ASSESSING CROP WATER REQUIREMENTS USING SATELLITE INDICES IN GOOGLE EARTH ENGINE

Abstract

Water scarcity is a critical challenge in modern agriculture. Traditional methods of monitoring crop water stress are time-consuming, expensive, and difficult to apply over large areas. This study demonstrates the use of the Google Earth Engine (GEE) cloud platform and high-resolution Sentinel-2 satellite imagery to evaluate crop moisture levels automatically. By calculating the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI) over a summer growing season (May to September), we tracked the dynamic relationship between plant health and water needs. The results show that sudden drops in NDWI accurately indicate periods of water stress, which can be directly correlated with the crop's vegetative growth stages shown by NDVI. This remote sensing approach provides a fast, automated, and cost-effective method for farmers and water managers to optimize irrigation schedules and save water resources.