysical Research: Biogeosciences, 124(11): 3232-3251 [DOI: 10.1029/2019JG005227http://dx.doi.org/10.1029/2019JG005227]
Liu L Y, Zhang X, Xie S, Liu X J, Song B W, Chen S Y and Peng D L. 2019e. Global white-sky and black-sky FAPAR retrieval using the energy balance residual method: algorithm and validation. Remote Sensing, 11(9): 1004 [DOI: 10.3390/rs11091004http://dx.doi.org/10.3390/rs11091004]
Liu L Z, Zhao W H, Wu J J, Liu S S, Teng Y G, Yang J H and Han X Y. 2019d. The impacts of growth and environmental parameters on solar-induced chlorophyll fluorescence at seasonal and diurnal scales. Remote Sensing, 11(17): 2002 [DOI: 10.3390/rs11172002http://dx.doi.org/10.3390/rs11172002]
Liu M B, Cao C X, Chen W and Wang X J. 2019f. Mapping canopy heights of poplar plantations in plain areas using ZY3-02 stereo and multispectral data. ISPRS International Journal of Geo-Information, 8(3): 106 [DOI: 10.3390/ijgi8030106http://dx.doi.org/10.3390/ijgi8030106]
Liu N, Zou B, Feng H H, Wang W, Tang Y Q and Liang Y. 2019g. Evaluation and comparison of multiangle implementation of the atmospheric correction algorithm, Dark Target, and Deep Blue aerosol products over China. Atmospheric Chemistry and Physics, 19(12): 8243-8268 [DOI: 10.5194/acp-19-8243-2019http://dx.doi.org/10.5194/acp-19-8243-2019]
Liu Q Y, Yu T and Gao H L. 2019h. Radiometric cross-calibration of GF-1 PMS sensor with a new BRDF model. Remote Sensing, 11(6): 707 [DOI: 10.3390/rs11060707http://dx.doi.org/10.3390/rs11060707]
Liu Q Y, Zhang T L, Li Y Z, Bu C F and Zhang Q F. 2019i. Comparative analysis of fractional vegetation cover estimation based on multi-sensor data in a semi-arid sandy area. Chinese Geographical Science, 29(1): 166-180 [DOI: 10.1007/s11769-018-1010-2http://dx.doi.org/10.1007/s11769-018-1010-2]
Liu R, Wen J, Wang X, Wang Z L, Li Z C, Xie Y, Zhu L and Li D P. 2019j. Derivation of vegetation optical depth and water content in the source region of the yellow river using the FY-3B microwave data. Remote Sensing, 11(13): 1536 [DOI: 10.3390/rs11131536http://dx.doi.org/10.3390/rs11131536]
Liu W W, Atherton J, Mottus M, Gastellu-Etchegorry J P, Malenovský Z, Raumonen P, Åkerblom M, Mäkipää R and Porcar-Castell A. 2019k. Simulating solar-induced chlorophyll fluorescence in a boreal forest stand reconstructed from terrestrial laser scanning measurements. Remote Sensing of Environment, 232: 111274 [DOI: 10.1016/j.rse.2019.111274http://dx.doi.org/10.1016/j.rse.2019.111274]
Liu X, Lv X B, Wu C M, Liu H, Huang H X, Li J, Li M M, Mao C and Zhou W X. 2020. Topographic correction method for high spatial resolution remote sensing data in mountainous area. Earth Science, 45(2): 645-662
柳潇, 吕新彪, 吴春明, 刘洪, 黄瀚霄, 李俊, 李敏敏, 毛晨, 周文孝. 2020. 面向高空间分辨率遥感影像的山区地形校正方法. 地球科学, 45(2): 645-662 [DOI: 10.3799/dqkx.2019.012http://dx.doi.org/10.3799/dqkx.2019.012]
Liu X J, Guanter L, Liu L Y, Damm A, Malenovský Z, Rascher U, Peng D L, Du S S and Gastellu-Etchegorry J P. 2019l. Downscaling of solar-induced chlorophyll fluorescence from canopy level to photosystem level using a random forest model. Remote Sensing of Environment, 231: 110772 [DOI: 10.1016/j.rse.2018.05.035http://dx.doi.org/10.1016/j.rse.2018.05.035]
Liu X J, Guo J, Hu J C and Liu L Y. 2019m. Atmospheric correction for tower-based solar-induced chlorophyll fluorescence observations at O2-A band. Remote Sensing, 11(3): 355 [DOI: 10.3390/rs11030355http://dx.doi.org/10.3390/rs11030355]
Liu X Y, Tang B H, Wu H, Tang R L, Li Z L and Shang G F. 2019n. A method for angular normalization of land surface temperature products based on component temperatures and fractional vegetation cover//Proceedings of the IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium. Yokohama: IEEE: 1849-1852 [DOI: 10.1109/IGARSS.2019.8899823http://dx.doi.org/10.1109/IGARSS.2019.8899823]
Liu X Y, Tang B H, Yan G J, Li Z L and Liang S L. 2019o. Retrieval of Global orbit drift corrected land surface temperature from long-term AVHRR Data. Remote Sensing, 11(23): 2843 [DOI: 10.3390/rs11232843http://dx.doi.org/10.3390/rs11232843]
Liu Y B, Wu G P, Ke C Q. 2016. Remote Sensing in Hydrology. Beijing: Science Press
刘元波, 吴桂平, 柯长青等. 2016. 水文遥感. 北京: 科学出版社
Liu Z H, Ballantyne A P and Cooper L A. 2019q. Biophysical feedback of global forest fires on surface temperature. Nature Communications, 10(1): 214 [DOI: 10.1038/s41467-018-08237-zhttp://dx.doi.org/10.1038/s41467-018-08237-z]
Liu Z Q, Lu X L, An S Q, Heskel M, Yang H L and Tang J W. 2019p. Advantage of multi-band solar-induced chlorophyll fluorescence to derive canopy photosynthesis in a temperate forest. Agricultural and Forest Meteorology, 279: 107691 [DOI: 10.1016/j.agrformet.2019.107691http://dx.doi.org/10.1016/j.agrformet.2019.107691]
Liu Z W, Li S N, Zhang Y S, Guo Y H, Wei W and Wang K X. 2019. Evaporation characteristics of alpine meadow in Tibetan Plateau and the influencing factors. Journal of Arid Land Resources and Environment, 33(9): 87-93
刘志伟, 李胜男, 张寅生, 郭燕红, 韦玮, 王坤鑫. 2019. 青藏高原高寒草原土壤蒸发特征及其影响因素. 干旱区资源与环境, 33(9): 87-93 [DOI: 10.13448/j.cnki.jalre.2019.270http://dx.doi.org/10.13448/j.cnki.jalre.2019.270]
Lu Y J, Cai H J, Jiang T T, Sun S K, Wang Y B, Zhao J F, Yu X and Sun J X. 2019. Assessment of global drought propensity and its impacts on agricultural water use in future climate scenarios. Agricultural and Forest Meteorology, 278: 107623 [DOI: 10.1016/j.agrformet.2019.107623http://dx.doi.org/10.1016/j.agrformet.2019.107623]
Luo S X, Song C Q, Liu K, Ke L H and Ma R H. 2019a. An effective low-cost remote sensing approach to reconstruct the long-term and dense time series of area and storage variations for large lakes. Sensors, 19(19): 4247 [DOI: 10.3390/s19194247http://dx.doi.org/10.3390/s19194247]
Luo W, Xu X L, Liu W, Liu M X, Li Z W, Peng T, Xu C H, Zhang Y H and Zhang R F. 2019b. UAV based soil moisture remote sensing in a karst mountainous catchment. CATENA, 174: 478-489 [DOI: 10.1016/j.catena.2018.11.017http://dx.doi.org/10.1016/j.catena.2018.11.017]
Lv S, Zeng Y, Su Z and Wen J. 2019. A closed-form expression of soil temperature sensing depth at L-band. IEEE Transations on Geoscience and Remote Sensing, 57(7): 4889-4897. [DOI: 10.119/TGRS. 2019. 2893687http://dx.doi.org/10.119/TGRS. 2019. 2893687]
Ma B, Li J C, Fan W J, Ren H Z, Xu X R, Cui Y K and Peng J J. 2018. Application of an LAI inversion algorithm based on the unified model of canopy bidirectional reflectance distribution function to the heihe river basin. Journal of Geophysical Research: Atmospheres, 123(18): 10671-10687 [DOI: 10.1029/2018JD028415http://dx.doi.org/10.1029/2018JD028415]
Ma J, Xiao X M, Miao R H, Li Y, Chen B Q, Zhang Y and Zhao B. 2019a. Trends and controls of terrestrial gross primary productivity of China during 2000-2016. Environmental Research Letters, 14(8): 084032 [DOI: 10.1088/1748-9326/ab31e4http://dx.doi.org/10.1088/1748-9326/ab31e4]
Ma Q, Wang K C and Wild M. 2015. Impact of geolocations of validation data on the evaluation of surface incident shortwave radiation from Earth System Models. Journal of Geophysical Research: Atmospheres, 120(14): 6825-6844 [DOI: 10.1002/2014jd022572http://dx.doi.org/10.1002/2014jd022572]
Ma R, Husi L T, Shang H Z, A’NA R, He J, Han X and Wang Z M. 2019. Estimation of downward surface shortwave radiation from Himawari-8 atmospheric products. Journal of Remote Sensing, 23(5): 924-934
马润, 胡斯勒图, 尚华哲, 阿娜日, 赫杰, 韩旭, 王子明. 2019. 基于葵花-8卫星大气产品的地表下行短波辐射计算. 遥感学报, 23(5): 924-934 [DOI: 10.11834/jrs.20198033http://dx.doi.org/10.11834/jrs.20198033]
Ma Y J, Li X Y, Liu L, Yang X F, Wu X C, Wang P, Lin H, Zhang G H and Miao C Y. 2019b. Evapotranspiration and its dominant controls along an elevation gradient in the Qinghai Lake watershed, northeast Qinghai-Tibet Plateau. Journal of Hydrology, 575: 257-268 [DOI: 10.1016/j.jhydrol.2019.05.019http://dx.doi.org/10.1016/j.jhydrol.2019.05.019]
Magruder L A and Brunt K M. 2018. Performance analysis of airborne photon-counting lidar data in preparation for the ICESat-2 mission. IEEE Transactions on Geoscience and Remote Sensing, 56(5): 2911-2918 [DOI: 10.1109/TGRS.2017.2786659http://dx.doi.org/10.1109/TGRS.2017.2786659]
Maykut G A and Church P E. 1973. Radiation climate of barrow Alaska, 1962-66. Journal of Applied Meteorology, 12(4): 620-628 [DOI: 10.1175/1520-0450(1973)012<0620:rcoba>2.0.co;2http://dx.doi.org/10.1175/1520-0450(1973)012<0620:rcoba>2.0.co;2]
Melnikova I, Awaya Y, Saitoh T M, Muraoka H, Sasai T. 2018. Estimation of leaf area index in a mountain forest of central japan with a 30-m spatial resolution based on landsat operational land imager imagery: An application of a simple model for seasonal monitoring. Remote Sensing, 10(2): 179 [DOI: 10.3390/rs10020179http://dx.doi.org/10.3390/rs10020179]
Meng X C, Cheng J, Zhao S H, Liu S H and Yao Y J. 2019. Estimating land surface temperature from landsat-8 data using the NOAA JPSS enterprise algorithm. Remote Sensing, 11(2): 155 [DOI: 10.3390/rs11020155http://dx.doi.org/10.3390/rs11020155]
