Scalable Water Balances from Earth Observations (SWEO): results from 50 years of remote sensing in hydrology

References

• Allan, J. A. (1997). Middle East water: Local and global issues. SOAS Water Issues Group, University of London.
   Google Scholar
• Allan, J. A. (2003). Virtual water – The water, food, and trade nexus. Useful concept or misleading metaphor? Water International, 28(1), 106–113. https://doi.org/10.1080/02508060.2003.9724812
   Web of Science ®Google Scholar
• Allen, R. G., Pereira, L. S., Raes, D. S., & Smith, M. (1998). Crop evapotranspiration. Guidelines for computing crop water requirements [Irrigation and Drainage Paper 56]. FAO
   Google Scholar
• Allen, R. G., Tasumi, M., Morse, A., Trezza, R., Wright, J. L., Bastiaanssen, W., Kramber, W., Lorite, I., & Robison, C. W. (2007). Satellite-based energy balance for mapping evapotranspiration with internalized calibration (Metric) – Applications. Journal of Irrigation and Drainage Engineering, 133(4), 395–406. https://doi.org/10.1061/(ASCE)0733-9437(2007)133:4(395)
   Google Scholar
• Anderson, M. C., Norman, J. M., Mecikalski, J. R., Otkin, J. A., & Kustas, W. P. (2007a). 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(D10). https://doi.org/10.1029/2006JD007506
   Web of Science ®Google Scholar
• Anderson, M. C., Norman, J. M., Mecikalski, J. R., Otkin, J. A., & Kustas, W. P. (2007b). A climatological study of evapotranspiration and moisture stress across the continental United States based on thermal remote sensing: 2. Surface moisture climatology. Journal of Geophysical Research: Atmospheres, 112(D11). https://doi.org/10.1029/2006JD007507
   Google Scholar
• Arino, O., Gross, D., Ranera, F., Leroy, M., Bicheron, P., Brockman, C., Defourny, P., Vancutsem, C., Achard, F., & Durieux, L. (2007). GlobCover: ESA service for global land cover from MERIS. 2007 IEEE international geoscience and remote sensing symposium, Piscataway, NJ: IEEE
   Google Scholar
• Bastiaanssen, W. G. M. (2009). Water accountants: The new generation of water management auditors. Inaugural Address, Delft University of Technology.
   Google Scholar
• Bastiaanssen, W. G. M., Karim, P., Rebelo, L.-M., Duan, Z., Senay, G., Muthuwatte, L., & Smakhtin, V. (2014). Earth-observation based assessment of the water production and water consumption of Nile Basin agro-ecosystems. Remote Sensing, 6(11), 10306–10334. https://doi.org/10.3390/rs61110306
   Web of Science ®Google Scholar
• Bastiaanssen, W. G. M., Pelgrum, H., Droogers, P., de Bruin, H. A. R., & Menenti, M. (1997). Area-average estimates of evaporation, wetness indicators and top soil moisture during two golden days in EFEDA. Agricultural and Forest Meteorology, 87(2–3), 119–137. https://doi.org/10.1016/S0168-1923(97)00020-8
   Web of Science ®Google Scholar
• Bastiaanssen, W. G. M., & Roebeling, R. A. (1993). Analysis of land-surface exchange processes in two agricultural regions in Spain using thematic mapper simulator data [IAHS Publication 407-416]. International Association of Hydrological Sciences.
   Google Scholar
• Budyko, M. I. (1974). Climate and life. Academic press.
