Satellite Hydrology: A New Era in Monitoring Groundwater, Wetlands, and Drought Dynamics

Authors

  • Yongheng Li

    Green Mine Engineering Research Center of Gansu Province, The Third Institute of Geology and Minerals Exploration, Gansu Provincial Bureau of Geology and Minerals Exploration and Development, Lanzhou 730050, China

DOI:

https://doi.org/10.30564/jees.v8i3.13103
Received: 7 January 2026 | Revised: 26 February 2026 | Accepted: 1 March 2026 | Published Online: 23 March 2026

Abstract

Satellite hydrology has reached a new era where a variety of earth observing systems, in conjunction with the development of data integrations, can provide a consistent monitoring of groundwater change, wetland dynamics, and drought evolution in regions where observations on the ground remain sparse or patchy. This synthesis review highlights the physical observables that are most useful to terrestrial hydrology: variable time-dependent gravity, passive and active microwave signals, optical reflectance, thermal infrared measurements, and altimetry, and this has been translated into hydrologic states and water fluxes: terrestrial water storage, ground water storage anomalies, inundation extent and hydroperiod, evapotranspiration, and multi-timescale drought indicators. We call attention to the role of satellite gravimetry in the transformation of basin-scale groundwater evaluation, synthetic aperture radar (SAR), and optical time series in transforming wetland science, however, to the characterization of hydroperiod and connectivity. In the case of drought, we recommend the use of both fast-responsive (soil moisture, evapotranspiration, thermal stress, and vegetation condition) in conjunction with slowly integrating storage anomalies to detect onset, intensification, and recovery, as well as rely on to diagnose the spread of deficits through the hydrologic system. In all these themes, we recognize the central action of enabling approaches that include multi-sensor fusion, data assimilation, and hybrid machine learning schemes in reducing ambiguity, scale gaps, and producing products of decision interest, and also highlighting ongoing issues in the quantification of uncertainty, consistency across long periods of time, and the separation of climate influence on human management of water. Research and operational priorities come as our final deduction to develop satellite hydrology to more trustworthy, explanation-seeking, and operationalizing monitoring designs of water security and ecosystem resilience.

Keywords:

Satellite Hydrology; Groundwater Storage; Wetlands; Drought Monitoring; Data Assimilation

References

[1] Penatti, N.C., Almeida, T.I.R.d., Ferreira, L.G., et al., 2015. Satellite-Based Hydrological Dynamics of the World's Largest Continuous Wetland. Remote Sensing of Environment. 170, 1–13. DOI: https://doi.org/10.1016/j.rse.2015.08.031

[2] Ndehedehe, C., 2022. Satellite Remote Sensing of Terrestrial Hydrology. Springer: Cham, Switzerland. DOI: https://doi.org/10.1007/978-3-030-99577-5

[3] Levizzani, V., Cattani, E., 2019. Satellite Remote Sensing of Precipitation and the Terrestrial Water Cycle in a Changing Climate. Remote Sensing. 11(19), 2301. DOI: https://doi.org/10.3390/rs11192301

[4] Cukier, D., Kon, F., 2018. A Maturity Model for Software Startup Ecosystems. Journal of Innovation and Entrepreneurship. 7(1), 14. DOI: https://doi.org/10.1186/s13731-018-0091-6

[5] Ehrensperger, R., Sauerwein, C., Breu, R., 2023. A Maturity Model for Digital Business Ecosystems from an IT Perspective. Journal of Universal Computer Science. 29(1), 34–72. DOI: https://doi.org/10.3897/jucs.79494

[6] Popereshnyak, S., Grinenko, S., Grinenko, O., et al., 2019. Methods for Assessing the Maturity Levels of Software Ecosystems. In Proceedings of the 2019 International Workshop on Cyber Hygiene (CybHyg 2019), Kyiv, Ukraine, 30 November 2019; pp. 251–261.

[7] Thomson, L., Kamalaldin, A., Sjödin, D., et al., 2022. A Maturity Framework for Autonomous Solutions in Manufacturing Firms: The Interplay of Technology, Ecosystem, and Business Model. International Entrepreneurship and Management Journal. 18(1), 125–152. DOI: https://doi.org/10.1007/s11365-020-00717-3

[8] Craft, C., 2022. Creating and Restoring Wetlands: From Theory to Practice. Elsevier: Amsterdam, The Netherlands. DOI: https://doi.org/10.1016/B978-0-12-823981-0.00002-2

[9] Yuan, S., Liang, X., Lin, T., et al., 2025. A Comprehensive Review of Remote Sensing in Wetland Classification and Mapping. arXiv preprint. arXiv:2504.10842. DOI: https://doi.org/10.48550/arXiv.2504.10842

[10] Hopkinson, C., Fuoco, B., Grant, T., et al., 2020. Wetland Hydroperiod Change along the Upper Columbia River Floodplain, Canada, 1984 to 2019. Remote Sensing. 12(24), 4084. DOI: https://doi.org/10.3390/rs12244084

