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Prasad S. Thenkabail, PhD

Dr. Prasad S. Thenkabail, Senior Scientist (ST), United States Geological Survey (USGS), is a world-recognized expert in remote sensing science with major contributions in the field sustained for nearly 40 years. Dr. Thenkabail’s career scientific achievements can be gauged by successfully making the list of world’s top 1% of scientists across fields (22 scientific fields and 176 sub-fields).

Global Food Security-Support Analysis Data at 30 m (GFSAD)

Global Food Security-Support Analysis Data at 30 m (GFSAD)

Global Hyperspectral Imaging Spectroscopy of Agricultural-Crops & Vegetation (GHISA)

Global Hyperspectral Imaging Spectroscopy of Agricultural-Crops & Vegetation (G…

Remote Sensing of Agriculture, Water, and Food Security

Dr. Thenkabail has conducted pioneering research in hyperspectral remote sensing of vegetation and in that of global croplands and their water use for food security. In hyperspectral remote sensing he has done cutting-edge research with wide implications in advancing remote sensing science in application to agriculture and vegetation. This body of work led to more than ten peer-reviewed research publications with high impact. For example, a single paper entitled “Hyperspectral vegetation indices and their relationships with agricultural crop characteristics” has received 1500 citations (3/14/23). In studies of global croplands for food and water security, he has led the release of the world’s first Landsat-derived: 1. global cropland extent product @ 30m (GCEP30), and 2. global rainfed and irrigated area product @ 30m (LGRIP30). This work demonstrates a “paradigm shift” in how remote sensing science is conducted. As per Google Scholar, the papers Dr. Thenkabail's research are cited 14,235 times. His h-index is 58 and i10-index is 113.

Dr. Thenkabail’s contributions to series of leading edited books on remote sensing science places him as a world leader in remote sensing science advances. He edited three-volume book entitled Remote Sensing Handbook published by Taylor and Francis, with 82 chapters and more than 2000 pages, widely considered a “magnus opus” encyclopedic standard reference for students, scholars, practitioners, and major experts in remote sensing science. He has recently completed editing Hyperspectral Remote Sensing of Vegetation published books by Taylor and Francis in four volumes with 50 chapters. This is the second edition that is currently in press and is a follow-up on the earlier single-volume Hyperspectral Remote Sensing of Vegetation. He has also edited a book on Remote Sensing of Global Croplands for Food Security.

He obtained his PhD from the Ohio State University in 1992 and has 168 publications including 9 books, 146 peer-reviewed journal articles, and 13 major data releases. Dr. Thenkabail is at the center of rendering scientific service to the world’s remote sensing community in roles that include Editor-in-Chief (2011-present) of Remote Sensing Open Access Journal and Associate Editor (2017-present) of American Society’s Journal Photogrammetric Engineering and Remote Sensing. Dr. Thenkabail was recognized as Fellow of the American Society of Photogrammetry and Remote Sensing (ASPRS) in 2023. His scientific papers have won several awards over the years demonstrating world class highest quality research. These include: 2023 Talbert Abrams Grand Award, the highest scientific paper award of the ASPRS, 2015 ASPRS ERDAS award for best scientific paper in remote sensing, and 1994 Autometric Award for the outstanding paper in remote sensing. He was a Landsat Science Team Member (2007-2011).

Professional Experience

  • 2022 - present - Senior Scientist (ST), United States Geological Survey (USGS) 

  • Oct. 2008-2022 - USGS: Supervisory Research Geographer-15 (2017-present), Research Geographer-15 (2011-2017), Research Geographer-14 (2008-2011), United States Geological Survey (USGS), Flagstaff, AZ.USA.

  • March 2003-Sept. 2008 - IWMI: Principal Researcher, Global Research Division group and Head of Remote Sensing and GIS Unit, International Water Management Institute (IWMI), Colombo, Sri Lanka.

  • April 1997-March 2003 - Yale University: Associate Research Scientist, Center for Earth Observation, Yale University, New Haven, CT,USA.

  • Nov. 1995-March 1997 - ICIMOD: Remote Sensing Specialist, International Center for Integrated Mountain Development (ICIMOD), Kathmandu, Nepal.

  • July 1992-Nov. 1995 - IITA: Remote Sensing Specialist, International Institute of Tropical Agriculture, Ibadan, Nigeria. 

  • Sept. 1998-June 1992 - OSU: Graduate Research Assistant, The Ohio State University, Columbus, Ohio.  