   Google Scholar
• Carlson, T. (2007). An overview of the triangle method for estimating surface evapotranspiration and soil moisture from satellite imagery. Sensors, 7(8), 1612–1629. https://doi.org/10.3390/s7081612
   Web of Science ®Google Scholar
• Carlson, T. N., Gillies, R. G., & Perry, E. (1994). A method to make use of thermal infrared temperature and NDVI measurements to infer surface soil water content and fractional vegetation cover. Remote Sensing Reviews, 9(1–2), 161–173. https://doi.org/10.1080/02757259409532220
   Google Scholar
• Chapagain, A. K., Hoekstra, A. Y., Savernije, H. H. G., & Gautum, R. (2006). The water footprint of cotton consumption: An assessment of the impact of worldwide consumption of cotton products on the water resources in the cotton producing countries. Ecological Economics, 60(1), 186–203. https://doi.org/10.1016/j.ecolecon.2005.11.027
   Web of Science ®Google Scholar
• Chukalla, A. D., Krol, M. S., & Hoekstra, A. Y. (2015). Green and blue water footprint reduction in irrigated agriculture: Effect of irrigation techniques, irrigation strategies and mulching. Hydrology and Earth System Sciences, 19(12), 4877–4891. https://doi.org/10.5194/hess-19-4877-2015
   Web of Science ®Google Scholar
• Davids, J. C., Davkota, N., Pandey, A., Prajapati, R., Ertis, B. A., Rutten, M. M., Lyon, S. W., Bogaard, T. A., & van de Giesen, N. (2019a). Soda-bottle science – Citizen science monsoon precipitation monitoring in Nepal. Frontiers in Earth Sciences, Hydrosphere, 7, 46. https://doi.org/10.3389/feart.2019.00046
   Google Scholar
• Davids, J. C., Rutten, M. M., Pandey, A., Devkota, N., van Oyen, W. D., Prajapati, R., & van de Giesen, N. (2019b). Citizen science flow – An assessment of simple streamflow measurement methods. Hydrology and Earth System Sciences, 23(2), 1045–1065. https://doi.org/10.5194/hess-23-1045-2019
   Web of Science ®Google Scholar
• Dinku, T., Funk, C., Peterson, P., Maidment, R., Tadesse, T., Gadain, H., & Ceccato, P. (2018). Validation of the CHIRPS satellite rainfall estimates over Eastern Africa. Quarterly Journal of the Royal Meteorological Society, 144(S1), 292–312. https://doi.org/10.1002/qj.3244
   Web of Science ®Google Scholar
• Dogrul, E. C., Kadir, T. N., & Brush, C. F. (2017). IWFM demand calculator, revision 63. Theoretical documentation and user’s manual. California Dept Water Resources.
   Google Scholar
• Duan, Z., & Bastiaanssen, W. G. M. (2013). Estimating water volume variations in lakes and reservoirs from four operational satellite altimetry databases and satellite imagery data. Remote Sensing of Environment, 134, 403–416. https://doi.org/10.1016/j.rse.2013.03.010
   Web of Science ®Google Scholar
• Engman, E. T., & Gurney, R. J. (1991). Remote sensing in hydrology. Chapman and Hall.
   Google Scholar
• Entekhabi, D., Njoku, E. G., O’Neill, P. E., Kellogg, K. H., Crow, W. T., Edelstein, W. N., Entin, J. K., Goodman, S. D., Jackson, T. J., Johnson, J., Kimball, J., Piepmeier, J. R., Koster, R. D., Martin, N., McDonald, K. C., Moghaddam, M., Moran, S., Reichle, R., Shi, J. C., … Van Zyl, J. (2010). The soil moisture active passive (SMAP) mission. Proceedings of the IEEE, 98(5), 704–716. https://doi.org/10.1109/JPROC.2010.2043918
   Web of Science ®Google Scholar
• Falkenmark, M., & Rockström, J. (2006). The new blue and green water paradigm: Breaking new ground for water resources planning and management. Journal of Water Resources Planning and Management ASCE, 132, 3. https://www.eqb.state.mn.us/sites/default/files/documents/Falkenamark_20493345.pdf
   Web of Science ®Google Scholar
• Feng, B., Zhou, L., Xie, D., Mao, Y., Gao, J., Xie, P., & Wu, P. (2021). A quantitative review of water footprint accounting and simulation for crop production based on publications during 2002–2018. Ecological Indicators, 120, 106962. https://doi.org/10.1016/j.ecolind.2020.106962
   Web of Science ®Google Scholar
• Frappart, F., & Bourrel, L. (2018). The use of remote sensing in hydrology. MDPI Books.
