Remote Sensing of the Terrestrial Water Cycle: Progress and Perspectives

TANG Qiuhong; ZHANG Xuejun; QI Youcun; CHEN Shaohui; JIA Guoqiang; MU Mengfei; YANG Jie; YANG Qiquan; HUANG Xin; YUN Xiaobo; LIU Xingcai; HUANG Zhongwei; TANG Yin

doi:10.13203/j.whugis20180174

Volume 43 Issue 12

Dec. 2018

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Geomatics and Information Science of Wuhan University > 2018 > 43(12): 1872-1884. > DOI: 10.13203/j.whugis20180174

TANG Qiuhong, ZHANG Xuejun, QI Youcun, CHEN Shaohui, JIA Guoqiang, MU Mengfei, YANG Jie, YANG Qiquan, HUANG Xin, YUN Xiaobo, LIU Xingcai, HUANG Zhongwei, TANG Yin. Remote Sensing of the Terrestrial Water Cycle: Progress and Perspectives[J]. Geomatics and Information Science of Wuhan University, 2018, 43(12): 1872-1884. DOI: 10.13203/j.whugis20180174

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Remote Sensing of the Terrestrial Water Cycle: Progress and Perspectives

TANG Qiuhong^{1, 2,},
ZHANG Xuejun^{1, 2},
QI Youcun¹,
CHEN Shaohui¹,
JIA Guoqiang^{1, 2},
MU Mengfei^{1, 2},
YANG Jie^{1, 3, 4},
YANG Qiquan^{1, 3, 4},
HUANG Xin^{3, 4},
YUN Xiaobo^{1, 2},
LIU Xingcai¹,
HUANG Zhongwei^{1, 2},
TANG Yin¹

1.
Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
2.
University of Chinese Academy of Sciences, Beijing 100049, China
3.
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
4.
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

Funds:

The National Natural Science Foundation of China 41730645

The National Natural Science Foundation of China 41790424

the National Science Foudation for Distinguished Young Scholars 41425002

More Information

Author Bio:
TANG Qiuhong, professor, specializes in the hydrology and remote sensing. E-mail: tangqh@igsnrr.ac.cn
Received Date: September 04, 2018
Published Date: December 04, 2018

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Abstract

Abstract

Satellite remote sensing has made great strides in the last few decades, which enables the long-term consistent observations of many variables of the terrestrial water cycle and thus advances the understanding of the terrestrial water cycle. This paper reviews the principles of remote sensing in retrieving key variables of the terrestrial water cycle, illustrates the progress of satellite remote sen-sing in hydrological applications, and discusses the future direction. Although most of hydrological states and fluxes variables are observable by remote sensing, closing terrestrial water budget with the remote sensing products is still an open question, suggesting more efforts are needed to improve the hydrological consistency of the remote sensing products. In the future, efforts should be made to develop new generation sensors and platforms for the consistent remote sensing products with finer spatiotemporal resolution. At the same time, efforts should be devoted to the evaluation of remote sen-sing products of the terrestrial water cycle through carrying out integrated field experiments.
- remote sensing,
- terrestrial water cycle,
- satellite,
- radar,
- water balance

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References (97)

