Biradar, CM, Thenkabail, PS, Platonov, A, Xiao, XM, Geerken, R, Noojipady, P, Turral, H, Vithanage, J (2008). Water productivity mapping methods using remote sensing. JOURNAL OF APPLIED REMOTE SENSING, 2, 23544.
The goal of this paper was to develop methods and protocols for water productivity mapping (WPM) using remote sensing data at multiple resolutions and scales in conjunction with field-plot data. The methods and protocols involved three broad categories: (a) Crop Productivity Mapping(CPM) (kg/m2); (b) Water Use (evapotranspiration) Mapping (WUM) (m3/m2); and (c) Water Productivity Mapping (WPM) (kg/m3). First, the CPMs were determined using remote sensing by: (i) Mapping crop types; (ii) modeling crop yield; and (iii) extrapolating models to larger areas. Second, WUM were derived using the Simplified Surface Energy Balance (SSEB) model. Finally, WPMs were produced by dividing CPMs and WUMs. The paper used data from Quickbird 2.44m, Indian Remote Sensing (IRS) Resoursesat-1 23.5m, Landsat-7 30m, and Moderate Resolution Imaging Spectroradiometer (MODIS) 250m and 500m, to demonstrate the methods for mapping water productivity (WP). In terms of physical water productivity (kilogram of yield produced per unit of water delivered), wheat crop had highest water productivity of 0.60 kg/m3 (WP), followed by rice with 0.5 kg/m3, and cotton with 0.42 kg/m3. In terms of economic value (dollar per unit of water delivered), cotton ranked highest at $ 0.5/m3 followed by wheat with $ 0.33/m3 and rice at $ 0.10/m3. The study successfully delineated the areas of low and high WP. An overwhelming proportion (50+%) of the irrigated areas were under low WP for all crops with only about 10% area in high WP.