{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T02:53:22Z","timestamp":1775271202126,"version":"3.50.1"},"reference-count":111,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2018,11,14]],"date-time":"2018-11-14T00:00:00Z","timestamp":1542153600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Scarcity of information on the water productivity of different water, land, and other ecosystems in Africa, hampers the optimal allocation of the limited water resources. This study presents an innovative method to quantify the spatial variability of biomass production, crop yield, and economic water productivity, in a data scarce landscape of the Pangani Basin. For the first time, gross return from carbon credits and other ecosystem services are considered, in the concept of Economic Water Productivity. The analysis relied on the MODIS satellite data of 250 m and eight-day resolutions, and the SEBAL model, utilizing Monteith\u2019s framework for ecological production. In agriculture, irrigated sugarcane and rice achieved the highest water productivities in both biophysical and economic values. Rainfed and supplementary irrigated banana and maize productivities were significantly lower than the potential values, reflecting a wide spatial variability. In natural landscapes, forest and wetland showed the highest biomass production. However, the transition to economic productivity was low but showed the potential to increase significantly when non-market goods and services were considered. Spatially explicit information, from both biophysical and economic water productivity, provides a holistic outlook of the socio-environmental and the economic water values of a land-use activity, and can identify areas for improvement, and trade-offs in river basins.<\/jats:p>","DOI":"10.3390\/rs10111802","type":"journal-article","created":{"date-parts":[[2018,11,14]],"date-time":"2018-11-14T10:58:22Z","timestamp":1542193102000},"page":"1802","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Mapping Ecological Production and Benefits from Water Consumed in Agricultural and Natural Landscapes: A Case Study of the Pangani Basin"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9407-0506","authenticated-orcid":false,"given":"Jeremiah K.","family":"Kiptala","sequence":"first","affiliation":[{"name":"School of Civil, Environmental and Geomatics Engineering, Jomo Kenyatta University of Agriculture and Technology, P.O. Box 62000, Nairobi 0200, Kenya"}]},{"given":"Marloes","family":"Mul","sequence":"additional","affiliation":[{"name":"IHE Delft, Institute for Water Education, Westvest 7, 2601 DA Delft, The Netherlands"}]},{"given":"Yasir","family":"Mohamed","sequence":"additional","affiliation":[{"name":"IHE Delft, Institute for Water Education, Westvest 7, 2601 DA Delft, The Netherlands"},{"name":"Hydraulic Research Center, P.O. Box 318, Wad Medani, Sudan"}]},{"given":"Wim G.M.","family":"Bastiaanssen","sequence":"additional","affiliation":[{"name":"IHE Delft, Institute for Water Education, Westvest 7, 2601 DA Delft, The Netherlands"},{"name":"Delft University of Technology (TU Delft), P.O. Box 5048, 2600 GA Delft, The Netherlands"}]},{"given":"Pieter","family":"Van der Zaag","sequence":"additional","affiliation":[{"name":"IHE Delft, Institute for Water Education, Westvest 7, 2601 DA Delft, The Netherlands"},{"name":"Delft University of Technology (TU Delft), P.O. 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