References

[1]	Tang Q, Durand M, Lettenmaier D P, et al. Satellite-based Observations of Hydrological Processes[J]. International Journal of Remote Sensing, 2010, 31(14):3661-3667 doi: 10.1080/01431161.2010.483496
[2]	Tapley B D, Bettadpur S, Ries J C, et al. GRACE Measurements of Mass Variability in the Earth System[J]. Science, 2004, 305(5683):503-505 doi: 10.1126/science.1099192
[3]	Sellers P J, Hall F G, Asrar G, et al. An Overview of the First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE)[J]. Journal of Geophysical Research Atmospheres, 1992, 97(D17):18345-18371 doi: 10.1029/92JD02111
[4]	李新, 李小文, 李增元, 等.黑河综合烟感联合实验研究进展:概述[J].遥感技术与应用, 2012, 27(5):637-649 Li Xin, Li Xiaowen, LI Zengyuan, et al. Progresses on the Watershed Allied Telemetry Experimental Research (WATER)[J]. Remote Sensing Technology and Application, 2012, 27(5):637-649
[5]	Arthur Y H, Ramesh K K, Steven N, et al. The Global Precipitation Measurement Mission[J]. Bulletin of the American Meteorological Society, 2014, 95(5):701-722 doi: 10.1175/BAMS-D-13-00164.1
[6]	唐国强, 万玮, 曾子悦, 等.全球降雨测量(GPM)计划及其最新进展综述[J].遥感技术与应用, 2015, 30(4):607-615 http://d.wanfangdata.com.cn/Periodical/ygjsyyy201504001 Tang Guoqiang, Wan Wei, Zeng Ziyue, et al. An Overview of the Global Precipitation Measurement (GPM) Mission and Its Latest Development[J]. Remote Sensing Technology and Application, 2015, 30(4):607-615 http://d.wanfangdata.com.cn/Periodical/ygjsyyy201504001
[7]	Kenneth J T, Marvin E B. Satellite Precipitation Products and Hydrologic Applications[J]. Water International, 2014, 39(3):360-380 doi: 10.1080/02508060.2013.870423
[8]	Kidd C, Levizzani V. Status of Satellite Precipitation Retrievals[J]. Hydrology and Earth System Sciences, 2011, 15(5):1109-1116 http://d.old.wanfangdata.com.cn/OAPaper/oai_doaj-articles_b829b41e975bf5747bfd6795739367b9
[9]	Barrett E C, Beaumont M J. Satellite Rainfall Monitoring:An Overview[J]. Remote Sensing Reviews, 1994, 11(1):23-48 http://d.old.wanfangdata.com.cn/NSTLQK/NSTL_QKJJ022166651/
[10]	Levizzani V, Amorati R, Meneguzzo F. A Review of Satellite Based Rainfall Estimation Methods[OL]. http://satmet.isac.cnr.it/papers/MUSIC-Rep-Sat_Precip-6.1.pdf, 2002
[11]	Francisco J T, Turk F J, Walt P, et al. Global Precipitation Measurement:Methods, Datasets and Applications[J]. Atmospheric Research, 2012, 104-105(1):70-97 http://d.old.wanfangdata.com.cn/NSTLQK/NSTL_QKJJ0226195120/
[12]	Arkin P A, Meisner B N. The Relationship Between Large-Scale Convective Rainfall and Cold Cloud over the Western Hemisphere During 1982-1984[J]. Monthly Weather Review, 1987, 115(115):51 doi: 10.1175-1520-0493(1987)115-0051-TRBLSC-2.0.CO%3b2/
[13]	Scofield R A, Kuligowski R J. Status and Outlook of Operational Satellite Precipitation Algorithms for Extreme-Precipitation Events[J]. Monthly Weather Review, 2003, 18(6):1037-1051 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=97f44b406822e0b4f52e983ec1c522cc
[14]	杨洪平, 万蓉, 石燕, 等.淮河流域"6.30"暴雨过程的三部雷达联合反演降水分析[C].新世纪气象科技创新与大气科学发展——中国气象学会2003年年会"03.7淮河大水的水文气象学问题"分会, 北京, 2003 Yang Hongping, Wan Rong, Shi Yan, et al. Three-Radar Combined Inversion Precipitation Analysis of "6.30" Heavy Rainfall in the Huaihe River Basin[C]. Meteorological Science and Technology Innovation and Atmospheric Science Deve-lopment in the New Century: Chinese Meteorological Society, Meeting of the "03.7 Huaihe Dashui Hydrometeorology" Subcommittee, Beijing, China, 2003
