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
Methods - QuickDRI
Methods - VegDRI
EROS Phenocam - Live
Spring Arriving at EROS
Challenges in Deriving Phenological Metrics
Methods for Deriving 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.
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
Phenology and climate relationships in aspen (Populus tremuloides Michx.) forest and woodland communities of southwestern Colorado
Application-ready expedited MODIS data for operational land surface monitoring of vegetation condition
Assessing the vegetation condition impacts of the 2011 drought across the U.S. southern Great Plains using the vegetation drought response index (VegDRI)
Remote sensing of land surface phenology
Merging remote sensing data and national agricultural statistics to model change in irrigated agriculture
Variability and trends in irrigated and non-irrigated croplands in the central U.S
The Vegetation Drought Response Index (VegDRI): An integration of satellite, climate, and biophysical data
Merging climate and multi-sensor time-series data in real-time drought monitoring across the U.S.A.
Drought Monitoring with VegDRI
Mapping irrigated lands at 250-m scale by merging MODIS data and National Agricultural Statistics
Phenological classification of the United States: A geographic framework for extending multi-sensor time-series data
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.
Science and Products
Methods - QuickDRI
Methods - VegDRI
EROS Phenocam - Live
Spring Arriving at EROS
Challenges in Deriving Phenological Metrics
Methods for Deriving 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.
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
Phenology and climate relationships in aspen (Populus tremuloides Michx.) forest and woodland communities of southwestern Colorado
Application-ready expedited MODIS data for operational land surface monitoring of vegetation condition
Assessing the vegetation condition impacts of the 2011 drought across the U.S. southern Great Plains using the vegetation drought response index (VegDRI)
Remote sensing of land surface phenology
Merging remote sensing data and national agricultural statistics to model change in irrigated agriculture
Variability and trends in irrigated and non-irrigated croplands in the central U.S
The Vegetation Drought Response Index (VegDRI): An integration of satellite, climate, and biophysical data
Merging climate and multi-sensor time-series data in real-time drought monitoring across the U.S.A.
Drought Monitoring with VegDRI
Mapping irrigated lands at 250-m scale by merging MODIS data and National Agricultural Statistics
Phenological classification of the United States: A geographic framework for extending multi-sensor time-series data
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.