   Google Scholar
• Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S., Husak, G., Rowland, J., Harrison, L., Hoell, A., & Michaelsen, J. (2015). The climate hazards infrared precipitation with stations – a new environmental record for monitoring extremes. Scientific Data, 2(1), 1–21. https://doi.org/10.1038/sdata.2015.66
   Web of Science ®Google Scholar
• Gates, D. M. (1964). Leaf temperature and transpiration 1. Agronomy Journal, 56(3), 273–277. https://doi.org/10.2134/agronj1964.00021962005600030007x
   Google Scholar
• Gillies, R. R., & Carlson, T. N. (1995). Thermal remote sensing of surface soil water content with partial vegetation cover for incorporation into climate models. Journal of Applied Meteorology and Climatology, 34(4), 745–756. https://doi.org/10.1175/1520-0450(1995)034<0745:TRSOSS>2.0.CO;2
   Google Scholar
• Hain, C. R., Crow, W. T., Mecikalski, J. R., Anderson, M. C., & Holmes, T. (2011). An intercomparison of available soil moisture estimates from thermal infrared and passive microwave remote sensing and land surface modeling. Journal of Geophysical Research: Atmospheres, 116(D15). https://doi.org/10.1029/2011JD015633
   Google Scholar
• Hall, D. K., Riggs, G. A., Salomonson, V. V., DiGirolamo, N. E., & Bayr, K. J. (2002). MODIS snow-cover products. Remote Sensing of Environment, 83(1–2), 181–194. https://doi.org/10.1016/S0034-4257(02)00095-0
   Web of Science ®Google Scholar
• Hessels, T. M. (2015). Comparison and validation of several open access remotely sensed rainfall products for the Nile Basin [ MSc thesis]. TU Delft.
   Google Scholar
• Hoekstra, A. Y. (2013). The water footprint of modern consumer society. Earthscan.
   Google Scholar
• Hsu, J., Huang, W.-R., Liu, P.-Y., & Li, X. (2021). Validation of CHIRPS precipitation estimates over Taiwan at multiple timescales. Remote Sensing, 13(2), 254. https://doi.org/10.3390/rs13020254
   Web of Science ®Google Scholar
• Jaafar, H., & Mourad, R. (2021). GYMEE: A global field-scale crop yield and ET mapper in Google Earth engine based on Landsat, weather, and soil data. Remote Sensing, 13(4), 773. https://doi.org/10.3390/rs13040773
   Web of Science ®Google Scholar
• Jackson, T. J., Bindish, R., Cosh, M. H., Zhao, T., Starks, P. J., Bosch, D. D., Seyfried, M., Moran, M. S., Goodrich, D. C., Kerr, Y. H., & Leroux, D. (2011). Validation of soil moisture and ocean salinity (SMOS) soil moisture over watershed networks in the US. IEEE Transactions on Geoscience and Remote Sensing, 50(5), 1530–1543. https://doi.org/10.1109/TGRS.2011.2168533
   Web of Science ®Google Scholar
• Jackson, R. D., Reginato, R. J., & Idso, S. B. (1977). Wheat canopy temperature: A practical tool for evaluating water requirements. Water Resources Research, 13(3), 651–656. https://doi.org/10.1029/WR013i003p00651
   Web of Science ®Google Scholar
• Jiang, D., Wang, J., Huang, Y., Zhou, K., Ding, X., & Fu, J. (2014). The review of GRACE data applications in terrestrial hydrology monitoring. Advances in Meteorology, 725131. https://doi.org/10.1155/2014/725131
   Google Scholar
• Karimi, P., Bastiaanssen, W. G. M., & Molden, D. (2013). Water accounting plus (WA+) – A water accounting procedure for complex river basins based on satellite measurements. Hydrology and Earth System Sciences, 17(7), 2459–2472. https://doi.org/10.5194/hess-17-2459-2013
   Web of Science ®Google Scholar
• Kerr, Y. H., Waldteufel, P., Richaume, P., Wigneron, J. P., Ferrazzoli, P., Mahmoodi, A., Al Bitar, A., Cabot, F., Gruhier, C., Juglea, S. E., Leroux, D., Mialon, A., & Delwart, S. (2012). The SMOS soil moisture retrieval algorithm. IEEE Transactions on Geoscience and Remote Sensing, 50(5), 1384–1403. https://doi.org/10.1109/TGRS.2012.2184548
   Web of Science ®Google Scholar
• Koutsouris, A. J., Chen, D., & Lyon, S. W. (2016). Comparing global precipitation data sets in Eastern Africa: A case study of Kilombero Valley, Tanzania. International Journal of Climatology, 36(4), 2000–2014. https://doi.org/10.1002/joc.4476
