陆表卫星遥感GLASS产品集的研发新进展
Updates on Global LAnd Surface Satellite （GLASS） products suite
- 2023年27卷第4期 页码：831-856
纸质出版日期： 2023-04-07
DOI： 10.11834/jrs.20232462
扫 描 看 全 文
浏览全部资源
扫码关注微信
摘要
GLASS（Global LAnd Surface Satellite）产品集是在中国国家高新技术研究和发展项目“十一五”和“十二五”863计划及“十三五”国家重点研发计划的支持下，经十余年努力研发而生成的多种陆表特征参数的高级卫星数据产品。与国际上同类产品相比较，它们具有一系列的独特特性，正得到国内外1000多家单位研究人员的使用，总下载量超过1.7 PB。本文概述了GLASS产品集算法的发展，产品特征，精度验证，以及这些产品的一些初步应用示例。同时还介绍了30 m分辨率的Hi-GLASS产品集，以及将来继续完善和发展GLASS产品的一些考虑。
Abstract
The Global LAnd Surface Satellite (GLASS) products suite includes high-level satellite products of land surface essential variables from multiple universities and research institutes. Producing the GLASS products suite has been undertaken since 2010. The suite spans from the initial five products to the current 16 products
which are generated mostly from the Advanced Very High-Resolution Radiometer and/or Moderate Resolution Imaging Spectroradiometer data. Some of the products have been previously introduced in the literature
and this study provides an update on the algorithm developments
validation accuracies
and their typical applications in all 16 products. This study also describes the Hi-GLASS products at 30 m resolution and some perspectives for further future improvement and development of the GLASS products.
Estimating land surface variables from satellite observations is an “ill-posed” inversion problem. For each pixel
the number of multispectral bands is usually smaller than the number of environmental variables
and the values of many spectral bands are highly correlated. Some novel solutions have been proposed to address the insufficient information in generating reliable GLASS products. We can identify at least four approaches. The first is based on the temporal signature of the satellite observations. A typical example is the MODIS Leaf Area Index (LAI) and the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) products generated using two-year observations simultaneously. The second uses an algorithm ensemble. A typical example is the evapotranspiration product based on integrating five estimation algorithms. The third uses multiple satellite observations. For example
the forest aboveground biomass product is based on optical
Lidar
and microwave data products. The last incorporates the physical model to generate the products
such as the gross primary production product.
The GLASS products have several unique features compared with similar products on the market
including the following:
(1) Several products are unique
such as the high-resolution (1 km) broadband emissivity and time-series forest aboveground biomass products.
(2) Most products have long time series (i.e.
over 40 years)
while most other similar global products start from approximately the year 2000
with a period of approximately 20 years.
(3) The radiation products
covering the world’s land and ocean surfaces
have a spatial resolution of 5 km
which is an order of magnitude higher than other such products in wide use
for example
the Global Energy and Water Exchanges
the Clouds and the Earth’s Radiant Energy System
and the International Satellite Cloud Climatology Project
which have spatial resolutions coarser than 100 km.
(4) Several long-time-series global products have the highest spatial resolution in the world
such as 250 m for the LAI
FAPAR
and albedo products and 5 km for snow cover extent. Moreover
the all-weather LST and near-surface air temperature products have a 1-km resolution.
(5) GLASS products are of high quality and accuracy.
Over 2000 peer-reviewed papers based on the GLASS products have been published. Their applications are distributed in many scientific disciplines and societal benefits areas.We will continue to improve the quality and accuracy of the existing GLASS products and produce more GLASS products with higher spatial resolutions.
关键词
卫星遥感陆表GLASS产品能量平衡碳循环
Keywords
satellite remote sensingland surfaceGLASS producesradiation and energy budgetcarbon cycle
references
Alkama R, Forzieri G, Duveiller G, Grassi G, Liang S L and Cescatti A. 2022. Vegetation-based climate mitigation in a warmer and greener world. Nature Communications, 13: 606 [DOI: 10.1038/s41467-022-28305-9http://dx.doi.org/10.1038/s41467-022-28305-9]
Cai W W, Yuan W P, Liang S L, Zhang X T, Dong W J, Xia J Z, Fu Y, Chen Y, Liu D and Zhang Q. 2014. Improved estimations of gross primary production using satellite-derived photosynthetically active radiation. Journal of Geophysical Research: Biogeosciences, 119(1): 110-123 [DOI: 10.1002/2013jg002456http://dx.doi.org/10.1002/2013jg002456]
Camacho F, Cernicharo J, Lacaze R, Baret F and Weiss M. 2013. GEOV1: LAI, FAPAR essential climate variables and Fcover global time series capitalizing over existing products. Part 2: validation and intercomparison with reference products. Remote Sensing of Environment, 137: 310-329 [DOI: 10.1016/j.rse.2013.02.030http://dx.doi.org/10.1016/j.rse.2013.02.030]
Carter C and Liang S L. 2019. Evaluation of ten machine learning methods for estimating terrestrial evapotranspiration from remote sensing. International Journal of Applied Earth Observation and Geoinformation, 78: 86-92 [DOI: 10.1016/j.jag.2019.01.020http://dx.doi.org/10.1016/j.jag.2019.01.020]
Chen J, He T, Jiang B and Liang S L. 2020. Estimation of all-sky all-wave daily net radiation at high latitudes from modis data. Remote Sensing of Environment, 245: 111842 [DOI: 10.1016/j.rse.2020.111842http://dx.doi.org/10.1016/j.rse.2020.111842]
Chen S Y, Zhang Y L, Wu Q L, Liu S H, Song C H, Xiao J F, Band L E and Vose J M. 2021a. Vegetation structural change and CO2 fertilization more than offset gross primary production decline caused by reduced solar radiation in China. Agricultural and Forest Meteorology, 296: 108207 [DOI: 10.1016/j.agrformet.2020.108207http://dx.doi.org/10.1016/j.agrformet.2020.108207]
Chen X N, Liang S L, Cao Y F and He T. 2016. Distribution, attribution, and radiative forcing of snow cover changes over China from 1982 to 2013. Climatic Change, 137(3): 363-377 [DOI: 10.1007/s10584-016-1688-zhttp://dx.doi.org/10.1007/s10584-016-1688-z]