  • Dec. 1984-Nov. 1986 - Mysore and Bangalore University: Teaching hydraulics and water resources, India.

  • The countries he has worked in include China, Cambodia, Indonesia, Israel, Syria, United States, Canada, Brazil, Uzbekistan, Bangladesh, India, Myanmar, Nepal, Sri Lanka, Republic of Benin, Burkina Faso, Cameroon, Central African Republic, Cote d'Ivoire, Gambia, Ghana, Mali, Nigeria, Senegal, Togo, Mozambique, and South Africa.

Education and Certifications

  • 1992 - Doctor of Philosophy (PhD) in Agricultural Engineering, The Ohio State University, Columbus, Ohio, USA.

  • 1983 - Master of Engineering (M.E.) in Hydraulics and Water Resources Engineering, Mysore University (India).

  • 1981 - Bachelor of Civil Engineering (B.E.), Mysore University (India).

Affiliations and Memberships*

  • Editor-in-Chief, Remote Sensing Open Access Journal; 2011-present.

  • Associate Editor, American Society of Photogrammetric Engineering and Remote Sensing (PE&RS), a Journal of the Imaging and Geospatial Information Society (ASPRS).

  • Editorial Advisory Board, International Society of Photogrammetry and Remote Sensing (ISPRS) Journal of Photo. & Remote Sensing, 2014-present.

  • Editorial Board Member, Remote Sensing of Environment (2007-2016)

  • Core member,  NASA South/Southeast Asia Research Initiative (SARI):  2014-present

  • Member, American Society of Photogrammetry and Remote Sensing (1988-present)

  • Chair: International Society of Photogrammetry and Remote Sensing (ISPRS) Working Group WG VIII/7: Land cover and its dynamics, including Agricultural & Urban Land Use (2013-2016)

  • Global Coordinator, Committee for Earth Observing Systems Agriculture Societal Beneficial Areas (CEOA SBA) (2010-2013)

  • Co-lead, IEEE “Water for the World” (2007-2011)

  • Member, Landsat Science Team (2007-2011)

Honors and Awards

  • 2023  Fellow, American Society of Photogrammetric Engineering and Remote Sensing (ASPRS)

  • 2023 Talbert Abrahms Grand Award, highest paper award from American Society of Photogrammetric Enginering and Remote Sensing (ASPRS).

  • 2022 - PESEP Scholar. The NASA-ISRO Professional Engineer and Scientist Exchange Program (PESEP). USA (NASA) and India (ISRO) scientific exchange scientist for 2022-2023. 

  • 2020 - Proposal evaluation panel for Israeli Ministry of Science and Technology, to their bi-national Italy-Israel joint laboratory in Precision Agriculture. 

  • 2019 - Advisory Board member, Taylor and Francis Inc., online library collection to support the United Nations’ Sustainable Development Goals (UN SDGs).

  • 2019 - USGS STAR award for supervision

  • 2019 - Member, NASA Surface Biology Geology (SBG)-Applications. For the SBG hyperspectral remote sensing mission (replacing former HyspIRI program).

  • 2019 - Member, NASA Calibration and Validation Working Group. For the SBG hyperspectral remote sensing mission (replacing former HyspIRI program).

  • 2019 - USGS 10-year service recognition

  • 2018 - The Excellent Reviewer of Remote Sensing of Environment

  • 2018 - Honored by the Arabian Gulf University, Bahrain and the Dubai-based International Center for Biosaline Agriculture (ICBA) for giving the keynote lecture. 

  • 2016 - NASA Group Achievement Award, 2016. (Member of Team) Fallowed Area Map

  • 2015 - ASPRS Best Scientific Paper Award, 2015: ASPRS ERDAS award for best scientific paper in remote sensing (given annually for the papers published in American Society of Photogrammetry

  • 2015 - Task Force Member NASA, SARI, 2015-present. South Asia Regional Initiative (SARI), A response to regional needs in Land Cover/Land Use Change (LCLUC) Science and Education (NASA)

  • 2015 - Innovations Inventory, PARIS21, 2015: 'Remote Sensing Data for Drought Assessment and Monitoring' monograph authors (as first author) is in the PARIS21.

  • 2013 - Panel chair, 2013, USGS RGE. For the Spring 2013 GIS and Remote Sensing USGS Research Grade Evaluation (RGE) panel.

  • 2008 - ASPRS President’s award for practical papers: American Society of Photogrammetry and Remote Sensing (ASPRS) John I. Davidson President’s Award for practical papers, 2008.