Ni W J, Zhang Z Y, Sun G Q and Liu Q H. 2019a. Modeling the stereoscopic features of mountainous forest landscapes for the extraction of forest heights from stereo imagery. Remote Sensing, 11(10): 1222 [DOI: 10.3390/rs11101222http://dx.doi.org/10.3390/rs11101222]
Ni Z Y, Huo H Y, Tang S H, Li Z L, Liu Z G, Xu S and Chen B L. 2019b. Assessing the response of satellite sun-induced chlorophyll fluorescence and MODIS vegetation products to soil moisture from 2010 to 2017: a case in Yunnan Province of China. International Journal of Remote Sensing, 40(5/6): 2278-2295 [DOI: 10.1080/01431161.2018.1506186http://dx.doi.org/10.1080/01431161.2018.1506186]
Nicodemus F E, Richmond J C, Hsia J J, Ginsberg I W and Limperis T L. 1977. Geometrical considerations and nomenclature for reflectance. UNT
Niu Z E, He H L, Zhu G F, Ren X L, Zhang L, Zhang K, Yu G R, Ge R, Li P, Zeng N and Zhu X B. 2019. An increasing trend in the ratio of transpiration to total terrestrial evapotranspiration in China from 1982 to 2015 caused by greening and warming. Agricultural and Forest Meteorology, 279: 107701 [DOI: 10.1016/j.agrformet.2019.107701http://dx.doi.org/10.1016/j.agrformet.2019.107701]
Painter T H, Rittger K, McKenzie C, Slaughter P, Davis R E and Dozier J. 2009. Retrieval of subpixel snow covered area, grain size, and albedo from MODIS. Remote Sensing of Environment, 113(4): 868-879 [DOI: 10.1016/j.rse.2009.01.001http://dx.doi.org/10.1016/j.rse.2009.01.001]
Pan H Z, Chen Z X, de Wit A and Ren J J. 2019b. Joint assimilation of leaf area index and soil moisture from sentinel-1 and sentinel-2 data into the WOFOST model for winter wheat yield estimation. Sensors, 19(14): 3161 [DOI: 10.3390/s19143161http://dx.doi.org/10.3390/s19143161]
Pan H Z, Chen Z X, Ren J Q, Li H and Wu S R. 2019a. Modeling winter wheat leaf area index and canopy water content with three different approaches using sentinel-2 multispectral instrument data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(2): 482-492 [DOI: 10.1109/JSTARS.2018.2855564http://dx.doi.org/10.1109/JSTARS.2018.2855564]
Park S H, Jung H S, Choi J and Jeon S. 2017. A quantitative method to evaluate the performance of topographic correction models used to improve land cover identification. Advances in Space Research, 60(7): 1488-1503 [DOI: 10.1016/j.asr.2017.06.054http://dx.doi.org/10.1016/j.asr.2017.06.054]
Peng H T, Ke C Q, Shen X Y, Li M M and Shao Z D. 2019a. Summer albedo variations in the Arctic Sea ice region from 1982 to 2015. International Journal of Climatology [DOI: 10.1002/joc.6379http://dx.doi.org/10.1002/joc.6379]
Peng J, Muller J P, Blessing S, Giering R, Danne O, Gobron N, Kharbouche S, Ludwig R, Müller B, Leng G Y, You Q L, Duan Z and Dadson S. 2019b. Can we use satellite-based FAPAR to detect drought? Sensors, 19(17): 3662 [DOI: 10.3390/s19173662http://dx.doi.org/10.3390/s19173662]
Peng X M, She J F, Zhang S H, Tan J Z and Li Y. 2019c. Evaluation of multi-reanalysis solar radiation products using global surface observations. Atmosphere, 10(2): 42 [DOI: 10.3390/atmos10020042http://dx.doi.org/10.3390/atmos10020042]
Peng Y D, Li W S, Luo X B and Li H. 2019d. A Geographically and temporally weighted regression model for spatial downscaling of MODIS land surface temperatures over urban heterogeneous regions. IEEE Transactions on Geoscience and Remote Sensing, 57(7): 5012-5027 [DOI: 10.1109/TGRS.2019.2895351http://dx.doi.org/10.1109/TGRS.2019.2895351]
Prata A J. 1996. A new long‐wave formula for estimating downward clear‐sky radiation at the surface. Quarterly Journal of the Royal Meteorological Society, 122(533): 1127-1151 [DOI: 10.1002/qj.49712253306http://dx.doi.org/10.1002/qj.49712253306]
Qi J B, Xie D H, Guo D S and Yan G J. 2017. A Large-scale emulation system for realistic three-dimensional (3-D) forest simulation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(11): 4834-4843 [DOI: 10.1109/JSTARS.2017.2714423http://dx.doi.org/10.1109/JSTARS.2017.2714423]
Qi J B, Xie D H, Yin T G, Yan G J, Gastellu-Etchegorry J P, Li L Y, Zhang W M, Mu X H and Norford L K. 2019a. LESS: largE-Scale remote sensing data and image simulation framework over heterogeneous 3D scenes. Remote Sensing of Environment, 221: 695-706 [DOI: 10.1016/j.rse.2018.11.036http://dx.doi.org/10.1016/j.rse.2018.11.036]
Qi J B, Yin T G, Xie D H and Gastellu-Etchegorry J P. 2019b. Hybrid scene structuring for accelerating 3D radiative transfer simulations. Remote Sensing, 11(22): 2637 [DOI: 10.3390/rs11222637http://dx.doi.org/10.3390/rs11222637]
Qian X, Qiu B and Zhang Y G. 2019. Widespread decline in vegetation photosynthesis in Southeast Asia due to the prolonged drought during the 2015/2016 El Niño. Remote Sensing, 11(8): 910 [DOI: 10.3390/rs11080910http://dx.doi.org/10.3390/rs11080910]
Qiao B J, Zhu L P, Wang J B, Ju J T, Ma Q F, Huang L, Chen H, Liu C and Xu T. 2019a. Estimation of lake water storage and changes based on bathymetric data and altimetry data and the association with climate change in the central Tibetan Plateau. Journal of Hydrology, 578: 124052 [DOI: 10.1016/j.jhydrol.2019.124052http://dx.doi.org/10.1016/j.jhydrol.2019.124052]
Qiao B J, Zhu L P and Yang R M. 2019b. Temporal-spatial differences in lake water storage changes and their links to climate change throughout the Tibetan Plateau. Remote Sensing of Environment, 222: 232-243 [DOI: 10.1016/j.rse.2018.12.037http://dx.doi.org/10.1016/j.rse.2018.12.037]
Qin W H and Gerstl S A W. 2000. 3-D scene modeling of semidesert vegetation cover and its radiation regime. Remote Sensing of Environment, 74(1): 145-162 [DOI: 10.1016/S0034-4257(00)00129-2http://dx.doi.org/10.1016/S0034-4257(00)00129-2]
Qiu B, Chen J M, Ju W M, Zhang Q and Zhang Y G. 2019a. Simulating emission and scattering of solar-induced chlorophyll fluorescence at far-red band in global vegetation with different canopy structures. Remote Sensing of Environment, 233: 111373 [DOI: 10.1016/j.rse.2019.111373http://dx.doi.org/10.1016/j.rse.2019.111373]
Qiu B, Li W K, Wang X Q, Shang L Y, Song C Q, Guo W D and Zhang Y G. 2019b. Satellite-observed solar-induced chlorophyll fluorescence reveals higher sensitivity of alpine ecosystems to snow cover on the Tibetan Plateau. Agricultural and Forest Meteorology, 271: 126-134 [DOI: 10.1016/j.agrformet.2019.02.045http://dx.doi.org/10.1016/j.agrformet.2019.02.045]
Qiu F, Chen J M, Croft H, Li J, Zhang Q, Zhang Y Q and Ju W M. 2019c. Retrieving leaf chlorophyll content by incorporating variable leaf surface reflectance in the PROSPECT model. Remote Sensing, 11(13): 1572 [DOI: 10.3390/rs11131572http://dx.doi.org/10.3390/rs11131572]
Qiu J X, Crow W T, Wagner W and Zhao T J. 2019d. Effect of vegetation index choice on soil moisture retrievals via the synergistic use of synthetic aperture radar and optical remote sensing. International Journal of Applied Earth Observation and Geoinformation, 80: 47-57 [DOI: 10.1016/j.jag.2019.03.015http://dx.doi.org/10.1016/j.jag.2019.03.015]
Qiu S, Zhu Z and He B B. 2019e. Fmask 4.0: improved cloud and cloud shadow detection in Landsats 4-8 and Sentinel-2 imagery. Remote Sensing of Environment, 231: 111205 [DOI: 10.1016/j.rse.2019.05.024http://dx.doi.org/10.1016/j.rse.2019.05.024]
Ran Y H and Li X. 2019. TanSat: a new star in global carbon monitoring from China. Science Bulletin, 64(5): 284-285 [DOI: 10.1016/j.scib.2019.01.019http://dx.doi.org/10.1016/j.scib.2019.01.019]
Salomonson V V and Appel I, 2004. Estimating fractional snow cover from MODIS using the normalized difference snow index. Remote Sensing of Environment, 89: 351-360
Santini F, Palombo A. 2019. Physically Based Approach for Combined Atmospheric and Topographic Corrections. Remote Sensing, 11(10): 1218 [DOI: https://doi.org/10.3390/rs11101218http://dx.doi.org/https://doi.org/10.3390/rs11101218]
Santos F, Meneses P and Hostert. 2019. Monitoring long-term forest dynamics with scarce data: a multi-date classification implementation in the Ecuadorian Amazon. European Journal of Remote Sensing, 52(sup1): 62-78 [DOI: 10.1080/22797254.2018.1533793http://dx.doi.org/10.1080/22797254.2018.1533793]
Schaepman-Strub G, Schaepman M E, Painter T H, Dangel S and Martonchik J V. 2006. Reflectance quantities in optical remote sensing—definitions and case studies. Remote Sensing of Environment, 103(1): 27-42 [DOI: 10.1016/j.rse.2006.03.002http://dx.doi.org/10.1016/j.rse.2006.03.002]
Shan N, Ju W M, Migliavacca M, Martini D, Guanter L, Chen J M, Goulas Y and Zhang Y G. 2019. Modeling canopy conductance and transpiration from solar-induced chlorophyll fluorescence. Agricultural and Forest Meteorology, 268: 189-201 [DOI: 10.1016/j.agrformet.2019.01.031http://dx.doi.org/10.1016/j.agrformet.2019.01.031]
Shao Z F, Pan Y, Diao C Y and Cai J J. 2019. Cloud detection in remote sensing images based on multiscale features-convolutional neural network. IEEE Transactions on Geoscience and Remote Sensing, 57(6): 4062-4076 [DOI: 10.1109/TGRS.2018.2889677http://dx.doi.org/10.1109/TGRS.2018.2889677]
Shi J and Dozier J. 2000a. Estimation of snow water equivalence using SIR-C/X-SAR. I. Inferring snow density and subsurface properties. IEEE Transactions on Geoscience and Remote Sensing, 38(6): 2465-2474 [DOI: 10.1109/36.885195http://dx.doi.org/10.1109/36.885195]
