|PATIL, VIRUPAKSHAGOUDA - King Saud University|
|SWAMY, RANGA - King Saud University|
|TOLA, E - King Saud University|
|MAREY, S - King Saud University|
|AL-DOSARI, A - King Saud University|
|BIRADAR, CHANDRA - International Center For Agricultural Research In The Dry Areas (ICARDA)|
Submitted to: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/17/2014
Publication Date: 6/1/2014
Citation: Patil, V., Swamy, R.M., Tola, E., Marey, S., Al-Dosari, A., Biradar, C.M., Gowda, P.H. 2014. Assessing agricultural water productivity in desert farming system of Saudi Arabia. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 10.1109/JSTARS.2014.2320592.
Interpretive Summary: In this study, water productivity of major crops was estimated using remote sensing data and analyzed by scientists from ARS and King Saud University. The evapotranspiration maps used in developing crop productivity maps was derived using a surface energy balance model. Analysis of the crop productivity maps indicated that wheat and alfalfa yielded highest and lowest water productivities, respectively.
Technical Abstract: The primary objective of this study was to assess the water productivity (WP) of the annual (wheat, barley, and corn)and biennial (alfalfa and Rhodes grass) crops cultivated under center-pivot irrigation located over desert areas of the Al-Kharj region in Saudi Arabia. The Surface Energy Balance Algorithm for Land (SEBAL) was applied to Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images to obtain evapotranspiration (ET) for assessing WP and irrigation performance (IP) of crops. Crop productivity (CP) was estimated using Normalized Difference Vegetation Index (NDVI) crop productivity models. The predicted CP for corn varied from 12,690 to 14,060 t/ha and from 6,000 to 7,370 t/ha for wheat. The annual CP for alfalfa and Rhodes grass was 42,450 and 58,210 kg/ha, respectively. The highest predicted WP was observed in wheat (0.80-2.01 kg/cu.m) and the lowest was in alfalfa (0.38-0.46 kg/cu.m). The deviation between SEBAL predicted ET-P and weather station recorded ET-W was 10%. The performance of the prediction models was assessed against the measured data. The overall mean bias/error of the predictions of CP, ET, and WP was 9.4%, -2.68%, and 9.65%, respectively; the root mean square error (RMSE) was 1,996 kg/ha, 2,107 cu.m/ha, and 0.09 kg/cu.m for CP, ET, and WP, respectively. When CP was converted into variations between the actual and predicted, the variations were 8% to 12% for wheat, 14% to 20% for corn, 17% to 35% for alfalfa, 3% to 38% for Rhodes grass, and 4% for barley.