Chen X N, Liang S L, Cao Y F, He T and Wang D D. 2015. Observed contrast changes in snow cover phenology in northern middle and high latitudes from 2001-2014. Scientific Reports, 5: 16820 [DOI: 10.1038/srep16820http://dx.doi.org/10.1038/srep16820]
Chen X N, Liang S L, He L, Yang Y P and Yin C. 2021b. A temporally consistent 8-Day 0,05° gap-free snow cover extent dataset over the northern hemisphere for the period 1981-2019. Earth System Science Data Discussions, 2021: 1-30 [DOI: 10.5194/essd-2021-279http://dx.doi.org/10.5194/essd-2021-279]
Chen Y, Liang S L, Ma H, Li B, He T and Wang Q. 2021c. An all-sky 1 km daily land surface air temperature product over Mainland China for 2003-2019 from MODIS and ancillary data. Earth System Science Data, 13(8): 4241-4261 [DOI: 10.5194/essd-13-4241-2021http://dx.doi.org/10.5194/essd-13-4241-2021]
Cheng J and Liang S L. 2013. Estimating global land surface broadband thermal-infrared emissivity using advanced very high resolution radiometer optical data. International Journal of Digital Earth, 6(S1): 34-49 [DOI: 10.1080/17538947.2013.783129http://dx.doi.org/10.1080/17538947.2013.783129]
Cheng J and Liang S L. 2014. Estimating the broadband longwave emissivity of global bare soil from the MODIS shortwave albedo product. Journal of Geophysical Research: Atmospheres, 119(2): 614-634 [DOI: 10.1002/2013JD020689http://dx.doi.org/10.1002/2013JD020689]
Cheng J and Liang S L. 2016. Global estimates for high-spatial-resolution clear-sky land surface upwelling Longwave radiation from Modis data. IEEE Transactions on Geoscience and Remote Sensing, 54(7): 4115-4129 [DOI: 10.1109/TGRS.2016.2537650http://dx.doi.org/10.1109/TGRS.2016.2537650]
Cheng J, Liang S L, Nie A X and Liu Q. 2018. Is there a physical linkage between surface emissive and reflective variables over non-vegetated surfaces? Journal of the Indian Society of Remote Sensing, 46(4): 591-596 [DOI: 10.1007/s12524-017-0713-7http://dx.doi.org/10.1007/s12524-017-0713-7]
Cheng J, Liang S L, Verhoef W, Shi L P and Liu Q. 2016. Estimating the hemispherical broadband longwave emissivity of global vegetated surfaces using a radiative transfer model. IEEE Transactions on Geoscience and Remote Sensing, 54(2): 905-917 [DOI: 10.1109/TGRS.2015.2469535http://dx.doi.org/10.1109/TGRS.2015.2469535]
Cheng J, Liang S L and Wang W H. 2017. Surface downward Longwave radiation//Liang S L, ed. Comprehensive Remote Sensing. Oxford, UK: Elsevier: 196-216 [DOI: 10.1016/B978-0-12-409548-9.10373-2http://dx.doi.org/10.1016/B978-0-12-409548-9.10373-2]
Cheng J, Liang S L, Weng F Z, Wang J D and Li X W. 2010. Comparison of radiative transfer models for simulating snow surface thermal infrared emissivity. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 3(3): 323-336 [DOI: 10.1109/JSTARS.2010.2050300http://dx.doi.org/10.1109/JSTARS.2010.2050300]
Cheng J, Liang S L, Yao Y J, Ren B Y, Shi L P and Liu H. 2014. A comparative study of three land surface broadband emissivity datasets from satellite data. Remote Sensing, 6(1): 111-134 [DOI: 10.3390/rs6010111http://dx.doi.org/10.3390/rs6010111]
Cheng J, Meng X C, Dong S Y and Liang S L. 2021. Generating the 30-M land surface temperature product over continental China and USA from Landsat 5/7/8 data. Science of Remote Sensing, 4: 100032 [DOI: 10.1016/j.srs.2021.100032http://dx.doi.org/10.1016/j.srs.2021.100032]
Cui D Y, Liang S L, Wang D D and Liu Z. 2021. A 1 km global dataset of historical (1979-2013) and future (2020-2100) Köppen–geiger climate classification and bioclimatic variables. Earth System Science Data, 13(11): 5087-5114 [DOI: 10.5194/essd-13-5087-2021http://dx.doi.org/10.5194/essd-13-5087-2021]
Dong L X, Hu J Y, Tang S H and Min M. 2013. Field validation of the GLASS land surface broadband emissivity database using pseudo-invariant sand dune sites in Northern China. International Journal of Digital Earth, 6(S1) 96-112 [DOI: 10.1080/17538947.2013.822573http://dx.doi.org/10.1080/17538947.2013.822573]
Druel A, Peylin P, Krinner G, Ciais P, Viovy N, Peregon A, Bastrikov V, Kosykh N and Mironycheva-Tokareva N. 2017. Towards a more detailed representation of high-latitude vegetation in the global land surface model orchidee (ORC-HL-VEGv1.0). Geoscientific Model Development, 10(12): 4693-4722 [DOI: 10.5194/gmd-10-4693-2017http://dx.doi.org/10.5194/gmd-10-4693-2017]
Feng Y B, Liu Q, Qu Y and Liang S L. 2016. Estimation of the ocean water albedo from remote sensing and meteorological reanalysis data. IEEE Transactions on Geoscience and Remote Sensing, 54(2): 850-868 [DOI: 10.1109/TGRS.2015.2468054http://dx.doi.org/10.1109/TGRS.2015.2468054]
Forzieri G, Miralles D G, Ciais P, Alkama R, Ryu Y, Duveiller G, Zhang K, Robertson E, Kautz M, Martens B, Jiang C Y, Arneth A, Georgievski G, Li W, Ceccherini G, Anthoni P, Lawrence P, Wiltshire A, Pongratz J, Piao S L, Sitch S, Goll D S, Arora V K, Lienert S, Lombardozzi D, Kato E, Nabel J E M S, Tian H Q, Friedlingstein P and Cescatti A. 2020. Increased control of vegetation on global terrestrial energy fluxes. Nature Climate Change, 10(4): 356-362 [DOI: 10.1038/s41558-020-0717-0http://dx.doi.org/10.1038/s41558-020-0717-0]
Gao X Y, Liang S L and He B. 2019. Detected global agricultural greening from satellite data. Agricultural and Forest Meteorology, 276-277: 107652 [DOI: 10.1016/j.agrformet.2019.107652http://dx.doi.org/10.1016/j.agrformet.2019.107652]
Gao X Y, Liang S L and Sauer J. 2020. Greening hiatus in eurasian boreal forests since 1997 caused by a wetting and cooling summer climate. Journal of Geophysical Research: Biogeosciences, 125(9): e2020JG005662 [DOI: 10.1029/2020JG005662http://dx.doi.org/10.1029/2020JG005662]
Good E J, Aldred F M, Ghent D J, Veal K L, Jimenez C. 2022. An analysis of the stability and trends in the LST_cci land surface temperature datasets over europe. Earth and Space Science. 9. [10.1029/2022EA002317http://dx.doi.org/10.1029/2022EA002317]
Guimberteau M, Zhu D, Maignan F, Huang Y, Yue C, Dantec-Nédélec S, Ottlé C, Jornet-Puig A, Bastos A, Laurent P, Goll D, Bowring S, Chang J F, Guenet B, Tifafi M, Peng S S, Krinner G, Ducharne A, Wang F X, Wang T, Wang X H, Wang Y L, Yin Z, Lauerwald R, Joetzjer E, Qiu C J, Kim H and Ciais P. 2018. ORCHIDEE-MICT (V8.4.1), a land surface model for the high latitudes: model description and validation. Geoscientific Model Development, 11(1): 121-163 [DOI: 10.5194/gmd-11-121-2018http://dx.doi.org/10.5194/gmd-11-121-2018]