[15]	刘晓阳, 杨洪平, 李建通, 等.新一代天气雷达定量估测降水系统(QPEGS)精度初评[C].中国气象学会2007年年会气象综合探测技术分会, 广州, 2007 Liu Xiaoyang, Yang Hongping, Li Jiantong, et al. The Preliminary Evaluation of the Accuracy of the New-generation Weather Radar Quantitative Precipi-tation System (QPEGS)[C]. The Meteorological Comprehensive Detection Technology Subcommittee of the 2007 Annual Meeting of the Chinese Meteorological Society, Guangzhou, China, 2007
[16]	Zhang J, Howard K, Langston C, et al. National Mosaic and Multi-Sensor QPE (NMQ) System:Description, Results, and Future Plans[J]. Bulletin of the American Meteorological Society, 2015, 92(92):1321-1338 http://d.old.wanfangdata.com.cn/NSTLQK/NSTL_QKJJ0224636570/
[17]	Zhang J, Howard K, Langston C, et al. Multi-Radar Multi-Sensor (MRMS) Quantitative Precipita-tion Estimation:Initial Operating Capabilities[J]. Bulletin of the American Meteorological Society, 2016 http://adsabs.harvard.edu/abs/2016JHyMe..17.1317W
[18]	刘黎平, 吴林林, 杨引明.基于模糊逻辑的分步式超折射地物回波识别方法的建立和效果分析[J].气象学报, 2007, 65(2):42-52, 65 http://d.old.wanfangdata.com.cn/Periodical/qxxb200702011 Liu Liping, Wu Linlin, Yang Yinming. Establishment and Effect Analysis of Step-by-Step Echo Recognition Method Based on Fuzzy Logic for Terrestrial Superrefractors[J]. Acta Meteorologica Sinica, 2007, 65(2):42-52, 65 http://d.old.wanfangdata.com.cn/Periodical/qxxb200702011
[19]	Tang L, Zhang J, Langston C, et al. A Physically Based Precipitation-Nonprecipitation Radar Echo Classifier Using Polarimetric and Environmental Data in a Real-Time National System[J]. Weather & Forecasting, 2014, 29(5):1106-1119 http://adsabs.harvard.edu/abs/2014WtFor..29.1106T
[20]	张培昌, 林炳干, 王登炎, 等.最优化法求Z-R关系及其在测定降水量中的精度[J].气象科学, 1992, 12(3):333-338 http://www.cnki.com.cn/Article/CJFDTotal-QXKX199203010.htm Zhang Peichang, Lin Binggan, Wang Dengyan, et al. Optimization of the Z-R Relationship and Its Accuracy in the Determination of Precipitation[J]. Journal of the Meteorologica Sinica, 1992, 12(3):333-338 http://www.cnki.com.cn/Article/CJFDTotal-QXKX199203010.htm
[21]	刘娟, 宋子忠, 刘东风, 等.分组Z-R关系及其在淮河流域雷达测雨中应用[J].气象科学, 1999, 19(2):213-219 http://kns.cnki.net/KCMS/detail/detail.aspx?filename=QXKX199902015&dbname=CJFD&dbcode=CJFQ Liu Juan, Song Zizhong, Liu Dongfeng, et al. Group Z-R Relationship and Its Application in Radar Rainfall Measurement in the Huaihe River Basin[J]. Journal of the Meteorological Sciences, 1999, 19(2):213-219 http://kns.cnki.net/KCMS/detail/detail.aspx?filename=QXKX199902015&dbname=CJFD&dbcode=CJFQ
[22]	汪瑛, 冯业荣, 蔡锦辉, 等.雷达定量降水动态分级Z-I关系估算方法[J].热带气象学报, 2011, 27(4):601-608 doi: 10.3969/j.issn.1004-4965.2011.04.018 Wang Ying, Feng Yerong, Cai Jinhui, et al. Radial Quantitative Precipitation Z-I Relationship Estimation Method[J]. Journal of Tropical Meteorology, 2011, 27(4):601-608 doi: 10.3969/j.issn.1004-4965.2011.04.018
[23]	Xu X, Howard K, Zhang J. An Automated Radar Technique for the Identification of Tropical Precipitation[J]. Journal of Hydrometeorology, 2008, 9(5):885-902 doi: 10.1175/2007JHM954.1
[24]	Zhang J, Qi Y C. A Real-Time Algorithm for the Correction of Brightband Effects in Radar-derived QPE[J]. Journal of Hydrometeorology, 2010, 11(5):1157-1171 doi: 10.1175/2010JHM1201.1
[25]	Qi Y, Zhang J, Zhang P. A Real-Time Automated Convective and Stratiform Precipitation Segregation Algorithm in Native Radar Coordinates[J]. Quarterly Journal of the Royal Meteorological Society, 2013, 139(677):2233-2240 doi: 10.1002/qj.v139.677
[26]	肖艳娇, 刘黎平, 李中华, 等.雷达反射率因子数据中的亮带自动识别和抑制[J].高原气象, 2010, 29(1):197-205 http://d.old.wanfangdata.com.cn/Periodical/gyqx201001023 Xiao Yanjiao, Liu Liping, Li Zhonghua, et al. Automatic Detection and Suppression of Bright Bands in Radar Reflectivity Data[J]. Plateau Meteorology, 2010, 29(1):197-205 http://d.old.wanfangdata.com.cn/Periodical/gyqx201001023