Shi J and Dozier J. 2000b. Estimation of snow water equivalence using SIR-C/X-SAR. II. Inferring snow depth and particle size. IEEE Transactions on Geoscience and Remote Sensing, 38(6): 2475-2488 [DOI: 10.1109/36.885196http://dx.doi.org/10.1109/36.885196]
Shi J C. 2012. An automatic algorithm on estimating sub-pixel snow cover from MODIS. Quaternary Sciences, 32(1): 6-15
施建成. 2012. MODIS亚像元积雪覆盖反演算法研究. 第四纪研究, 32(1): 6-15 [DOI: 10.3969/j.issn.1001-7410.2012.01.01http://dx.doi.org/10.3969/j.issn.1001-7410.2012.01.01]
Shi J C, Dong X L, Zhao T J, Du J Y, Jiang L M, Du Y, Liu H, Wang Z Z, Ji D B and Xiong C. 2014. WCOM: the science scenario and objectives of a global water cycle observation mission//Proceedings of 2014 IEEE Geoscience and Remote Sensing Symposium. Quebec City: IEEE: 3646-3649 [DOI: 10.1109/IGARSS.2014.6947273http://dx.doi.org/10.1109/IGARSS.2014.6947273]
Shi J C, Xiong C and Jiang L M. 2016. Review of snow water equivalent microwave remote sensing. Science China Earth Sciences, 59(4): 731-745 [DOI: 10.1007/s11430-015-5225-0http://dx.doi.org/10.1007/s11430-015-5225-0]
Shi J C, Xiong C and Jiang L M. 2016. Review of snow water equivalent microwave remote sensing. Science China Earth Sciences, 46(4): 529-543
施建成, 熊川, 蒋玲梅. 2016. 雪水当量主被动微波遥感研究进展. 中国科学(B辑), 46(4): 529-543 [DOI: 10.1360/N072015-00031http://dx.doi.org/10.1360/N072015-00031]
Shi Y R, Zhang Y F and Li R Y. 2019. Local-scale urban energy balance observation under various sky conditions in a humid subtropical region. Journal of Applied Meteorology and Climatology, 58(7): 1573-1591 [DOI: 10.1175/jamc-d-18-0273.1http://dx.doi.org/10.1175/jamc-d-18-0273.1]
Shimizu K, Ota T, Mizoue N and Yoshida S. 2018. Assessments of preprocessing methods for Landsat time series images of mountainous forests in the tropics. Journal of Forest Research, 23(3): 139-148 [DOI: 10.1080/13416979.2018.1434034http://dx.doi.org/10.1080/13416979.2018.1434034]
Shui T T, Liu J, Xiao Y and Shi L Y. 2019. Effects of snow cover on urban surface energy exchange: observations in Harbin, China during the winter season. International Journal of Climatology, 39(3): 1230-1242 [DOI: 10.1002/joc.5873http://dx.doi.org/10.1002/joc.5873]
Singh S, Sood V, Kaur R and Prashar S. 2019. An efficient algorithm for detection of seasonal snow cover variations over undulating North Indian Himalayas, India. Advances in Space Research, 64(2): 314-327 [DOI: 10.1016/j.asr.2019.04.016http://dx.doi.org/10.1016/j.asr.2019.04.016]
Smith L C. 1997. Satellite remote sensing of river inundation area, stage, and discharge: a review. Hydrological Processes, 11(10): 1427-1439 [DOI: 10.1002/http://dx.doi.org/10.1002/
(SICI)1099-1085(199708)11:10<1427::AID-HYP473>3.0.CO;2-S]
Smith L C and Pavelsky T M. 2009. Remote sensing of volumetric storage changes in lakes. Earth Surface Processes and Landforms, 34(10): 1353-1358 [DOI: 10.1002/esp.1822http://dx.doi.org/10.1002/esp.1822]
Sola I, González-Audícana M and Álvarez-Mozos J. 2016. Multi-criteria evaluation of topographic correction methods. Remote Sensing of Environment, 184: 247-262 [DOI: 10.1016/j.rse.2016.07.002http://dx.doi.org/10.1016/j.rse.2016.07.002]
Song L and Wu R G. 2019. Intraseasonal snow cover variations over Western Siberia and associated atmospheric processes. Journal of Geophysical Research: Atmospheres, 124(16): 8994-9010 [DOI: 10.1029/2019JD030479http://dx.doi.org/10.1029/2019JD030479]
Su W, Huang J X, Liu D S and Zhang M Z. 2019a. Retrieving corn canopy leaf area index from multitemporal landsat imagery and terrestrial LiDAR data. Remote Sensing, 11(5): 572 [DOI: 10.3390/rs11050572http://dx.doi.org/10.3390/rs11050572]
Su W, Sun Z P, Chen W H, Zhang X D, Yao C, Wu J Y, Huang J X and Zhu D H. 2019b. Joint retrieval of growing season corn canopy LAI and leaf chlorophyll content by fusing sentinel-2 and MODIS images. Remote Sensing, 11(20): 2409 [DOI: 10.3390/rs11202409http://dx.doi.org/10.3390/rs11202409]
Sui Y L, He B and Fu T J. 2019. Energy-based cloud detection in multispectral images based on the SVM technique. International Journal of Remote Sensing, 40(14): 5530-5543 [DOI: 10.1080/01431161.2019.1580788http://dx.doi.org/10.1080/01431161.2019.1580788]
Sun J, Shi S, Yang J, Gong W, Qiu F, Wang L C, Du L and Chen B W. 2019. Wavelength selection of the multispectral lidar system for estimating leaf chlorophyll and water contents through the PROSPECT model. Agricultural and Forest Meteorology, 266-267: 43-52 [DOI: 10.1016/j.agrformet.2018.11.035http://dx.doi.org/10.1016/j.agrformet.2018.11.035]
Sun Q, Jiao Q J and Dai H Y. 2019. Research on retrieving corn canopy chlorophyll content under different leaf inclination angle distribution types based on spectral indices. Spectroscopy and Spectral Analysis, 39(7): 2257-2263
孙奇, 焦全军, 戴华阳. 2019. 基于光谱指数的不同叶倾角分布下玉米冠层叶绿素含量反演. 光谱学与光谱分析, 39(7): 2257-2263
Swinbank W C. 1963. Long‐wave radiation from clear skies. Quarterly Journal of the Royal Meteorological Society, 89(381): 339-348 [DOI: 10.1002/qj.49708938105http://dx.doi.org/10.1002/qj.49708938105]
Tan J C, NourEldeen N, Mao K B, Shi J C, Li Z L, Xu T R and Yuan Z J. 2019a. Deep learning convolutional neural network for the retrieval of land surface temperature from AMSR2 data in China. Sensors, 19(3): 2987 [DOI: 10.3390/s19132987http://dx.doi.org/10.3390/s19132987]
Tan S, Wu B F and Yan N N. 2019b. A method for downscaling daily evapotranspiration based on 30-m surface resistance. Journal of Hydrology, 577: 123882 [DOI: 10.1016/j.jhydrol.2019.06.054http://dx.doi.org/10.1016/j.jhydrol.2019.06.054]
Tang R L, Li Z L and Sun X M. 2013. Temporal upscaling of instantaneous evapotranspiration: an intercomparison of four methods using eddy covariance measurements and MODIS data. Remote Sensing of Environment, 138: 102-118 [DOI: 10.1016/j.rse.2013.07.001http://dx.doi.org/10.1016/j.rse.2013.07.001]
Tang R L, Li Z L, Huo X, Jiang Y Z, Tang B H and Wu H. 2019. A re-examination of two methods for estimating daily evapotranspiration from remotely sensed instantaneous observations. International Journal of Remote Sensing, 40(5/6): 1981-1995
Tang R Y, Zhao X, Zhou T, Jiang B, Wu D H and Tang B J. 2018. Assessing the impacts of urbanization on albedo in Jing-Jin-Ji Region of China. Remote Sensing, 10(7): 1096 [DOI: 10.3390/rs10071096http://dx.doi.org/10.3390/rs10071096]
Tao G F, Jia K, Zhao X, Wei X Q, Xie X H, Zhang X W, Wang B, Yao Y J and Zhang X T. 2019a. Generating high spatio-temporal resolution fractional vegetation cover by Fusing GF-1 WFV and MODIS data. Remote Sensing, 11(19): 2324 [DOI: 10.3390/rs11192324http://dx.doi.org/10.3390/rs11192324]
Tao L L, Wang G J, Chen W J, Chen X, Li J and Cai Q K. 2019b. Soil moisture retrieval from SAR and optical data using a combined model. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(2): 637-647 [DOI: 10.1109/JSTARS.2019.2891583http://dx.doi.org/10.1109/JSTARS.2019.2891583]
Tao M H, Wang J, Li R, Wang L L, Wang L C, Wang Z F, Tao J H, Che H Z and Chen L F. 2019c. Performance of MODIS high-resolution MAIAC aerosol algorithm in China: characterization and limitation. Atmospheric Environment, 213: 159-169 [DOI: 10.1016/j.atmosenv.2019.06.004http://dx.doi.org/10.1016/j.atmosenv.2019.06.004]
Verrelst J, Camps-Valls G, Muñoz-Marí J, Rivera J P, Veroustraete F, Clevers J G P W and Moreno J. 2015. Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties-A review. ISPRS Journal of Photogrammetry and Remote Sensing, 108: 273-290 [DOI: 10.1016/j.isprsjprs.2015.05.005http://dx.doi.org/10.1016/j.isprsjprs.2015.05.005]
Wahr J, Molenaar M and Bryan F. 1998. Time variability of the Earth’s gravity field: hydrological and oceanic effects and their possible detection using GRACE. Journal of Geophysical Research, 103(B12): 30205-30229 [DOI: 10.1029/98JB02844http://dx.doi.org/10.1029/98JB02844]
Wang C G, Duan S B, Zhang X Y, Wu H, Gao M F and Leng P. 2019a. An alternative split-window algorithm for retrieving land surface temperature from Visible Infrared Imaging Radiometer Suite data. International Journal of Remote Sensing, 40(5/6): 1640-1654 [DOI: 10.1080/01431161.2018.1492180http://dx.doi.org/10.1080/01431161.2018.1492180]
Wang G X, Jiang L M, Shi J C, Liu X J, Yang J W and Cui H Z. 2019b. Snow-covered area retrieval from Himawari–8 AHI imagery of the Tibetan Plateau. Remote Sensing, 11(20): 2391 [DOI: 10.3390/rs11202391http://dx.doi.org/10.3390/rs11202391]
Wang H, Mao K B, Mu F Y, Shi J C, Yang J, Li Z L and Qin Z H. 2019c. A split window algorithm for retrieving land surface temperature from FY-3D MERSI-2 data. Remote Sensing, 11(18): 2083 [DOI: 10.3390/rs11182083http://dx.doi.org/10.3390/rs11182083]
Wang H B, Li X, Ma M G and Geng L Y. 2019d. Improving estimation of gross primary production in dryland ecosystems by a model-data fusion approach. Remote Sensing, 11(3): 225 [DOI: 10.3390/rs11030225http://dx.doi.org/10.3390/rs11030225]