Han F, Zhang Q, Buyantuev A, Niu J M, Liu P T, Li X H, Kang S R L, Zhang J, Chang C M and Li Y P. 2015. Effects of climate change on phenology and primary productivity in the desert steppe of Inner Mongolia. Journal of Arid Land, 7(2): 251-263 [DOI: 10.1007/s40333-014-0042-4http://dx.doi.org/10.1007/s40333-014-0042-4]
He T, Liang S L, Wang D D, Cao Y F, Gao F, Yu Y Y and Feng M. 2018. Evaluating land surface albedo estimation from landsat mss, Tm, Etm +, and oli data based on the unified direct estimation approach. Remote Sensing of Environment, 204: 181-196 [DOI: 10.1016/j.rse.2017.10.031http://dx.doi.org/10.1016/j.rse.2017.10.031]
He T, Liang S L, Yu Y Y, Liu Q D, Gao F and Liu Q. 2013. Greenland surface albedo changes 1981-2012 from satellite observations. Environmental Research Letters, 8(4): 044043 [DOI: 10.1088/1748-9326/8/4/044043http://dx.doi.org/10.1088/1748-9326/8/4/044043]
Hu L, Fan W J, Ren H Z, Liu S H, Cui Y K and Zhao P. 2018. Spatiotemporal dynamics in vegetation gpp over the great khingan mountains using GLASS products from 1982 to 2015. Remote Sensing, 10(3): 488 [DOI: 10.3390/rs10030488http://dx.doi.org/10.3390/rs10030488]
Huang X J, Zheng Y, Zhang H, Lin S R, Liang S L, Li X Q, Ma M G and Yuan W P. 2022. High spatial resolution vegetation gross primary production product: algorithm and validation. Science of Remote Sensing, 5: 100049 [DOI: 10.1016/j.srs.2022.100049http://dx.doi.org/10.1016/j.srs.2022.100049]
Huang Y Y, Gerber S, Huang T Y and Lichstein J W. 2016. Evaluating the drought response of Cmip5 models using global gross primary productivity, leaf area, precipitation, and soil moisture data. Global Biogeochemical Cycles, 30(12): 1827-1846 [DOI: 10.1002/2016GB005480http://dx.doi.org/10.1002/2016GB005480]
Hulley G C, Hook S J, Manning E, Lee S Y and Fetzer E. 2009. Validation of the atmospheric infrared sounder (AIRS) Version 5 land surface emissivity product over the namib and kalahari deserts. Journal of Geophysical Research: Atmospheres, 114(D19): D19104 [DOI: 10.1029/2009jd012351http://dx.doi.org/10.1029/2009jd012351]
Jia A L, Liang S L and Wang D D. 2022. Generating a 2-Km, all-sky, hourly land surface temperature product from advanced baseline imager data. Remote Sensing of Environment, 278: 113105 [DOI: 10.1016/j.rse.2022.113105http://dx.doi.org/10.1016/j.rse.2022.113105]
Jia A L, Ma H, Liang S L and Wang D D. 2021. Cloudy-sky land surface temperature from VIIRS and MODIS satellite data using a surface energy balance-based method. Remote Sensing of Environment, 263: 112566 [DOI: 10.1016/j.rse.2021.112566http://dx.doi.org/10.1016/j.rse.2021.112566]
Jia K, Liang S L, Gu X F, Baret F, Wei X Q, Wang X X, Yao Y J, Yang L Q and Li Y W. 2016. Fractional vegetation cover estimation algorithm for Chinese Gf-1 wide field view Data. Remote Sensing of Environment, 177: 184-191 [DOI: 10.1016/j.rse.2016.02.019http://dx.doi.org/10.1016/j.rse.2016.02.019]
Jia K, Liang S L, Liu S H, Li Y W, Xiao Z Q, Yao Y J, Jiang B, Zhao X, Wang X X, Xu S and Cui J. 2015. Global land surface fractional vegetation cover estimation using general regression neural networks from MODIS surface reflectance. IEEE Transactions on Geoscience and Remote Sensing, 53(9): 4787-4796 [DOI: 10.1109/tgrs.2015.2409563http://dx.doi.org/10.1109/tgrs.2015.2409563]
Jia K, Liang S L, Wei X Q, Yao Y J, Yang L Q, Zhang X T and Liu D Y. 2018a. Validation of global land surface satellite (GLASS) fractional vegetation cover product from modis data in an agricultural region. Remote Sensing Letters, 9(9): 847-856 [DOI: 10.1080/2150704x.2018.1484958http://dx.doi.org/10.1080/2150704x.2018.1484958]
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. 2019. 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 W X, Liu M, Wang D D, He H L, Shi P L, Li Y N and Wang Y F. 2018b. Uncertainty in simulating regional gross primary productivity from satellite-based models over Northern China grassland. Ecological Indicators, 88: 134-143 [DOI: 10.1016/j.ecolind.2018.01.028http://dx.doi.org/10.1016/j.ecolind.2018.01.028]
Jiang B, Liang S L, Jia A L, Xu J L, Zhang X T, Xiao Z Q, Zhao X, Jia K and Yao Y J. 2019. Validation of the surface daytime net radiation product from version 4.0 GLASS product suite. IEEE Geoscience and Remote Sensing Letters, 16(4): 509-513 [DOI: 10.1109/LGRS.2018.2877625http://dx.doi.org/10.1109/LGRS.2018.2877625]
Jiang B, Zhang Y, Liang S L, Wohlfahrt G, Arain A, Cescatti A, Georgiadis T, Jia K, Kiely G, Lund M, Montagnani L, Magliulo V, Ortiz P S, Oechel W, Vaccari F P, Yao Y J and Zhang X T. 2015. Empirical estimation of daytime net radiation from shortwave radiation and ancillary information. Agricultural and Forest Meteorology, 211-212: 23-36 [DOI: 10.1016/j.agrformet.2015.05.003http://dx.doi.org/10.1016/j.agrformet.2015.05.003]
Jiang B, Zhang Y, Liang S L, Zhang X T and Xiao Z Q. 2014. Surface daytime net radiation estimation using artificial neural networks. Remote Sensing, 6(11): 11031-11050 [DOI: 10.3390/rs61111031http://dx.doi.org/10.3390/rs61111031]
Jiang B, Liang S L, Ma H, Zhang X T, Xiao Z Q, Zhao X, Jia K, Yao Y J and Jia A L. 2016. GLASS daytime all-wave netradiation product: algorithm development and preliminary validation. Remote Sensing, 8(3): 222 [DOI: 10.3390/rs8030222http://dx.doi.org/10.3390/rs8030222]
Jiang F X, Xie X H, Liang S L, Wang Y B, Zhu B W, Zhang X T and Chen Y C. 2021. Loess plateau evapotranspiration intensified by land surface radiative forcing associated with ecological restoration. Agricultural and Forest Meteorology, 311: 108669 [DOI: 10.1016/j.agrformet.2021.108669http://dx.doi.org/10.1016/j.agrformet.2021.108669]
Jiapaer G, Liang S L, Yi Q X and Liu J P. 2015. Vegetation dynamics and responses to recent climate change in xinjiang using leaf area index as an indicator. Ecological Indicators, 58: 64-76 [DOI: 10.1016/j.ecolind.2015.05.036http://dx.doi.org/10.1016/j.ecolind.2015.05.036]
Jin M L and Liang S L. 2006. An Improved land surface emissivity parameter for land surface models using global remote sensing observations. Journal of Climate, 19(12): 2867-2881 [DOI: 10.1175/JCLI3720.1http://dx.doi.org/10.1175/JCLI3720.1]