[27]	黄旋旋, 朱科锋, 赵坤.利用雷达反射率因子垂直廓线改进复杂地形下的台风降水估测精度[J].气象, 2017, 43(10):1198-1212 doi: 10.7519/j.issn.10000526.2017.10.004 Huang Xuanxuan, Zhu Kefeng, Zhao Kun. Improvement of Typhoon Precipitation Estimation Accuracy Under Complex Terrain Using Radar Reflectivity Factor Vertical Profiles[J]. Meteorological Monthly, 2017, 43(10):1198-1212 doi: 10.7519/j.issn.10000526.2017.10.004
[28]	肖艳娇, 刘黎平.新一代天气雷达网资料的三维格点化及拼图方法研究[J].气象学报, 2006, 64(5):647-656 doi: 10.3321/j.issn:0577-6619.2006.05.011 Xiao Yanjiao, Liu Liping. Research on Three-dimensional Lattice Point and Puzzle Method of Weather Radar Network Data of New Generation[J]. Acta Meteorologica Sinica, 2006, 64(5):647-656 doi: 10.3321/j.issn:0577-6619.2006.05.011
[29]	Zhang J, Howard K, Gourley J J. Constructing Three-Dimensional Multiple-Radar Reflectivity Mosaics:Examples of Convective Storms and Stratiform Rain Echoes[J]. Journal of Atmospheric & Oceanic Technology, 2005, 22(1):30-42 http://adsabs.harvard.edu/abs/2005jatot..22...30z
[30]	Langston C, Zhang J, Howard K. Four-Dimensional Dynamic Radar Mosaic[J]. Journal of Atmospheric & Oceanic Technology, 2007, 24(5):776-790 http://d.old.wanfangdata.com.cn/NSTLQK/NSTL_QKJJ025088464/
[31]	Tabary P. The New French Operational Radar Rainfall Product. Part I:Methodology[J]. Weather & Forecasting, 2007, 22(3):393-408 http://adsabs.harvard.edu/abs/2007WtFor..22..409T
[32]	Qi Y, Zhang J. A Physically Based Two-Dimensional Seamless Reflectivity Mosaic for Radar QPE in the MRMS System[J]. Journal of Hydrometeorology, 2017, 18(5):1327-1340 doi: 10.1175/JHM-D-16-0197.1
[33]	唐国强, 龙笛, 万玮, 等.全球水遥感技术及其应用研究的综述与展望[J].中国科学:技术科学, 2015, 45:1013-1023 http://kns.cnki.net/KCMS/detail/detail.aspx?filename=JEXK201510002&dbname=CJFD&dbcode=CJFQ Tang Guoqiang, Long Di, Wan Wei, et al. An Overview and Outlook of Global Water Remote Sen-sing Technology and Applications[J]. Scientia Sinica, 2015, 45:1013-1023 http://kns.cnki.net/KCMS/detail/detail.aspx?filename=JEXK201510002&dbname=CJFD&dbcode=CJFQ
[34]	Luo X, Chen J M, Liu J, et al. Comparison of Big-leaf, Two-Big-Leaf and Two-Leaf Upscaling Schemes for Evapotranspiration Estimation Using Coupled Carbon-water Modelling[J]. Journal of Geophysical Research Biogeosciences, 2018, DOI: 10.1002/2017JG003978
[35]	Wagle P, Bhattarai N, Gowda P H, et al. Perfor-mance of Five Surface Energy Balance Models for Estimating Daily Evapotranspiration in High Biomass Sorghum[J]. Journal of Photogrammetry & Remote Sen-sing, 2017, 128:192-203 http://adsabs.harvard.edu/abs/2017JPRS..128..192W
[36]	杨永民, 李璐, 庞治国, 等.基于理论参数空间的遥感蒸散模型构建及验证[J].遥感技术与应用, 2016, 31(2):324-331 http://d.old.wanfangdata.com.cn/Periodical/ygjsyyy201602016 Yang Yongmin, Li Lu, Pang Zhiguo, et al. A Remote Sensing Evapotranspiration Model for Land Based on the Theoretical Trapezoid Space[J]. Remote Sensing Technology and Application, 2016, 31(2):324-331 http://d.old.wanfangdata.com.cn/Periodical/ygjsyyy201602016
[37]	Mu Q Z, Zhao M S, Running S W. Improvements to a MODIS Global Terrestrial Evapotranspiration Algorithm[J]. Remote Sensing of Environment, 2011, 115(8):1781-1800 doi: 10.1016/j.rse.2011.02.019
[38]	Zhang K, Kimball J S, Nemani R R, et al. A Continuous Satellite-derived Global Record of Land Surface Evapotranspiration from 1983 to 2006[J]. Water Resources Research, 2010, 46:W09522 doi: 10.1029-2009WR008800/
[39]	Vinukollu R K, Meynadier R, Sheffield J, et al. Multi-model, Multi-sensor Estimates of Glo-bal Evapotranspiration:Climatology, Uncertainties and Trends[J]. Hydrological Processes, 2011, 25(26):3993-4010 doi: 10.1002/hyp.8393
