Water productivity mapping using remote sensing data of various resolutions to support more crop per drop

Xueliang Cai; Prasad S. Thenkabail; Chandrashekhar M. Biradar; Alexander Platonov; Muralikrishna Gumma; Venkateswarlu Dheeravath; Yafit Cohen; Naftali Goldshleger; Eyal Ben-Dor; Victor Alchanatis; Jagath Vithanage; Anputhas Markandu

doi:10.1117/1.3257643

1 October 2009 Water productivity mapping using remote sensing data of various resolutions to support "more crop per drop"

Xueliang Cai, Prasad S. Thenkabail, Chandrashekhar M. Biradar, Alexander Platonov, Muralikrishna Gumma, Venkateswarlu Dheeravath, Yafit Cohen, Naftali Goldshleger, Eyal Ben-Dor, Victor Alchanatis, Jagath Vithanage, Anputhas Markandu

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 3, Issue 1, 033557 (October 2009). https://doi.org/10.1117/1.3257643

Abstract

The overarching goal of this research was to map crop water productivity using satellite sensor data at various spectral, spatial, radiometric, and temporal resolutions involving: (a) Moderate Resolution Imaging Spectroradiometer (MODIS) 500m, (b) MODIS 250m, (c) Landsat enhanced thematic mapper plus (ETM+) 60m thermal, (d) Indian Remote Sensing Satellite (IRS) 23.5 m, and (e) Quickbird 2.44 m data. The spectro-biophysical models were developed using IRS and Quickbird satellite data for wet biomass, dry biomass, leaf area index, and grain yield for 5 crops: (a) cotton, (b) maize, (c) winter wheat, (d) rice, and (e) alfalfa in the Sry Darya basin, Central Asia. Crop-specific productivity maps were developed by applying the best spectro-biophysical models for the respective delineated crop types. Water use maps were produced using simplified surface energy balance (SSEB) model by multiplying evaporative fraction derived from Landsat ETM+ thermal data by potential ET. The water productivity (WP) maps were then derived by dividing the crop productivity maps by water use maps. The results of cotton crop, an overwhelmingly predominant crop in Central Asian Study area, showed that about 55% area had low WP of < 0.3 kg/m³, 34% had moderate WP of 0.3-0.4 kg/m³, and only 11% area had high WP > 0.4 kg/m³. The trends were similar for other crops. These results indicated that there is highly significant scope to increase WP (to grow "more crop per drop") through better water and cropland management practices in the low WP areas, which will substantially enhance food security of the ballooning populations without having to increase: (a) cropland areas, and\or (b) irrigation water allocations.

Citation Download Citation

Xueliang Cai, Prasad S. Thenkabail, Chandrashekhar M. Biradar, Alexander Platonov, Muralikrishna Gumma, Venkateswarlu Dheeravath, Yafit Cohen, Naftali Goldshleger, Eyal Ben-Dor, Victor Alchanatis, Jagath Vithanage, and Anputhas Markandu "Water productivity mapping using remote sensing data of various resolutions to support "more crop per drop"," Journal of Applied Remote Sensing 3(1), 033557 (1 October 2009). https://doi.org/10.1117/1.3257643

Published: 1 October 2009

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available