Kanniah K D, Beringer J, Hutley L B, Tapper N J and Zhu X. 2009. Evaluation of collections 4 and 5 of the MODIS gross primary productivity product and algorithm improvement at a tropical Savanna site in Northern Australia. Remote Sensing of Environment, 113(9): 1808-1822 [DOI: 10.1016/j.rse.2009.04.013http://dx.doi.org/10.1016/j.rse.2009.04.013]
Kindermann G E, McCallum I, Fritz S and Obersteiner M. 2008. A global forest growing stock, biomass and carbon map based on fao statistics. Silva Fennica, 42(3): 387-396 [DOI: 10.14214/sf.244http://dx.doi.org/10.14214/sf.244]
Li B, Liang S L, Liu X B, Ma H, Chen Y, Liang T and He T. 2021a. Estimation of all-sky 1 km land surface temperature over the conterminous United States. Remote Sensing of Environment, 266: 112707 [DOI: 10.1016/j.rse.2021.112707http://dx.doi.org/10.1016/j.rse.2021.112707]
Li R Q, Gao Y H, Chen D L, Zhang Y X and Li S S. 2018. Contrasting vegetation changes in dry and humid regions of the Tibetan Plateau over recent decades. Sciences in Cold and Arid Regions, 10(6): 482-492 [DOI: 10.3724/sp.j.1226.2018.00482http://dx.doi.org/10.3724/sp.j.1226.2018.00482]
Li S, Weigand J and Ganguly S. 2017. The potential for climate impacts from widespread deployment of utility-scale solar energy installations: an environmental remote sensing perspective. Journal of Remote Sensing and GIS, 6: 1-6 [DOI: 10.4172/2469-4134.1000190http://dx.doi.org/10.4172/2469-4134.1000190]
Li X L, Liang S L, Yu G R, Yuan W P, Cheng X, Xia J Z, Zhao T B, Feng J M, Ma Z G, Ma M G, Liu S M, Chen J Q, Shao C L, Li S G, Zhang X D, Zhang Z Q, Chen S P, Ohta T, Varlagin A, Miyata A, Takagi K, Saiqusa N and Kato T. 2013. Estimation of gross primary production over the terrestrial ecosystems in China. Ecological Modelling, 261-262: 80-92 [DOI: 10.1016/j.ecolmodel.2013.03.024http://dx.doi.org/10.1016/j.ecolmodel.2013.03.024]
Li X X, Liang S L and Jin H A. 2021b. An effective method for generating spatiotemporally continuous 30 M vegetation products. Remote Sensing, 13(4): 719 [DOI: 10.3390/rs13040719http://dx.doi.org/10.3390/rs13040719]
Li Y F, Sui X X, Yao Y J, Cheng H X, Zhang L L, Wang L, Ning J, Shang K, Yang J M, Yu R Y and Liu L. 2021c. Evaluation of six satellite-based terrestrial latent heat flux products in the vegetation dominated Haihe River basin of North China. Forests, 12(12): 1632 [DOI: 10.3390/f12121632http://dx.doi.org/10.3390/f12121632]
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. 2021. Some thoughts on the development of quantitative remote sensing in China. National Remote Sensing Bulletin, 25(9): 1889-1895
梁顺林. 2021. 中国定量遥感发展的一些思考. 遥感学报, 25(9): 1889-1895 [DOI: 10.11834/jrs.20211516http://dx.doi.org/10.11834/jrs.20211516]
Liang S L, Cheng J, Jia K, Jiang B, Liu Q, Liu S H, Xiao Z Q, Xie X H, Yao Y J, Yuan W P, Zhang X T and Zhao X. 2016a. Recent progress in land surface quantitative remote sensing. Journal of Remote Sensing, 20(5): 875-898
梁顺林, 程洁, 贾坤, 江波, 刘强, 刘素红, 肖志强, 谢先红, 姚云军, 袁文平, 张晓通, 赵祥. 2016a. 陆表定量遥感反演方法的发展新动态. 遥感学报, 20(5): 875-898 [DOI: 10.11834/jrs.20166258http://dx.doi.org/10.11834/jrs.20166258]
Liang S L, Cheng J, Jia K, Jiang B, Liu Q, Xiao Z Q, Yao Y J, Yuan W P, Zhang X T, Zhao X and Zhou J. 2021. The global land surface satellite (Glass) product suite. Bulletin of the American Meteorological Society, 102(2): E323-E337 [DOI: 10.1175/bams-d-18-0341.1http://dx.doi.org/10.1175/bams-d-18-0341.1]
Liang S L, Tang S H, Zhang J, Xu B, Cheng J, Cheng X, Gong P, Jia K, Jiang B, Li A N, Liu S H, Qiu H, Xiao Z Q, Xie X H, Yang J, Yang J G, Yao Y J, Yu G R, Zhang X T and Zhao X. 2016b. Production of the global climate data records and applications to climate change studies. Journal of Remote Sensing, 20(6): 1491-1499
梁顺林, 唐世浩, 张杰, 徐冰, 程洁, 程晓, 宫鹏, 贾坤, 江波, 李爱农, 刘素红, 邱红, 肖志强, 谢先红, 杨军, 杨俊刚, 姚云军, 于贵瑞, 张晓通, 赵祥. 2016b. 全球气候数据集生成及气候变化应用研究. 遥感学报, 20(6): 1491-1499 [DOI: 10.11834/jrs.20166359http://dx.doi.org/10.11834/jrs.20166359]
Liang S L, Zhang J, Chen L J, Zhao X and Chen J. 2017. Production and Applications of the Global Remote Sensing Products. Beijing: Science Press
梁顺林, 张杰, 陈利军, 赵祥, 杨军. 2017. 全球变化遥感产品的生产与应用. 北京: 科学出版社
Liang S L, Zhang X T, Xiao Z Q, Cheng J, Liu Q and Zhao X 2013a. Global Land Surface Satellite (GLASS) Products: Algorithms, Validation and Analysis. Cham: Springer: 1-167 [DOI: 10.1007/978-3-319-02588-9http://dx.doi.org/10.1007/978-3-319-02588-9]
Liang S L, Zhang X T, Xiao Z Q, Cheng J, Liu Q and Zhao X. 2014. Global LAnd Surface Satellite (GLASS) Products: Algorithms, Validation and Analysis. Beijing: Higher Education Press
梁顺林, 张晓通, 肖志强, 程洁, 刘强, 赵祥. 2014. 全球陆表特征参量(GLASS)产品算法、验证与分析. 北京: 高教出版社
Liang S L, Zhao X, Liu S H, Yuan W P, Cheng X, Xiao Z Q, Zhang X T, Liu Q, Cheng J, Tang H R, Qu Y H, Bo Y C, Qu Y, Ren H Z, Yu K and Townshend J. 2013b. A long-term global land surface satellite (GLASS) data-set for environmental studies. International Journal of Digital Earth, 6(S1): 5-33 [DOI: 10.1080/17538947.2013.805262http://dx.doi.org/10.1080/17538947.2013.805262]
Liu H, Gong P, Wang J, Clinton N, Bai Y Q and Liang S L. 2020. Annual dynamics of global land cover and its long-term changes from 1982 to 2015. Earth System Science Data, 12(2): 1217-1243 [DOI: 10.5194/essd-12-1217-2020http://dx.doi.org/10.5194/essd-12-1217-2020]
Lin H, Li S W, Xing J, He T, Yang J and Wang Q X. 2021. High resolution aerosol optical depth retrieval over urban areas from Landsat-8 oli images. Atmospheric Environment, 261: 118591 [DOI: 10.1016/j.atmosenv.2021.118591http://dx.doi.org/10.1016/j.atmosenv.2021.118591]
Liu N F, Liu Q, Wang L Z, Liang S L, Wen J G, Qu Y and Liu S H. 2013. A statistics-based temporal filter algorithm to map spatiotemporally continuous shortwave albedo from modis data. Hydrology and Earth System Sciences, 17(6): 2121-2129 [DOI: 10.5194/hess-17-2121-2013http://dx.doi.org/10.5194/hess-17-2121-2013]
Liu P L, Hao L, Pan C, Zhou D C, Liu Y Q and Sun G. 2017. Combined effects of climate and land management on watershed vegetation dynamics in an arid environment. Science of the Total Environment, 589: 73-88 [DOI: 10.1016/j.scitotenv.2017.02.210http://dx.doi.org/10.1016/j.scitotenv.2017.02.210]
