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]