   Web of Science ®Google Scholar
• Laipelt, L., Kayser, R. H. B., Ruhoff, A. S., Ruhoff, A., Bastiaanssen, W., Erickson, T. A., & Melton, F. (2021). Long-term monitoring of evapotranspiration using the SEBAL algorithm and Google Earth engine cloud computing. ISPRS Journal of Photogrammetry and Remote Sensing, 178, 81–96. https://doi.org/10.1016/j.isprsjprs.2021.05.018
   Web of Science ®Google Scholar
• Lehner, B., Lierman, C. R., Revenga, C., Vörösmarty, C., Fekete, B., Crouzet, P., Döll, P., Endejan, M., Frenken, K., Magome, J., Nilsson, C., Robertson, J. C., Rödel, R., Sindorf, N., & Wisser, D. (2011). High‐resolution mapping of the world’s reservoirs and dams for sustainable river‐flow management. Frontiers in Ecology and the Environment, 9(9), 494–502. https://doi.org/10.1890/100125
   Web of Science ®Google Scholar
• Lekshmi, S., Singh, D. N., & Baghini, M. S. (2014). A critical review of soil moisture measurement. Measurement, 54, 92–105. https://doi.org/10.1016/j.measurement.2014.04.007
   Web of Science ®Google Scholar
• Long, D., Longuevergne, L., & Scanlon, B. R. F. (2014). Uncertainty in evapotranspiration from land surface modeling, remote sensing, and GRACE satellites. Water Resources Research, 50(2), 1131–1151. https://doi.org/10.1002/2013WR014581
   Web of Science ®Google Scholar
• Madugundu, R., Al-Gaadi, K. A., Toler, E., Hassaballa, A. A., & Kayad, A. G. (2018). Utilization of Landsat-8 data for the estimation of carrot and maize crop water footprint under the arid climate of Saudi Arabia. PLOS ONE, 13(2), e0192830. https://doi.org/10.1371/journal.pone.0192830
   PubMed Web of Science ®Google Scholar
• McCulloch, J. S., & Robinson, M. (1993). History of forest hydrology. Journal of Hydrology, 150(2–4), 189–216. https://doi.org/10.1016/0022-1694(93)90111-L
   Web of Science ®Google Scholar
• Mekonnen, M. M., & Hoekstra, A. Y. (2016). Four billion people facing severe water scarcity. Science Advances, 2(2), e1500323. https://doi.org/10.1126/sciadv.1500323
   PubMed Web of Science ®Google Scholar
• Menenti, M. (1980). Defining relationships between surface characteristics and actual evaporation rate. Meeting of working group 2 of the TELLUS project. Joint Research Centre of the European Communities.
   Google Scholar
• Miro, M. E., & Famiglietti, J. S. (2018). Downscaling GRACE remote sensing datasets to high-resolution groundwater storage change maps of California’s Central Valley. Remote Sensing, 10(1), 143. https://doi.org/10.3390/rs10010143
   Web of Science ®Google Scholar
• Mo, T., Choudhury, J., Schmugge, T. J., Wang, J. R., & Jackson, T. J. (1982). A model for microwave emission from vegetation‐covered fields. Journal of Geophysical Research: Oceans, 87(C13), 11229–11237. https://doi.org/10.1029/JC087iC13p11229
   Google Scholar
• Molden, D. (1997). Accounting for water use and productivity. International Water Management Institute.
   Google Scholar
• Monteith, J. L. (1977). Climate and the efficiency of crop production in Britain. Philosophical Transactions of the Royal Society of London. B, Biological Sciences, 281(980), 277–294. https://doi.org/10.1098/rstb.1977.0140
   Web of Science ®Google Scholar
• Moreira, A. A., Ruhoff, A. L., Roberti, D. R., Souza, V. D. A., da Rocha, H. R., & Paiva, R. C. D. D. (2019). Assessment of terrestrial water balance using remote sensing data in South America. Journal of Hydrology, 575, 131–147. https://doi.org/10.1016/j.jhydrol.2019.05.021
   Web of Science ®Google Scholar
• Nawaz, M., Farooq, I. M., & Irfan, M. (2021). Validation of CHIRPS satellite-based precipitation dataset over Pakistan. Atmospheric Research, 248, 105289. https://doi.org/10.1016/j.atmosres.2020.105289
   Web of Science ®Google Scholar
• Neale, C. M., & Cosh, M. H. (2012). Remote sensing and hydrology. IAHS Press.
