NATIONAL PHD COURSE IN EARTH OBSERVATION

Thesis title: Proximal and Remote sensing integration for improved accuracy and representativeness in crop yield gap estimation

Food loss and waste have become a critical global challenge, intensifying environmental pressure on land and water resources and generating avoidable greenhouse gas emissions across the entire food supply chain. According to the Food and Agriculture Organization, food loss and waste affect society by compromising all four dimensions of food security (utilization, access, availability, and stability). One major strategy to prevent unsustainable agricultural expansion and environmental degradation, while simultaneously increasing food production to feed a growing global population, is to enhance agricultural output from existing croplands by closing crop yield gaps. Consequently, the quantification of yield gaps and accurate yield prediction play crucial roles in ensuring food security. Crop growth models are among the most powerful numerical tools available for assessing the effects of sustainable land management practices on crop development and yield. Although these models are mechanistically based and capable of simulating a wide range of conditions, they require empirical parameterization which is typically informed by observations from plot-scale experiments. Although plot-scale data are highly accurate, they often lack representativeness because experimental designs tend to minimize natural variability, such as soil and topographic heterogeneity, through the use of blocking, contiguous layouts, and other controlled setups. As a result, yields obtained from plot-scale experiments frequently exceed those observed at the field scale due to inherent locational bias. This scale mismatch has long raised questions about the validity of extrapolating results from plot-scale studies to entire fields or regions, and about the extent to which such findings can reliably inform field-scale conclusions. In this context, the integration of remote sensing data, although less precise than plot-level measurements, provides the advantage of capturing the spatial variability of vegetation parameters and yield, thereby increasing representativeness and strengthening model parameterization and yield forecasting at the field scale. For these reasons, the present study aims to evaluate innovative methods for integrating proximal and remote sensing data to estimate crop yields. The research is structured into three chapters: 1) A global meta-analysis quantifying the effect of water stress and combined water and biotic stress on crop yield gaps. 2) The inversion of the SCOPE radiative transfer model to estimate the Leaf Area Index, Leaf Chlorophyll Content, and Canopy Chlorophyll Content of durum wheat and potato from multispectral and hyperspectral UAV imagery. 3) Modeling the impact of climate change on durum wheat yields across representative Mediterranean environments using CERES-Wheat. The meta-analysis shows that water stress reduced yields by 60% per hectare and 71% per plant, with Pulses, Cereals, and Horticultural crops most affected—Pulses exhibiting losses up to 163% per plant. Biotic stress further amplified these losses, as observed in melon, where fungal infections increased yield gaps from 31% to 63%. Climate zones also played a significant role, with reductions reaching up to 62% in warm temperate regions with dry summers. The results of the first chapter of this thesis underscore the need for field-based data to refine global yield gap estimates and to strengthen local agronomic research for climate adaptation. The inversion of SCOPE showed good agreement between vegetation parameters extracted from remote sensing and those measured in the field, and optimization of the cost function further improved accuracy. These results highlight the potential of SCOPE inversion for robust, physically based monitoring of crop biophysical and biochemical traits. Modeling the impact of climate change on durum wheat yields across representative Mediterranean environments enabled accurate yield predictions under future climate scenarios. Simulations identified irrigation as a key adaptation strategy to reduce yield losses and highlighted the importance of integrated management, supported by process-based crop models, for developing climate-resilient Mediterranean cropping systems.