предыдущий|следующий

A review on water resources stereoscopic monitoring systems based on multisource data

  • role： First author第一作者
  • Affiliation：

    State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China

  • Email：yan-117@mails.tsinghua.edu.cn
  • Introduction：岩腊，1994年生, 男, 硕士研究生, 研究方向为多源遥感数据融合及水文水资源应用。E-mail: yan-117@mails.tsinghua.edu.cn
YAN La1,  
  • role： Corresponding author通信作者
  • Affiliation：

    State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China

  • Email：dlong@tsinghua.edu.cn
  • Introduction：龙笛，1982年生, 男, 研究员，研究方向为遥感水文与水资源。E-mail: dlong@tsinghua.edu.cn
LONG Di1*,  
  • Affiliation：

    State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China

BAI Liangliang1,  
  • Affiliation：

    State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China

ZHANG Caijin1,  
  • Affiliation：

    State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China

HAN Zhongying1,  
  • Affiliation：

    State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China

LI Xingdong1,  
  • Affiliation：

    State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China

WANG Wen2,  
  • Affiliation：

    Spatial Information Research Center, Changjiang Science Institute,Wuhan 430010, China

SHEN Shaohong3,  
  • Affiliation：

    Department of Water Resources, China Institute of Water Resources and Hydropower Research,Beijing 100038, China

YE Yuntao4

реферат

Water resources monitoring is the foundation of water resources management. China is implementing the strictest water resources management system toward precise and dynamic management in terms of water quantity, water quality, and water use efficiency, with higher requirements for water resources monitoring. Water resources components are characterized by large spatial and temporal differences. Currently, the water resources monitoring system is based mainly on ground monitoring. However, it is difficult to carry out large-scale and long-term monitoring based solely on ground measurements. There are still knowledge gaps in the fine and dynamic water resources management. With continuous and rapid increases in satellite data, this paper examines the use of multi-mission satellite data, ground observation systems, and land data assimilation systems to jointly develop a stereoscopic monitoring system, which would be of value to address the monitoring gaps for water cycle components and to enhance the monitoring capability of water resources in China, with important implications for stereoscopic water resources monitoring for other countries and regions globally.

ключеви́че слова́

remote sensing;multisource data;stereoscopic monitoring;coordinated mechanisms;water resources management