  • 2007 - Special achievement in GIS award from ESRI, awarded by ESRI President Mr. Jack Dangermond during the 2007 annual ESRI conference in San Diego.

  • 2006 - Best team award for my remote sensing and GIS team @ the International Water Management Institute (IWMI) during Institute’s Annual Research Meeting 2006.

  • 2005 - Best paper award (5 best paper awards given) by International Water Management Institute (IWMI) during Institute’s Annual Research Meeting 2005.

  • 2004 - Best paper award (5 best paper awards given) by International Water Management Institute (IWMI) during Institute’s Annual Research Meeting 2004.

  • 2001 - Member, Scientific Advisory Board, Rapideye, a Private German Satellite Company.

  • 1994 - Autometric award for outstanding paper by American Society of Photogrammetry and Remote Sensing (ASPRS).

Abstracts and Presentations

  • As a result of Dr. Thenkabail’s scientific accomplishments, standing, and stature, he is a highly sought-after speaker. Since 2011, he has given 117 talks (averaging ~12 per year) of which 40% (47/117) were invited. He has been invited as a speaker in Bahrain, Brazil, Canada, China, Egypt, Germany, India, Indonesia, Israel, Myanmar, Thailand, Vietnam, and various places in USA (e.g., Purdue, OSU).

Science and Products

link
waterSMART Banner Image 2

Global Crop Water Productivity and Savings through waterSMART (GCWP)

The waterSMART (Sustain and Manage America’s Resources for Tomorrow) project places technical information and tools in the hands of stakeholders that allow them to answer pertinent questions regarding water availability. Two goals of waterSMART are to 1) establish water availability and its use based on an understanding of the past and present water users and to 2) project water availability and...
By
Western Geographic Science Center
link

Global Crop Water Productivity and Savings through waterSMART (GCWP)

The waterSMART (Sustain and Manage America’s Resources for Tomorrow) project places technical information and tools in the hands of stakeholders that allow them to answer pertinent questions regarding water availability. Two goals of waterSMART are to 1) establish water availability and its use based on an understanding of the past and present water users and to 2) project water availability and...
Learn More
link
Global Food Security-Support

Global Food-and-Water Security-support Analysis Data (GFSAD)

The GFSAD is a NASA funded project (2023-2028) to provide highest-resolution global cropland data and their water use that contributes towards global food-and-water security in the twenty-first century. The GFSAD products are derived through multi-sensor remote sensing data (e.g., Landsat-series, Sentinel-series, MODIS, AVHRR), secondary data, and field-plot data and aims at documenting cropland...
By
Western Geographic Science Center
link

Global Food-and-Water Security-support Analysis Data (GFSAD)

The GFSAD is a NASA funded project (2023-2028) to provide highest-resolution global cropland data and their water use that contributes towards global food-and-water security in the twenty-first century. The GFSAD products are derived through multi-sensor remote sensing data (e.g., Landsat-series, Sentinel-series, MODIS, AVHRR), secondary data, and field-plot data and aims at documenting cropland...
Learn More
link
USGS

Increasing data accessibility by adding existing datasets and capabilities to a cutting-edge visualization app to enable cross-community use

We will collate and publish existing datasets from collaborators and ingest them into a visualization app to help researchers with machine learning model-building and hypothesis-making. These data collation and app development methods could help other researchers increase their data accessibility.
By
Community for Data Integration (CDI)
link

Increasing data accessibility by adding existing datasets and capabilities to a cutting-edge visualization app to enable cross-community use

We will collate and publish existing datasets from collaborators and ingest them into a visualization app to help researchers with machine learning model-building and hypothesis-making. These data collation and app development methods could help other researchers increase their data accessibility.
Learn More
link
USGS science for a changing world logo

Processing a new generation of hyperspectral data on the Cloud using Pangeo

We aim to migrate our research workflow from a closed system to an open framework, increasing flexibility and transparency in our science and accessibility of our data. Our hyperspectral data of agricultural crops are crucial for training/ validating machine learning algorithms to study food security, land use, etc. Generating such data is resource-intensive and requires expertise, proprietary
By
Community for Data Integration (CDI)
link

Processing a new generation of hyperspectral data on the Cloud using Pangeo

We aim to migrate our research workflow from a closed system to an open framework, increasing flexibility and transparency in our science and accessibility of our data. Our hyperspectral data of agricultural crops are crucial for training/ validating machine learning algorithms to study food security, land use, etc. Generating such data is resource-intensive and requires expertise, proprietary
Learn More
link
Lorem ipsum dolor sit amet

