Weather and climate shocks (such as droughts, floods, and heat waves) can imperil climate-sensitive agricultural systems in food-insecure regions, threatening the livelihoods and nutritional status of vulnerable populations in these areas. Satellite remote sensing offers an inexpensive, timely solution to monitor conditions on the Earth’s surface and has become increasingly used to generate data used in decision-making by private and public actors. In this webinar, we discuss recent advances in two satellite products, land surface temperature (LST) and solar-induced chlorophyll fluorescence (SIF), that can significantly improve monitoring for drought-stressed crops and crop productivity. The spatial and temporal variation traceable through LST is critical for identifying the governing land-atmosphere interactions that affect crop growth. Similarly, SIF presents an optical signal of plants’ photosynthetic machinery, thus providing direct functional information about photosynthesis. The recent advent of satellite SIF remote sensing holds great promise for near-real-time crop growth monitoring.

This webinar is the first of a two-part webinar to present new data and findings from ongoing research under the United States Agency for International Development (USAID)-funded project “Harnessing Big Data and Machine Learning to Feed the Future”, based at Cornell University. Researchers and analysts from operational agencies are invited to join these events for a presentation and discussion of key principles, data sources, methods, and applications.