References

  1. 1.
    Allan M G, Hamilton D P, Hicks B J and Brabyn L. 2011. Landsat remote sensing of chlorophyll a concentrations in central North Island lakes of New Zealand. International Journal of Remote Sensing, 32(7):2037-2055
  2. 2.
    Anderson M C, Norman J M, Mecikalski J R, Otkin J A and Kustas W P. 2007. A climatological study of evapotranspiration and moisture stress across the continental United States based on thermal remote sensing: 1. Model formulation. Journal of Geophysical Research: Atmospheres, 112: D10117
  3. 3.
    Bai L L, Cai J B, Liu Y, Chen H, Zhang B Z and Huang L X. 2017. Responses of field evapotranspiration to the changes of cropping pattern and groundwater depth in large irrigation district of Yellow River basin. Agricultural Water Management, 188: 1-11
  4. 4.
    Bai L L, Long D and Yan L. 2019. Estimation of surface soil moisture with downscaled land surface temperatures using a data fusion approach for heterogeneous agricultural land. Water Resources Research, 55(2): 1105-1128
  5. 5.
    Bartalis Z, Wagner W, Naeimi V, Hasenauer S, Scipal K, Bonekamp H, Figa J and Anderson C. 2007. Initial soil moisture retrievals from the METOP‐A Advanced Scatterometer (ASCAT). Geophysical Research Letters, 34: L20401
  6. 6.
    Bjerklie D M, Dingman S L, Vorosmarty C J, Bolster C H and Congalton R G. 2003. Evaluating the potential for measuring river discharge from space. Journal of Hydrology, 278(1-4): 17-38
  7. 7.
    Brakenridge G R, Nghiem S V, Anderson E and Chien S. 2005. Space‐based measurement of river runoff. Eos, Transactions American Geophysical Union, 86(19): 185-188
  8. 8.
    Brocca L, Hasenauer S, Lacava T, Melone F, Moramarco T, Wagner W, Dorigo W, Matgen P, Martínez-Fernández J and Llorens P. 2011. Soil moisture estimation through ASCAT and AMSR-E sensors: An intercomparison and validation study across Europe. Remote Sensing of Environment, 115(12): 3390-3408
  9. 9.
    Brocca L, Massari C, Ciabatta L, Moramarco T, Penna D, Zuecco G, Pianezzola L, Borga M, Matgen P and Martínez-Fernández J. 2015. Rainfall estimation from in situ soil moisture observations at several sites in Europe: an evaluation of the SM2RAIN algorithm. Journal of Hydrology and Hydromechanics, 63(3): 201-209
  10. 10.
    Brocca L, Moramarco T, Melone F and Wagner W. 2013. A new method for rainfall estimation through soil moisture observations. Geophysical Research Letters, 40(5): 853-858
  11. 11.
    Brocca L, Pellarin T, Crow W T, Ciabatta L, Massari C, Ryu D, Su C H, Rüdiger C and Kerr Y. 2016. Rainfall estimation by inverting SMOS soil moisture estimates: A comparison of different methods over Australia. Journal of Geophysical Research: Atmospheres, 121(20): 12062-12079
  12. 12.
    Cai Y. 2013. National Water Resources Monitoring Capacity Building Project and its Progress. Water Resources Information, (6):5-10
  13. 13.
    Cammalleri C, Anderson M, Gao F, Hain C and Kustas W. 2013. A data fusion approach for mapping daily evapotranspiration at field scale. Water Resources Research, 49(8): 4672-4686
  14. 14.
    Chan S, Bindlish R, O'Neill P, Jackson T, Njoku E, Dunbar S, Chaubell J, Piepmeier J, Yueh S and Entekhabi D. 2018. Development and assessment of the SMAP enhanced passive soil moisture product. Remote Sensing of Environment, 204: 931-941
  15. 15.
    Cho E, Su C-H, Ryu D, Kim H and Choi M. 2017. Does AMSR2produce better soil moisture retrievals than AMSR-E over Australia? Remote Sensing of Environment, 188: 95-105
  16. 16.
    Choi M and Hur Y. 2012. A microwave-optical/infrared disaggregation for improving spatial representation of soil moisture using AMSR-E and MODIS products. Remote Sensing of Environment, 124: 259-269
  17. 17.
    Ciabatta L, Marra A C, Panegrossi G, Casella D, Sanò P, Dietrich S, Massari C and Brocca L. 2017. Daily precipitation estimation through different microwave sensors: Verification study over Italy. Journal of Hydrology, 545: 436-450
  18. 18.