Wang J, Li H P, Lu H Y, Zhang R Q and Cao X S. 2019. Dry-wet edge based on land surface temperature and leaf area index and estimation of regional evapotranspiration. Arid Zone Research, 36(2): 395-402
王军, 李和平, 鹿海员, 张瑞强, 曹雪松. 2019. 基于地表温度和叶面积指数的干湿限研究及区域蒸散发估算, 36(2): 395-402 [DOI: 10.13866/j.azr.2019.02.15http://dx.doi.org/10.13866/j.azr.2019.02.15]
Wang J, Yan Q W, Tan X L and Zou Y J. 2019. Vegetation coverage dynamics and its driving factors in Inner Mongolia based on FVC information entropy. Forest Resources Management, (4): 159-167
王瑾, 闫庆武, 谭学玲, 邹雅婧. 2019. 内蒙古地区植被覆盖动态及驱动因素分析. 林业资源管理, (4): 159-167 [DOI: 10.13466/j.cnki.lyzygl.2019.04.023]
Wang K C and Dickinson R E. 2012. A review of global terrestrial evapotranspiration: observation, modeling, climatology, and climatic variability. Reviews of Geophysics, 50(2):
RG2005 [DOI: 10.1029/2011RG000373http://dx.doi.org/10.1029/2011RG000373]
Wang L F, Zhang S C, Zhang C L, Che T, Su L, Wan T H and Zhang X J. 2019. The study of ground-based GPS retrieved snow depth in Altay. Desert and Oasis Meteorology, 13(1): 93-98
王力福, 张双成, 张成龙, 车涛, 苏磊, 万田荷, 张晓娟. 2019. 地基GPS用于阿勒泰积雪深度反演研究. 沙漠与绿洲气象, 13(1): 93-98 [DOI: 10.12057/j.issn.1002-0799.2019.01.013http://dx.doi.org/10.12057/j.issn.1002-0799.2019.01.013]
Wang L J, Guo N, Wang W and Zuo H C. 2019e. Optimization of the local split-window algorithm for FY-4A land surface temperature retrieval. Remote Sensing, 11(17): 2016 [DOI: 10.3390/rs11172016http://dx.doi.org/10.3390/rs11172016]
Wang L L, Liu J K, Gao Z Q, Li Y B, Huang M, Fan S H, Zhang X Y, Yang Y J, Miao S G, Zou H, Sun Y L, Chen Y and Yang T. 2019f. Vertical observations of the atmospheric boundary layer structure over Beijing urban area during air pollution episodes. Atmospheric Chemistry and Physics, 19(10): 6949-6967 [DOI: 10.5194/acp-19-6949-2019http://dx.doi.org/10.5194/acp-19-6949-2019]
Wang L S, Kaban M K, Thomas M, Chen C and Ma X. 2019g. The challenge of spatial resolutions for GRACE-based estimates volume changes of larger man-made lake: the case of China’s three gorges reservoir in the Yangtze River. Remote Sensing, 11(1): 99 [DOI: 10.3390/rs11010099http://dx.doi.org/10.3390/rs11010099]
Wang M M, He G J, Zhang Z M, Wang G Z, Wang Z H, Yin R Y, Cui S A, Wu Z J and Cao X J. 2019h. A radiance-based split-window algorithm for land surface temperature retrieval: theory and application to MODIS data. International Journal of Applied Earth Observation and Geoinformation, 76: 204-217 [DOI: 10.1016/j.jag.2018.11.015http://dx.doi.org/10.1016/j.jag.2018.11.015]
Wang M, Jia X J, Ge J W and Qian Q F. 2019i. Changes in the relationship between the interannual variation of eurasian snow cover and spring SAT over Eastern Eurasia. Journal of Geophysical Research: Atmospheres, 124(2): 468-487 [DOI: 10.1029/2018JD029077http://dx.doi.org/10.1029/2018JD029077]
Wang M M, Zhang Z J, Hu T and Liu X G. 2019j. A practical single-channel algorithm for land surface temperature retrieval: application to landsat series data. Journal of Geophysical Research: Atmospheres, 124(1): 299-316 [DOI: 10.1029/2018JD029330http://dx.doi.org/10.1029/2018JD029330]
Wang P, Li D, Liao W L, Rigden A and Wang W. 2019k. Contrasting evaporative responses of ecosystems to heatwaves traced to the opposing roles of vapor pressure deficit and surface resistance. Water Resources Research, 55(6): 4550-4563 [DOI: 10.1029/2019wr024771http://dx.doi.org/10.1029/2019wr024771]
Wang R K, Wang J, Zhu G F, Li H Y, Shao D H and Hao X H. 2019l. Estimation of surface latent heat fluxes in an oasis utilizing a two-source energy balance model based on land surface temperature decomposition. Journal of Applied Remote Sensing, 13(3): 034504 [DOI: 10.1117/1.JRS.13.034504http://dx.doi.org/10.1117/1.JRS.13.034504]
Wang S, Garcia M, Bauer-Gottwein P, Jakobsen J, Zarco-Tejada P J, Bandini F, Paz V S and Ibrom A. 2019m. High spatial resolution monitoring land surface energy, water and CO2 fluxes from an Unmanned Aerial System. Remote Sensing of Environment, 229: 14-31 [DOI: 10.1016/j.rse.2019.03.040http://dx.doi.org/10.1016/j.rse.2019.03.040]
Wang S H, Ju W M, Peñuelas J, Cescatti A, Zhou Y Y, Fu Y S, Huete A, Liu M and Zhang Y G. 2019n. Urban-rural gradients reveal joint control of elevated CO2 and temperature on extended photosynthetic seasons. Nature Ecology and Evolution, 3(7): 1076-1085 [DOI: 10.1038/s41559-019-0931-1http://dx.doi.org/10.1038/s41559-019-0931-1]
Wang T, Tang R L, Li Z L, Jiang Y Z, Liu M, Tang B H and Wu H. 2019. Temporal upscaling methods for daily evapotranspiration estimation from remotely sensed instantaneous observations. Journal of Remote Sensing, 23(5): 813-830
王桐, 唐荣林, 李召良, 姜亚珍, 刘萌, 唐伯惠, 吴骅. 2019. 遥感反演蒸散发的日尺度扩展方法研究进展. 遥感学报, 23(5): 813-830 [DOI: 10.11834/jrs.20197434http://dx.doi.org/10.11834/jrs.20197434]
Wang X and Xu F. 2019. A PolinSAR inversion error model on polarimetric system parameters for forest height mapping. IEEE Transactions on Geoscience and Remote Sensing, 57(8): 5669-5685 [DOI: 10.1109/TGRS.2019.2901531http://dx.doi.org/10.1109/TGRS.2019.2901531]
Wang X R, Qiu B, Li W K and Zhang Q. 2019o. Impacts of drought and heatwave on the terrestrial ecosystem in China as revealed by satellite solar-induced chlorophyll fluorescence. Science of the Total Environment, 693: 133627 [DOI: 10.1016/j.scitotenv.2019.133627http://dx.doi.org/10.1016/j.scitotenv.2019.133627]
Wang Y L, Huang X D, Wang J S, Zhou M Q and Liang T G. 2019p. AMSR2 snow depth downscaling algorithm based on a multifactor approach over the Tibetan Plateau, China. Remote Sensing of Environment, 231: 111268DOI: 10.1016/j.rse.2019.111268http://dx.doi.org/10.1016/j.rse.2019.111268]
Wang Y B, Ma J, Xiao X M, Wang X X, Dai S Q and Zhao B. 2019q. Long-term dynamic of Poyang Lake surface water: a mapping work based on the google earth engine cloud platform. Remote Sensing, 11(3): 313 [DOI: 10.3390/rs11030313http://dx.doi.org/10.3390/rs11030313
Wang Y, Ni W J, Sun G Q, Chi H, Zhang Z Y and Guo Z F. 2019r. Slope-adaptive waveform metrics of large footprint lidar for estimation of forest aboveground biomass. Remote Sensing of Environment, 224: 386-400 [DOI: 10.1016/j.rse.2019.02.017http://dx.doi.org/10.1016/j.rse.2019.02.017]
Wei J, Li Z Q, Peng Y R, Sun L and Yan X. 2019a. A regionally robust high-spatial-resolution aerosol retrieval algorithm for MODIS images over eastern China. IEEE Transactions on Geoscience and Remote Sensing, 57(7): 4748-4757 [DOI: 10.1109/tgrs.2019.2892813http://dx.doi.org/10.1109/tgrs.2019.2892813]
Wei J, Tang X G, Gu Q, Wang M, Ma M G and Han X J. 2019b. Using solar-induced chlorophyll fluorescence observed by OCO-2 to Predict Autumn crop production in China. Remote Sensing, 11(14): 1715 [DOI: 10.3390/rs11141715http://dx.doi.org/10.3390/rs11141715]
Wei S S, Fang H L, Schaaf C, He L M and Chen J M. 2019c. Global 500 m clumping index product derived from MODIS BRDF data (2001-2017). Remote Sensing of Environment, 232: 111296 [DOI: 10.1016/j.rse.2019.111296http://dx.doi.org/10.1016/j.rse.2019.111296]
Wei Y, Zhang X T, Hou N, Zhang W Y, Jia K and Yao Y J. 2019d. Estimation of surface downward shortwave radiation over China from AVHRR data based on four machine learning methods. Solar Energy, 177: 32-46 [DOI: 10.1016/j.solener.2018.11.008http://dx.doi.org/10.1016/j.solener.2018.11.008]
Wen J G, Liu Q, Tang Y, Dou B C, You D Q, Xiao Q, Liu Q H and Li X W. 2015. Modeling land surface reflectance coupled BRDF for HJ-1/CCD data of rugged terrain in Heihe River Basin, China. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(4): 1506-1518 [DOI: 10.1109/JSTARS.2015.2416254http://dx.doi.org/10.1109/JSTARS.2015.2416254]
Wen J Q, Liu Q, Xiao Q, Liu Q H, You D Q, Hao D L, Wu S B and Lin X W. 2018. Characterizing land surface anisotropic reflectance over rugged terrain: a review of concepts and recent developments. Remote Sensing, 10(3): 370 [DOI: 10.3390/rs10030370http://dx.doi.org/10.3390/rs10030370]
Widlowski J L, Côté J F and Béland M. 2014. Abstract tree crowns in 3D radiative transfer models: impact on simulated open-canopy reflectances. Remote Sensing of Environment, 142: 155-175 [DOI: 10.1016/j.rse.2013.11.016http://dx.doi.org/10.1016/j.rse.2013.11.016]
Wild M, Folini D, Hakuba M Z, Schär C, Seneviratne S I, Kato S, Rutan D, Ammann C, Wood E F and König-Langlo G. 2015. The energy balance over land and oceans: an assessment based on direct observations and CMIP5 climate models. Climate Dynamics, 44(11/12): 3393-3429 [DOI: 10.1007/s00382-014-2430-zhttp://dx.doi.org/10.1007/s00382-014-2430-z]
Wu C J, Wang C C, Shen P, Zhu J J, Fu H Q and Gao H. 2019a. Forest height estimation using PolInSAR optimal normal matrix constraint and cross-iteration method. IEEE Geoscience and Remote Sensing Letters, 16(8): 1245-1249 [DOI: 10.1109/LGRS.2019.2895869http://dx.doi.org/10.1109/LGRS.2019.2895869]
Wu C Y, Cao G C, Chen K L, E C Y, Mao Y H, Zhao S K, Wang Q, Su X Y and Wei Y L. 2019b. Remotely sensed estimation and mapping of soil moisture by eliminating the effect of vegetation cover. Journal of Integrative Agriculture, 18(2): 316-327 [DOI: 10.1016/S2095-3119(18)61988-4http://dx.doi.org/10.1016/S2095-3119(18)61988-4]
