Remote Sensing-Based Tools for Agricultural and Climate Monitoring

Combining satellite-based remote sensing, agroclimatic modeling, and crop-specific biophysical indicators, this work presents an integrated and scalable methodology for agricultural and climate monitoring. Emphasizing three representative case studies on the Iberian Peninsula (industrial tomato in Lebrija, open-field strawberry in Aznalcázar, and orange orchards in Tavernes de la Valldigna), the study uses land surface temperature (LST) anomalies, vegetation indices (NDVI, MSAVI), and a Water Balance Index (WBI) derived from remote sensing platforms including Landviewer and Crop Monitoring. Using Hargreaves-based models combined with crop coefficients, over 180 satellite images were investigated over two full growing seasons (2023–2024) and monthly evapotranspiration and effective rainfall values were computed. With R2 values ranging from 0.72 to 0.83 and significance levels below 0.01, a new yield estimate equation combining WBI, NDVI, and ΔLST has shown strong predictive performance across crops. Particularly in early and mid-season development stages, correlation studies also showed that MSAVI routinely outperformed NDVI in identifying crop water stress. While perennial crops such as orange respond more slowly but are still sufficiently modeled, the results verified that shallow-rooted, short-cycle crops such tomato and strawberry show greater sensitivity to hydrological variation. Particularly for smallholder farmers and regional planners working under climate variability and limited field data, the framework offers a practical, low-cost solution for early identification of water stress and yield forecasting. In the end, this chapter emphasizes the need of including Earth observation technologies into decision-making procedures for sustainable agricultural development in Mediterranean and semi-arid surroundings.