Description
Solar-Induced Chlorophyll Fluorescence (SIF) provides a unique remote sensing approach for measuring photosynthetic activity, offering real-time insights into vegetation stress and productivity that surpass traditional indices (e.g. Normalized Difference Vegetation Index (NDVI)) in sensitivity and accuracy. This intermediate training builds on a previous Applied Remote Sensing Training Program (ARSET) training that introduces the SIF measurement and covers several case studies of the impact of floods and droughts on agricultural systems and the impacts of fire on forested ecosystems.
Participants will learn fundamental principles of SIF remote sensing and its practical applications for monitoring vegetation dynamics across cropland and natural systems. The course demonstrates how SIF data can track crop phenological cycles, assess agricultural drought impacts, evaluate wildfire damage and recovery patterns, and quantify relationships between SIF observations and Gross Primary Production (GPP). Participants will gain hands-on experience analyzing SIF datasets from NASA missions including the Orbiting Carbon Observatory-2 (OCO-2) and Orbiting Carbon Observatory-3 (OCO-3), as well as using gap-filled data products derived using machine learning techniques.