Solar Induced Fluorescence (SIF) Observations for Assessing Vegetation Changes Related to Floods, Drought, and Fire Impacts
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.
ARSET también ofrece esta capacitación en español. Conozca más sobre la agenda y cómo inscribirse: https://go.nasa.gov/3VyuDin
Objectives
By the end of this training attendees will be able to:
- Recognize how the Solar Induced Fluorescence (SIF) is measured and used as a complementary measurement to commonly used indices (NDVI) for land and vegetation applications.
- Identify advantages and limitations of using space-based SIF measurements to monitor and evaluate vegetation health and condition.
- Run a given Jupyter Notebook to generate a Snapshot Area Map (SAM) plot from OCO-3 data for selected regions of interest to analyze and evaluate vegetation and land change due to fire impacts.
- Run a given Jupyter Notebook using GoSIF data for selected regions of interest to produce visualizations to analyze and interpret episodic land change due to droughts and floods.
- Compare how SIF products aggregated in space and time using open source tools are used to study vegetation change across different regions in a variety of science and applied use cases.
Target Audience
- Researchers, analysts, and end users who are interested in learning how to use SIF to study vegetative change for agriculture, land management, pre-fire conditions, and droughts.
- Students and academics who are interested in assessing vegetative change with satellite data and understanding SIF.
Course Format
- Three, 2-hour parts. Each part includes a 30-min Q&A session.