    Cho E, Jacobs J M and Vuyovich C M. 2020. The Value of Long‐Term (40 years) Airborne Gamma Radiation SWE Record for Evaluating Three Observation‐Based Gridded SWE Data Sets by Seasonal Snow and Land Cover Classifications. Water Resources Research, 56: 1-23
  19. 19.
    Colliander A, Jackson T J, Chan S, O'Neill P, Bindlish R, Cosh M, Caldwell T, Walker J, Berg A and McNairn H. 2018. An assessment of the differences between spatial resolution and grid size for the SMAP enhanced soil moisture product over homogeneous sites. Remote Sensing of Environment, 207: 65-70
  20. 20.
    Cui W J, Xia L H, Xie X T and Pan C H. 2017. A model of dissolved oxygen in the Pearl River estuary based on measured spectrum. Journal of Guangzhou University, 16(6): 84-92
  21. 21.
    Cui Y L, Dong B, Li Y H and Cai X L. 2007. Assessment indicators and scales of water saving in agricultural irrigation. Transactions of the Chinese Society of Agricultural Engineering, 23(7): 1-7
  22. 22.
    Draper D W, Newell D A, Wentz F J, Krimchansky S, Skofronick-Jackson G M. 2015. The Global Precipitation Measurement (GPM) Microwave Imager (GMI): Instrument Overview and Early On-Orbit Performance. IEEE Journal of selected topics in applied earth observations and remote sensing, 8(7): 3452-3462
  23. 23.
    Fang X, Duan H T, Cao Z G, Shen M and Ge X S. 2018. Remote monitoring of cyanobacterial blooms using multi-source satellite data: A case of Yuqiao Reservoir, Tianjin. Journal of Lake Sciences, 30(4): 967-978
  24. 24.
    Feng W, Zhong M, Lemoine J M, Biancale R, Hsu H T and Xia J. 2013. Evaluation of groundwater depletion in North China using the Gravity Recovery and Climate Experiment (GRACE) data and ground‐based measurements. Water Resources Research, 49(4): 2110-2118
  25. 25.
    Feng X, Li J, Cheng W, Fu B, Wang Y and Lü Y. 2017. Evaluation of AMSR-E retrieval by detecting soil moisture decrease following massive dryland re-vegetation in the Loess Plateau, China. Remote Sensing of Environment, 196: 253-264
  26. 26.
    Filippucci P, Tarpanelli A, Massari C, Serafini A, Strati V, Alberi M, Raptis K G C, Mantovani F and Brocca L. 2020. Soil moisture as a potential variable for tracking and quantifying irrigation: A case study with proximal gamma-ray spectroscopy data. Advances in Water Resources, 136: 103502
  27. 27.
    Gelsinari S, Doble R, Daly E and Pauwels V R N. 2020. Feasibility of improving groundwater modeling by assimilating evapotranspiration rates. Water Resources Research, 56, e2019WR025983
  28. 28.
    Gokmen M, Vekerdy Z, Verhoef A, Verhoef W, Batelaan O and Van der Tol C. 2012. Integration of soil moisture in SEBS for improving evapotranspiration estimation under water stress conditions. Remote Sensing of Environment, 121: 261-274
  29. 29.
    Guo J Y, Chang X T, Gao Y g, Sun J L and Hwang C. 2009. Lake level variations monitored with satellite altimetry waveform retracking. IEEE journal of selected topics in applied earth observations and remote sensing, 2(2): 80-86
  30. 30.
    Guo P, Wu F D, Dai J G, Wang H H, Xu L P and Zhang G S. 2017. Comparison of farmland crop classification methods based on visible light images of unmanned aerial vehicles. Transactions of the Chinese Society of Agricultural Engineering, 33(13): 112-119
  31. 31.
    Hasan S, Montzka C, Rüdiger C, Ali M, Bogena H R, Vereecken H. 2014. Soil moisture retrieval from airborne L-band passive microwave using high resolution multispectral data. ISPRS Journal of Photogrammetry and Remote Sensing, 91: 59-71
  32. 32.
    He L, Hong Y, Wu X L, Ye N, Walker J P and Chen X N. 2018. Investigation of SMAP Active–Passive Downscaling Algorithms Using Combined Sentinel-1 SAR and SMAP Radiometer Data. IEEE transactions on Geoscience and remote sensing, 56(8): 4906-4918
  33. 33.
    Hu H, Hu G X and Li X H. 2017. Summarization of remote sensing monitoring of water quality. Inner Mongolia Environmental Sciences, 29(8): 158-160
  34. 34.
