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Description

Flash droughts are rapidly developing drought events that severely affect vegetation health, resulting in reduced crop yields and increased wildfire risk. Unlike long-term droughts, which develop gradually and can persist for multiple years, flash droughts emerge over a period of weeks and occur on subseasonal to seasonal timescale. Timely detection is essential for effective monitoring and response.

This intermediate training focuses on the use of solar-induced fluorescence (SIF) observations from satellites to detect the early vegetation stress associated with flash droughts and to assess their impacts on natural and managed ecosystems. Participants will learn how SIF measurements from the Orbiting Carbon Observatory-2 (OCO-2) provide inisights on changes in photosynthetic activity, enabling the early detection of vegetation stress before visible changes occur.

Because vegetation responses are closely linked to water availability, the training also incorporates soil moisture observations from the Soil Moisture Active Passive (SMAP) mission. This additional dataset helps characterize land surface moisture conditions to improve flash drought detection.

Together, SIF and soil moisture observations provide a powerful framework for monitoring the onset and evolution of flash droughts, and they can offer skillful predictions of flash drought with multiple weeks of lead time compared to conventional meteorological and hydrological drought indicators.

The methodology will be demonstrated through recent historic examples of flash droughts, while highlighting how the methodology can be adapted for flash drought monitoring across global regions.

Prerequisites

Objectives

By the end of this training attendees will be able to:

  • Identify the causes, risks, and consequences of flash droughts as compared to slowly-evolving droughts.
  • Recognize how SIF data can be used for the detection and prediction of flash drought events.
  • Compare the advantages and limitations of SIF, soil moisture, and meteorological data to detect the onset of flash drought.
  • Synthesize Solar-Induced Fluorescence Rapid Change Index (SIF-RCI) time series data for a selected region using a provided Jupyter Notebook.
  • Retrieve and manipulate SMAP Soil Moisture (SM) and land process model-based Soil Water Deficit Index (SWDI) time series, and compare with SIF-RCI time series data using a provided Jupyter Notebook.
  • Compare the implications of different methods for flagging and identifying flash droughts in real-world scenarios.

Target Audience

  • National and international water resources managers at local, state, and federal levels. 
  • Public and private water utilities, natural resources conservation organizations, and fisheries and aquaculture organizations.
  • Academic faculty and students.

Course Format

  • The complete course consists of two 2-hour parts, with Part 1 offered on September 24, 2026 and Part 2 on September 29, 2026.
  • On each day, there are two opportunities to take the course (identical offerings):
    • Session A: 12:00 p.m. to 2:00 p.m. EDT (UTC-4)
    • Session B: 3:00 p.m. to 5:00 p.m. EDT (UTC-4)
  • Each part will include a 30-minute live Q&A.
  • Those who attend Parts 1 and 2 and complete the homework by the due date will receive a certificate of attendance.

Sessions

Part 1: Introduction to Flash Drought Monitoring using SIF and Soil Moisture

  • Part 1 Introduction
  • Intro to Flash Drought Monitoring using Satellite Data and Derived Products
  • Deriving Flash Drought Indicators from SIF and SM
  • Part 1 Summary
  • Q&A Session

ARSET Instructor: Erika Podest (JPL/Caltech)

Guest Instructors: Nick Parazoo (JPL/Caltech), Jackie Ryan (JPL/Caltech)

Part 2: Flash Drought Detection and Prediction

  • Part 2 Introduction
  • Flash Drought Detection and Prediction
  • How to Detect Flash Drought
  • Part 2 Summary
  • Q&A Session

ARSET Instructor: Erika Podest (JPL/Caltech)

Guest Instructor: Jackie Ryan (JPL/Caltech)

Details

Last Updated

Aug. 12, 2026

Published

Aug. 12, 2026

Data Center/Project

Applied Remote Sensing Training Program (ARSET)