[40]	Miralles D G, Jiménez C, Jung M, et al. The WACMOS-ET Project-Part 2:Evaluation of Glo-bal Terrestrial Evaporation Data Sets[J]. Hydrology & Earth System Sciences Discussions, 2016, 12(1):10651-10700
[41]	Chen Shaohui. A Trous Wavelet Based Four-Dimensional Evapotranspiration Assimilation[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sen-sing, 2016, 9(1):467-474 doi: 10.1109/JSTARS.2015.2501542
[42]	徐沛, 张超.土壤水分遥感反演研究进展[J].林业资源管理, 2015(4):151-160 http://d.old.wanfangdata.com.cn/Periodical/zrzhxb200606020 Xu Pei, Zhang Chao. Progress of Research on Retrieval of Soil Moisture Based on Remote Sensing[J]. Forest Resources Management, 2015(4):151-160 http://d.old.wanfangdata.com.cn/Periodical/zrzhxb200606020
[43]	余凡, 李海涛, 张承明, 等.利用双极化微波遥感数据反演土壤水分的新方法[J].武汉大学学报·信息科学版, 2014, 39(2):225-228 http://ch.whu.edu.cn/CN/abstract/abstract2939.shtml Yu Fan, Li Haitao, Zhang Chengming, et al. A New Approach for Surface Soil Moisture Retrieving Using Two-polarized Microwave Remote Sensing Data[J]. Geomatics and Information Science of Wuhan University, 2014, 39(2):225-228 http://ch.whu.edu.cn/CN/abstract/abstract2939.shtml
[44]	Peng J, Loew A, Merlin O, et al. A Review of Spatial Downscaling of Satellite Remotely Sensed Soil Moisture[J]. Reviews of Geophysics, 2017, 55(2):341-366 doi: 10.1002/rog.v55.2
[45]	吴黎, 张有智, 解文欢, 等.土壤水分的遥感监测方法概述[J].国土资源遥感, 2014, 26(2):19-26 http://d.old.wanfangdata.com.cn/Periodical/gtzyyg201402004 Wu Li, Zhang Youzhi, Xie Wenhuan, et al. Summary of Remote Sensing Methods for Monitoring Soil Moisture[J]. Remote Sensing for Land and Resources, 2014, 26(2):19-26 http://d.old.wanfangdata.com.cn/Periodical/gtzyyg201402004
[46]	陈书林, 刘元波, 温作民.卫星遥感反演土壤水分研究综述[J].地球科学进展, 2012, 27(11):1192-1203 http://d.old.wanfangdata.com.cn/Periodical/dqkxjz201211002 Chen Shulin, Liu Yuanbo, Wen Zuomin. Satellite Retrieval of Soil Moisture:An Overview[J]. Advances in Earth Science, 2012, 27(11):1192-1203 http://d.old.wanfangdata.com.cn/Periodical/dqkxjz201211002
[47]	Kerr Y H, Waldteufel P, Richaume P, et al. The SMOS Soil Moisture Retrieval Algorithm[J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50:1384-1403 doi: 10.1109/TGRS.2012.2184548
[48]	Kolassa J, Reichle R H, Liu Q, et al. Estimating Surface Soil Moisture from SMAP Observations Using a Neural Network Technique[J]. Remote Sensing of Environment, 2018, 204:43-59 doi: 10.1016/j.rse.2017.10.045
[49]	胡猛, 冯起, 席海洋.遥感技术监测干旱区土壤水分研究进展[J].土壤通报, 2013, 44(5):1270-1275 http://d.old.wanfangdata.com.cn/Periodical/trtb201305041 Hu Meng, Feng Qi, Xi Haiyang. Progress of Monitoring Soil Moisture by Remote Sensing in Arid Areas[J]. Chinese Journal of Soil Science, 2013, 44(5):1270-1275 http://d.old.wanfangdata.com.cn/Periodical/trtb201305041
[50]	杨红娟, 丛振涛, 雷志栋.谐波法与双源模型耦合估算土壤热通量和地表蒸散发[J].武汉大学学报·信息科学版, 2009, 34(6):706-710 http://ch.whu.edu.cn/CN/abstract/abstract1350.shtml Yang Hongjuan, Cong Zhentao, Lei Zhidong. Coupling of the Harmonic Analysis Method and Two-source Energy Model to Estimate Soil Heat Flux and Evapotranspiration[J]. Geomatics and Information Science of Wuhan University, 2009, 34(6):706-710 http://ch.whu.edu.cn/CN/abstract/abstract1350.shtml
[51]	Dietz A J, Kuenzer C, Gessner U, et al. Remote Sensing of Snow-A Review of Available Methods[J]. International Journal of Remote Sensing, 2012, 33(13):4094-4134 doi: 10.1080/01431161.2011.640964
[52]	Yang J, Jiang L, Shi J, et al. Monitoring Snow Cover Using Chinese Meteorological Satellite Data over China[J]. Remote Sen-sing of Environment, 2014, 143:192-203 doi: 10.1016/j.rse.2013.12.022
