Jesslyn Brown
Jesslyn Brown is a research geographer with the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center in Sioux Falls, South Dakota, USA. Jess's main interests involve improving our understanding of changes in terrestrial vegetation related to climate and other driving forces and advancing the use of remote sensing imagery in applications.
Jesslyn Brown is a research geographer with the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center in Sioux Falls, South Dakota, USA, where she has worked for 30 years. Since finishing her graduate program at the University of Nebraska—Lincoln in 1990, she has worked in applied geographic research utilizing remote sensing approaches. Jess’s main interests involve improving our understanding of changes in terrestrial vegetation related to climate and other driving forces and advancing the use of remotely sensed imagery for applications including drought early warning, tracking vegetation phenology (i.e., seasonal dynamics), and mapping land cover and land use. Jess was a member of the Global Land Cover Characteristics team that created the first map of global land cover at a 1-km resolution in the 1990s. From 2001 to 2017, she led multiple projects mainly focused on developing new monitoring tools to improve agricultural drought monitoring capabilities in the U.S. in a strong collaboration with the University of Nebraska-Lincoln’s National Drought Mitigation Center. During that time, she also led efforts to investigate recent land use change specifically focused on irrigated agriculture across the country. In 2017, she began a new role leading the Land Change Monitoring Assessment and Projection (LCMAP) science team. LCMAP is a relatively new USGS initiative developing an end-to-end capability to use the deep Landsat record to continuously track and characterize changes in land cover state and condition and translate the information into assessments of current and historical processes of cover and change.
Science and Products
Monitoring landscape dynamics in central U.S. grasslands with harmonized Landsat-8 and Sentinel-2 time series data
Optimizing a remote sensing production efficiency model for macro-scale GPP and yield estimation in agroecosystems
Exploring relationships of spring green-up to moisture and temperature across Wyoming, U.S.A
Multi-year data from satellite- and ground-based sensors show details and scale matter in assessing climate’s effects on wetland surface water, amphibians, and landscape conditions
Drought and land-cover conditions in the Great Plains
Priority questions in multidisciplinary drought research
Challenges in complementing data from ground-based sensors with satellite-derived products to measure ecological changes in relation to climate – lessons from temperate wetland-upland landscapes
Effect of NOAA satellite orbital drift on AVHRR-derived phenological metrics
Building the vegetation drought response index for Canada (VegDRI-Canada) to monitor agricultural drought: first results
Assessing the evolution of soil moisture and vegetation conditions during the 2012 United States flash drought
Exploring drought controls on spring phenology
The integration of geophysical and enhanced Moderate Resolution Imaging Spectroradiometer Normalized Difference Vegetation Index data into a rule-based, piecewise regression-tree model to estimate cheatgrass beginning of spring growth
Non-USGS Publications**
**Disclaimer: The views expressed in Non-USGS publications are those of the author and do not represent the views of the USGS, Department of the Interior, or the U.S. Government.
Methods - QuickDRI
Methods - VegDRI
Spring Arriving at EROS
EROS Phenocam - Live
Methods for Deriving Metrics
Challenges in Deriving Phenological Metrics
Deriving Phenological Metrics from NDVI
Data Smoothing - Reducing the "Noise" in NDVI
NDVI from Other Sensors
NDVI from AVHRR
Remote Sensing Phenology
NDVI, the Foundation for Remote Sensing Phenology
Participated in these Eyes on Earth podcast episodes.
Science and Products
Monitoring landscape dynamics in central U.S. grasslands with harmonized Landsat-8 and Sentinel-2 time series data
Optimizing a remote sensing production efficiency model for macro-scale GPP and yield estimation in agroecosystems
Exploring relationships of spring green-up to moisture and temperature across Wyoming, U.S.A
Multi-year data from satellite- and ground-based sensors show details and scale matter in assessing climate’s effects on wetland surface water, amphibians, and landscape conditions
Drought and land-cover conditions in the Great Plains
Priority questions in multidisciplinary drought research
Challenges in complementing data from ground-based sensors with satellite-derived products to measure ecological changes in relation to climate – lessons from temperate wetland-upland landscapes
Effect of NOAA satellite orbital drift on AVHRR-derived phenological metrics
Building the vegetation drought response index for Canada (VegDRI-Canada) to monitor agricultural drought: first results
Assessing the evolution of soil moisture and vegetation conditions during the 2012 United States flash drought
Exploring drought controls on spring phenology
The integration of geophysical and enhanced Moderate Resolution Imaging Spectroradiometer Normalized Difference Vegetation Index data into a rule-based, piecewise regression-tree model to estimate cheatgrass beginning of spring growth
Non-USGS Publications**
**Disclaimer: The views expressed in Non-USGS publications are those of the author and do not represent the views of the USGS, Department of the Interior, or the U.S. Government.
Methods - QuickDRI
Methods - VegDRI
Spring Arriving at EROS
EROS Phenocam - Live
Methods for Deriving Metrics
Challenges in Deriving Phenological Metrics
Deriving Phenological Metrics from NDVI
Data Smoothing - Reducing the "Noise" in NDVI
NDVI from Other Sensors
NDVI from AVHRR
Remote Sensing Phenology
NDVI, the Foundation for Remote Sensing Phenology
Participated in these Eyes on Earth podcast episodes.