[11] Nhamo, L., Magidi, J., Dickens, C., 2017. Determining Wetland Spatial Extent and Seasonal Variations of the Inundated Area Using Multispectral Remote Sensing. Water SA. 43(4), 543–552. DOI: https://doi.org/10.4314/wsa.v43i4.02

[12] McCabe, M.F., Rodell, M., Alsdorf, D.E., et al., 2017. The Future of Earth Observation in Hydrology. Hydrology and Earth System Sciences. 21(7), 3879–3914. DOI: https://doi.org/10.5194/hess-21-3879-2017

[13] Papa, F., Frappart, F., 2021. Surface Water Storage in Rivers and Wetlands Derived from Satellite Observations: A Review of Current Advances and Future Opportunities for Hydrological Sciences. Remote Sensing. 13(20), 4162. DOI: https://doi.org/10.3390/rs13204162

[14] Sheffield, J., Wood, E.F., Pan, M., et al., 2018. Satellite Remote Sensing for Water Resources Management: Potential for Supporting Sustainable Development in Data-Poor Regions. Water Resources Research. 54(12), 9724–9758. DOI: https://doi.org/10.1029/2017WR022437

[15] Brisco, B., 2015. Mapping and Monitoring Surface Water and Wetlands with Synthetic Aperture Radar. In: Tiner, R.W., Lang, M.W., Klemas, V. (Eds.). Remote Sensing of Wetlands: Applications and Advances. CRC Press: Boca Raton, FL, USA. pp. 119–136.

[16] Sahour, H., Kemink, K.M., O’Connell, J., 2021. Integrating SAR and Optical Remote Sensing for Conservation-Targeted Wetlands Mapping. Remote Sensing. 14(1), 159. DOI: https://doi.org/10.3390/rs14010159

[17] Amani, M., 2018. Combination of Optical and SAR Remote Sensing Data for Wetland Mapping and Monitoring [Master’s Thesis]. Memorial University of Newfoundland: St. John’s, NL, Canada.

[18] Cumming, G.S., Cumming, D.H., Redman, C.L., 2006. Scale Mismatches in Social-Ecological Systems: Causes, Consequences, and Solutions. Ecology and Society. 11(1), 14. DOI: https://doi.org/10.5751/ES-01569-110114

[19] Merchant, M., 2025. Enhanced Characterization of Wet Arctic Ecosystems Using Earth Observation Satellite Data and Machine Learning [PhD Thesis]. University of Guelph: Guelph, ON, Canada.

[20] Pelosi, C., Goulard, M., Balent, G., 2010. The Spatial Scale Mismatch between Ecological Processes and Agricultural Management: Do Difficulties Come from Underlying Theoretical Frameworks? Agriculture, Ecosystems & Environment. 139(4), 455–462. DOI: https://doi.org/10.1016/j.agee.2010.09.004

[21] Ascough, J., Maier, H., Ravalico, J., et al., 2008. Future Research Challenges for Incorporation of Uncertainty in Environmental and Ecological Decision-Making. Ecological Modelling. 219(3–4), 383–399. DOI: https://doi.org/10.1016/j.ecolmodel.2008.07.015

[22] Jiang, D., Wang, K., 2019. The Role of Satellite-Based Remote Sensing in Improving Simulated Streamflow: A Review. Water. 11(8), 1615. DOI: https://doi.org/10.3390/w11081615

[23] Turnbull, L., Wainwright, J., Brazier, R.E., 2008. A Conceptual Framework for Understanding Semi-Arid Land Degradation: Ecohydrological Interactions across Multiple Space and Time Scales. Ecohydrology. 1(1), 23–34. DOI: https://doi.org/10.1002/eco.4

[24] Yan, D., Wang, Y., Qin, D., et al., 2025. Hydrological Geography: Theoretical Framework, Research Progress, and Future Development Directions. Geographical Research Bulletin. 4, 186–224. DOI: https://doi.org/10.50908/grb.4.0_186

[25] Trenberth, K.E., 1999. Conceptual Framework for Changes of Extremes of the Hydrological Cycle with Climate Change. Climatic Change. 42(1), 327–339. DOI: https://doi.org/10.1023/A:1005488920935

[26] Bogena, H., White, T., Bour, O., et al., 2018. Toward Better Understanding of Terrestrial Processes through Long-Term Hydrological Observatories. Vadose Zone Journal. 17(1), 1–10. DOI: https://doi.org/10.2136/vzj2018.10.0194

[27] Duan, S.-B., Han, X.-J., Huang, C., et al., 2020. Land Surface Temperature Retrieval from Passive Microwave Satellite Observations: State-of-the-Art and Future Directions. Remote Sensing. 12(16), 2573. DOI: https://doi.org/10.3390/rs12162573

[28] Njoku, E.G., 2023. Surface Temperature Estimation over Land Using Satellite Microwave Radiometry. In: Choudhury, B.J., Kerr, Y.H., Njoku, E.G., et al. (Eds.). Passive Microwave Remote Sensing of Land–Atmosphere Interactions. CRC Press: London, UK. pp. 509–530.