    Huang Q, Long D, Du M D, Zeng C, Qiao G, Li X D, Hou A Z and Hong Y. 2018. Discharge estimation in high-mountain regions with improved methods using multisource remote sensing: A case study of the Upper Brahmaputra River. Remote Sensing of Environment, 219: 115-134
  35. 35.
    Huang Y, Chen X, Bao A M, Liu T and Feng X W. 2009. Daily flow modeling in arid ungauged basin. Advances in Water Science, 20(3): 332-336
  36. 36.
    Huffman G J, Adler R F, Bolvin D T, Gu, G J, Nelkin E J, Bowman K P, Hong Y, Stocker E F, Wolff D B. 2007. The TRMM multisatellite precipitation analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales, Journal of Hydrometeorology, 8(1): 38-55
  37. 37.
    Jackson T J, Bindlish R, Cosh M, Gasiewski A, Stankov B, Klein M, Weber B and Zavorotny V. 2005. Soil moisture experiments 2004 (SMEX04) polarimetric scanning radiometer, AMSR-E and heterogeneous landscapes, International Geoscience and Remote Sensing Symposium, 2, 1114-1117
  38. 38.
    Jalilvand E, Tajrishy M, Hashemi S A G Z and Brocca L. 2019. Quantification of irrigation water using remote sensing of soil moisture in a semi-arid region. Remote Sensing of Environment, 231: 111226
  39. 39.
    Jiang L, Yang Y T and Shang S H. 2013. Evaluation on irrigation efficiency of irrigation district in arid region based on evapotranspiration estimated from remote sensing data. Transactions of the Chinese Society of Agricultural Engineering, 29(20): 95-101
  40. 40.
    Jiang C. 2018. Exploration and practice of application of Gaofen satellite data in remote sensing monitoring of water environment in River Basin. Satelite Application, (1): 39-42
  41. 41.
    Kerr Y H, Waldteufel P, Wigneron J-P, Martinuzzi J, Font J and Berger M. 2001. Soil moisture retrieval from space: The Soil Moisture and Ocean Salinity (SMOS) mission. IEEE transactions on Geoscience and remote sensing, 39(8): 1729-1735
  42. 42.
    Kim H, Parinussa R, Konings A G, Wagner W, Cosh M H, Lakshmi V, Zohaib M and Choi M. 2018. Global-scale assessment and combination of SMAP with ASCAT (active) and AMSR2 (passive) soil moisture products. Remote Sensing of Environment, 204: 260-275
  43. 43.
    Kim K, Park J, Baik J and Choi M. 2017. Evaluation of topographical and seasonal feature using GPM IMERG and TRMM 3B42 over Far-East Asia. Atmospheric Research, 187: 95-105
  44. 44.
    Koster R D, Brocca L, Crow W T, Burgin M S and De Lannoy G J. 2016. Precipitation estimation using L‐band and C‐band soil moisture retrievals. Water Resources Research, 52(9): 7213-7225
  45. 45.
    Kummerow C, Barnes W, Kozu T, Shiue J, Simpson J. 1998. The Tropical Rainfall Measuring Mission (TRMM) sensor package. Journal of atmospheric and oceanic technology,15(3): 809-817
  46. 46.
    Kummerow C, Simpson J, Thiele O, Barnes W, Chang A T C, Stocker E, Adler R F, Hou A, Kakar R, Wentz F, Ashcroft P, Kozu T, Hong Y, Okamoto K, Iguchi T, Kuroiwa H, Im E, Haddad Z, Huffman G, Ferrier B, Olson W S, Zipser E, Smith E A, Wilheit T T, North G, Krishnamurti T, Nakamura K. 2000. The status of the Tropical Rainfall Measuring Mission (TRMM) after two years in orbit. Journal of applied meteorology, 39(12): 1965-1982
  47. 47.
    Kustas W P, Norman J M, Anderson M C and French A N. 2003. Estimating subpixel surface temperatures and energy fluxes from the vegetation index–radiometric temperature relationship. Remote Sensing of Environment, 85(4): 429-440
  48. 48.
    Lautenbacher C C. 2006. The global earth observation system of systems: Science serving society. Space Policy, 22(1): 8-11
  49. 49.
    Li X D, Long D, Huang Q, Han P F, Zhao F Y and Wada Y. 2019. High-temporal-resolution water level and storage change data sets for lakes on the Tibetan Plateau during 2000–2017 using multiple altimetric missions and Landsat-derived lake shoreline positions. Earth System Science Data Discussions, 11(4): 1603-1627
  50. 50.
    Li Y M, Huang J Z, Lu W N and Shi J Z. 2006. Model-based remote sensing on the concentration of suspended sediments in Taihu Lake. Oceanologia et Limnologia Sinica, 37(2): 171-177
  51. 51.