   Google Scholar
• Ojha, N., Merlin, O., Suere, C., & Escorihuela, M. J. (2021). Extending the spatio-temporal applicability of DISPATCH soil moisture downscaling algorithm: A study case using SMAP, MODIS and Sentinel-3 data. Frontiers in Environmental Science, 9, 40. https://doi.org/10.3389/fenvs.2021.555216
   Web of Science ®Google Scholar
• Owe, M., & Van de Griend, A. A. (1998). Comparison of soil moisture penetration depths for several bare soils at two microwave frequencies and implications for remote sensing. Water Resources Research, 34(9), 2319–2327. https://doi.org/10.1029/98WR01469
   Web of Science ®Google Scholar
• Paca, V., Espinoza-Davales, G. E., Moreira, D. M., & Comair, G. (2020). Variability of trends in precipitation across the Amazon River Basin determined from the CHIRPS precipitation product and from station records. Water, 12(5), 1244. https://doi.org/10.3390/w12051244
   Web of Science ®Google Scholar
• Pang, Z., Qin, X., Jiang, W. S., Fu, J., Yang, K., Lu, J., Qu, W., Li, L., & Li, X. (2020). The review of soil moisture multi-scale verification methods. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, 5(3), 395–399. https://doi.org/10.5194/isprs-annals-V-3-2020-395-2020
   Google Scholar
• Parajka, J., & Blöschl, G. (2006). Validation of MODIS snow cover images over Austria. Hydrology and Earth System Sciences, 10(5), 679–689. https://doi.org/10.5194/hess-10-679-2006
   Web of Science ®Google Scholar
• Perry, C. (2013). ABCDE+ F: A framework for thinking about water resources management. Water International, 38(1), 95–107. https://doi.org/10.1080/02508060.2013.754618
   Web of Science ®Google Scholar
• Pietroniro, A., & Prowse, T. D. (2002). Applications of remote sensing in hydrology. Hydrological Processes, 16(8), 1537–1541. https://doi.org/10.1002/hyp.1018
   Web of Science ®Google Scholar
• Poortinga, A., Bastiaanssen, W. G. M., Simons, G., Saah, D., Senay, G., Fenn, M., Bean, B., & Kadyszewski, J. (2017). A self-calibrating runoff and streamflow remote sensing model for ungauged basins using open-access Earth observation data. Remote Sensing, 9(1), 86. https://doi.org/10.3390/rs9010086
   Web of Science ®Google Scholar
• Rittger, K., Painter, T. M., & Dozier, J. (2013). Assessment of methods for mapping snow cover from MODIS. Advances in Water Resources, 51, 367–380. https://doi.org/10.1016/j.advwatres.2012.03.002
   Web of Science ®Google Scholar
• Rodell, M., & Famiglietti, J. S. (2002). The potential for satellite-based monitoring of groundwater storage changes using GRACE: The high plains aquifer, Central US. Journal of Hydrology, 263(1–4), 245–256. https://doi.org/10.1016/S0022-1694(02)00060-4
   Web of Science ®Google Scholar
• Rosema, A., & Fiselier, J. (1990). Meteosat-based evapotranspiration and thermal inertia mapping for monitoring transgression in the Lake Chad region and Niger Delta. International Journal of Remote Sensing, 11(5), 741–752. https://doi.org/10.1080/01431169008955054
   Web of Science ®Google Scholar
• Savoca, M. E., Senay, G. B., Maupin, M. A., Kenny, J. F., & Perry, C. A. (2013). Actual evapotranspiration modeling using the operational simplified surface energy balance (SSEBop) approach. US Dept Interior, US Geological Survey.