Wu H and Li W. 2019. Downscaling land surface temperatures using a random forest regression model with multitype predictor variables. IEEE Access, 7: 21904-21916 [DOI: 10.1109/ACCESS.2019.2896241http://dx.doi.org/10.1109/ACCESS.2019.2896241]
Wu H B. 2019. Studies on changes in water level and storage of Bosten Lake based on satellite-borne radar altimetry data. Journal of Water Resources and Water Engineering, 30(3): 9-16, 23
吴红波. 2019. 基于星载雷达测高资料估计博斯腾湖水位-水量变化研究. 水资源与水工程学报, 30(3): 9-16, 23 [DOI: 10.11705/j.issn.1672-643X.2019.03.02http://dx.doi.org/10.11705/j.issn.1672-643X.2019.03.02]
Wu J H, Zhong B, Tian S F, Yang A X and Wu J J. 2019c. Downscaling of urban land surface temperature based on multi-factor geographically weighted regression. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(8): 2897-2911 [DOI: 10.1109/JSTARS.2019.2919936http://dx.doi.org/10.1109/JSTARS.2019.2919936]
Wu P H, Yin Z X, Yang H, Wu Y L and Ma X S. 2019d. Reconstructing geostationary satellite land surface temperature imagery based on a multiscale feature connected convolutional neural network. Remote Sensing, 11(3): 300 [DOI: 10.3390/rs11030300http://dx.doi.org/10.3390/rs11030300]
Wu S B, Wen J G, Gastellu-Etchegorry J P, Liu Q H, You D Q, Xiao Q, Hao D L, Lin X W and Yin T G. 2019e. The definition of remotely sensed reflectance quantities suitable for rugged terrain. Remote Sensing of Environment, 225: 403-415 [DOI: 10.1016/j.rse.2019.01.005http://dx.doi.org/10.1016/j.rse.2019.01.005]
Wu S B, Wen J G, Lin X W, Hao D L, You D Q, Xiao Q, Liu Q H and Yin T G. 2019f. Modeling discrete forest anisotropic reflectance over a sloped surface with an extended GOMS and SAIL model. IEEE Transactions on Geoscience and Remote Sensing, 57(2): 944-957 [DOI: 10.1109/TGRS.2018.2863605http://dx.doi.org/10.1109/TGRS.2018.2863605]
Wu S B, Wen J G, Xiao Q, Liu Q H, Hao D L, Lin X W and You D Q. 2019g. Derivation of kernel functions for kernel-driven reflectance model over sloping terrain. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(2): 396-409 [DOI: 10.1109/Jstars.2018.2854771http://dx.doi.org/10.1109/Jstars.2018.2854771]
Wu T T, Han L and Liu Q. 2019h. A novel algorithm for differentiating cloud from snow sheets using Landsat 8 OLI imagery. Advances in Space Research, 64(1): 79-87 [DOI: 10.1016/j.asr.2019.03.014http://dx.doi.org/10.1016/j.asr.2019.03.014]
Wu X D, Xiao Q, Wen J G and You D Q. 2019i. Direct comparison and triple collocation: which is more reliable in the validation of coarse-scale satellite surface albedo products. Journal of Geophysical Research: Atmospheres, 124(10): 5198-5213 [DOI: 10.1029/2018jd029937http://dx.doi.org/10.1029/2018jd029937]
Wu X D, Xiao Q, Wen J G, You D Q and Hueni A. 2019j. Advances in quantitative remote sensing product validation: overview and current status. Earth-Science Reviews, 196: 102875 [DOI: 10.1016/j.earscirev.2019.102875http://dx.doi.org/10.1016/j.earscirev.2019.102875]
Xia H P, Chen Y H, Li Y and Quan J L. 2019. Combining kernel-driven and fusion-based methods to generate daily high-spatial-resolution land surface temperatures. Remote Sensing of Environment, 224: 259-274 [DOI: 10.1016/j.rse.2019.02.006http://dx.doi.org/10.1016/j.rse.2019.02.006]
Xiao X X, Zhang T J, Zhong X Y, Li X D and Li Y X. 2019. Spatiotemporal variation of snow depth in the Northern Hemisphere from 1992 to 2016. The Cryosphere Discussions [DOI: 10.5194/tc-2019-33http://dx.doi.org/10.5194/tc-2019-33]
Xiao X X, Zhang T J, Zhong X Y, Shao W W and Li X D. 2018. Support vector regression snow-depth retrieval algorithm using passive microwave remote sensing data. Remote Sensing of Environment, 210: 48-64 [DOI: 10.1016/j.rse.2018.03.008http://dx.doi.org/10.1016/j.rse.2018.03.008]
Xie J K, Xu Y P, Wang Y T, Gu H T, Wang F M and Pan S L. 2019a. Influences of climatic variability and human activities on terrestrial water storage variations across the Yellow River basin in the recent decade. Journal of Hydrology, 579: 124218 [DOI: 10.1016/j.jhydrol.2019.124218http://dx.doi.org/10.1016/j.jhydrol.2019.124218]
Xie M M, Wang Z Q, Huete A, Brown L A, Wang H Y, Xie Q Y, Xu X P and Ding Y L. 2019b. Estimating peanut leaf chlorophyll content with dorsiventral leaf adjusted indices: minimizing the impact of spectral differences between adaxial and abaxial leaf surfaces. Remote Sensing, 11(18): 2148 [DOI: 10.3390/rs11182148http://dx.doi.org/10.3390/rs11182148]
Xie X Y, Li A N, Jin H A, Tan J B, Wang C B, Lei G B, Zhang Z J, Bian J H and Nan X. 2019c. Assessment of five satellite-derived LAI datasets for GPP estimations through ecosystem models. Science of the Total Environment, 690: 1120-1130 [DOI: 10.1016/j.scitotenv.2019.06.516http://dx.doi.org/10.1016/j.scitotenv.2019.06.516]
Xu J, Yao Y J, Tan K R, Li Y F, Liu S M, Shang K, Jia K, Zhang X T, Chen X W and Bei X Y. 2019a. Integrating latent heat flux products from MODIS and landsat data using multi-resolution kalman filter method in the midstream of Heihe River Basin of Northwest China. Remote Sensing, 11(15): 1787 [DOI: 10.3390/rs11151787http://dx.doi.org/10.3390/rs11151787]
Xu M Z, Liu R G, Chen J M, Liu Y, Shang R, Ju W M, Wu C Y and Huang W J. 2019b. Retrieving leaf chlorophyll content using a matrix-based vegetation index combination approach. Remote Sensing of Environment, 224: 60-73 [DOI: 10.1016/j.rse.2019.01.039http://dx.doi.org/10.1016/j.rse.2019.01.039]
Xu S, Cheng J and Zhang Q. 2019c. Reconstructing all-weather land surface temperature using the bayesian maximum entropy method over the Tibetan Plateau and Heihe River Basin. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(9): 3307-3316 [DOI: 10.1109/JSTARS.2019.2921924http://dx.doi.org/10.1109/JSTARS.2019.2921924]
Xu T R, Guo Z X, Xia Y L, Ferreira V G, Liu S M, Wang K C, Yao Y J, Zhang X J and Zhao C S. 2019d. Evaluation of twelve evapotranspiration products from machine learning, remote sensing and land surface models over conterminous United States. Journal of Hydrology, 578: 124105 [DOI: 10.1016/j.jhydrol.2019.124105http://dx.doi.org/10.1016/j.jhydrol.2019.124105]
Xu T R, He X L, Bateni S M, Auligne T, Liu S M, Xu Z W, Zhou J and Mao K B. 2019e. Mapping regional turbulent heat fluxes via variational assimilation of land surface temperature data from polar orbiting satellites. Remote Sensing of Environment, 221: 444-461 [DOI: 10.1016/j.rse.2018.11.023http://dx.doi.org/10.1016/j.rse.2018.11.023]
Xu X J, Du H Q, Fan W L, Hu J G, Mao F J and Dong H. 2019f. Long-term trend in vegetation gross primary production, phenology and their relationships inferred from the FLUXNET data. Journal of Environmental Management, 246: 605-616 [DOI: 10.1016/j.jenvman.2019.06.023http://dx.doi.org/10.1016/j.jenvman.2019.06.023]
Xu X Q, Lu J S, Zhang N, Yang T C, He J Y, Yao X, Cheng T, Zhu Y, Cao W X and Tian Y C. 2019g. Inversion of rice canopy chlorophyll content and leaf area index based on coupling of radiative transfer and Bayesian network models. ISPRS Journal of Photogrammetry and Remote Sensing, 150: 185-196 [DOI: 10.1016/j.isprsjprs.2019.02.013http://dx.doi.org/10.1016/j.isprsjprs.2019.02.013]
Yang B, Knyazikhin Y, Xie D, Zhao H, Zhang J and Wu Y. Influence of Leaf Specular Reflection on Canopy Radiative Regime Using an Improved Version of the Stochastic Radiative Transfer Model. Remote Sens. 2018, 10, 1632
Yang C J, Huang H, Ni J and Yang D F. 2019a. Effects of Topographic normalization on the relationship between tropical forest biomass and landsat TM images. Journal of the Indian Society of Remote Sensing, 47(4): 595-601 [DOI: 10.1007/s12524-018-0902-zhttp://dx.doi.org/10.1007/s12524-018-0902-z]
Yang G, Sun W W, Shen H F, Meng X C and Li J L. 2019b. An integrated method for reconstructing daily MODIS land surface temperature data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(3): 1026-1040
Yang H T, Xu H Q, Shi T T and Chen S M. 2019. Fractional vegetation cover change based on vegetation seasonal variation correction: a case in Lianjiang County, Fujian Province, China. Chinese Journal of Applied Ecology, 30(1): 285-291
杨绘婷, 徐涵秋, 施婷婷, 陈善沐. 2019. 基于植被信息季节变换的植被覆盖度变化——以福建省连江县为例. 应用生态学报, 30(1): 285-291 [DOI: 10.13287/j.1001-9332.201901.016http://dx.doi.org/10.13287/j.1001-9332.201901.016]
Yang J, Jin S H, Xiao X M, Jin C, Xia J H, Li X M and Wang S J. 2019e. Local climate zone ventilation and urban land surface temperatures: towards a performance-based and wind-sensitive planning proposal in megacities. Sustainable Cities and Society, 47: 101487 [DOI: 10.1016/j.scs.2019.101487http://dx.doi.org/10.1016/j.scs.2019.101487]
Yang J F and Zhang D J. 2019. Soil moisture estimation with a remotely sensed dry edge determination based on the land surface temperature-vegetation index method. Journal of Applied Remote Sensing, 13(2): 024511 [DOI: 10.1117/1.JRS.13.024511http://dx.doi.org/10.1117/1.JRS.13.024511]
Yang J W, Jiang L M, Luojus K, Pan J M, Lemmetyinen J, Takala M and Wu S L. 2019d. Snow depth estimation and historical data reconstruction over china based on a random forest machine learning approach. The Cryosphere, in press, 2020 [DOI: 10.5194/tc-2019-161http://dx.doi.org/10.5194/tc-2019-161]