Global Hyperspectral Imaging Spectral-library of Agricultural-Crops & Vegetation (GHISA)

This webpage showcases the key research advances made in hyperspectral remote sensing of agricultural crops and vegetation over the last 50 years. There are three focus areas:
By
Ecosystems Mission Area, Land Change Science Program, Western Geographic Science Center
link

Global Hyperspectral Imaging Spectral-library of Agricultural-Crops & Vegetation (GHISA)

This webpage showcases the key research advances made in hyperspectral remote sensing of agricultural crops and vegetation over the last 50 years. There are three focus areas:
Learn More
link
USGS science for a changing world logo

Global Croplands and Their Water Use for Food Security in the Twenty-first Century

Global climate change is putting unprecedented pressure on global croplands and their water use, vital for ensuring future food security for the world's rapidly expanding human population. The end of the green green revolution (productivity per unit of land) era has meant declining global per capita agricultural production requiring immediate policy responses to safeguard food security amidst glob
By
John Wesley Powell Center for Analysis and Synthesis
link

Global Croplands and Their Water Use for Food Security in the Twenty-first Century

Global climate change is putting unprecedented pressure on global croplands and their water use, vital for ensuring future food security for the world's rapidly expanding human population. The end of the green green revolution (productivity per unit of land) era has meant declining global per capita agricultural production requiring immediate policy responses to safeguard food security amidst glob
Learn More

DESIS and PRISMA spectral library of agricultural crops in California's Central Valley in the 2021 Growing Season

Here we provide Germany’s Deutsches Zentrum fu¨r Luft- und Raumfahrt (DLR) Earth Sensing Imaging Spectrometer (DESIS) and Italy’s ASI’s PRecursore IperSpettrale della Missione Applicativa (PRISMA) Derived Spectral Library of Agricultural Crops for California. It was developed using DESIS and PRISMA hyperspectral data acquired in August 2021 near Fresno, CA. The 4-bin DESIS image has 60 bands with
By
Western Geographic Science Center

Data Supporting Automated Cropland Fallow Algorithm (ACFA) for the Northern Great Plains of USA

This data release documents the steps performed to create classified cropland fallow maps for the Northern Great Plains region of the United States from the years 2010 to 2019. The data release consists of the following: (i) an XML metadata file, (ii) a table of reference data (iii) two decision tree models, and (iv) 10 single band GeoTIFFs. The XML file named ‘Metadata.xml’ describes steps that w
By
Western Geographic Science Center

PlanetScope and DESIS spectral library of agricultural crops in California's Central Valley for the 2020 growing season

Here we provide information for the PlanetScope and d Deutsches Zentrum fur Luft- und Raumfahrt (DLR) Earth Sensing Imaging Spectrometer (DESIS) Derived Spectral Library of Agricultural Crops in California which was developed using PlanetScope Dove-R high spatial resolution data and DESIS hyperspectral data acquired for 2020. PlanetScope images are available through Planet Labs (2022). The DESIS i
By
Western Geographic Science Center

Download rates of the Global Food-Security-Support-Analysis Data at 30-m Resolution (GFSAD30) Cropland-Extent Products

The data was collected to track the usage and downloads of the Global Food Security-support Analysis Data at 30 meters (GFSAD30) Cropland Extent Product. This data supports an Open File Report titled Global Food-Security-Support-Analysis Data at 30-m Resolution (GFSAD30) Cropland-Extent Products - Download Analysis. The GFSAD30 data is available for download on the National Aeronautics and Spate A
By
Western Geographic Science Center

Dr. Thenkabail’s contributions to series of leading edited books on remote sensing science places him as a world leader in remote sensing science advances. He edited three-volume book entitled Remote Sensing Handbook published by Taylor and Francis, with 82 chapters and more than 2000 pages, widely considered a “magnus opus” encyclopedic standard reference for students, scholars, practitioners, and major experts in remote sensing science. 

He has recently completed editing Hyperspectral Remote Sensing of Vegetation published books by Taylor and Francis in four volumes with 50 chapters: This is the second edition that is currently in press and is a follow-up on the earlier single-volume Hyperspectral Remote Sensing of Vegetation (also published by Taylor and Francis Inc.). He has also edited  a book on Remote Sensing of Global Croplands for Food Security (Taylor and Francis). These books are widely used and widely referenced in institutions worldwide.