   Google Scholar
• Schmugge, T. J., Kustas, W. P., Ritchie, W. P., Jackson, T. J., & Rango, A. (2002). Remote sensing in hydrology. Advances in Water Resources, 25(8–12), 1367–1385. https://doi.org/10.1016/S0309-1708(02)00065-9
   Web of Science ®Google Scholar
• Schmugge, T. J., O’Neill, P. E., & Wang, J. (1986). Passive microwave soil moisture research. IEEE Transactions on Geoscience and Remote Sensing GE, 24(1), 12–22. https://doi.org/10.1109/TGRS.1986.289584
   Web of Science ®Google Scholar
• Schultz, G. A. (1988). Remote sensing in hydrology. Journal of Hydrology, 100(1–3), 239–265. https://doi.org/10.1016/0022-1694(88)90187-4
   Web of Science ®Google Scholar
• Scott, C. A., Bastiaanssen, W. G. M., & Ahmed, M. (2003). Mapping root zone soil moisture using remotely sensed optical imagery. Journal of Irrigation and Drainage Engineering, 129(5), 326–335. https://doi.org/10.1061/(ASCE)0733-9437(2003)129:5(326)
   Web of Science ®Google Scholar
• Senay, G. B., Gouda, P. H., Kagone, S., Bohms, S., Howell, T., Friedrichs, M., Marek, T., & Verdin, J. (2014). Evaluating the SSEBop approach for evapotranspiration mapping with Landsat data using lysimetric observations in the semi-arid Texas high plains. Hydrology and Earth System Sciences Discussions, 11(1), 723–756. https://doi.org/10.5194/hessd-11-723-2014
   Google Scholar
• Shilpakar, R. L., Bastiaanssen, W. G. M., & Molden, D. (2011). A remote sensing-based approach for water accounting in the East Rapti River Basin, Nepal. Himalayan Journal of Sciences, 7(9), 15–30. https://doi.org/10.3126/hjs.v7i9.5785
   Google Scholar
• Simon, G. W. H., Droogers, P., Contreras, S., Sieber, J., & Bastiaanssen, W. (2020b). Virtual tracers to detect sources of water and track water reuse across a river basin. Water, 12(8), 2315. https://doi.org/10.3390/w12082315
   Web of Science ®Google Scholar
• Simons, G., Bastiaanssen, W. G. M., Cheema, M. J. M., Ahmad, B., & Immerzeel, W. W. (2020a). A novel method to quantify consumed fractions and non-consumptive use of irrigation water: Application to the Indus basin irrigation system of Pakistan. Agricultural Water Management, 236, 106174. https://doi.org/10.1016/j.agwat.2020.106174
   Web of Science ®Google Scholar
• Soer, G. (1977). The TERGRA model: A mathematical model for the simulation of the daily behaviour of crop surface temperature and actual evapotranspiration. Instituut voor Culturtechniek en Waterhuishouding, The Netherlands.
   Google Scholar
• Sriwongsitanon, N., Suwawong, T., Thianpopinug, S., Williams, J., Jia, L., & Bastiaanssen, W. (2020). Validation of seven global remotely sensed ET products across Thailand using water balance measurements and land use classifications. Journal of Hydrology: Regional Studies, 30, 100709. https://doi.org/10.1016/j.ejrh.2020.100709
   Google Scholar
• Steduto, P., Raes, D., Hsiao, T. C, Fereres, E., Heng, L. K., Howell, T. A., Evett, S. R., Rojas-Lara, B. A., Farahani, H. J., & Izzi, G. (2009). Concepts and applications of AquaCrop: The FAO crop water productivity model. In W. Cao, J. W. White, E. Wang (Eds.), Crop modelling and decision support (pp. 177–191). Springer.
   Google Scholar
• Teixeira, A. H. D. C., Padovani, C. R., Andrade, R. G., Leivas, J., Victoria, D., & Galdino, S. (2015). Use of MODIS images to quantify the radiation and energy balances in the Brazilian pantanal. Remote Sensing, 7(11), 14597–14619. https://doi.org/10.3390/rs71114597
   Web of Science ®Google Scholar
• Vishwakarma, B. D., Zhang, J., & Sneeuw, N. (2021). Downscaling GRACE total water storage change using partial least squares regression. Scientific Data, 8(1), 1–13. https://doi.org/10.1038/s41597-021-00862-6
   PubMed Web of Science ®Google Scholar
• World Economic Forum. (2014). Global risks 2014. www.weforum.org/risks
   Google Scholar
• Wu, B.-F., Xiang, J., Yan, N., & Du, X. (2008). ETWatch for monitoring regional evapotranspiration with remote sensing. Advances in Water Science, 19(5), 671–678. http://skxjz.nhri.cn/en/article/id/490
   Google Scholar
• Yang, X., Pavelsky, T. M., Allen, G. H., & Donchyts, G. (2019). RivWidthCloud: An automated Google Earth engine algorithm for river width extraction from remotely sensed imagery. IEEE Geoscience and Remote Sensing Letters, 17(2), 217–221. https://doi.org/10.1109/LGRS.2019.2920225
   Web of Science ®Google Scholar
• Yan, N., Wu, B., & Zhu, W. (2020). Assessment of agricultural water productivity in arid China. Water, 12(4), 1161. https://doi.org/10.3390/w12041161
   Web of Science ®Google Scholar