Advances in spectral and structural remote sensing are transforming howwe study and monitor plant ecophysiology across scales, from individualtrees to entire agricultural regions. This lecture will explore howhyperspectral imaging, LiDAR-based 3D canopy modeling, and artificialintelligence can be integrated to quantify plant functional traits, monitorcrop dynamics, and support precision agriculture. Through three casestudies, we will demonstrate the power of these approaches in capturingstructural and physiological complexity: (1) Satellite-based detection ofbloom shifts and phenological patterns in California’s almond orchards,revealing climate-driven variations in flowering dynamics; (2) Fusion ofthermal, multispectral, and LiDAR data to estimate plant water status andits relationship to fruit cracking, linking spectral signals with physiologicalstress responses; and (3) Crop-type mapping and multi-year monitoringof Israeli agricultural systems using Sentinel-1 and Sentinel-2 datacombined with machine learning for national-scale agriculturalassessment. Together, these studies illustrate how spectralecophysiology, combining remote sensing and artificial intelligent, offersnew opportunities to bridge plant function, management, andsustainability in agricultural landscapes under changing environmentalconditions.