[29] Meng, L., Yan, C., Lv, S., et al., 2024. Synthetic Aperture Radar for Geosciences. Reviews of Geophysics. 62(3), e2023RG000821. DOI: https://doi.org/10.1029/2023RG000821

[30] Lu, J., Wang, W., Wang, X., et al., 2023. Active Array Antennas for High Resolution Microwave Imaging Radar. Springer: Singapore. DOI: https://doi.org/10.1007/978-981-99-1475-3

[31] Zhou, Y., Dong, J., Xiao, X., et al., 2017. Open Surface Water Mapping Algorithms: A Comparison of Water-Related Spectral Indices and Sensors. Water. 9(4), 256. DOI: https://doi.org/10.3390/w9040256

[32] Thompson, D.R., Guanter, L., Berk, A., et al., 2019. Retrieval of Atmospheric Parameters and Surface Reflectance from Visible and Shortwave Infrared Imaging Spectroscopy Data. Surveys in Geophysics. 40(3), 333–360. DOI: https://doi.org/10.1007/s10712-018-9488-9

[33] Kalma, J.D., McVicar, T.R., McCabe, M.F., 2008. Estimating Land Surface Evaporation: A Review of Methods Using Remotely Sensed Surface Temperature Data. Surveys in Geophysics. 29(4), 421–469. DOI: https://doi.org/10.1007/s10712-008-9037-z

[34] Sun, G., Ranson, K., Kharuk, V., et al., 2003. Validation of Surface Height from Shuttle Radar Topography Mission Using Shuttle Laser Altimeter. Remote Sensing of Environment. 88(4), 401–411. DOI: https://doi.org/10.1016/j.rse.2003.09.001

[35] Brenner, A.C., DiMarzio, J.P., Zwally, H.J., 2007. Precision and Accuracy of Satellite Radar and Laser Altimeter Data over the Continental Ice Sheets. IEEE Transactions on Geoscience and Remote Sensing. 45(2), 321–331. DOI: https://doi.org/10.1109/TGRS.2006.887172

[36] Hunt, R.J., Walker, J.F., Krabbenhoft, D.P., 1999. Characterizing Hydrology and the Importance of Ground-Water Discharge in Natural and Constructed Wetlands. Wetlands. 19(2), 458–472. DOI: https://doi.org/10.1007/BF03161777

[37] Milly, P.C.D., 1994. Climate, Soil Water Storage, and the Average Annual Water Balance. Water Resources Research. 30(7), 2143–2156. DOI: https://doi.org/10.1029/94WR00586

[38] Hipel, K.W., Ben-Haim, Y., 2002. Decision Making in an Uncertain World: Information-Gap Modeling in Water Resources Management. IEEE Transactions on Systems, Man, and Cybernetics Part C. 29(4), 506–517. DOI: https://doi.org/10.1109/5326.798765

[39] Trigg, M.A., Michaelides, K., Neal, J.C., et al., 2013. Surface Water Connectivity Dynamics of a Large Scale Extreme Flood. Journal of Hydrology. 505, 138–149. DOI: https://doi.org/10.1016/j.jhydrol.2013.09.035

[40] Chen, J., Li, Y., Shu, L., et al., 2023. The Influence of the 2022 Extreme Drought on Groundwater Hydrodynamics in the Floodplain Wetland of Poyang Lake Using a Modeling Assessment. Journal of Hydrology. 626, 130194. DOI: https://doi.org/10.1016/j.jhydrol.2023.130194

[41] Safeeq, M., Fares, A., 2016. Groundwater and Surface Water Interactions in Relation to Natural and Anthropogenic Environmental Changes. In: Fares, A. (Ed.). Emerging Issues in Groundwater Resources. Springer: Cham, Switzerland. pp. 289–326. DOI: https://doi.org/10.1007/978-3-319-32008-3_11

[42] Baker, A.A., Treble, P.C., Andersen, M.S., et al., 2015. Caves: Observatories of Australia's Diffuse Groundwater Recharge History. National Centre for Groundwater Research and Training: Bedford Park, Australia.

[43] Liu, Y., 2019. Impacts of Climate Variation and Change on Hydrologic and Vegetation Dynamics [PhD Thesis]. Duke University: Durham, NC, USA.

[44] Lin, N., Jiang, R., Liu, Q., et al., 2022. Quantifying the Spatiotemporal Variation of Evapotranspiration of Different Land Cover Types and the Contribution of Its Associated Factors in the Xiliao River Plain. Remote Sensing. 14(2), 252. DOI: https://doi.org/10.3390/rs14020252

[45] Bourdin, D.R., Fleming, S.W., Stull, R.B., 2012. Streamflow Modelling: A Primer on Applications, Approaches and Challenges. Atmosphere-Ocean. 50(4), 507–536. DOI: https://doi.org/10.1080/07055900.2012.734276

[46] Band, L.E., 1999. Spatial Hydrography and Landforms. In: Longley, P.A., Goodchild, M.F., Maguire, D.J., et al. (Eds.). Geographical Information Systems. John Wiley & Sons: New York, NY, USA. pp. 527–542.