Liu W F, Wei X H, Li Q, Fan H B, Duan H L, Wu J P, Giles-Hansen K and Zhang H. 2016. Hydrological recovery in two large forested watersheds of southeastern China: the importance of watershed properties in determining hydrological responses to reforestation. Hydrology and Earth System Sciences, 20: 4747-4756 [DOI: 10.5194/hess-2016-327http://dx.doi.org/10.5194/hess-2016-327]
Liu X B, Liang S L, Li B, Ma H and He T. 2021. Mapping 30 M fractional forest cover over china’s three-north region from Landsat-8 data using ensemble machine learning methods. Remote Sensing, 13(13): 2592 [DOI: 10.3390/rs13132592http://dx.doi.org/10.3390/rs13132592]
Liu X Y, Tang B H, Yan G J, Li Z L and Liang S L. 2019. 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]
Lu Y R, Liu Q, Li X, Li X H, Liu L, Xiao S and Sun M Y. 2020. An algorithm for producing 250 m global albedo product and validation. Journal of Geo-Information Science, 22(2): 328-335
陆彦蓉, 刘强, 李霞, 李秀红, 刘璐, 肖洒, 孙美莹. 2020. 全球250 m反照率产品算法及验证. 地球信息科学学报, 22(2): 328-335 [DOI: 10.12082/dqxxkx.2020.190184http://dx.doi.org/10.12082/dqxxkx.2020.190184]
Ma H and Liang S L. 2022. Development of the GLASS 250-M leaf area index product (Version 6) from MODIS data using the bidirectional LSTM deep learning model. Remote Sensing of Environment, 273: 112985 [DOI: 10.1016/j.rse.2022.112985http://dx.doi.org/10.1016/j.rse.2022.112985]
Ma H, Liang S L, Shi H Y and Zhang Y. 2021. An optimization approach for estimating multiple land surface and atmospheric variables from the geostationary advanced himawari imager top-of-atmosphere observations. IEEE Transactions on Geoscience and Remote Sensing, 59(4): 2888-2908 [DOI: 10.1109/TGRS.2020.3007118http://dx.doi.org/10.1109/TGRS.2020.3007118]
Ma H, Liang S L, Xiao Z Q and Wang D D. 2018. Simultaneous estimation of multiple land surface parameters from VIIRS optical-thermal data. IEEE Geoscience and Remote Sensing Letters, 15(1): 156-160 [DOI: 10.1109/LGRS.2017.2779040http://dx.doi.org/10.1109/LGRS.2017.2779040]
Ma H, Liang S L, Xiong C H, Wang Q and Jia A L. 2022a. Global land surface 250 m 8 d fraction of absorbed photosynthetically active radiation (FAPAR) product from 2000 to 2020. Earth System Science Data, 14(12): 5333-5347 [DOI: 10.5194/essd-14-5333-2022http://dx.doi.org/10.5194/essd-14-5333-2022]
Ma H, Liang S L, Zhu Z L and He T. 2022b. Developing a land continuous variable estimator to generate daily land products from landsat data. IEEE Transactions on Geoscience and Remote Sensing, 60: 4406619 [DOI: 10.1109/TGRS.2021.3121272http://dx.doi.org/10.1109/TGRS.2021.3121272]
Ma H, Liu Q, Liang S L and Xiao Z Q. 2017a. Simultaneous estimation of leaf area index, fraction of absorbed photosynthetically active radiation, and surface albedo from multiple-satellite data. IEEE Transactions on Geoscience and Remote Sensing, 55(8): 4334-4354 [DOI: 10.1109/TGRS.2017.2691542http://dx.doi.org/10.1109/TGRS.2017.2691542]
Ma J, Zhou J, Göttsche F-M, Liang S L, Wang S F and Li M S. 2020. A global long-term (1981-2000) land surface temperature product for NOAA AVHRR. Earth System Science Data, 12(4): 3247-3268 [DOI: 10.5194/essd-12-3247-2020http://dx.doi.org/10.5194/essd-12-3247-2020]
Ma J Y, Yan X D, Dong W J and Chou J M. 2015. Gross primary production of global forest ecosystems has been overestimated. Scientific Reports, 5: 10820 [DOI: 10.1038/srep10820http://dx.doi.org/10.1038/srep10820]
Ma N, Szilagyi J, Zhang Y S and Liu W B. 2019. Complementary‐relationship‐based modeling of terrestrial evapotranspiration across China during 1982-2012: validations and spatiotemporal analyses. Journal of Geophysical Research: Atmospheres, 124(8): 4326-4351 [DOI: 10.1029/2018JD029850http://dx.doi.org/10.1029/2018JD029850]
Ma R, Zhang L, Tian X J, Zhang J C, Yuan W P, Zheng Y, Zhao X and Kato T. 2017b. Assimilation of remotely-sensed leaf area index into a dynamic vegetation model for gross primary productivity estimation. Remote Sensing, 9(3): 188 [DOI: 10.3390/rs9030188http://dx.doi.org/10.3390/rs9030188]
Ma Y C, He T, Liang S L, Wen J G, Gastellu-Etchegorry J P, Chen J, Ding A X and Feng S Q. 2022c. Landsat snow-free surface albedo estimation over sloping terrain: algorithm development and evaluation. IEEE Transactions on Geoscience and Remote Sensing, 60: 4408914 [DOI: 10.1109/TGRS.2022.3149762http://dx.doi.org/10.1109/TGRS.2022.3149762]
Meng X C, Cheng J and Liang S L. 2017. Estimating land surface temperature from Feng Yun-3C/Mersi data using a new land surface emissivity scheme. Remote Sensing, 9(12): 1247 [DOI: 10.3390/rs9121247http://dx.doi.org/10.3390/rs9121247]
Mercury M, Green R, Hook S, Oaida B, Wu W, Gunderson A and Chodas M. 2012. Global cloud cover for assessment of optical satellite observation opportunities: a hyspiri case study. Remote Sensing of Environment, 126: 62-71 [DOI: 10.1016/j.rse.2012.08.007http://dx.doi.org/10.1016/j.rse.2012.08.007]
Mocko D M, Kumar S V, Peters-Lidard C D and Wang S G. 2021. Assimilation of vegetation conditions improves the representation of drought over agricultural areas. Journal of Hydrometeorology, 22(5): 1085-1098 [DOI: 10.1175/JHM-D-20-0065.1http://dx.doi.org/10.1175/JHM-D-20-0065.1]
Mu B H, Zhao X, Wu D H, Wang X Y, Zhao J C, Wang H Y, Zhou Q, Du X Z and Liu N J. 2021. Vegetation cover change and its attribution in China from 2001 to 2018. Remote Sensing, 13(3): 496 [DOI: 10.3390/rs13030496http://dx.doi.org/10.3390/rs13030496]
Mu X H, Huang S, Ren H Z, Yan G J, Song W J and Ruan G Y. 2015. Validating Geov1 fractional vegetation cover derived from coarse-resolution remote sensing images over croplands. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(2): 439-446 [DOI: 10.1109/JSTARS.2014.2342257http://dx.doi.org/10.1109/JSTARS.2014.2342257]
Pasquato M, Medici C, Friend A D and Francés F. 2015. Comparing two approaches for parsimonious vegetation modelling in semiarid regions using satellite data. Ecohydrology, 8(6): 1024-1036 [DOI: 10.1002/eco.1559http://dx.doi.org/10.1002/eco.1559]
Piao S L, Yin G D, Tan J G, Cheng L, Huang M T, Li Y, Liu R G, Mao J F, Myneni R B, Peng S S, Poulter B, Shi X Y, Xiao Z Q, Zeng N, Zeng Z Z and Wang Y P. 2015. Detection and attribution of vegetation greening trend in China over the last 30 years. Global Change Biology, 21(4): 1601-1609 [DOI: 10.1111/gcb.12795http://dx.doi.org/10.1111/gcb.12795]