    Liang S L, Bai R, Chen X N, Cheng J, Fan W J, He T, Jia K, Jiang B, Jiang L M, Jiao Z T, Liu Y B, Ni W J, Qiu F, Song L L, Sun L, Tang B H, Wen J G, Wu G P, Xie D H, Yao Y J, Yuan W P, Zhang Y G, Zhang Y Z, Zhang Y T, Zhang X T, Zhao T J and Zhao X. 2020. Review of China's land surface quantitative remote sensing development in 2019. Journal of Remote Sensing, 24(6): 609-662
  52. 52.
    Liu J K, Zhong S Q and Liang W H. 2015. Extraction on crops planting structure based on multi-temporal Landsat8 OLI images. Remote Sensing Technology and Application, 30(4): 775-783
  53. 53.
    Liu X F, Duan H T and Ma R H. 2010. The spatial heterogeneity of water quality variables in Lake Taihu. Journal of Lake Sciences, 2010(3):63-70
  54. 54.
    Long D, Bai L L, Yan L, Zhang C J, Yang W T, Lei H M, Quan J L, Meng X Y and Shi C X. 2019. Generation of spatially complete and daily continuous surface soil moisture of high spatial resolution. Remote Sensing of Environment, 233: 111364
  55. 55.
    Long D, Chen X, Scanlon B R, Wada Y, Hong Y, Singh V P, Chen Y, Wang C, Han Z and Yang W. 2016. Have GRACE satellites overestimated groundwater depletion in the Northwest India Aquifer? Scientific reports, 6: 24398
  56. 56.
    Long D, Longuevergne L and Scanlon B R. 2015. Global analysis of approaches for deriving total water storage changes from GRACE satellites. Water Resources Research, 51(4): 2574-2594
  57. 57.
    Long D, Scanlon B R, Longuevergne L, Sun A Y, Fernando D N and Save H. 2013. GRACE satellite monitoring of large depletion in water storage in response to the 2011 drought in Texas. Geophysical Research Letters, 40(13): 3395-3401
  58. 58.
    Long D and Singh V P. 2012a. A modified surface energy balance algorithm for land (M-SEBAL) based on a trapezoidal framework. Water Resources Research, 48, W02528
  59. 59.
    Long D and Singh V P. 2012b. A Two-source Trapezoid Model for Evapotranspiration (TTME) from satellite imagery. Remote Sensing of Environment, 121, 370-388
  60. 60.
    Long D and Singh V P. 2010. Integration of the GG model with SEBAL to produce time series of evapotranspiration of high spatial resolution at watershed scales. Journal of Geophysical Research-Atmospheres, 115: D21128
  61. 61.
    Ma J W and Qin S X. 2012. Recent advances and development of data assimilation algorithms. Advances in Earth Sciences, 27(7): 747-757
  62. 62.
    Massari C, Crow W and Brocca L. 2017. An assessment of the performance of global rainfall estimates without ground-based observations. Hydrology and earth system sciences, 21(9): 4347
  63. 63.
    McCabe M, Wood E F, Wójcik R, Pan M, Sheffield J, Gao H and Su H. 2008. Hydrological consistency using multi-sensor remote sensing data for water and energy cycle studies. Remote Sensing of Environment, 112(2): 430-444
  64. 64.
    McCabe M F, Rodell M, Alsdorf D E, Miralles D G, Uijlenhoet R, Wagner W, Lucieer A, Houborg R, Verhoest N E and Franz T E. 2017. The future of Earth observation in hydrology. Hydrology and earth system sciences, 21(7): 3879
  65. 65.
    Merlin O, Escorihuela M J, Mayoral M A, Hagolle O, Al Bitar A and Kerr Y. 2013. Self-calibrated evaporation-based disaggregation of SMOS soil moisture: An evaluation study at 3 km and 100 m resolution in Catalunya, Spain. Remote Sensing of Environment, 130: 25-38
  66. 66.
    Peng J, Loew A, Merlin O and Verhoest N E. 2017. A review of spatial downscaling of satellite remotely sensed soil moisture. Reviews of Geophysics, 55(2): 341-366
  67. 67.
    Prakash S, Mitra A K, Pai D S, AghaKouchak A. 2016. From TRMM to GPM:How well can heavy rainfall be detected from space? Advances in water resources, 88: 1-7
  68. 68.
    Purdy A J, Fisher J B, Goulden M L, Colliander A, Halverson G, Tu K and Famiglietti J S. 2018. SMAP soil moisture improves global evapotranspiration. Remote Sensing of Environment, 219: 1-14
  69. 69.
    Qin Y H, Wu T H, Wu X D, Li R, Xie C W, Qiao Y P, Hu G J, Zhu X F, Wang W H and Shang W. 2017. Assessment of reanalysis soil moisture products in the permafrost regions of the central of the Qinghai–Tibet Plateau. Hydrological Processes, 31(26): 4647-4659