 

 

 

Filter Total Items: 77

Machine learning and new-generation spaceborne hyperspectral data advance crop type mapping

Hyperspectral sensors provide near-continuous spectral data that can facilitate advancements in agricultural crop classification and characterization, which are important for addressing global food and water security issues. We investigated two new-generation hyperspectral sensors, Germany’s Deutsches Zentrum für Luft‐ und Raumfahrt Earth Sensing Imaging Spectrometer (DESIS) and Italy’s PRecursore
Authors
Itiya Aneece, Prasad Thenkabail, Richard L. McCormick, Alifu Haireti, Daniel Foley, Adam Oliphant, Pardhasaradhi Teluguntla
By
Western Geographic Science Center

Automated Cropland Fallow Algorithm (ACFA) for the Northern Great Plains of USA

Cropland fallowing is choosing not to plant a crop during a season when a crop is normally planted. It is an important component of many crop rotations and can improve soil moisture and health. Knowing which fields are fallow is critical to assess crop productivity and crop water productivity, needed for food security assessments. The annual spatial extent of cropland fallows is poorly understood
Authors
Adam Oliphant, Prasad Thenkabail, Pardhasaradhi Teluguntla, Itiya Aneece, Daniel Foley, Richard L. McCormick
By
Western Geographic Science Center

Crop water productivity from cloud-Based landsat helps assess California’s water savings

Demand for food and water are increasing while the extent of arable land and accessible fresh water are decreasing. This poses global challenges as economies continue to develop and the population grows. With agriculture as the leading consumer of water, better understanding how water is used to produce food may help support the increase of Crop Water Productivity (CWP; kg/m3), the ratio of crop o
Authors
Daniel Foley, Prasad Thenkabail, Adam Oliphant, Itiya Aneece, Pardhasaradhi Teluguntla
By
Western Geographic Science Center

Mapping vegetation index-derived actual evapotranspiration across croplands using the Google Earth Engine platform

Precise knowledge of crop water consumption is essential to better manage agricultural water use, particularly in regions where most countries struggle with increasing water and food insecurity. Approaches such as cloud computing and remote sensing (RS) have facilitated access, process, and visualization of big geospatial data to map and monitor crop water requirements. To find the most reliable V
Authors
Neda Abbasi, Hamideh Nouri, Kamel Didan, Armando Barreto-Muñoz, Sattar Chavoshi Borujeni, Christian Opp, Pamela L. Nagler, Prasad Thenkabail, Stefan Siebert
By
Ecosystems Mission Area, Southwest Biological Science Center, Western Geographic Science Center

New generation hyperspectral sensors DESIS and PRISMA provide improved agricultural crop classifications

Using new remote sensing technology to study agricultural crops will support advances in food and water security. The recently launched, new generation spaceborne hyperspectral sensors, German DLR Earth Sensing Imaging Spectrometer (DESIS) and Italian PRecursore IperSpettrale della Missione Applicativa (PRISMA), provide unprecedented data in hundreds of narrow spectral bands for the study of the E
Authors
Itiya Aneece, Prasad Thenkabail
By
Western Geographic Science Center

New generation hyperspectral data From DESIS compared to high spatial resolution PlanetScope data for crop type classification

Thoroughly investigating the characteristics of new generation hyperspectral and high spatial resolution spaceborne sensors will advance the study of agricultural crops. Therefore, we compared the performances of hyperspectral Deutsches Zentrum fur Luftund Raumfahrt- (DLR) Earth Sensing Imaging Spectrometer (DESIS) and high spatial resolution PlanetScope in classifying eight crop types in Californ
Authors
Itiya Aneece, Daniel Foley, Prasad Thenkabail, Adam Oliphant, Pardhasaradhi G. Teluguntla
By
Western Geographic Science Center

Multiple agricultural cropland products of South Asia developed using Landsat-8 30 m and MODIS 250 m data using machine learning on the Google Earth Engine (GEE) cloud and spectral matching techniques (SMTs) in support of food and water security

Cropland products are of great importance in water and food security assessments, especially in South Asia, which is home to nearly 2 billion people and 230 million hectares of net cropland area. In South Asia, croplands account for about 90% of all human water use. Cropland extent, cropping intensity, crop watering methods, and crop types are important factors that have a bearing on the quantity,
Authors
Murali Krishna Gumma, Prasad Thenkabail, Pranay Panjala, Pardhasaradhi Teluguntla, Takashi Yamano, Ismail Mohammad
By
Western Geographic Science Center

Global food-security-support-analysis data at 30-m resolution (GFSAD30) cropland-extent products—Download Analysis