Qin W M, Wang L C, Zhang M, Niu Z G, Luo M, Lin A W and Hu B. 2019. First effort at constructing a high-density photosynthetically active radiation dataset during 1961-2014 in China. Journal of Climate, 32(10): 2761-2780 [DOI: 10.1175/JCLI-D-18-0590.1http://dx.doi.org/10.1175/JCLI-D-18-0590.1]
Qu Y, Liang S L, Liu Q, Li X J, Feng Y B and Liu S H. 2016. Estimating Arctic Sea-Ice shortwave albedo from modis data. Remote Sensing of Environment, 186: 32-46 [DOI: 10.1016/j.rse.2016.08.015http://dx.doi.org/10.1016/j.rse.2016.08.015]
Qu Y, Liu Q, Liang S L, Wang L Z, Liu N F and Liu S H. 2014. Direct-estimation algorithm for mapping daily land-surface broadband albedo from MODIS data. IEEE Transactions on Geoscience and Remote Sensing, 52(2): 907-919 [DOI: 10.1109/TGRS.2013.2245670http://dx.doi.org/10.1109/TGRS.2013.2245670]
Restrepo-Coupe N, da Rocha H R, Hutyra L R, da Araujo A C, Borma L S, Christoffersen B, Cabral O M R, de Camargo P B, Cardoso F L, da Costa A C L, Fitzjarrald D R, Goulden M L, Kruijt B, Maia J M F, Malhi Y S, Manzi A O, Miller S D, Nobre A D, von Randow C, Sá L D A, Sakai R K, Tota J, Wofsy S C, Zanchi F B and Saleska S R. 2013. What drives the seasonality of photosynthesis across the Amazon basin? A cross-site analysis of eddy flux tower measurements from the brasil flux network. Agricultural and Forest Meteorology, 182-183: 128-144 [DOI: 10.1016/j.agrformet.2013.04.031http://dx.doi.org/10.1016/j.agrformet.2013.04.031]
Schwarz M, Folini D, Yang S, Allan R P and Wild M. 2020. Changes in atmospheric shortwave absorption as important driver of dimming and brightening. Nature Geoscience, 13(2): 110-115 [DOI: 10.1038/s41561-019-0528-yhttp://dx.doi.org/10.1038/s41561-019-0528-y]
Shang K, Yao Y J, Liang S L, Zhang Y H, Fisher J B, Chen J Q, Liu S M, Xu Z W, Zhang Y, Jia K, Zhang X T, Yang J M, Bei X Y, Guo X Z, Yu R Y, Xie Z J and Zhang L L. 2021. DNN-MET: a deep neural networks method to integrate satellite-derived evapotranspiration products, eddy covariance observations and ancillary information. Agricultural and Forest Meteorology, 308-309: 108582 [DOI: 10.1016/j.agrformet.2021.108582http://dx.doi.org/10.1016/j.agrformet.2021.108582]
Slessarev E W, Lin Y, Bingham N L, Johnson J E, Dai Y, Schimel J P and Chadwick O A. 2016. Water balance creates a threshold in soil ph at the global scale. Nature, 540(7634): 567-569 [DOI: 10.1038/nature20139http://dx.doi.org/10.1038/nature20139]
Song L S, Liu S M, Kustas W P, Nieto H, Sun L, Xu Z W, Skaggs T H, Yang Y, Ma M G, Xu T R, Tang X G and Li Q P. 2018. Monitoring and validating spatially and temporally continuous daily evaporation and transpiration at River basin scale. Remote Sensing of Environment, 219: 72-88 [DOI: 10.1016/j.rse.2018.10.002http://dx.doi.org/10.1016/j.rse.2018.10.002]
Sui S and Sun L. 2022. Comparative analysis of several typical landsat 8 oli cloud detection methods. Remote Sensing, 14(3): 719 [DOI: 10.3390/rs14030719http://dx.doi.org/10.3390/rs14030719]
Tesemma Z K, Wei Y, Peel M C and Western A W. 2015. The effect of year-to-year variability of leaf area index on variable infiltration capacity model performance and simulation of runoff. Advances in Water Resources, 83: 310-322 [DOI: 10.1016/j.advwatres.2015.07.002http://dx.doi.org/10.1016/j.advwatres.2015.07.002]
Tian X, Yan M, Van Der Tol C, Li Z Y, Su Z B, Chen E X, Li X, Li L H, Wang X F, Pan X D, Gao L S and Han Z T. 2017. Modeling forest above-ground biomass dynamics using multi-source data and incorporated models: a case study over the qilian mountains. Agricultural and Forest Meteorology, 246: 1-14 [DOI: 10.1016/j.agrformet.2017.05.026http://dx.doi.org/10.1016/j.agrformet.2017.05.026]
Valipour M, Dietrich J. 2022. Developing ensemble mean models of satellite remote sensing, climate reanalysis, and land surface models. Theoretical and Applied Climatology volume 150: 909-926. [10.1007/s00704-022-04185-3]
Verma M, Friedl M A, Law B E, Bonal D, Kiely G, Black T A, Wohlfahrt G, Moors E J, Montagnani L, Marcolla B, Toscano P, Varlagin A, Roupsard O, Cescatti A, Arain M A and D’Odorico P. 2015. Improving the performance of remote sensing models for capturing intra- and inter-annual variations in daily GPP: an analysis using global FLUXNET tower data. Agricultural and Forest Meteorology, 214-215: 416-429 [DOI: 10.1016/j.agrformet.2015.09.005http://dx.doi.org/10.1016/j.agrformet.2015.09.005]
Wang D D, Liang S L, He T and Shi Q Q. 2015a. Estimation of daily surface shortwave net radiation from the combined modis data. IEEE Transactions on Geoscience and Remote Sensing, 53(10): 5519-5529 [DOI: 10.1109/tgrs.2015.2424716http://dx.doi.org/10.1109/tgrs.2015.2424716]
Wang Q F, Zheng H, Zhu X J and Yu G R. 2015b. Primary estimation of Chinese terrestrial carbon sequestration during 2001-2010. Science Bulletin, 60(6): 577-590 [DOI: 10.1007/s11434-015-0736-9http://dx.doi.org/10.1007/s11434-015-0736-9]
Wang L C, Zhu H J, Lin A W, Zou L, Qin W M, and Du Q Y. 2017. Evaluation of the latest MODIS GPP products across multiple biomes using Global Eddy Covariance Flux Data. Remote Sensing, 9: 418 [DOI:10.3390/rs9050418http://dx.doi.org/10.3390/rs9050418]
Wang W, Zhou J, Wen X, Long Z, Zhong H, Ma J, Ding L, Qi D. 2022. All-weather near-surface air temperature estimation based on satellite data over the Tibetan Plateau. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 15: 3340–3350. [10.1109/JSTARS.2022.3161800]
Wu P, Su Y, Duan S, Li X, Yang H, Zeng C, Ma, X, Wu Y, Shen H. 2022. A two-step deep learning framework for mapping gapless all-weather land surface temperature using thermal infrared and passive microwave data. Remote Sensing of Environment. 277, 113070. [10.1016/j.rse.2022.113070http://dx.doi.org/10.1016/j.rse.2022.113070]
Xia M, Jia K, Zhao W W, Liu S L, Wei X Q and Wang B. 2021. Spatio-temporal changes of ecological vulnerability across the Qinghai-Tibetan Plateau. Ecological Indicators, 123: 107274 [DOI: 10.1016/j.ecolind.2020.107274http://dx.doi.org/10.1016/j.ecolind.2020.107274]
Xiao Z Q, Liang S L and Jiang B. 2017. Evaluation of four long time-series global leaf area index products. Agricultural and Forest Meteorology, 246: 218-230 [DOI: 10.1016/j.agrformet.2017.06.016http://dx.doi.org/10.1016/j.agrformet.2017.06.016]