  70. 70.
    Ran Q, Pan Y, Wang Y R, Chen L H and Xu H L. 2013. Estimation of annual groundwater exploitation in Haihe River Basin by use of GRACE satellite data. Advances in Science Technology of Water Resources, (2): 42-46
  71. 71.
    Rodell M, Chen J, Kato H, Famiglietti J S, Nigro J and Wilson C R. 2007. Estimating groundwater storage changes in the Mississippi River basin (USA) using GRACE. Hydrogeology Journal, 15(1): 159-166
  72. 72.
    Rodell M, Houser P, Jambor U, Gottschalck J, Mitchell K, Meng C-J, Arsenault K, Cosgrove B, Radakovich J and Bosilovich M. 2004. The global land data assimilation system. Bulletin of the American Meteorological Society, 85(3): 381-394
  73. 73.
    Rodell M, Velicogna I and Famiglietti J S. 2009. Satellite-based estimates of groundwater depletion in India. Nature, 460(7258): 999-1002
  74. 74.
    Scanlon B R, Long uevergne L and Long D. 2012. Ground referencing GRACE satellite estimates of groundwater storage changes in the California Central Valley, USA. Water Resources Research, 48(4): W04520
  75. 75.
    Shang S H, Jiang L and Yang Y T. 2015. Review of remote sensing-based assessment method for irrigation and crop water use efficiency. Transactions of the Chinese Society for Agricultural Machinery, 46(10): 81-92
  76. 76.
    Shi C X, Xie Z H, Qian H, Liang M L and Yang X C. 2011. China land soil moisture EnKF data assimilation based on satellite remote sensing data. Science China Earth Sciences, 54(9): 1430-1440
  77. 77.
    Shi J C, Xiong C and Jiang L M. 2016. Review of snow water equivalent microwave remote sensing. Science China Earth Sciences, 46(4): 529-543
  78. 78.
    Sichangi A W, Wang L, Yang K, Chen D, Wang Z, Li X, Zhou J, Liu W and Kuria D. 2016. Estimating continental river basin discharges using multiple remote sensing data sets. Remote Sensing of Environment, 179: 36-53
  79. 79.
    Strassberg G, Scanlon B R and Rodell M. 2007. Comparison of seasonal terrestrial water storage variations from GRACE with groundwater‐level measurements from the High Plains Aquifer (USA). Geophysical Research Letters, 34(14): L14402
  80. 80.
    Tang G Q, Long D, Wan W, Zeng Z Y, Guo X L and Hong Y. 2015. An overview and outlook of global water remote sensing technology and applications. Scientia Sinica Technologica, 45(10): 1013-1023
  81. 81.
    Tang G Q, Long D, Hong Y, Gao J Y and Wan W. 2018. Documentation of multifactorial relationships between precipitation and topography of the Tibetan Plateau using spaceborne precipitation radars. Remote Sensing of Environment, 208: 82-96
  82. 82.
    Tang G Q, Ma Y Z, Long D, Zhong L Z, Hong Y. 2016. Evaluation of GPM Day-1 IMERG and TMPA Version-7 legacy products over Mainland China at multiple spatiotemporal scales. Journal of hydrology, 533: 152-167
  83. 83.
    Tang G Q, Wen Y X, Gao J Y, Long D, Ma Y Z, Wan W and Hong Y. 2017. Similarities and differences between three coexisting spaceborne radars in global rainfall and snowfall estimation. Water Resources Research, 53: 3835-3853
  84. 84.
    Trudel M, Leconte R and Paniconi C. 2014. Analysis of the hydrological response of a distributed physically-based model using post-assimilation (EnKF) diagnostics of streamflow and in situ soil moisture observations. Journal of Hydrology, 514: 192-201
  85. 85.
    Wagner W, Hahn S, Kidd R, Melzer T, Bartalis Z, Hasenauer S, Figa-Saldaña J, de Rosnay P, Jann A and Schneider S. 2013. The ASCAT soil moisture product: A review of its specifications, validation results, and emerging applications. Meteorologische Zeitschrift, 22(1): 5-33
  86. 86.
    Wan W, Xiao P f, Feng X Z, Li H, Ma R H, Duan H T and Zhao L M. 2014. Monitoring lake changes of Qinghai-Tibetan Plateau over the past 30 years using satellite remote sensing data. Chinese Science Bulletin, 59(8), 701-714
  87. 87.