[47] Muste, M., Lee, K., Kim, D., et al., 2020. Revisiting Hysteresis of Flow Variables in Monitoring Unsteady Streamflows. Journal of Hydraulic Research. 58(6), 867–887. DOI: https://doi.org/10.1080/00221686.2020.1786742

[48] Gahegan, M., Ehlers, M., 2000. A Framework for the Modelling of Uncertainty between Remote Sensing and Geographic Information Systems. ISPRS Journal of Photogrammetry and Remote Sensing. 55(3), 176–188. DOI: https://doi.org/10.1016/S0924-2716(00)00018-6

[49] von Clarmann, T., Degenstein, D.A., Livesey, N.J., et al., 2020. Overview: Estimating and Reporting Uncertainties in Remotely Sensed Atmospheric Composition and Temperature. Atmospheric Measurement Techniques. 13(8), 4393–4436. DOI: https://doi.org/10.5194/amt-13-4393-2020

[50] Milani, L., 2012. Multi-Sensor Satellite Precipitation Estimate for Hydrogeological Hazard Mitigation [PhD Thesis]. Università degli Studi di Ferrara: Ferrara, Italy.

[51] Faridani Bardaskan, F., 2023. Novel Satellite-Based Methodologies for Multi-Sensor and Multi-Scale Environmental Monitoring to Preserve Natural Capital [PhD Thesis]. University of Basilicata Matera: Matera, Italy.

[52] Khan, S.I., Chang, N.-B., Hong, Y., et al., 2024. Remote Sensing Technologies for Multi-Scale Hydrological Studies: Advances and Perspectives. In: Thenkabail, P.S. (Ed.). Remote Sensing Handbook, Volume V. CRC Press: Boca Raton, FL, USA. pp. 37–64. DOI: https://doi.org/10.1201/9781003541400-4

[53] Woods, D., 2023. Integrated Hydrologic Validation of Satellite Precipitation over the United States [PhD Thesis]. The University of Oklahoma: Norman, OK, USA.

[54] Zektser, I., Loaiciga, H.A., 1993. Groundwater Fluxes in the Global Hydrologic Cycle: Past, Present and Future. Journal of Hydrology. 144(1–4), 405–427. DOI: https://doi.org/10.1016/0022-1694(93)90182-9

[55] Kuang, X., Liu, J., Scanlon, B.R., et al., 2024. The Changing Nature of Groundwater in the Global Water Cycle. Science. 383(6686), eadf0630. DOI: https://doi.org/10.1126/science.adf0630

[56] Mishra, N., Khare, D., Gupta, K.K., et al., 2014. Impact of Land Use Change on Groundwater: A Review. Advances in Water Resource and Protection. 2(2), 28–41.

[57] Becker, M.W., 2006. Potential for Satellite Remote Sensing of Ground Water. Groundwater. 44(2), 306–318. DOI: https://doi.org/10.1111/j.1745-6584.2005.00123.x

[58] Adams, K.H., Reager, J.T., Rosen, P., et al., 2022. Remote Sensing of Groundwater: Current Capabilities and Future Directions. Water Resources Research. 58(10), e2022WR032219. DOI: https://doi.org/10.1029/2022WR032219

[59] Bobba, A., Bukata, R., Jerome, J., 1992. Digitally Processed Satellite Data as a Tool in Detecting Potential Groundwater Flow Systems. Journal of Hydrology. 131(1–4), 25–62. DOI: https://doi.org/10.1016/0022-1694(92)90212-E

[60] Eamus, D., Zolfaghar, S., Villalobos-Vega, R., et al., 2015. Groundwater-Dependent Ecosystems: Recent Insights from Satellite and Field-Based Studies. Hydrology and Earth System Sciences. 19(10), 4229–4256. DOI: https://doi.org/10.5194/hess-19-4229-2015

[61] Humphrey, V., Rodell, M., Eicker, A., 2023. Using Satellite-Based Terrestrial Water Storage Data: A Review. Surveys in Geophysics. 44(5), 1489–1517. DOI: https://doi.org/10.1007/s10712-022-09754-9

[62] Feng, W., Shum, C.K., Zhong, M., et al., 2018. Groundwater Storage Changes in China from Satellite Gravity: An Overview. Remote Sensing. 10(5), 674. DOI: https://doi.org/10.3390/rs10050674

[63] MacDougall, M.D.J., 2019. Impact of Long- and Short-Term Geodynamic Processes on Hydrocarbon Reservoirs in the Grand Banks [Master’s Thesis]. Queen’s University: Kingston, ON, Canada.