Xiao Z Q, Liang S L and Sun R. 2018. Evaluation of three long time series for global fraction of absorbed photosynthetically active radiation (FAPAR) products. IEEE Transactions on Geoscience and Remote Sensing, 56(9): 5509-5524 [DOI: 10.1109/TGRS.2018.2818929http://dx.doi.org/10.1109/TGRS.2018.2818929]
Xiao Z Q, Liang S L, Sun R, Wang J D and Jiang B. 2015. Estimating the fraction of absorbed photosynthetically active radiation from the MODIS data based GLASS leaf area index product. Remote Sensing of Environment, 171: 105-117 [DOI: 10.1016/j.rse.2015.10.016http://dx.doi.org/10.1016/j.rse.2015.10.016]
Xiao Z Q, Liang S L, Wang J D, Chen P, Yin X J, Zhang L Q and Song J L. 2014. Use of general regression neural networks for generating the GLASS leaf area index product from time-series modis surface reflectance. IEEE Transactions on Geoscience and Remote Sensing, 52(1): 209-223 [DOI: 10.1109/tgrs.2013.2237780http://dx.doi.org/10.1109/tgrs.2013.2237780]
Xiao Z Q, Liang S L, Wang J D, Xiang Y, Zhao X and Song J L. 2016a. Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. IEEE Transactions on Geoscience and Remote Sensing, 54(9): 5301-5318 [DOI: 10.1109/TGRS.2016.2560522http://dx.doi.org/10.1109/TGRS.2016.2560522]
Xiao Z Q, Wang T T, Liang S L and Sun R. 2016b. Estimating the fractional vegetation cover from GLASS leaf area index product. Remote Sensing, 8(4): 337 [DOI: 10.3390/rs8040337http://dx.doi.org/10.3390/rs8040337]
Xu B D, Li J, Park T, Liu Q H, Zeng Y L, Yin G F, Zhao J, Fan W L, Yang L, Knyazikhin Y and Myneni R B. 2018. An integrated method for validating long-term leaf area index products using global networks of site-based measurements. Remote Sensing of Environment, 209: 134-151 [DOI: 10.1016/j.rse.2018.02.049http://dx.doi.org/10.1016/j.rse.2018.02.049]
Xu J L, Liang S L, Ma H and He T. 2022b. Generating 5 km resolution 1981-2018 daily global land surface longwave radiation products from AVHRR shortwave and longwave observations using densely connected convolutional neural networks. Remote Sensing of Environment, 280: 113223 [DOI: 10.1016/j.rse.2022.113223http://dx.doi.org/10.1016/j.rse.2022.113223]
Xu J L, Liang S L and Jiang B. 2022a. A global long-term (1981-2019) daily land surface radiation budget product from AVHRR satellite data using a residual convolutional neural network. Earth System Science Data, 14(5): 2315-2341 [DOI: 10.5194/essd-14-2315-2022http://dx.doi.org/10.5194/essd-14-2315-2022]
Xu S, Cheng J and Zhang Q. 2021. a random forest-based data fusion method for obtaining all-weather land surface temperature with high spatial resolution. Remote Sensing, 13(11): 2211 [DOI: 10.3390/rs13112211http://dx.doi.org/10.3390/rs13112211]
Xu X J, Zhou G M, Liu S G, Du H Q, Mo L F, Shi Y J, Jiang H, Zhou Y F and Liu E B. 2013. Implications of ice storm damages on the water and carbon cycle of bamboo forests in southeastern China. Agricultural and Forest Meteorology, 177: 35-45 [DOI: 10.1016/j.agrformet.2013.04.005http://dx.doi.org/10.1016/j.agrformet.2013.04.005]
Yang F and Cheng J. 2020. A framework for estimating cloudy sky surface downward longwave radiation from the derived active and passive cloud property parameters. Remote Sensing of Environment, 248: 111972 [DOI: 10.1016/j.rse.2020.111972http://dx.doi.org/10.1016/j.rse.2020.111972]
Yang L Q, Jia K, Liang S L, Liu J C and Wang X X. 2016. Comparison of four machine learning methods for generating the GLASS fractional vegetation cover product from modis data. Remote Sensing, 8(8): 682 [DOI: 10.3390/rs8080682http://dx.doi.org/10.3390/rs8080682]
Yao Y J, Liang S L, Li X L, Hong Y, Fisher J B, Zhang N N, Chen J Q, Cheng J, Zhao S H, Zhang X T, Jiang B, Sun L, Jia K, Wang K C, Chen Y, Mu Q Z and Feng F. 2014. Bayesian multimodel estimation of global terrestrial latent heat flux from eddy covariance, meteorological, and satellite observations. Journal of Geophysical Research: Atmospheres, 119(8): 4521-4545 [DOI: 10.1002/2013JD020864http://dx.doi.org/10.1002/2013JD020864]
Yao Y J, Liang S L, Li X L, Zhang Y H, Chen J Q, Jia K, Zhang X T, Fisher J B, Wang X Y, Zhang L L, Xu J, Shao C L, Posse G, Li Y N, Magliulo V, Varlagin A, Moors E J, Boike J, Macfarlane C, Kato T, Buchmann N, Billesbach D P, Beringer J, Wolf S, Papuga S A, Wohlfahrt G, Montagnani L, Ardö J, Paul-Limoges E, Emmel C, Hörtnagl L, Sachs T, Gruening C, Gioli B, López-Ballesteros A, Steinbrecher R and Gielen B. 2017. Estimation of high-resolution terrestrial evapotranspiration from landsat data using a simple taylor skill fusion method. Journal of Hydrology, 553: 508-526 [DOI: 10.1016/j.jhydrol.2017.08.013http://dx.doi.org/10.1016/j.jhydrol.2017.08.013]
Ye S C, Feng H H, Zou B, Ding Y, Zhu S J, Li F and Dong G T. 2021. Satellite-based estimation of the influence of land use and cover change on the surface shortwave radiation budget in a humid basin. Remote Sensing, 13(8): 1447 [DOI: 10.3390/rs13081447http://dx.doi.org/10.3390/rs13081447]
Yuan W P, Cai W W, Xia J Z, Chen J Q, Liu S G, Dong W J, Merbold L, Law B, Arain A, Beringer J, Bernhofer C, Black A, Blanken P D, Cescatti A, Chen Y, Francois L, Gianelle D, Janssens I A, Jung M, Kato T, Kiely G, Liu D, Marcolla B, Montagnani L, Raschi A, Roupsard O, Varlagin A and Wohlfahrt G. 2014. Global comparison of light use efficiency models for simulating terrestrial vegetation gross primary production based on the lathuile database. Agricultural and Forest Meteorology, 192-193: 108-120 [DOI: 10.1016/j.agrformet.2014.03.007http://dx.doi.org/10.1016/j.agrformet.2014.03.007]
Yuan W P, Liu S G, Yu G R, Bonnefond J M, Chen J Q, Davis K, Desai A R, Goldstein A H, Gianelle D, Rossi F, Suyker A E and Verma S B. 2010. Global estimates of evapotranspiration and gross primary production based on MODIS and global meteorology data. Remote Sensing of Environment, 114(7): 1416-1431 [DOI: 10.1016/j.rse.2010.01.022http://dx.doi.org/10.1016/j.rse.2010.01.022]
Yuan W P, Liu S G, Zhou G S, Zhou G Y, Tieszen L L, Baldocchi D, Bernhofer C, Gholz H, Goldstein A H, Goulden M L, Hollinger D Y, Hu Y M, Law B E, Stoy P C, Vesala T and Wofsy S C. 2007. Deriving a light use efficiency model from eddy covariance flux data for predicting daily gross primary production across biomes. Agricultural and Forest Meteorology, 143(3-4): 189-207 [DOI: 10.1016/j.agrformet.2006.12.001http://dx.doi.org/10.1016/j.agrformet.2006.12.001]