    Wang D W, Huang C L and Gu J. 2016. Impact of Penetration Depth on L-band Microwave Brightness Temperature in Arid Region based on L-MEB Model. Remote Sensing Technology and Application, 31(3): 580-589
  88. 88.
    Wang H, Wang J H and Hu P. 2011. Exploration and analysis of key technological support for the strictest managerial system of water resources. China Water Resources, (6): 28-29,32
  89. 89.
    Wang W and Lu H. 2016. Progress in application of remote sensing data in hydrological simulation. Remote Sensing Technology Application, 30(6): 1042-1050
  90. 90.
    Wu X, Zhou J, Wang H, Li Y and Zhong B. 2015. Evaluation of irrigation water use efficiency using remote sensing in the middle reach of the Heihe river, in the semi‐arid Northwestern China. Hydrological Processes, 29(9): 2243-2257
  91. 91.
    Xia Y L, Sheffield J, Ek M B, Dong J R, Chaney N, Wei H L, Meng J and Wood E F. 2014. Evaluation of multi-model simulated soil moisture in NLDAS-2. Journal of Hydrology, (20): 95-101
  92. 92.
    Xu B D, Li J, Xin X Z, Zeng Y L and Yin G F. 2015. Review of methods for evaluating representativeness of ground station observation. Journal of Remote Sensing, 19(5): 703-718
  93. 93.
    Yang G J, Weng Q H, Pu R L, Gao F, Sun C H, Li H and Zhao C J. 2016. Evaluation of ASTER-like daily land surface temperature by fusing ASTER and MODIS data during the HiWATER-MUSOEXE. Remote Sensing, 8(1): 75
  94. 94.
    Yang J X, SU H and Wang Y P. 2010. DisTrad Model for Thermal Sub-pixel Mapping in High Vegetation Area. Remote Sensing Technology Application, 25(3): 346-352
  95. 95.
    Yang Y T, Long D, Shang S H. 2013. Remote estimation of terrestrial evapotranspiration without using meteorological data. Geophysical Research Letters, 40: 3026-3030
  96. 96.
    Yang Y T, Shang S H and Jiang L. 2012. Remote sensing temporal and spatial patterns of evapotranspiration and the responses to water management in a large irrigation district of North China. Agricultural and Forest Meteorology, 164: 112-122
  97. 97.
    Zeng C, Long D, Shen H F, Wu P H, Cui Y K and Hong Y. 2018. A two-step framework for reconstructing remotely sensed land surface temperatures contaminated by cloud. ISPRS Journal of Photogrammetry and Remote Sensing, 141: 30-45
  98. 98.
    Zhang B, Li J S, Shen Q, Wu Y H, Zhang F F and Wang S L. 2019. Key Technologies and Systems of Surface Water Environment Monitoring by Remote Sensing. Environmental Monitoring in China, 35(4): 1-9
  99. 99.
    Zhang C H and Wang G Q. 2015. Water security, and hydroscience and technology in China: Focuses and frontiers. Scientia Sinica Technologica, 45(10): 1007-1012
  100. 100.
    Zhang J H, Xu Y, Yao F M, Wang P J, Guo W J, Li L and Yang L M. 2010. Advances in estimation methods of vegetation water content based on optical remote sensing techniques. Science China Technological Sciences, 40(10): 1121-1129
  101. 101.
    Zhang K, Zhang K, Niu P T and Gao L. 2018. Research Progress of Water Quality Monitoring Technique Based on Remote Sensing. Modern Mining, 34(11): 171-174,202
  102. 102.
    Zhang L F, Peng M Y, Sun X J, Cen Y and Tong Q X. 2019. Progress and bibliometric analysis of remote sensing data fusion methods (1992—2018). Journal of Remote Sensing, 23(4): 600-616
  103. 103.
    Zhang Y, Pan M and Wood E F. 2016. On creating global gridded terrestrial water budget estimates from satellite remote sensing. Surveys in Geophysics, 37: 249-268
  104. 104.
    Zhu X L, Chen J, Gao F, Chen X H and Masek J G. 2010. An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions. Remote Sensing of Environment, 114(11): 2610-2623
  105. 105.
    Zhu Z and Shi C X. 2014. Simulation and evaluation of CLDAS and GLDAS soil moisture data in China. Science Technology and Engineering, 14(32): 138-144
  106. 106.
    Zhou Y D, He B Y, Kou J F, Liang S W, Hu S X and Hu K. 2006. Inversion of Lake Trophic Level Index in Wuhan Area Based on GF-1 Images. Resources and Environment in the Yangtze Basin, 27(6): 1307-1314

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