[53]	Tang Q H, Gao H L, Lu H, et al. Remote Sen-sing:Hydrology[J]. Progress in Physical Geography, 2009, 33(4):490-509 doi: 10.1177/0309133309346650
[54]	Hall D K, Riggs G A, Salomonson V V, et al. MODIS Snow-cover Products[J]. Remote Sensing of Environment, 2002, 83(1):181-194 http://d.old.wanfangdata.com.cn/Periodical/skxjz201003008
[55]	Hall D K, Riggs G A. MODIS/Terra Snow Cover 8-Day L3 Global 500 m Grid, Version 6[C]. NASA National Snow and Ice Data Center Distributed Active Archive Center, Boulder, Colorado, USA, 2016
[56]	Frei A, Tedesco M, Lee S, et al. A Review of Global Satellite-derived Snow Products[J]. Advances in Space Research, 2012, 50:1007-1029 doi: 10.1016/j.asr.2011.12.021
[57]	Parajka J, Pepe M, Rampini A, et al. A Regional Snowline Method for Estimating Snow Cover from MODIS During Cloud Cover[J]. Journal of Hydrology, 2010, 381:203-212 doi: 10.1016/j.jhydrol.2009.11.042
[58]	王子龙, 胡石涛, 付强, 等.积雪参数遥感反演研究进展[J].东北农业大学学报, 2016, 47(9):100-106 doi: 10.3969/j.issn.1005-9369.2016.09.014 Wang Zilong, Hu Shitao, Fu Qiang, et al. Research Progress on Remote Sensing Inversion of Snow Parameters[J]. Journal of Northeast Agricultural University, 2016, 47(9):100-106 doi: 10.3969/j.issn.1005-9369.2016.09.014
[59]	Foster J L, Hall D K, Chang A T C. Remote Sen-sing of Snow[J]. Eos Transactions American Geophysical Union, 2013, 68(32):682-684
[60]	Tedesco M, Kelly R, Foster J L, et al. AMSR-E/Aqua Daily L3 Global Snow Water Equivalent EASE-Grids, Version 2[C]. NASA National Snow and Ice Data Center Distributed Active Archive Center, Boulder, Colorado, USA, 2004
[61]	于灵雪, 张树文, 卜坤, 等.雪数据集研究综述[J].地理科学, 2013, 33(7):878-883 http://www.cnki.com.cn/Article/CJFDTotal-DLKX201307016.htm Yu Lingxue, Zhang Shuwen, Bu Kun, et al. A Review of Snow Dataset Research[J]. Scientia Geographica Sinica, 2013, 33(7):878-883 http://www.cnki.com.cn/Article/CJFDTotal-DLKX201307016.htm
[62]	Gao Y, Xie H, Lu N, et al. Toward Advanced Daily Cloud-free Snow Cover and Snow Water Equivalent Products from Terra-Aqua MODIS and Aqua AMSR-E Measurements[J]. Journal of Hydrology, 2010, 385(1-4):23-35 doi: 10.1016/j.jhydrol.2010.01.022
[63]	Foster J, Hall L D K, Eylander J B, et al. A Blended Glabal Snow Product Using Visible, Passive Microwave and Scatterometer Satellite Data[J]. Intemational Journal of Remote Sensing, 2011, 32(5):1371-1395 doi: 10.1080/01431160903548013
[64]	Helfrich S R, Mcnamara D, Ramsay B H, et al. Enhancements to, and Forthcoming Developments in the Interactive Multisensor Snow and Ice Mapping System (IMS)[J]. Hydrological Processes, 2010, 21(12):1576-1586 doi: 10.1002/hyp.6720/pdf
[65]	Vörösmarty C J, Green P, Salisbury J, et al. Glo-bal Water Resources:Vulnerability from Climate Change and Population Growth[J]. Science, 2000, 289(5477):284-288 doi: 10.1126/science.289.5477.284
[66]	骆剑承, 盛永伟, 沈占锋, 等.分步迭代的多光谱遥感水体信息高精度自动提取[J].遥感学报, 2009, 13(4):610-615 http://d.old.wanfangdata.com.cn/Periodical/ygxb200904005 Luo Jiancheng, Sheng Yongwei, Shen Zhanfeng, et al. Highly Accurate Automatic Extraction of Multi-spectral Remote Sensing Water Information with Step Iteration[J]. Journal of Remote Sensing, 2009, 13(4):610-615 http://d.old.wanfangdata.com.cn/Periodical/ygxb200904005
[67]	李建, 周屈, 陈晓玲, 等.近岸/内陆典型水环境要素定量遥感空间尺度问题研究[J].武汉大学学报·信息科学版, 2018, 43(6):937-942 http://ch.whu.edu.cn/CN/abstract/abstract6135.shtml Li Jian, Zhou Qu, Chen Xiaoling, et al. Spatial Scale Study on Quantitative Remote Sensing of Highly Dynamic Coastal/Inland Waters[J]. Geomatics and Information Science of Wuhan University, 2018, 43(6):937-942 http://ch.whu.edu.cn/CN/abstract/abstract6135.shtml