Yang J W, Jiang L M, Wu S L, Wang G X, Wang J and Liu X J. 2019c. Development of a snow depth estimation algorithm over China for the FY-3D/MWRI. Remote Sensing, 11(3): 977 [DOI: 10.3390/rs11080977http://dx.doi.org/10.3390/rs11080977]
Yang M, Xu W B, Li J W, Zhou Z Y and Lu Y. 2019f. A modified version of BRDF model based on Kubelka-Munk theory for coating materials. Optik, 193: 162982 [DOI: 10.1016/j.ijleo.2019.162982http://dx.doi.org/10.1016/j.ijleo.2019.162982]
Yang Q Q, Huang X and Tang Q H. 2019g. The footprint of urban heat island effect in 302 Chinese cities: temporal trends and associated factors. Science of the Total Environment, 655: 652-62 [DOI: 10.1016/j.scitotenv.2018.11.171http://dx.doi.org/10.1016/j.scitotenv.2018.11.171]
Yang X B, Wang C, Pan F F, Nie S, Xi X H and Luo S Z. 2019h. Retrieving leaf area index in discontinuous forest using ICESat/GLAS full-waveform data based on gap fraction model. ISPRS Journal of Photogrammetry and Remote Sensing, 148: 54-62 [DOI: 10.1016/j.isprsjprs.2018.12.010http://dx.doi.org/10.1016/j.isprsjprs.2018.12.010]
Yang Y, Chen R S, Song Y X, Han C T, Liu J F and Liu Z W. 2019i. Sensitivity of potential evapotranspiration to meteorological factors and their elevational gradients in the Qilian Mountains, northwestern China. Journal of Hydrology, 568: 147-159 [DOI: 10.1016/j.jhydrol.2018.10.069http://dx.doi.org/10.1016/j.jhydrol.2018.10.069]
Yang Y T and Roderick M L. 2019. Radiation, surface temperature and evaporation over wet surfaces. Quarterly Journal of the Royal Meteorological Society, 145(720): 1118-1129 [DOI: 10.1002/qj.3481http://dx.doi.org/10.1002/qj.3481]
Yao T D, Xue Y K, Chen D L, Chen F H, Thompson L, Cui P, Koike T, Lau W K M, Lettenmaier D, Mosbrugger V, Zhang R H, Xu B Q, Dozier J, Gillespie T, Gu Y, Kang S C, Piao S L, Sugimoto S, Ueno K, Wang L, Wang W C, Zhang F, Sheng Y W, Guo W D, Ailikun, Yang X X, Ma Y M, Shen S S P, Su Z B, Chen F, Liang S L, Liu Y M, Singh V P, Yang K, Yang D Q, Zhao X Q, Qian Y, Zhang Y and Li Q. 2019a. Recent third pole's rapid warming accompanies cryospheric melt and water cycle intensification and interactions between monsoon and environment: multidisciplinary approach with observations, modeling, and analysis. Bulletin of the American Meteorological Society, 100(3): 423-444 [DOI: 10.1175/Bams-D-17-0057.1http://dx.doi.org/10.1175/Bams-D-17-0057.1]
Yao Y J, Zhang Y H, Liu Q, Liu S M, Jia K, Zhang X T, Xu Z W, Xu T R, Chen J Q and Fisher J B. 2019b. Evaluation of a satellite-derived model parameterized by three soil moisture constraints to estimate terrestrial latent heat flux in the Heihe River basin of Northwest China. Science of the Total Environment, 695: 133787 [DOI: 10.1016/j.scitotenv.2019.133787http://dx.doi.org/10.1016/j.scitotenv.2019.133787]
Yin C, Lopez-Baeza E, Martin-Neira M, Fernandez-Moran R, Yang L, Navarro-Camba E A, Egido A, Mollfulleda A, Li W Q, Cao Y C, Zhu B and Yang D K. 2019a. Intercomparison of soil moisture retrieved from GNSS-R and from passive L-band radiometry at the Valencia anchor station. Sensors, 19(8): 1900 [DOI: 10.3390/s19081900http://dx.doi.org/10.3390/s19081900]
Yin G F, Li A N, Wu S B, Fan W L, Zeng Y L, Yan K, Xu B D, Li J and Liu Q H. 2018. PLC: a simple and semi-physical topographic correction method for vegetation canopies based on path length correction. Remote Sensing of Environment, 215: 184-198 [DOI: 10.1016/j.rse.2018.06.009http://dx.doi.org/10.1016/j.rse.2018.06.009]
Yin G F, Verger A, Qu Y H, Zhao W, Xu B D, Zeng Y L, Liu K, Li J and Liu Q H. 2019b. Retrieval of high spatiotemporal resolution leaf area index with gaussian processes, wireless sensor network, and satellite data fusion. Remote Sensing, 11(3): 244 [DOI: 10.3390/rs11030244http://dx.doi.org/10.3390/rs11030244]
Yin W J, Hu L T, Han S C, Zhang M L and Teng Y G. 2019c. Reconstructing terrestrial water storage variations from 1980 to 2015 in the Beishan Area of China. Geofluids, 2019: 3874742 [DOI: 10.1155/2019/3874742http://dx.doi.org/10.1155/2019/3874742]
Yu B, Shang S H, Zhu W B, Gentine P and Cheng Y. 2019a. Mapping daily evapotranspiration over a large irrigation district from MODIS data using a novel hybrid dual-source coupling model. Agricultural and Forest Meteorology, 276-277: 107612 [DOI: 10.1016/j.agrformet.2019.06.011http://dx.doi.org/10.1016/j.agrformet.2019.06.011]
Yu K G, Li Y W and Chang X. 2019b. Snow depth estimation based on combination of pseudorange and carrier phase of GNSS dual-frequency signals. IEEE Transactions on Geoscience and Remote Sensing, 57(3): 1817-1828 [DOI: 10.1109/tgrs.2018.2869284http://dx.doi.org/10.1109/tgrs.2018.2869284]
Yu L J, Yang Q H, Zhou M Y, Lenschow D H, Wang X Q, Zhao J C, Sun Q Z, Tian Z X, Shen H and Zhang L. 2019c. The variability of surface radiation fluxes over landfast sea ice near Zhongshan Station, East Antarctica during austral spring. International Journal of Digital Earth, 12(8): 860-877 [DOI: 10.1080/17538947.2017.1304458http://dx.doi.org/10.1080/17538947.2017.1304458]
Yu S S, Xin X Z, Liu Q H, Zhang H L and Li L. 2019d. An improved parameterization for retrieving clear-sky downward longwave radiation from satellite thermal infrared data. Remote Sensing, 11(4): 425 [DOI: 10.3390/rs11040425http://dx.doi.org/10.3390/rs11040425]
Yu W P, Ma M G, Yang H, Tan J L and Li X L. 2019e. Supplement of the radiance-based method to validate satellite-derived land surface temperature products over heterogeneous land surfaces. Remote Sensing of Environment, 230: 111188 [DOI: 10.1016/j.rse.2019.05.007http://dx.doi.org/10.1016/j.rse.2019.05.007]
Yu Y C, Shi J C, Wang T X, Letu H S, Yuan P F, Zhou W and Hu L. 2019f. Evaluation of the himawari-8 Shortwave Downward Radiation (SWDR) product and its comparison with the CERES-SYN, MERRA-2, and ERA-Interim datasets. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(2): 519-532 [DOI: 10.1109/jstars.2018.2851965http://dx.doi.org/10.1109/jstars.2018.2851965]
Yuan W P, Zheng Y, Piao S L, Ciais P, Lombardozzi D, Wang Y P, Ryu Y, Chen G X, Dong W J, Hu Z M, Jain A K, Jiang C Y, Kato E, Li S H, Lienert S, Liu S G, Nabel J E S M, Qin Z C, Quine T, Sitch S, Smith W K, Wang F, Wu C Y, Xiao Z Q and Yang S. 2019. Increased atmospheric vapor pressure deficit reduces global vegetation growth. Science Advances, 5(8):
eaax1396 [DOI: 10.1126/sciadv.aax1396http://dx.doi.org/10.1126/sciadv.aax1396]
Zeng Q,Cheng J and Dong L 2019. Assessment of the long-term high spatial resolution Global LAnd Surface Satellite (GLASS) surface longwave radiation product using ground measurements. IEEE Journal of Selected Topics in Applied Earth Observstions and Remote Sensing
Zeng Z Y, Gan Y J, Kettner A J, Yang Q, Zeng C, Brakenridge G R and Hong Y. 2020. Towards high resolution flood monitoring: an integrated methodology using passive microwave brightness temperatures and Sentinel synthetic aperture radar imagery. Journal of Hydrology, 582: 124377 [DOI: 10.1016/j.jhydrol.2019.124377http://dx.doi.org/10.1016/j.jhydrol.2019.124377]
Zhan X C, Xiao Z Q, Jiang J Y and Shi H Y. 2019.A Data Assimilation Method for Simultaneously Estimating the Multiscale Leaf Area Index from Time-Series Multi-Resolution Satellite Observations. IEEE Transactions on Geoscience and Remote Sensing, 57(11): 9344-9361 [DOI: 10.1109/TGRS.2019.2926392http://dx.doi.org/10.1109/TGRS.2019.2926392]
Zhang A Y and Zhang X L. 2019. Land surface temperature retrieved from Landsat-8 and comparison with MODIS temperature product. Journal of Beijing Forestry University, 41(3): 1-13
张爱因, 张晓丽. Landsat-8地表温度反演及其与MODIS温度产品的对比分析. 北京林业大学学报, 41(3): 1-13 [DOI: 10.13332/j.1000-1522.20180234http://dx.doi.org/10.13332/j.1000-1522.20180234]
Zhang G D, Zhou H M, Wang C J, Xue H Z, Wan g J D and Wan H W. 2019b. Time series high-resolution land surface albedo estimation based on the ensemble kalman filter algorithm. Remote Sensing, 11(7): 753 [DOI: 10.3390/rs11070753http://dx.doi.org/10.3390/rs11070753]
Zhang G Q, Chen W F and Xie H J. 2019a. Tibetan Plateau's lake level and volume changes from NASA's ICESat/ICESat‐2 and Landsat missions. Geophysical Research Letters, 46(22): 13107-13118 [DOI: 10.1029/2019GL085032http://dx.doi.org/10.1029/2019GL085032]
Zhang G Q, Yao T D, Chen W F, Zheng G X, Shum C K, Yang K, Piao S L, Sheng Y W, Yi S, Li J L, O'Reilly C M, Qi S H, Shen S S P, Zhang H B and Jia Y Y. 2019c. Regional differences of lake evolution across China during 1960s–2015 and its natural and anthropogenic causes. Remote Sensing of Environment, 221: 386-404 [DOI: 10.1016/j.rse.2018.11.038http://dx.doi.org/10.1016/j.rse.2018.11.038]
Zhang H B, Zhang F, Zhang G Q, Che T, Yan W, Ye M and Ma N. 2019d. Ground-based evaluation of MODIS snow cover product V6 across China: implications for the selection of NDSI threshold. Science of the Total Environment, 651: 2712-2726 [DOI: 10.1016/j.scitotenv.2018.10.128http://dx.doi.org/10.1016/j.scitotenv.2018.10.128]