[64] Ndehedehe, C., 2023. Satellite Hydrology Programmes: Capabilities and Benefits. In Hydro-Climatic Extremes in the Anthropocene. Springer: Cham, Switzerland. pp. 81–133. DOI: https://doi.org/10.1007/978-3-031-37727-3_4

[65] Tolomio, M., Borin, M., 2018. Water Table Management to Save Water and Reduce Nutrient Losses from Agricultural Fields: 6 Years of Experience in North-Eastern Italy. Agricultural Water Management. 201, 1–10. DOI: https://doi.org/10.1016/j.agwat.2018.01.009

[66] Vlotman, W., Jansen, H., 2003. Controlled Drainage for Integrated Water Management. In Proceedings of the 9th International Drainage Workshop of ICID, Utrecht, The Netherlands, 10–13 September 2003.

[67] Liu, S., Huang, S., Xie, Y., et al., 2019. Assessing the Non-Stationarity of Low Flows and Their Scale-Dependent Relationships with Climate and Human Forcing. Science of the Total Environment. 687, 244–256. DOI: https://doi.org/10.1016/j.scitotenv.2019.06.025

[68] Liu, P., Din, A.H.M., Hamden, M.H., 2023. A Review of Satellite-Based Monitoring of Groundwater Storage Changes and Depletion Consequences. IOP Conference Series: Earth and Environmental Science. 1274, 012004. DOI: https://doi.org/10.1088/1755-1315/1274/1/012004

[69] Gao, H., 2015. Satellite Remote Sensing of Large Lakes and Reservoirs: From Elevation and Area to Storage. Wiley Interdisciplinary Reviews: Water. 2(2), 147–157. DOI: https://doi.org/10.1002/wat2.1065

[70] Döll, P., Schmied, H.M., Schuh, C., et al., 2014. Global-Scale Assessment of Groundwater Depletion and Related Groundwater Abstractions: Combining Hydrological Modeling with Information from Well Observations and GRACE Satellites. Water Resources Research. 50(7), 5698–5720. DOI: https://doi.org/10.1002/2014WR015595

[71] Condon, L.E., Kollet, S., Bierkens, M.F.P., et al., 2021. Global Groundwater Modeling and Monitoring: Opportunities and Challenges. Water Resources Research. 57(12), e2020WR029500. DOI: https://doi.org/10.1029/2020WR029500

[72] Gebremedhin, M.A., 2024. Integrating In-Situ Data with Satellite-Derived Products to Assess Surface-Groundwater Interactions and Sustainability of Groundwater Resources in Semi-Arid Environment. DATA Archiving and Networked Services (DANS): The Hague, The Netherlands.

[73] Fankhauser, K., Macharia, D., Coyle, J., et al., 2022. Estimating Groundwater Use and Demand in Arid Kenya through Assimilation of Satellite Data and In-Situ Sensors with Machine Learning toward Drought Early Action. Science of the Total Environment. 831, 154453. DOI: https://doi.org/10.1016/j.scitotenv.2022.154453

[74] Raju, D., 2025. Groundwater Depletion and Sustainability in Merced County, California: Analyzing Current Trends Scenarios Using GIS Tools [Master’s Thesis]. The University of Arizona: Tucson, AZ, USA.

[75] Masood, A., Tariq, M.A.U.R., Hashmi, M.Z.U.R., et al., 2022. An Overview of Groundwater Monitoring through Point-to Satellite-Based Techniques. Water. 14(4), 565. DOI: https://doi.org/10.3390/w14040565

[76] Riaz, A., Nijhuis, S., Bobbink, I., 2025. The Role of Spatial Planning in Landscape-Based Groundwater Recharge: A Systematic Literature Review. Water. 17(6), 862. DOI: https://doi.org/10.3390/w17060862

[77] Ndehedehe, C., 2022. Groundwater from Space. In Satellite Remote Sensing of Terrestrial Hydrology. Springer: Cham, Switzerland. pp. 211–230. DOI: https://doi.org/10.1007/978-3-030-99577-5_9

[78] Shaikh, M., Birajdar, F., 2024. Artificial Intelligence in Groundwater Management: Innovations, Challenges, and Future Prospects. International Journal of Science and Research Archive. 11(1), 502–512. DOI: https://doi.org/10.30574/ijsra.2024.11.1.0105

[79] Davis, G.B., Rayner, J.L., Donn, M.J., 2023. Advancing “Autonomous” Sensing and Prediction of the Subsurface Environment: A Review and Exploration of the Challenges for Soil and Groundwater Contamination. Environmental Science and Pollution Research. 30(8), 19520–19535. DOI: https://doi.org/10.1007/s11356-022-25125-8

[80] Pourmorad, S., Kabolizade, M., Dimuccio, L.A., 2024. Artificial Intelligence Advancements for Accurate Groundwater Level Modelling: An Updated Synthesis and Review. Applied Sciences. 14(16), 7358. DOI: https://doi.org/10.3390/app14167358

[81] Sharma, L.K., Naik, R., 2024. Wetland Ecosystems. In Conservation of Saline Wetland Ecosystems: An Initiative towards UN Decade of Ecological Restoration. Springer: Singapore. pp. 3–32. DOI: https://doi.org/10.1007/978-981-97-5069-6_1

[82] Tiner, R.W., 2016. Wetland Indicators: A Guide to Wetland Formation, Identification, Delineation, Classification, and Mapping. CRC Press: Boca Raton, FL, USA.