Yuan W P, Zheng Y, Piao S, 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 M S, 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.aax139http://dx.doi.org/10.1126/sciadv.aax139]
Zeng Q and Cheng J. 2021. Estimating high-spatial resolution surface daily longwave radiation from the instantaneous global land surface satellite (GLASS) longwave radiation product. International Journal of Digital Earth, 14(11): 1674-1704 [DOI: 10.1080/17538947.2021.1966526http://dx.doi.org/10.1080/17538947.2021.1966526]
Zeng Q, Cheng J and Dong L X. 2020. 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 Observations and Remote Sensing, 13: 2032-2055 [DOI: 10.1109/JSTARS.2020.2992472http://dx.doi.org/10.1109/JSTARS.2020.2992472]
Zeng Z Z, Piao S L, Li L Z X, Zhou L M, Ciais P, Wang T, Li Y, Lian X, Wood E F, Friedlingstein P, Mao J F, Estes L D, Myneni R B, Peng S S, Shi X Y, Seneviratne S I and Wang Y P. 2017. Climate mitigation from vegetation biophysical feedbacks during the past three decades. Nature Climate Change, 7(6): 432-436 [DOI: 10.1038/nclimate3299http://dx.doi.org/10.1038/nclimate3299]
Zhan C and Liang S L. 2022. Improved estimation of the global top-of-atmosphere albedo from AVHRR data. Remote Sensing of Environment, 269: 112836 [DOI: 10.1016/j.rse.2021.112836http://dx.doi.org/10.1016/j.rse.2021.112836]
Zhang X D, Huang A N, Dai Y J, Li W P, Gu C L, Yuan H, Wei N, Zhang Y L, Qiu B and Cai S X. 2022a. Influences of 3D Sub‐grid terrain radiative effect on the performance of colm over Heihe River Basin, Tibetan Plateau. Journal of Advances in Modeling Earth Systems, 14(1): e2021MS002654 [DOI: 10.1029/2021MS002654http://dx.doi.org/10.1029/2021MS002654]
Zhang X D, Zhou J, Liang S L, Chai L N, Wang D D and Liu J. 2020a. Estimation of 1-Km all-weather remotely sensed land surface temperature based on reconstructed spatial-seamless satellite passive microwave brightness temperature and thermal infrared data. ISPRS Journal of Photogrammetry and Remote Sensing, 167: 321-344 [DOI: 10.1016/j.isprsjprs.2020.07.014http://dx.doi.org/10.1016/j.isprsjprs.2020.07.014]
Zhang X D, Zhou J, Liang S L and Wang D D. 2021. A practical reanalysis data and thermal infrared remote sensing data merging (RTM) method for reconstruction of a 1-Km all-weather land surface temperature. Remote Sensing of Environment, 260: 112437 [DOI: 10.1016/j.rse.2021.112437http://dx.doi.org/10.1016/j.rse.2021.112437]
Zhang X T, Liang S L, Zhou G Q, Wu H R and Zhao X. 2014. Generating global land surface satellite incident shortwave radiation and photosynthetically active radiation products from multiple satellite data. Remote Sensing of Environment, 152: 318-332 [DOI: 10.1016/j.rse.2014.07.003http://dx.doi.org/10.1016/j.rse.2014.07.003]
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. 2019a. An operational approach for generating the global land surface downward shortwave radiation product from modis data. IEEE Transactions on Geoscience and Remote Sensing, 57(7): 4636-4650 [DOI: 10.1109/TGRS.2019.2891945http://dx.doi.org/10.1109/TGRS.2019.2891945]
Zhang Y F, Liang S L, Zhu Z L, Ma H and He T. 2022b. Soil moisture content retrieval from landsat 8 data using ensemble learning. ISPRS Journal of Photogrammetry and Remote Sensing, 185: 32-47 [DOI: 10.1016/j.isprsjprs.2022.01.005http://dx.doi.org/10.1016/j.isprsjprs.2022.01.005]
Zhang Y Z and Liang S L. 2020. Fusion of multiple gridded biomass datasets for generating a global forest aboveground biomass map. Remote Sensing, 12(16): 2559 [DOI: 10.3390/rs12162559http://dx.doi.org/10.3390/rs12162559]
Zhang Y Z, Liang S L and Yang L. 2019b. 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 Y Z, Ma J, Liang S L, Li X S and Liu J D. 2022c. A stacking ensemble algorithm for improving the biases of forest aboveground biomass estimations from multiple remotely sensed datasets. GIScience and Remote Sensing, 59(1): 234-249 [DOI: 10.1080/15481603.2021.2023842http://dx.doi.org/10.1080/15481603.2021.2023842]
Zhang Y Z, Ma J, Liang S L, Li X S and Li M Y. 2020b. An evaluation of eight machine learning regression algorithms for forest aboveground biomass estimation from multiple satellite data Products. Remote Sensing, 12(24): 4015 [DOI: 10.3390/rs12244015http://dx.doi.org/10.3390/rs12244015]
Zhao X, Liang S L, Liu S H, Yuan W P, Xiao Z Q, Liu Q, Cheng J, Zhang X T, Tang H R, Zhang X, Liu Q, Zhou G Q, Xu S and Yu K. 2013. The global land surface satellite (GLASS) remote sensing data processing system and products. Remote Sensing, 5(5): 2436-2450 [DOI: 10.3390/rs5052436http://dx.doi.org/10.3390/rs5052436]
Zheng Y, Shen R Q, Wang Y W, Li X Q, Liu S G, Liang S L, Chen J M, Ju W M, Zhang L and Yuan W P. 2020. Improved estimate of global gross primary production for reproducing its long-term variation, 1982-2017. Earth System Science Data, 12(4): 2725-2746 [DOI: 10.5194/essd-12-2725-2020http://dx.doi.org/10.5194/essd-12-2725-2020]
Zhou J, Liang S L, Cheng J, Wang Y J and Ma J. 2019. 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]
Zhu H J, Lin A W, Wang L C, Xia Y and Zou L. 2016a. Evaluation of modis gross primary production across multiple biomes in China using eddy covariance flux data. Remote Sensing, 8(5): 395 [DOI: 10.3390/rs8050395http://dx.doi.org/10.3390/rs8050395]
Zhu Z C, Piao S, Myneni R B, Huang M T, Zeng Z Z, Canadell J G, Ciais P, Sitch S, Friedlingstein P, Arneth A, Cao C X, Cheng L, Kato E, Koven C, Li Y, Lian X, Liu Y W, Liu R G, Mao J F, Pan Y Z, Peng S S, Peñuelas J, Poulter B, Pugh T A M, Stocker B D, Viovy N, Wang X H, Wang Y P, Xiao Z Q, Yang H, Zaehle S and Zeng N. 2016b. Greening of the earth and its drivers. Nature Climate Change, 6(8): 791-795 [DOI: 10.1038/nclimate3004http://dx.doi.org/10.1038/nclimate3004]
文章被引用时，请邮件提醒。
提交
相关作者
暂无数据
相关机构
北京科技大学 自动化学院, 北京市工业波谱成像工程技术研究中心
宁波大学 信息科学与工程学院
宁波市镇海区气象局
自然资源部 城市国土资源监测与仿真重点实验室
南京大学 国际地球系统科学研究所
选择翻译语言
- 地址：北京市海淀区北四环西路19号 《遥感学报》编辑部 邮编：100190
- 联系电话：010-58887052 Email：nrsb@aircas.ac.cn
- 技术支持由北京北大方正电子有限公司提供 京ICP备20021838号-9
京公网安备11010802024621 - 本系统建议在Chrome、 IE9+ 以上版本浏览器阅读本站内容，360浏览器请切换至极速模式
- Cookies帮助我们提供服务并提供个性化体验。使用本网站，即表示您同意我们使用Cookies
0
回到顶部