{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T11:29:27Z","timestamp":1775734167024,"version":"3.50.1"},"reference-count":13,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,12,21]],"date-time":"2022-12-21T00:00:00Z","timestamp":1671580800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"WeMAST-Wetlands Monitoring and Assessment project"},{"name":"Africa Union and the European Union"},{"name":"Global Monitoring for Environment and Security (GMES)"},{"name":"Africa Support Programme"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In line with the United Nations Sustainable Development Goal (SDG) 6, the main goal of the Special Issue on \u201cRemote sensing for water resources and environmental management\u201d was to solicit papers from a diverse range of scientists around the world on the use of cutting-edge remote sensing technologies to assess and monitor freshwater quality, quantity, availability, and management to ensure water security. Special consideration was given to scientific manuscripts that covered, but were not limited to, the development of geospatial techniques and remote sensing applications for detecting, quantifying, and monitoring freshwater water resources, identifying potential threats to water resources and agriculture, as well as other themes related to water resources and environmental management at various spatial scales. The Special Issue attracted over thirteen peer-reviewed scientific articles, with the majority of manuscripts originating from China. Most of the studies made use of satellite datasets, ranging from coarse spatial resolution data, such as the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO), to medium spatial resolution data, such as the Landsat series, ERA5, Modern-Era Retrospective Analysis for Research and Application Land version 2 reanalysis product (MERRA2), CLSM and NOAH ET, and MODIS (Moderate Resolution Imaging Spectroradiometer). Google Earth Engine (GEE) data, together with big data processing techniques, such as the remote sensing-based energy balance model (ALEXI\/DisALEXI approach) and the STARFM data fusion technique, were used for analyzing geospatial datasets. Overall, this Special Issue demonstrated significant knowledge gaps in various big data image processing techniques and improved computing processes in assessing and monitoring water resources and the environment at various spatial and temporal scales.<\/jats:p>","DOI":"10.3390\/rs15010018","type":"journal-article","created":{"date-parts":[[2022,12,21]],"date-time":"2022-12-21T04:28:55Z","timestamp":1671596935000},"page":"18","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Remote Sensing for Water Resources and Environmental Management"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3456-8991","authenticated-orcid":false,"given":"Timothy","family":"Dube","sequence":"first","affiliation":[{"name":"Institute for Water Studies, Department of Earth Science, University of the Western Cape, Private Bag X17, Bellville, Cape Town 7535, South Africa"}]},{"given":"Munyaradzi D.","family":"Shekede","sequence":"additional","affiliation":[{"name":"Department of Geography Geospatial Sciences and Earth Observation (GGEO), University of Zimbabwe, P. Bag MP 167, Mt Pleasant, Harare, Zimbabwe"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0983-1276","authenticated-orcid":false,"given":"Christian","family":"Massari","sequence":"additional","affiliation":[{"name":"National Research Council, Via Madonna Alta 126, 06128 Perugia, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Yang, X., Wang, N., Chen, A., Li, Z., Liang, Q., and Zhang, Y. (2022). Impacts of Climate Change, Glacier Mass Loss and Human Activities on Spatiotemporal Variations in Terrestrial Water Storage of the Qaidam Basin, China. Remote Sens., 14.","DOI":"10.3390\/rs14092186"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"102914","DOI":"10.1016\/j.pce.2020.102914","article-title":"Satellite monitoring of surface water variability in the drought prone Western Cape, South Africa","volume":"124","author":"Bhaga","year":"2021","journal-title":"Phys. Chem. Earth"},{"key":"ref_3","first-page":"100689","article-title":"Advancements in the satellite sensing of the impacts of climate and variability on bush encroachment in savannah rangelands","volume":"25","author":"Maphanga","year":"2022","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1342","DOI":"10.1080\/19475705.2022.2072774","article-title":"Fine-scale multi-temporal and spatial analysis of agricultural drought in agro-ecological regions of Zimbabwe","volume":"13","author":"Sharara","year":"2022","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Varghese, D., Radulovi\u0107, M., Stojkovi\u0107, S., and Crnojevi\u0107, V. (2021). Reviewing the potential of sentinel-2 in assessing the drought. Remote Sens., 13.","DOI":"10.3390\/rs13173355"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Zhu, B., Bai, Y., Zhang, Z., He, X., Wang, Z., Zhang, S., and Dai, Q. (2022). Satellite Remote Sensing of Water Quality Variation in a Semi-Enclosed Bay (Yueqing Bay) under Strong Anthropogenic Impact. Remote Sens., 14.","DOI":"10.3390\/rs14030550"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Matlhodi, B., Kenabatho, P.K., Parida, B.P., and Maphanyane, J.G. (2021). Analysis of the future land use land cover changes in the gaborone dam catchment using ca-markov model: Implications on water resources. Remote Sens., 13.","DOI":"10.3390\/rs13132427"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Magidi, J., Nhamo, L., Mpandeli, S., and Mabhaudhi, T. (2021). Application of the random forest classifier to map irrigated areas using google earth engine. Remote Sens., 13.","DOI":"10.3390\/rs13050876"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3761","DOI":"10.1016\/j.ecolmodel.2011.09.009","article-title":"Modeling urban land use change by the integration of cellular automaton and Markov model","volume":"222","author":"Guan","year":"2011","journal-title":"Ecol. Modell."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Dlamini, M., Chirima, G., Sibanda, M., Adam, E., and Dube, T. (2021). Characterizing Leaf Nutrients of Wetland Plants and Agricultural Crops with Nonparametric Approach Using Sentinel-2 Imagery Data. Remote Sens., 13.","DOI":"10.3390\/rs13214249"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Carpintero, E., Anderson, M.C., Andreu, A., Hain, C., Gao, F., Kustas, W.P., and Gonz\u00e1lez-Dugo, M.P. (2021). Estimating evapotranspiration of mediterranean oak savanna at multiple temporal and spatial resolutions. Implications for water resources management. Remote Sens., 13.","DOI":"10.3390\/rs13183701"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Ochege, F.U., Shi, H., Li, C., Ma, X., Igboeli, E.E., and Luo, G. (2021). Assessing satellite, land surface model and reanalysis evapotranspiration products in the absence of in-situ in central asia. Remote Sens., 13.","DOI":"10.3390\/rs13245148"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Kostianoy, A.G., Lebedev, S.A., Kostianaia, E.A., and Prokofiev, Y.A. (2022). Interannual Variability of Water Level in Two Largest Lakes of Europe. 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