[83] Hu, S., Niu, Z., Chen, Y., 2017. Global Wetland Datasets: A Review. Wetlands. 37(5), 807–817. DOI: https://doi.org/10.1007/s13157-017-0927-z

[84] Ozesmi, S.L., Bauer, M.E., 2002. Satellite Remote Sensing of Wetlands. Wetlands Ecology and Management. 10(5), 381–402. DOI: https://doi.org/10.1023/A:1020908432489

[85] Guo, M., Li, J., Sheng, C., et al., 2017. A Review of Wetland Remote Sensing. Sensors. 17(4), 777. DOI: https://doi.org/10.3390/s17040777

[86] Niu, Z., Gong, P., Cheng, X., et al., 2009. Geographical Characteristics of China’s Wetlands Derived from Remotely Sensed Data. Science in China Series D: Earth Sciences. 52(6), 723–738. DOI: https://doi.org/10.1007/s11430-009-0075-2

[87] Adam, E., Mutanga, O., Rugege, D., 2010. Multispectral and Hyperspectral Remote Sensing for Identification and Mapping of Wetland Vegetation: A Review. Wetlands Ecology and Management. 18(3), 281–296. DOI: https://doi.org/10.1007/s11273-009-9169-z

[88] Rundquist, D.C., Narumalani, S., Narayanan, R.M., 2001. A Review of Wetlands Remote Sensing and Defining New Considerations. Remote Sensing Reviews. 20(3), 207–226. DOI: https://doi.org/10.1080/02757250109532435

[89] Klemas, V., 2013. Remote Sensing of Emergent and Submerged Wetlands: An Overview. International Journal of Remote Sensing. 34(18), 6286–6320. DOI: https://doi.org/10.1080/01431161.2013.800656

[90] Guo, Z., Wu, L., Huang, Y., et al., 2022. Water-Body Segmentation for SAR Images: Past, Current, and Future. Remote Sensing. 14(7), 1752. DOI: https://doi.org/10.3390/rs14071752

[91] Nath, R.K., Deb, S., 2010. Water-Body Area Extraction from High Resolution Satellite Images—An Introduction, Review, and Comparison. International Journal of Image Processing. 3(6), 353–372.

[92] Rouzegari, N., Bolboli Zadeh, M., Jimenez Arellano, C., et al., 2025. Passive Microwave Imagers, Their Applications, and Benefits: A Review. Remote Sensing. 17(9), 1654. DOI: https://doi.org/10.3390/rs17091654

[93] Rodríguez-Rodríguez, M., Halmos, L., Jiménez-Bonilla, A., et al., 2025. Assessing the Impact of Groundwater Extraction and Climate Change on a Protected Playa-Lake System in the Southern Iberian Peninsula: La Ratosa Natural Reserve. Geographies. 5(2), 21. DOI: https://doi.org/10.3390/geographies5020021

[94] Kissel, A.M., Halabisky, M., Scherer, R.D., et al., 2020. Expanding Wetland Hydroperiod Data via Satellite Imagery for Ecological Applications. Frontiers in Ecology and the Environment. 18(8), 432–438. DOI: https://doi.org/10.1002/fee.2233

[95] Michielotto, A., 2025. Management Strategies for Coastal Ecosystems in the Face of Climate Change and Increasing Human Pressure [PhD Thesis]. University of Padua: Padua, Italy.

[96] Alves, B., Angnuureng, D.B., Morand, P., et al., 2020. A Review on Coastal Erosion and Flooding Risks and Best Management Practices in West Africa: What Has Been Done and Should Be Done. Journal of Coastal Conservation. 24(3), 38. DOI: https://doi.org/10.1007/s11852-020-00755-7

[97] Sáez-Ardura, F., Parra-Salazar, M., Vallejos-Romero, A., et al., 2025. Exploring the Socio-Environmental Regulation of Water—A Systematic Review of Sustainable Watershed Management. Sustainability. 17(4), 1588. DOI: https://doi.org/10.3390/su17041588

[98] Rafiei, V., Nejadhashemi, A., Mushtaq, S., et al., 2022. Groundwater-Surface Water Interactions at Wetland Interface: Advancement in Catchment System Modeling. Environmental Modelling & Software. 152, 105407. DOI: https://doi.org/10.1016/j.envsoft.2022.105407

[99] Volik, O., Petrone, R., Price, J., 2023. Wetlands as Integral Parts of Surface Water–Groundwater Interactions in the Athabasca Oil Sands Area: Review and Synthesis. Environmental Reviews. 32(2), 145–172. DOI: https://doi.org/10.1139/er-2023-0064

