Stav Gil Bar

Dryland forests, which cover 19% of global drylands, play a crucial role in carbon sequestration, biodiversity, and climate resilience, yet remain under-monitored due to the sparse distribution of flux tower networks. Gross primary productivity (GPP), a key indicator of ecosystem function, is particularly sensitive to aridity. However, widely used satellite-based GPP products, such as MODIS and GDMP (with a resolution of>300 m), fail to capture the fine-scale heterogeneity of dryland forests. We developed a high-resolution semi-empirical GPP model for Mediterranean conifer forests by integrating Sentinel-2–derived chlorophyll absorption (αλ) with tower-based photosynthetically active radiation (PARin). Because αλ represents both fAPAR and LUE, it serves as a dynamic indicator of stress. The model calibrated using data from eight flux tower sites spanning an aridity index range of 0.137–0.523, showed strong agreement with measured GPP (y = 0.86x, R² = 0.89, RMSE = 0.81), validating αλ × PARin as a robust proxy. This study provides one of the first demonstrations that Sentinel-2–derived chlorophyll absorption can reliably estimate GPP in Mediterranean dryland forests. The framework enables monitoring of ecosystem productivity and stress at management-relevant scales. Comparisons with MODIS and GDMP will benchmark performance, while future applications will extend the model across Israeli pine forests to assess how GPP, LUE, and αλ vary with precipitation, temperature, vapor pressure deficit, topography, and soil. Importantly, the approach also enables the evaluation of species-specific responses of Pinus halepensis and P. brutia to water and temperature stress. This work advances understanding of forest drought responses and provides valuable insights for dryland forest management and restoration.