Zhang J C and Zhou W Z. 2019. Spatial-temporal changes of fraction of absorbed photosynthetically active radiation in Qinling-Daba Mountains from 2006 to 2015. Chinese Journal of Ecology, 38(5): 1453-1463
章金城, 周文佐. 2019. 2006-2015年秦巴山区植被光合有效辐射吸收比例的时空变化特征. 生态学杂志, 38(5): 1453-1463 [DOI: 10.13292/j.1000-4890.201905.018http://dx.doi.org/10.13292/j.1000-4890.201905.018]
Zhang J L, Lu C, Xu H and Wang G X. 2019e. Estimating aboveground biomass of Pinus densata-dominated forests using Landsat time series and permanent sample plot data. Journal of Forestry Research, 30(5): 1689-1706 [DOI: 10.1007/s11676-018-0713-7http://dx.doi.org/10.1007/s11676-018-0713-7]
Zhang K, Zhu G F, Ma J Z, Yang Y T, Shang S S and Gu C J. 2019f. Parameter analysis and estimates for the MODIS evapotranspiration algorithm and multiscale verification. Water Resources Research, 55(3): 2211-2231 [DOI: 10.1029/2018WR023485http://dx.doi.org/10.1029/2018WR023485]
Zhang L F, Qiao N, Huang C P and Wang S H. 2019g. Monitoring drought effects on vegetation productivity using satellite solar-induced chlorophyll fluorescence. Remote Sensing, 11(4): 378 [DOI: 10.3390/rs11040378http://dx.doi.org/10.3390/rs11040378]
Zhang L L, Yao Y J, Bei X Y, Jia K, Zhang X T, Xie X H, Jiang B, Shang K, Xu J and Chen X W. 2019h. Assessing the remotely sensed evaporative drought index for drought monitoring over Northeast China. Remote Sensing, 11(17): 1960 [DOI: 10.3390/rs11171960http://dx.doi.org/10.3390/rs11171960]
Zhang L X, Zhou D C, Fan J W, Guo Q, Chen S P, Wang R H and Li Y Z. 2019i. Contrasting the performance of eight satellite-based GPP models in water-limited and temperature-limited grassland ecosystems. Remote Sensing, 11(11): 1333 [DOI: 10.3390/rs11111333http://dx.doi.org/10.3390/rs11111333]
Zhang Q, Wan Z, Hemmings B and Abbasov F. 2019j. Reducing black carbon emissions from Arctic shipping: solutions and policy implications. Journal of Cleaner Production, 241: 118261 [DOI: 10.1016/j.jclepro.2019.118261http://dx.doi.org/10.1016/j.jclepro.2019.118261]
Zhang Q, Zhang X K, Li Z H, Wu Y F and Zhang Y G. 2019k. Comparison of Bi-hemispherical and hemispherical-conical configurations for in situ measurements of solar-induced chlorophyll fluorescence. Remote Sensing, 11(22): 2642 [DOI: 10.3390/rs11222642http://dx.doi.org/10.3390/rs11222642]
Zhang Q Q, Pan Y, Gong H L, Zheng L Q and Zhu Y Q. 2019. The impact of different GRACE filtering methods on inversing terrestrial water storage change in southwestern Karst Area. Earth Science, 44(9): 2955-2962
张青全, 潘云, 宫辉力, 郑龙群, 诸云强. 2019. 不同滤波方法对GRACE反演西南岩溶区陆地水储量变化的影响. 地球科学, 44(9): 2955-2962 [DOI: 10.3799/dqkx.2019.153http://dx.doi.org/10.3799/dqkx.2019.153]
Zhang R, Zhou X H, Ouyang Z T, Avitabile V, Qi J G, Chen J Q and Giannico V. 2019l. Estimating aboveground biomass in subtropical forests of China by integrating multisource remote sensing and ground data. Remote Sensing of Environment, 232: 111341 [DOI: 10.1016/j.rse.2019.111341http://dx.doi.org/10.1016/j.rse.2019.111341]
Zhang S, Shi C X, Shen R P and Wu J. 2019o. Improved assimilation of fengyun-3 satellite-based snow cover fraction in northeastern China. Journal of Meteorological Research, 33(5): 960-975 [DOI: 10.1007/s13351-019-8205-zhttp://dx.doi.org/10.1007/s13351-019-8205-z]
Zhang S H, Li X G, She J F and Peng X M. 2019n. Assimilating remote sensing data into GIS-based all sky solar radiation modeling for mountain terrain. Remote Sensing of Environment, 231: 111239 [DOI: 10.1016/j.rse.2019.111239http://dx.doi.org/10.1016/j.rse.2019.111239]
Zhang S Q, Chen H, Fu Y, Niu H H, Yang Y and Zhang B X. 2019m. Fractional vegetation cover estimation of different vegetation types in the Qaidam Basin. Sustainability, 11(3): 864 [DOI: 10.3390/su11030864http://dx.doi.org/10.3390/su11030864]
Zhang T, Zhou C X and Zheng L. 2019r. Analysis of the temporal-spatial changes in surface radiation budget over the Antarctic sea ice region. Science of the Total Environment, 666: 1134-1150 [DOI: 10.1016/j.scitotenv.2019.02.264http://dx.doi.org/10.1016/j.scitotenv.2019.02.264]
Zhang T T, Wang T, Krinner G, Wang X Y, Gasser T, Peng S S, Piao S L and Yao T D. 2019q. The weakening relationship between Eurasian spring snow cover and Indian summer monsoon rainfall. Science Advances, 5(3):
eaau8932 [DOI: 10.1126/sciadv.aau8932http://dx.doi.org/10.1126/sciadv.aau8932]
Zhang W B, Yang X C, Manlike A, Jin Y X, Zheng F L, Guo J, Shen G, Zhang Y J and Xu B. 2019s. Comparative study of remote sensing estimation methods for grassland fractional vegetation coverage – a grassland case study performed in Ili prefecture, Xinjiang, China. International Journal of Remote Sensing, 40(5/6): 2243-2258 [DOI: 10.1080/01431161.2018.1508918http://dx.doi.org/10.1080/01431161.2018.1508918]
Zhang W Y, Zhang X T, Li W H, Hou N, Wei Y, Jia K, Yao Y J and Cheng J. 2019t. Evaluation of Bayesian multimodel estimation in surface incident shortwave radiation simulation over high latitude areas. Remote Sensing, 11(15): 1776 [DOI: 10.3390/rs11151776http://dx.doi.org/10.3390/rs11151776]
Zhang X, Liu L Y, Chen X D, Xie S and Lei L P. 2019w. A novel multitemporal cloud and cloud shadow detection method using the integrated cloud z-scores model. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(1): 123-134 [DOI: 10.1109/JSTARS.2018.2889150http://dx.doi.org/10.1109/JSTARS.2018.2889150]
Zhang X H, He Y, Wang C, Xu F, Li X H, Tan C W, Chen D M, Wang G J and Shi L X. 2019z. Estimation of corn canopy chlorophyll content using derivative spectra in the O2–a absorption band. Frontiers in Plant Science, 10: 1047 [DOI: 10.3389/fpls.2019.01047http://dx.doi.org/10.3389/fpls.2019.01047]
Zhang X N, Jiao Z T, Dong Y D, He T, Ding A X, Yin S Y, Zhang H, Cui L, Chang Y X, Guo J and Xie R. 2019u. Development of the direct-estimation albedo algorithm for snow-free landsat TM albedo retrievals using field flux measurements. IEEE Transactions on Geoscience and Remote Sensing, 58(3): 1550-1567 [DOI: 10.1109/TGRS.2019.2946598http://dx.doi.org/10.1109/TGRS.2019.2946598]
Zhang X T, Liang S L, Wang G X, Yao Y J, Jiang B and Cheng J. 2016. Evaluation of the reanalysis surface incident shortwave radiation products from NCEP, ECMWF, GSFC, and JMA using satellite and surface observations. Remote Sensing, 8(3): 225 [DOI: 10.3390/rs8030225http://dx.doi.org/10.3390/rs8030225]
Zhang X T, Liang S L, Wild M and Jiang B. 2015. Analysis of surface incident shortwave radiation from four satellite products. Remote Sensing of Environment, 165: 186-202 [DOI: 10.1016/j.rse.2015.05.015http://dx.doi.org/10.1016/j.rse.2015.05.015]
Zhang X T, Wang D D, Liu Q, Yao Y J, Jia K, He T, Jiang B, Wei Y, Ma H, Zhao X, Li W H, and Liang S L. 2019v. An operational approach for generating the global land surface downward shortwave radiation product from MODIS data. IEEE Transactions on Geoscience and Remote Sensing, 57, 4636-4650 [DOI: 10.1109/TGRS.2019.2891945http://dx.doi.org/10.1109/TGRS.2019.2891945]
Zhang Y Z, Liang S L and Yang L. 2019x. A review of regional and global gridded forest biomass datasets. Remote Sensing, 11(23): 2744 [DOI: 10.3390/rs11232744http://dx.doi.org/10.3390/rs11232744]
Zhang Z Y, Chen J M, Guanter L, He L M and Zhang Y G. 2019y. From canopy‐leaving to total canopy far‐red fluorescence emission for remote sensing of photosynthesis: first results from TROPOMI. Geophysical Research Letters, 46(21): 12030-12040 [DOI: 10.1029/2019gl084832http://dx.doi.org/10.1029/2019gl084832]
Zhang Z Y, Wu W L, Fan M, Wei J, Tan Y H and Wang Q. 2019p. Evaluation of MAIAC aerosol retrievals over China. Atmospheric Environment, 202: 8-16 [DOI: 10.1016/j.atmosenv.2019.01.013http://dx.doi.org/10.1016/j.atmosenv.2019.01.013]
Zhao F, Dai X, Verhoef W, Guo Y Q, van der Tol C, Li Y G and Huang Y B. 2016. FluorWPS: a Monte Carlo ray-tracing model to compute sun-induced chlorophyll fluorescence of three-dimensional canopy. Remote Sensing of Environment, 187: 385-399 [DOI: 10.1016/j.rse.2016.10.036http://dx.doi.org/10.1016/j.rse.2016.10.036]
Zhao F, Li Y G, Dai X, Verhoef W, Guo Y Q, Shang H, Gu X F, Huang Y B, Yu T and Huang J X. 2015. Simulated impact of sensor field of view and distance on field measurements of bidirectional reflectance factors for row crops. Remote Sensing of Environment, 156: 129-142 [DOI: 10.1016/j.rse.2014.09.011http://dx.doi.org/10.1016/j.rse.2014.09.011]
Zhao G S, Dong J W, Cui Y P, Liu J Y, Zhai J, He T, Zhou Y Y and Xiao X M. 2019a. Evapotranspiration-dominated biogeophysical warming effect of urbanization in the Beijing-Tianjin-Hebei region, China. Climate Dynamics, 52(1/2): 1231-1245 [DOI: 10.1007/s00382-018-4189-0http://dx.doi.org/10.1007/s00382-018-4189-0]
Zhao L F, Shen Z F, Li C M, Guo M, Sun Y and Gao L J. 2019b. Evaluating the estimation of net radiation based on MODIS data and CoLM: a case study in the Tibetan Plateau. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(2): 460-470 [DOI: 10.1109/jstars.2019.2893600http://dx.doi.org/10.1109/jstars.2019.2893600]
Zhao T J. 2018. Recent advances of L-band application in the passive microwave remote sensing of soil moisture and its prospects. Progress in Geography, 37(2): 198-213
