Leveraging remotely-sensed vegetation indices to evaluate crop coefficients and actual irrigation requirements in the water-stressed Maipo River Basin of Central Chile

Abstract

Assessment of irrigation needs for agricultural production has commonly relied on reference crop coefficients derived using limited climatic conditions. This work focused on evaluating actual crop coefficients (Kc) by leveraging available satellite data from Landsat 8 Operational Land Imager (OLI) and Terra Moderate Resolution Imaging Spectroradiometer (MODIS). This method built on the linear relationship and variability between reference Kc from the Food and Agricultural Organization (FAO) and specific site response in Normalized Difference Vegetation Index (NDVI). A regional model was developed for the water-stressed Maipo River Basin of Central Chile during the growing seasons of maize from 2018 to 2022. Throughout model validation, NDVI-based Kc predictions of high quality were found with R-squared scores ranging from 0.77 to 0.97. Relying on Earth observations, this approach provided site-specific Kc values and estimates of actual evapotranspiration without requiring site instruments and/or particular knowledge of farming schedules. Overall, this study has the potential to assist individuals and organizations around the world in improving irrigation management approaches, and advance precision agriculture in the context of water scarcity.