[68]	McFeeters S K. The Use of the Normalized Difference Water Index (NDWI) in the Delineation of Open Water Features[J]. International Journal of Remote Sensing, 1996, 17(7):1425-1432 doi: 10.1080/01431169608948714
[69]	徐涵秋.利用改进的归一化差异水体指数(MNDWI)提取水体信息的研究[J].遥感学报, 2005, 9(5):589-595 http://cdmd.cnki.com.cn/Article/CDMD-11415-2006194729.htm Xu Hanqiu. Study on Extracting Water Information Using Improved Normalized Differential Water Index (MNDWI)[J]. Journal of Remote Sensing, 2005, 9(5):589-595 http://cdmd.cnki.com.cn/Article/CDMD-11415-2006194729.htm
[70]	殷亚秋, 李家国, 余涛, 等.基于高分辨率遥感影像的面向对象水体提取方法研究[J].测绘通报, 2015(1):81-85 http://d.old.wanfangdata.com.cn/Periodical/chtb201501016 Yin Yaqiu, Li Jiaguo, Yu Tao, et al. Object-oriented Water Body Extraction Method Based on High Resolution Remote Sensing Images[J]. Bulletin of Surveying and Mapping, 2015(1):81-85 http://d.old.wanfangdata.com.cn/Periodical/chtb201501016
[71]	Pekel J F, Vancutsem C, Bastin L, et al. A Near Real-Time Water Surface Detection Method Based on HSV Transformation of MODIS Multi-spectral Time Series Data[J]. Remote Sensing of Environment, 2014, 140:704-716 doi: 10.1016/j.rse.2013.10.008
[72]	廖安平, 陈利军, 陈军, 等.全球陆表水体高分辨率遥感制图[J].中国科学:地球科学, 2014, 44(8):1634-1645 http://www.cnki.com.cn/Article/CJFDTOTAL-JDXK201408004.htm Liao Anping, Chen Lijun, Chen Jun, et al. High Resolution Remote Sensing Mapping of Global Land Surface Waters[J]. Science China:Earth Science, 2014, 44(8):1634-1645 http://www.cnki.com.cn/Article/CJFDTOTAL-JDXK201408004.htm
[73]	孙娜, 高志强, 王晓晶, 等.基于高分遥感影像的黄土高原地区水体高精度提取[J].国土资源遥感, 2017, 29(4):173-178 http://d.old.wanfangdata.com.cn/Periodical/gtzyyg201704026 Sun Na, Gao Zhiqiang, Wang Xiaojing, et al. Highly Accurate Extraction of Water Body in the Loess Plateau Based on High Resolution Remote Sensing Images[J]. Remote Sensing for Land & Resources, 2017, 29(4):173-178 http://d.old.wanfangdata.com.cn/Periodical/gtzyyg201704026
[74]	Donchyts G, Baart F, Winsemius H, et al. Earth's Surface Water Change over the Past 30 Years[J]. Nature Climate Change, 2016, 6(9):810 doi: 10.1038/nclimate3111
[75]	Gorelick N, Hancher M, Dixon M, et al. Google Earth Engine:Planetary-Scale Geospatial Analysis for Everyone[J]. Remote Sensing of Environment, 2017, 202:18-27 doi: 10.1016/j.rse.2017.06.031
[76]	Pekel J F, Cottam A, Gorelick N, et al. High-Reso-lution Mapping of Global Surface Water and Its Long-term Changes[J]. Nature, 2016, 540(7633):418 doi: 10.1038/nature20584
[77]	Trochim E D, Prakash A, Kane D L, et al. Remote Sensing of Water Tracks[J]. Earth & Space Science, 2016(3), DOI: 10.1002/2015EA000112
[78]	Hugue F, Lapointe M, Eaton B C, et al. Satellite-based Remote Sensing of Running Water Habitats at Large Riverscape Scales:Tools to Analyze Habitat Heterogeneity for River Ecosystem Management[J]. Geomorphology, 2016, 253:353-369 doi: 10.1016/j.geomorph.2015.10.025
[79]	Gao H. Satellite Remote Sensing of Large Lakes and Reservoirs:From Elevation and Area to Storage[J]. Wiley Interdisciplinary Reviews Water, 2015, 2(2):147-157 doi: 10.1002/wat2.1065
[80]	Tapley B D, Bettadpur S, Watkins M, et al. The Gravity Recovery and Climate Experiment:Mission Overview and Early Results[J]. Geophysical Research Letters, 2004, 31:9607-9610 doi: 10.1029/2004GL019920/full
[81]	Long D, Shen Y, Sun A, et al. Drought and Flood Monitoring for a Large Karst Plateau in Southwest China Using Extended GRACE Data[J]. Remote Sensing of Environment, 2014, 155:145-160 doi: 10.1016/j.rse.2014.08.006
