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The recent climate dynamics characterized by unpredictability and a series of extreme events pose challenges to society at various levels, particularly threatening agricultural production. The development of increasingly sophisticated models and computers combined with remote sensing techniques can serve as a means to safeguard the agricultural domain. The aim of this work is to develop a computational tool, named CROPORBIT, designed to operate at a regional scale for estimating crop yield. The capabilities of this tool have a significant positive impact on water management, crop health monitoring, and quantifying damage from extreme meteorological events, such as high temperatures. CROPORBIT combined the radiative model METRIC with a Photosynthetically Active Radiation-based model. Essential inputs for the tool include Landsat 8 and 9 satellite imagery and daily meteorological data retrieved from the regional network stations. The tool performs a multi-temporal analysis of crop growth, involving the interpolation of ET, stress coefficient, and dry biomass accumulation maps, which are then transformed into crop yield maps by applying a harvest index coefficient. CROPORBIT underwent validation in a series of soybean and corn fields situated in the low-lying plain of the Veneto Region, where crop yield maps were recorded by combine harvesters. The preliminary results have shown that CROPORBIT can predict the average crop yield with a good approximation while it was less performing in capturing the field yield variability. The main issues have proven to be the scarcity of clear-sky conditions imagery and the estimation of the harvest index variability. This research establishes the foundation for future investigations, emphasizing the need for improvements in spatial and time resolution. Enhancements in these aspects may lead to improved outcomes in terms of both accuracy and spatial variability.
Gabrieli et al. (Mon,) studied this question.