IntroductionThe global food-security-support-analysis data at 30-meter resolution (GFSAD30) cropland-extent product is a project to provide high-resolution global cropland-extent data relating to water use. It is the first global-land-cover map focusing exclusively on agriculture with a 30-meter spatial resolution. The overarching goal of the GFSAD30 project is to produce consistent and unbiased e
Authors
Adam Oliphant, Prasad Thenkabail, Pardhasaradhi Teluguntla
By
Western Geographic Science Center

Classifying crop types using two generations of hyperspectral sensors (Hyperion and DESIS) with machine learning on the cloud

Advances in spaceborne hyperspectral (HS) remote sensing, cloud-computing, and machine learning can help measure, model, map and monitor agricultural crops to address global food and water security issues, such as by providing accurate estimates of crop area and yield to model agricultural productivity. Leveraging these advances, we used the Earth Observing-1 (EO-1) Hyperion historical archive and
Authors
Itiya Aneece, Prasad Thenkabail
By
Western Geographic Science Center

Global cropland-extent product at 30-m resolution (GCEP30) derived from Landsat satellite time-series data for the year 2015 using multiple machine-learning algorithms on Google Earth Engine cloud

Executive SummaryGlobal food and water security analysis and management require precise and accurate global cropland-extent maps. Existing maps have limitations, in that they are (1) mapped using coarse-resolution remote-sensing data, resulting in the lack of precise mapping location of croplands and their accuracies; (2) derived by collecting and collating national statistical data that are often
Authors
Prasad S. Thenkabail, Pardhasaradhi G. Teluguntla, Jun Xiong, Adam Oliphant, Russell G. Congalton, Mutlu Ozdogan, Murali Krishna Gumma, James C. Tilton, Chandra Giri, Cristina Milesi, Aparna Phalke, Richard Massey, Kamini Yadav, Temuulen Sankey, Ying Zhong, Itiya Aneece, Daniel Foley
By
Western Geographic Science Center, International Programs

Hyperspectral narrowband data propel gigantic leap in the earth remote sensing

Hyperspectral narrowbands (HNBs) capture data as nearly continuous “spectral signatures” rather than a “few spectral data points” along the electromagnetic spectrum as with multispectral broadbands (MBBs). Almost all of satellite remote sensing of the Earth in the twentieth century was conducted using MBB data from sensors such as the Landsat-series, Advanced Very High-Resolution Radiometer (AVHRR
Authors
Prasad Thenkabail, Itiya Aneece, Pardhasaradhi Teluguntla, Adam Oliphant
By
Western Geographic Science Center

Mapping croplands of Europe, Middle East, Russia, and Central Asia using Landsat 30-m data, machine learning algorithms and Google Earth Engine

Accurate and timely information on croplands is important for environmental, food security, and policy studies. Spatially explicit cropland datasets are also required to derive information on crop type, crop yield, cropping intensity, as well as irrigated areas. Large area defined as continental to global cropland mapping is challenging due to differential manifestation of croplands, wide range
Authors
Aparna Phalke, Mutlu Ozdogan, Prasad Thenkabail, Tyler Erickson, Noel Gorelick
By
Western Geographic Science Center

Led the release of the world’s first Landsat 30-m derived global cropland extent product.The data is already widely used worldwide and is downloadable from the NASA\USGS LP DAAC site:

https://lpdaac.usgs.gov/news/release-of-gfsad-30-meter-cropland-extent-products/

The GFSAD30 project accomplished their goal!

The GFSAD30 project accomplished their goal!

The global croplands mapping project has succesfully mapped croplands across the entire planet in order to understand the security of food world wide...

Read Article
New Map of Worldwide Croplands Supports Food and Water Security

New Map of Worldwide Croplands Supports Food and Water Security

India has the highest net cropland area while South Asia and Europe are considered agricultural capitals of the world.

Read Article
New Remote Sensing Handbook Published

New Remote Sensing Handbook Published

A newly published, three-volume “Remote Sensing Handbook” is a comprehensive coverage of all remote sensing topics written by over 300 leading global...
Read Article

Science and Products

link
waterSMART Banner Image 2

Global Crop Water Productivity and Savings through waterSMART (GCWP)

The waterSMART (Sustain and Manage America’s Resources for Tomorrow) project places technical information and tools in the hands of stakeholders that allow them to answer pertinent questions regarding water availability. Two goals of waterSMART are to 1) establish water availability and its use based on an understanding of the past and present water users and to 2) project water availability and...
By
Western Geographic Science Center
link

Global Crop Water Productivity and Savings through waterSMART (GCWP)

