Intellligent sustainable agricultural water practice using multi sensor spatiotemporal evolution

Area of Science:

• Environmental Science
• Remote Sensing
• Agricultural Science

Background:

• Unsustainable water extraction for irrigation threatens agriculture, especially under climate change.
• Monitoring agricultural water use is crucial for sustainable practices.

Purpose of the Study:

• To monitor the spatiotemporal evolution of agriculture in Al-Qassim, Saudi Arabia, using multi-sensor satellite data.
• To assess vegetation changes and their relationship with climate and water resources.

Main Methods:

• Utilized Landsat suite, GRACE, GRACE-FO, and Planetscope satellite data for vegetation indices (NDVI, SAVI, MSAVI2).
• Applied Mann Kendall trend analysis and linear regression to analyze temporal patterns and correlations.
• Validated vegetation results using high-resolution Planetscope data.

Main Results:

• Water extraction showed a decreasing trend (-10.24 ± 1.4 mm/year) from 2003-2020.
• Annual rainfall exhibited a decreasing trend, while annual temperature showed an increasing trend (1982-2020).
• Strong positive correlations were found between vegetation indices and rainfall (lagged), and strong negative correlations with temperature.

Conclusions:

• Satellite remote sensing is effective for monitoring agricultural changes and water resource dynamics.
• Climate change significantly impacts vegetation health in the region.
• Effective water resource management strategies are essential for sustainable agriculture in arid regions.