[100] Xin, P., Wilson, A., Shen, C., et al., 2022. Surface Water and Groundwater Interactions in Salt Marshes and Their Impact on Plant Ecology and Coastal Biogeochemistry. Reviews of Geophysics. 60(1), e2021RG000740. DOI: https://doi.org/10.1029/2021RG000740

[101] Dronova, I., 2015. Object-Based Image Analysis in Wetland Research: A Review. Remote Sensing. 7(5), 6380–6413. DOI: https://doi.org/10.3390/rs70506380

[102] Mahdavi, S., Salehi, B., Granger, J., et al., 2018. Remote Sensing for Wetland Classification: A Comprehensive Review. GIScience & Remote Sensing. 55(5), 623–658. DOI: https://doi.org/10.1080/15481603.2017.1419602

[103] Moomaw, W.R., Chmura, G.L., Davies, G.T., et al., 2018. Wetlands in a Changing Climate: Science, Policy and Management. Wetlands. 38(2), 183–205. DOI: https://doi.org/10.1007/s13157-018-1023-8

[104] Gallant, A.L., 2015. The Challenges of Remote Monitoring of Wetlands. Remote Sensing. 7(8), 10938–10950. DOI: https://doi.org/10.3390/rs70810938

[105] Panu, U., Sharma, T., 2002. Challenges in Drought Research: Some Perspectives and Future Directions. Hydrological Sciences Journal. 47(S1), S19–S30. DOI: https://doi.org/10.1080/02626660209493019

[106] Shiau, J.-T., 2023. Causality-Based Drought Propagation Analyses among Meteorological Drought, Hydrologic Drought, and Water Shortage. Science of the Total Environment. 888, 164216. DOI: https://doi.org/10.1016/j.scitotenv.2023.164216

[107] AghaKouchak, A., Mirchi, A., Madani, K., et al., 2021. Anthropogenic Drought: Definition, Challenges, and Opportunities. Reviews of Geophysics. 59(2), e2019RG000683. DOI: https://doi.org/10.1029/2019RG000683

[108] Van Loon, A.F., 2015. Hydrological Drought Explained. Wiley Interdisciplinary Reviews: Water. 2(4), 359–392. DOI: https://doi.org/10.1002/wat2.1085

[109] Wu, J., Liu, Z., Yao, H., et al., 2018. Impacts of Reservoir Operations on Multi-Scale Correlations between Hydrological Drought and Meteorological Drought. Journal of Hydrology. 563, 726–736. DOI: https://doi.org/10.1016/j.jhydrol.2018.06.053

[110] Ma, J., Gao, J., 2023. Cascading Effects of Drought in Xilin Gol Temperate Grassland, China. Scientific Reports. 13(1), 10926. DOI: https://doi.org/10.1038/s41598-023-38002-2

[111] Seneviratne, S.I., Corti, T., Davin, E.L., et al., 2010. Investigating Soil Moisture–Climate Interactions in a Changing Climate: A Review. Earth-Science Reviews. 99(3–4), 125–161. DOI: https://doi.org/10.1016/j.earscirev.2010.02.004

[112] Wanniarachchi, S., Sarukkalige, R., 2022. A Review on Evapotranspiration Estimation in Agricultural Water Management: Past, Present, and Future. Hydrology. 9(7), 123. DOI: https://doi.org/10.3390/hydrology9070123

[113] Gouveia, C., Trigo, R., Beguería, S., et al., 2017. Drought Impacts on Vegetation Activity in the Mediterranean Region: An Assessment Using Remote Sensing Data and Multi-Scale Drought Indicators. Global and Planetary Change. 151, 15–27. DOI: https://doi.org/10.1016/j.gloplacha.2016.06.011

[114] Yao, C., Shum, C., Luo, Z., et al., 2022. An Optimized Hydrological Drought Index Integrating GNSS Displacement and Satellite Gravimetry Data. Journal of Hydrology. 614, 128647. DOI: https://doi.org/10.1016/j.jhydrol.2022.128647

[115] AghaKouchak, A., Farahmand, A., Melton, F.S., et al., 2015. Remote Sensing of Drought: Progress, Challenges and Opportunities. Reviews of Geophysics. 53(2), 452–480. DOI: https://doi.org/10.1002/2014RG000456

[116] Nie, W., Kumar, S.V., Arsenault, K.R., et al., 2021. Towards Effective Drought Monitoring in the Middle East and North Africa (MENA) Region: Implications from Assimilating Leaf Area Index and Soil Moisture into the Noah-MP Land Surface Model for Morocco. Hydrology and Earth System Sciences Discussions. 26(9), 1–36. DOI: https://doi.org/10.5194/hess-2021-263

[117] Zaitchik, B.F., Tuholske, C., 2021. Earth Observations of Extreme Heat Events: Leveraging Current Capabilities to Enhance Heat Research and Action. Environmental Research Letters. 16(11), 111002. DOI: https://doi.org/10.1088/1748-9326/ac30c0

