000028623 001__ 28623
000028623 041__ $$aEnglish
000028623 100__ $$aZwart, S. J.
000028623 245__ $$aWatpro: A Remote Sensing Based Model for Mapping Water Productivity of Wheat
000028623 260__ $$c2010
000028623 300__ $$a1628-1636
000028623 507__ $$yDavid Molden, dmarticles
000028623 520__ $$aWater productivity in agriculture needs to be improved significantly in the coming decades to secure food supply to a growing world population. To assess on a global scale where water productivity can be improved and what the causes are for not reaching its potential, the current levels must be understood. This paper describes the development and validation of a WATer PROductivity (WATPRO) model for wheat that is based on remote sensing-derived input data sets, and that can be applied at local to global scales. The model is a combination of Monteith's theoretical framework for dry matter production in plants and an energy balance model to assess actual evapotranspiration. It is shown that by combining both approaches, the evaporative fraction and the atmospheric transmissivity, two parameters which are usually difficult to estimate spatially, can be omitted. Water productivity can then be assessed from four spatial variables: broadband surface albedo, the vegetation index NDVI, the extraterrestrial radiation and air temperature. A sensitivity analysis revealed that WATPRO is most sensitive to changes in NDVI and least sensitive to changes in air temperature. The WATPRO model was applied at 39 locations where water productivity was measured under experimental conditions. The correlation between measured and modelled water productivity was low, and this can be mainly attributed to differences in scales and in the experimental and modelling periods. A comparison with measurements from farmer's fields in areas surrounded by other wheat fields located in Sirsa District, NW India, showed an improved correlation. Although not a validation, a comparison with SEBAL-derived water productivity in the same region in India proved that WATPRO can spatially predict water productivity with the same spatial variation. © 2010 Elsevier B.V.
000028623 700__ $$aBastiaanssen, W. G. M.
000028623 700__ $$aDe Fraiture, C.
000028623 700__ $$aMolden, D. J.
000028623 773__ $$pAgricultural Water Management
000028623 773__ $$v97 (10)
000028623 773__ $$a10.1016/j.agwat.2010.05.017
000028623 8564_ $$uhttp://login.oaresciences.org/whalecomwww.scopus.com/whalecom0/inward/record.url?eid=2-s2.0-77954542845&partnerID=40&md5=a5d2f62b6730eeea5761d9c289a83ca9$$yExternal link
000028623 980__ $$aARTICLE