赵天杰. 2018. 被动微波反演土壤水分的L波段新发展及未来展望. 地理科学进展, 37(2): 198-213 [DOI: 10.18306/dlkxjz.2018.02.003http://dx.doi.org/10.18306/dlkxjz.2018.02.003]
Zhao T J, Shi J C, Lv L, Xu H, Chen D, Cui Q, Jackson T J,Yan G, Jia L, Chen L, Zhao K, Zheng X, Zhao L, Zheng C, Ji D, Xiong C, Wang T, Li R, Pan J, Wen J, Yu C, Zheng Y, Jiang L, Chai L, Lu H, Yao P, Ma J. Lv H, Wu J, Zhao W, Yang N, Guo P, Li Y, Hu L, Geng D and Zhang Z. 2020. Soil moisture experiment in the Luan River supporting new satellite mission opportunities. Remote Sensing of Environment, 240(20). [DOI: 10.1016/j.res.2020.111680http://dx.doi.org/10.1016/j.res.2020.111680]
Zhao W, Duan S B, Li A N and Yin G F. 2019d. A practical method for reducing terrain effect on land surface temperature using random forest regression. Remote Sensing of Environment, 221: 635-649 [DOI: 10.1016/j.rse.2018.12.008http://dx.doi.org/10.1016/j.rse.2018.12.008]
Zhao Y X, Yan C H, Lu S, Wang P, Qiu G Y and Li R L. 2019c. Estimation of chlorophyll content in intertidal mangrove leaves with different thicknesses using hyperspectral data. Ecological Indicators, 106: 105511 [DOI: 10.1016/j.ecolind.2019.105511http://dx.doi.org/10.1016/j.ecolind.2019.105511]
Zheng C L, Jia L, Hu G C and Lu J. 2019a. Earth Observations-based evapotranspiration in northeastern Thailand. Remote Sensing, 11(2): 138 [DOI: 10.3390/rs11020138http://dx.doi.org/10.3390/rs11020138]
Zheng L, Zhao G S, Dong J W, Ge Q S, Tao J, Zhang X Z, Qi Y C, Doughty R B and Xiao X M. 2019b. Spatial, temporal, and spectral variations in albedo due to vegetation changes in China's grasslands. ISPRS Journal of Photogrammetry and Remote Sensing, 152: 1-12 [DOI: 10.1016/j.isprsjprs.2019.03.020http://dx.doi.org/10.1016/j.isprsjprs.2019.03.020]
Zheng W, Shao J L and Gao H. 2019c. Songhua river basin flood monitoring using multi-source satellite remote sensing data//Proceedings of IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium. Yokohama: IEEE: 9760-9763 [DOI: 10.1109/IGARSS.2019.8897834http://dx.doi.org/10.1109/IGARSS.2019.8897834]
Zheng Y T, Ren H Z, Guo J X, Ghent D, Tansey K, Hu X B, Nie J and Chen S S. 2019d. Land surface temperature retrieval from sentinel-3A Sea and land surface temperature radiometer, using a split-window algorithm. Remote Sensing, 11(6): 650 [DOI: 10.3390/rs11060650http://dx.doi.org/10.3390/rs11060650]
Zhong L, Ma Y M, Hu Z Y, Fu Y F, Hu Y Y, Wang X, Cheng M L and Ge N. 2019. Estimation of hourly land surface heat fluxes over the Tibetan Plateau by the combined use of geostationary and polar-orbiting satellites. Atmospheric Chemistry and Physics, 19(8): 5529-5541 [DOI: 10.5194/acp-19-5529-2019http://dx.doi.org/10.5194/acp-19-5529-2019]
Zhou C X, Zhang T and Zheng L. 2019a. The characteristics of surface albedo change trends over the Antarctic Sea Ice region during recent decades. Remote Sensing, 11(7): 821 [DOI: 10.3390/rs11070821http://dx.doi.org/10.3390/rs11070821]
Zhou D C, Xiao J F, Bonafoni S, Berger C, Deilami K, Zhou Y Y, Frolking S, Yao R, Qiao Z and Sobrino J A. 2019b. Satellite remote sensing of surface urban heat islands: progress, challenges, and perspectives. Remote Sensing, 11(1): 48 [DOI: 10.3390/rs11010048http://dx.doi.org/10.3390/rs11010048]
Zhou F C, Li Z L, Wu H, Duan S B, Song X N and Yan G J. 2019c. A remote sensing method for retrieving land surface emissivity and temperature in cloudy areas: a case study over South China. International Journal of Remote Sensing, 40(5/6): 1724-1735 [DOI: 10.1080/01431161.2018.1519288http://dx.doi.org/10.1080/01431161.2018.1519288]
Zhou H M, Liang S L, He T, Wang J D, Bo Y C and Wang D D. 2019d. Evaluating the spatial representativeness of the MODerate resolution image spectroradiometer albedo product (MCD43) at AmeriFlux Sites. Remote Sensing, 11(5): 547 [DOI: 10.3390/rs11050547http://dx.doi.org/10.3390/rs11050547]
Zhou J, Liang S L, Cheng J, Wang Y J and Ma J. 2019e. The GLASS land surface temperature product. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(2): 493-507 [DOI: 10.1109/JSTARS.2018.2870130http://dx.doi.org/10.1109/JSTARS.2018.2870130]
Zhou J, Wang L, Zhang Y S, Guo Y H, Li X P and Liu W B. 2015. Exploring the water storage changes in the largest lake (Selin Co) over the Tibetan Plateau during 2003-2012 from a basin-wide hydrological modeling. Water Resources Research, 51(10): 8060-8086 [DOI: 10.1002/2014WR015846http://dx.doi.org/10.1002/2014WR015846]
Zhou L, Mu H W, Ma H J and Chen G X. 2019. Remote sensing estimation on yield of winter wheat in North China based on convolutional neural network. Transactions of the Chinese Society of Agricultural Engineering, 35(15): 119-128
周亮, 慕号伟, 马海姣, 陈高星. 2019. 基于卷积神经网络的中国北方冬小麦遥感估产. 农业工程学报, 35(15): 119-128 [DOI: 10.11975/j.issn.1002-6819.2019.15.016http://dx.doi.org/10.11975/j.issn.1002-6819.2019.15.016]
Zhou Q T, Fellows A, Flerchinger G N and Flores A N. 2019g. Examining interactions between and among predictors of net ecosystem exchange: a machine learning approach in a semi-arid landscape. Scientific Reports, 9(1): 2222 [DOI: 10.1038/s41598-019-38639-yhttp://dx.doi.org/10.1038/s41598-019-38639-y]
Zhou R W, Zhang Y P, Song Q H, Lin Y X, Sha L Q, Jin Y Q, Liu Y T, Fei X H, Gao J B, He Y L, Li T Y and Wang S S. 2019f. Relationship between gross primary production and canopy colour indices from digital camera images in a rubber (Hevea brasiliensis) plantation, Southwest China. Forest Ecology and Management, 437: 222-231 [DOI: 10.1016/j.foreco.2019.01.019http://dx.doi.org/10.1016/j.foreco.2019.01.019]
Zhou W, Shi J C, Wang T X, Peng B, Husi L, Yu Y and Zhao R. 2019h. New Methods for deriving clear‐sky surface longwave downward radiation based on remotely sensed data and ground measurements. Earth and Space Science, 6(11): 2071-2086 [DOI: 10.1029/2019EA000754http://dx.doi.org/10.1029/2019EA000754]
Zhou W C, Han Z, Han H Y, Wang Y Q, Zhang X W and Wu Y S. 2019l. Characteristics of L-band radio frequency interference detected via the soil moisture active passive radiometer in China and its offshore areas. Results in Physics, 12: 1859-1865 [DOI: 10.1016/j.rinp.2019.01.062http://dx.doi.org/10.1016/j.rinp.2019.01.062]
Zhou X, Huang W, Zhang J, Kong W, Casa R and Huang Y. 2019i. A novel combined spectral index for estimating the ratio of carotenoid to chlorophyll content to monitor crop physiological and phenological status. International Journal of Applied Earth Observation and Geoinformation, 76: 128-142 [DOI: 10.1016/j.jag.2018.10.012http://dx.doi.org/10.1016/j.jag.2018.10.012]
Zhou X W and Xin Q C. 2019. Improving satellite-based modelling of gross primary production in deciduous broadleaf forests by accounting for seasonality in light use efficiency. International Journal of Remote Sensing, 40(3): 931-955 [DOI: 10.1080/01431161.2018.1519285http://dx.doi.org/10.1080/01431161.2018.1519285]
Zhou Y, Dong J W, Xiao X M, Liu R G, Zhou Z H, Zhou G S and Ge Q S. 2019j. Continuous monitoring of lake dynamics on the Mongolian Plateau using all available Landsat imagery and Google Earth Engine. Science of the Total Environment, 689: 366-380 [DOI: 10.1016/j.scitotenv.2019.06.341http://dx.doi.org/10.1016/j.scitotenv.2019.06.341]
Zhu L P, Ju J T, Qiao B J, Yang R M, Liu C and Han B P. 2019. Recent lake changes of the Asia Water Tower and their climate response: progress, problems and prospects. Chinese Science Bulletin, 64(27): 2796-2806
朱立平, 鞠建廷, 乔宝晋, 杨瑞敏, 刘翀, 韩博平. 2019. “亚洲水塔”的近期湖泊变化及气候响应: 进展、问题与展望. 科学通报, 64(27): 2796-2806 [DOI: 10.1360/TB-2019-0185http://dx.doi.org/10.1360/TB-2019-0185]
Zhu Q, Luo Y L, Xu Y P, Tian Y and Yang T T. 2019. Satellite soil moisture for agricultural drought monitoring: assessment of SMAP-derived soil water deficit Index in Xiang River Basin, China. Remote Sensing, 11(3): 362 [DOI: 10.3390/rs11030362http://dx.doi.org/10.3390/rs11030362]
Zhu Z and Woodcock C E. 2012. Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sensing of Environment, 118: 83-94 [DOI: 10.1016/j.rse.2011.10.028http://dx.doi.org/10.1016/j.rse.2011.10.028]
Zhuo W, Huang J X, Li L, Zhang X D, Ma H Y, Gao X R, Huang H, Xu B D and Xiao X M. 2019. Assimilating soil moisture retrieved from sentinel-1 and sentinel-2 data into WOFOST model to improve winter wheat yield estimation. Remote Sensing, 11(13): 1618 [DOI: 10.3390/rs11131618http://dx.doi.org/10.3390/rs11131618]
Zou L, Wang L C, Li J R, Lu Y B, Gong W and Niu Y. 2019a. Global surface solar radiation and photovoltaic power from Coupled Model Intercomparison Project Phase 5 climate models. Journal of Cleaner Production, 224: 304-324 [DOI: 10.1016/j.jclepro.2019.03.268http://dx.doi.org/10.1016/j.jclepro.2019.03.268]
Zou M Z, Kang S Z, Niu J and Lu H N. 2019b. Untangling the effects of future climate change and human activity on evapotranspiration in the Heihe agricultural region, Northwest China. Journal of Hydrology: 124323 [DOI: 10.1016/j.jhydrol.2019.124323http://dx.doi.org/10.1016/j.jhydrol.2019.124323]
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