[118] Roundy, J.K., Wood, E.F., 2015. The Attribution of Land–Atmosphere Interactions on the Seasonal Predictability of Drought. Journal of Hydrometeorology. 16(2), 793–810. DOI: https://doi.org/10.1175/JHM-D-14-0121.1

[119] Zhang, L., Yu, X., Zhou, T., et al., 2023. Understanding and Attribution of Extreme Heat and Drought Events in 2022: Current Situation and Future Challenges. Advances in Atmospheric Sciences. 40(11), 1941–1951. DOI: https://doi.org/10.1007/s00376-023-3171-x

[120] Yu, R., Chen, S., Xie, Y., et al., 2025. Foundation Models for Environmental Science: A Survey of Emerging Frontiers. arXiv preprint. arXiv:2504.04280. DOI: https://doi.org/10.48550/arXiv.2504.04280

[121] Schwartz, C.C., 2024. Further Developing Drought Early Warning Information Systems Using Mixed-Methods and Multiple Streams of Data [PhD Thesis]. The University of Nebraska–Lincoln: Lincoln, NE, USA.

[122] Tauro, F., Selker, J., van de Giesen, N., et al., 2018. Measurements and Observations in the XXI Century (MOXXI): Innovation and Multi-Disciplinarity to Sense the Hydrological Cycle. Hydrological Sciences Journal. 63(2), 169–196. DOI: https://doi.org/10.1080/02626667.2017.1420191

[123] Schultz, G.A., 1988. Remote Sensing in Hydrology. Journal of Hydrology. 100(1–3), 239–265. DOI: https://doi.org/10.1016/0022-1694(88)90187-4

[124] Xu, X., Li, J., Tolson, B.A., 2014. Progress in Integrating Remote Sensing Data and Hydrologic Modeling. Progress in Physical Geography. 38(4), 464–498. DOI: https://doi.org/10.1177/0309133314536583

[125] Schumann, G., Bates, P.D., Horritt, M.S., et al., 2009. Progress in Integration of Remote Sensing-Derived Flood Extent and Stage Data and Hydraulic Models. Reviews of Geophysics. 47(4). DOI: https://doi.org/10.1029/2008RG000274

[126] Liu, Y., Weerts, A.H., Clark, M., et al., 2012. Advancing Data Assimilation in Operational Hydrologic Forecasting: Progresses, Challenges, and Emerging Opportunities. Hydrology and Earth System Sciences. 16(10), 3863–3887. DOI: https://doi.org/10.5194/hess-16-3863-2012

[127] Moradkhani, H., 2008. Hydrologic Remote Sensing and Land Surface Data Assimilation. Sensors. 8(5), 2986–3004. DOI: https://doi.org/10.3390/s8052986

[128] Meehan, T.G., 2022. Advancements in Measuring and Modeling the Mechanical and Hydrological Properties of Snow and Firn: Multi-Sensor Analysis, Integration, and Algorithm Development [PhD Thesis]. Boise State University: Boise, ID, USA.

[129] Hasan, F., Medley, P., Drake, J., et al., 2024. Advancing Hydrology through Machine Learning: Insights, Challenges, and Future Directions Using the CAMELS, Caravan, GRDC, CHIRPS, PERSIANN, NLDAS, GLDAS, and GRACE Datasets. Water. 16(13), 1904. DOI: https://doi.org/10.3390/w16131904

[130] McMillan, H.K., Westerberg, I.K., Krueger, T., 2018. Hydrological Data Uncertainty and Its Implications. Wiley Interdisciplinary Reviews: Water. 5(6), e1319. DOI: https://doi.org/10.1002/wat2.1319

[131] Lopez, T., Al Bitar, A., Biancamaria, S., et al., 2020. On the Use of Satellite Remote Sensing to Detect Floods and Droughts at Large Scales. Surveys in Geophysics. 41(6), 1461–1487. DOI: https://doi.org/10.1007/s10712-020-09618-0

[132] Kallache, M., Rust, H., Kropp, J., 2005. Trend Assessment: Applications for Hydrology and Climate Research. Nonlinear Processes in Geophysics. 12(2), 201–210. DOI: https://doi.org/10.5194/npg-12-201-2005

[133] Edwards, W.K., Bellotti, V., Dey, A.K., et al., 2003. The Challenges of User-Centered Design and Evaluation for Infrastructure. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI 2003), Ft. Lauderdale, FL, USA, 5–10 April 2003; pp. 297–304. DOI: https://doi.org/10.1145/642611.642664

Downloads

How to Cite

Li, Y. (2026). Satellite Hydrology: A New Era in Monitoring Groundwater, Wetlands, and Drought Dynamics. Journal of Environmental & Earth Sciences, 8(3), 198–231. https://doi.org/10.30564/jees.v8i3.13103

Issue

Article Type

Review