The waterSMART (Sustain and Manage America’s Resources for Tomorrow) project places technical information and tools in the hands of stakeholders that allow them to answer pertinent questions regarding water availability. Two goals of waterSMART are to 1) establish water availability and its use based on an understanding of the past and present water users and to 2) project water availability and...
Learn More
link
Global Food Security-Support

Global Food-and-Water Security-support Analysis Data (GFSAD)

The GFSAD is a NASA funded project (2023-2028) to provide highest-resolution global cropland data and their water use that contributes towards global food-and-water security in the twenty-first century. The GFSAD products are derived through multi-sensor remote sensing data (e.g., Landsat-series, Sentinel-series, MODIS, AVHRR), secondary data, and field-plot data and aims at documenting cropland...
By
Western Geographic Science Center
link

Global Food-and-Water Security-support Analysis Data (GFSAD)

The GFSAD is a NASA funded project (2023-2028) to provide highest-resolution global cropland data and their water use that contributes towards global food-and-water security in the twenty-first century. The GFSAD products are derived through multi-sensor remote sensing data (e.g., Landsat-series, Sentinel-series, MODIS, AVHRR), secondary data, and field-plot data and aims at documenting cropland...
Learn More
link
USGS

Increasing data accessibility by adding existing datasets and capabilities to a cutting-edge visualization app to enable cross-community use

We will collate and publish existing datasets from collaborators and ingest them into a visualization app to help researchers with machine learning model-building and hypothesis-making. These data collation and app development methods could help other researchers increase their data accessibility.
By
Community for Data Integration (CDI)
link

Increasing data accessibility by adding existing datasets and capabilities to a cutting-edge visualization app to enable cross-community use

We will collate and publish existing datasets from collaborators and ingest them into a visualization app to help researchers with machine learning model-building and hypothesis-making. These data collation and app development methods could help other researchers increase their data accessibility.
Learn More
link
USGS science for a changing world logo

Processing a new generation of hyperspectral data on the Cloud using Pangeo

We aim to migrate our research workflow from a closed system to an open framework, increasing flexibility and transparency in our science and accessibility of our data. Our hyperspectral data of agricultural crops are crucial for training/ validating machine learning algorithms to study food security, land use, etc. Generating such data is resource-intensive and requires expertise, proprietary
By
Community for Data Integration (CDI)
link

Processing a new generation of hyperspectral data on the Cloud using Pangeo

We aim to migrate our research workflow from a closed system to an open framework, increasing flexibility and transparency in our science and accessibility of our data. Our hyperspectral data of agricultural crops are crucial for training/ validating machine learning algorithms to study food security, land use, etc. Generating such data is resource-intensive and requires expertise, proprietary
Learn More
link
Lorem ipsum dolor sit amet

Global Hyperspectral Imaging Spectral-library of Agricultural-Crops & Vegetation (GHISA)

This webpage showcases the key research advances made in hyperspectral remote sensing of agricultural crops and vegetation over the last 50 years. There are three focus areas:
By
Ecosystems Mission Area, Land Change Science Program, Western Geographic Science Center
link

Global Hyperspectral Imaging Spectral-library of Agricultural-Crops & Vegetation (GHISA)

This webpage showcases the key research advances made in hyperspectral remote sensing of agricultural crops and vegetation over the last 50 years. There are three focus areas:
Learn More
link
USGS science for a changing world logo

Global Croplands and Their Water Use for Food Security in the Twenty-first Century

Global climate change is putting unprecedented pressure on global croplands and their water use, vital for ensuring future food security for the world's rapidly expanding human population. The end of the green green revolution (productivity per unit of land) era has meant declining global per capita agricultural production requiring immediate policy responses to safeguard food security amidst glob
By
John Wesley Powell Center for Analysis and Synthesis
link

Global Croplands and Their Water Use for Food Security in the Twenty-first Century

Global climate change is putting unprecedented pressure on global croplands and their water use, vital for ensuring future food security for the world's rapidly expanding human population. The end of the green green revolution (productivity per unit of land) era has meant declining global per capita agricultural production requiring immediate policy responses to safeguard food security amidst glob
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DESIS and PRISMA spectral library of agricultural crops in California's Central Valley in the 2021 Growing Season

