Intellligent sustainable agricultural water practice using multi sensor spatiotemporal evolution.

The amount of water taken from non-renewable resources such as aquifers to fulfill irrigation requirements is rarely monitored, putting sustainable agriculture under threat in the face of changing climate. In the present research, an attempt was made to apply multi-sensor (Landsat suite, GRACE, GRACE-FO) satellite data to monitor spatiotemporal evolution of agriculture for the Al-Qassim region, Kingdom of Saudi Arabia (KSA). For this purpose, time series of NDVI (Normalized Difference Vegetation Index), SAVI (Soil-Adjusted Vegetation Index), and MSAVI2 (Modified Soil-Adjusted Vegetation 2) was utilized to assess vegetation pattern change in the study area. The present investigation used High-resolution Planetscope (PS) nanosatellite data to validate the vegetation results. Mann Kendall trend analysis and linear regression were performed to study the temporal pattern, and the relationship between vegetation, GRACE, and climate variables was performed from 1984 to 2020. Water extraction based on the averaged value of JPL GWS and CSR GWS showed a decreasing trend of -10.24 ± 1.4 mm/year from 2003-2020. The annual rainfall showed a decreasing trend, while the annual temperature showed an increasing trend from 1982-2020. The correlation of vegetation indices with rainfall of one-month lag showed a significantly better relationship of 0.74, 0.74, and 0.75, respectively, for NDVI, SAVI, and MSAVI2. The correlation between temperature and all three vegetation indices is a strong negative correlation: -0.85 for NDVI and -0.9 for SAVI and MSAVI.