[82]	Biancamaria S, Lettenmaier D P, Pavelsky T M. The SWOT Mission and Its Capabilities for Land Hydrology[J]. Remote Sensing and Water Resources, 2016, 55:117-147 doi: 10.1007/978-3-319-32449-4
[83]	Crétaux J F, Biancamaria S, Arsen A, et al. Global Surveys of Reservoirs and Lakes from Satellites and Regional Application to the Syrdarya River Basin[J]. Environmental Research Letters, 2015, 10:015002 doi: 10.1088/1748-9326/10/1/015002
[84]	Lee H, Durand M, Jung H C, et al. Characterization of Surface Water Storage Changes in Arctic Lakes Using Simulated SWOT Measurements[J]. International Journal of Remote Sensing, 2010, 31:3931-3953 doi: 10.1080/01431161.2010.483494
[85]	Gao H, Birkett C, Lettenmaier D P. Global Monitoring of Large Reservoir Storage from Satellite Remote Sensing[J]. Water Resources Research, 2012, 48:1-12 doi: 10.1016/j.advwatres.2012.07.002
[86]	Zhang S, Gao H, Naz B S. Monitoring Reservoir Rtorage in South Asia from Multisatellite Remote Sensing[J]. Water Resources Research, 2014, 50:8927-8943 doi: 10.1002/2014WR015829
[87]	Keys T A, Scott D T. Monitoring Volumetric Fluctuations in Tropical Lakes and Reservoirs Using Satellite Remote Sensing[J]. Lake & Reservoir Management, 2017, 34(3):154-166 doi: 10.1080/10402381.2017.1402226
[88]	Zhang S, Gao H. A Novel Algorithm for Monitoring Reservoirs Under All-weather Conditions at a High Temporal Resolution Through Passive Microwave Remote Sensing[J]. Geophysical Research Letters, 2016, 43:8052-8059 doi: 10.1002/2016GL069560
[89]	Politi E, Maccallum S, Cutler M E J, et al. Selection of a Network of Large Lakes and Reservoirs Suitable for Global Environmental Change Analysis Using Earth Observation[J]. International Journal of Remote Sensing, 2016, 37(13):3042-3060 doi: 10.1080/01431161.2016.1192702
[90]	McCabe M F, Wood E F, Wojcik R, et al. Hydrological Consistency Using Multi-sensor Remote Sensing Data for Water and Energy Cycle Studies[J]. Remote Sensing of Environment, 2008, 112:430-444 doi: 10.1016/j.rse.2007.03.027
[91]	Sheffield J, Ferguson C R, Troy T J, et al. Closing the Terrestrial Water Budget from Satellite Remote Sensing[J]. Geophysical Research Letters, 2009, 36:L07403 http://www.cabdirect.org/abstracts/20103055296.html
[92]	Gao H, Tang Q, Ferguson C R, et al. Estimating the Water Budget of Major US River Basins Via Remote Sensing[J]. International Journal of Remote Sensing, 2010, 31:3955-3978 doi: 10.1080/01431161.2010.483488
[93]	Wang H, Guan H, Gutiérrez-Jurado H A, et al. Examination of Water Budget Using Satellite Pro-ducts over Australia[J]. Journal of Hydrology, 2014, 511:546-554 doi: 10.1016/j.jhydrol.2014.01.076
[94]	Sahoo A K, Pan M, Troy T J, et al. Reconciling the Global Terrestrial Water Budget Using Satellite Remote Sensing[J]. Remote Sensing of Environment, 2011, 115:1850-1865 doi: 10.1016/j.rse.2011.03.009
[95]	Pan M, Sahoo A K, Troy T J, et al. Multisource Estimation of Long-term Terrestrial Water Budget for Major Global River Basins[J]. Journal of Climate, 2012, 25:3191-3206 doi: 10.1175/JCLI-D-11-00300.1
[96]	Zhang Y, Pan M, Wood E F. On Creating Global Gridded Terrestrial Water Budget Estimates from Satellite Remote Sensing[J]. Surveys in Geophy-sics, 2016, 37:249-268 doi: 10.1007/s10712-015-9354-y
[97]	Shi J, Dong X, Zhao T, et al. WCOM: The Science Scenario and Objectives of a Global Water Cycle Observation Mission[C]. IEEE Geoscience and Remote Sensing Symposium, Quebec City, Canada, 2014

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