Advances in spectral and structural remote sensing are transforming how
we study and monitor plant ecophysiology across scales, from individual
trees to entire agricultural regions. This lecture will explore how
hyperspectral imaging, LiDAR-based 3D canopy modeling, and artificial
intelligence can be integrated to quantify plant functional traits, monitor
crop dynamics, and support precision agriculture. Through three case
studies, we will demonstrate the power of these approaches in capturing
structural and physiological complexity: (1) Satellite-based detection of
bloom shifts and phenological patterns in California’s almond orchards,
revealing climate-driven variations in flowering dynamics; (2) Fusion of
thermal, multispectral, and LiDAR data to estimate plant water status and
its relationship to fruit cracking, linking spectral signals with physiological
stress responses; and (3) Crop-type mapping and multi-year monitoring
of Israeli agricultural systems using Sentinel-1 and Sentinel-2 data
combined with machine learning for national-scale agricultural
assessment. Together, these studies illustrate how spectral
ecophysiology, combining remote sensing and artificial intelligent, offers
new opportunities to bridge plant function, management, and
sustainability in agricultural landscapes under changing environmental
conditions.
LOCATION: TRANSP:OPAQUE URL:https://www.weizmann.ac.il/EPS/events END:VEVENT END:VCALENDAR