Here we provide Germany’s Deutsches Zentrum fu¨r Luft- und Raumfahrt (DLR) Earth Sensing Imaging Spectrometer (DESIS) and Italy’s ASI’s PRecursore IperSpettrale della Missione Applicativa (PRISMA) Derived Spectral Library of Agricultural Crops for California. It was developed using DESIS and PRISMA hyperspectral data acquired in August 2021 near Fresno, CA. The 4-bin DESIS image has 60 bands with
By
Western Geographic Science Center

Data Supporting Automated Cropland Fallow Algorithm (ACFA) for the Northern Great Plains of USA

This data release documents the steps performed to create classified cropland fallow maps for the Northern Great Plains region of the United States from the years 2010 to 2019. The data release consists of the following: (i) an XML metadata file, (ii) a table of reference data (iii) two decision tree models, and (iv) 10 single band GeoTIFFs. The XML file named ‘Metadata.xml’ describes steps that w
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Western Geographic Science Center

PlanetScope and DESIS spectral library of agricultural crops in California's Central Valley for the 2020 growing season

Here we provide information for the PlanetScope and d Deutsches Zentrum fur Luft- und Raumfahrt (DLR) Earth Sensing Imaging Spectrometer (DESIS) Derived Spectral Library of Agricultural Crops in California which was developed using PlanetScope Dove-R high spatial resolution data and DESIS hyperspectral data acquired for 2020. PlanetScope images are available through Planet Labs (2022). The DESIS i
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Western Geographic Science Center

Download rates of the Global Food-Security-Support-Analysis Data at 30-m Resolution (GFSAD30) Cropland-Extent Products

The data was collected to track the usage and downloads of the Global Food Security-support Analysis Data at 30 meters (GFSAD30) Cropland Extent Product. This data supports an Open File Report titled Global Food-Security-Support-Analysis Data at 30-m Resolution (GFSAD30) Cropland-Extent Products - Download Analysis. The GFSAD30 data is available for download on the National Aeronautics and Spate A
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Western Geographic Science Center

Dr. Thenkabail’s contributions to series of leading edited books on remote sensing science places him as a world leader in remote sensing science advances. He edited three-volume book entitled Remote Sensing Handbook published by Taylor and Francis, with 82 chapters and more than 2000 pages, widely considered a “magnus opus” encyclopedic standard reference for students, scholars, practitioners, and major experts in remote sensing science. 

He has recently completed editing Hyperspectral Remote Sensing of Vegetation published books by Taylor and Francis in four volumes with 50 chapters: This is the second edition that is currently in press and is a follow-up on the earlier single-volume Hyperspectral Remote Sensing of Vegetation (also published by Taylor and Francis Inc.). He has also edited  a book on Remote Sensing of Global Croplands for Food Security (Taylor and Francis). These books are widely used and widely referenced in institutions worldwide.

 

 

 

Filter Total Items: 77

Machine learning and new-generation spaceborne hyperspectral data advance crop type mapping

Hyperspectral sensors provide near-continuous spectral data that can facilitate advancements in agricultural crop classification and characterization, which are important for addressing global food and water security issues. We investigated two new-generation hyperspectral sensors, Germany’s Deutsches Zentrum für Luft‐ und Raumfahrt Earth Sensing Imaging Spectrometer (DESIS) and Italy’s PRecursore
Authors
Itiya Aneece, Prasad Thenkabail, Richard L. McCormick, Alifu Haireti, Daniel Foley, Adam Oliphant, Pardhasaradhi Teluguntla
By
Western Geographic Science Center

Automated Cropland Fallow Algorithm (ACFA) for the Northern Great Plains of USA

Cropland fallowing is choosing not to plant a crop during a season when a crop is normally planted. It is an important component of many crop rotations and can improve soil moisture and health. Knowing which fields are fallow is critical to assess crop productivity and crop water productivity, needed for food security assessments. The annual spatial extent of cropland fallows is poorly understood
Authors
Adam Oliphant, Prasad Thenkabail, Pardhasaradhi Teluguntla, Itiya Aneece, Daniel Foley, Richard L. McCormick
By
Western Geographic Science Center

Crop water productivity from cloud-Based landsat helps assess California’s water savings

Demand for food and water are increasing while the extent of arable land and accessible fresh water are decreasing. This poses global challenges as economies continue to develop and the population grows. With agriculture as the leading consumer of water, better understanding how water is used to produce food may help support the increase of Crop Water Productivity (CWP; kg/m3), the ratio of crop o
Authors
Daniel Foley, Prasad Thenkabail, Adam Oliphant, Itiya Aneece, Pardhasaradhi Teluguntla
By
Western Geographic Science Center

Mapping vegetation index-derived actual evapotranspiration across croplands using the Google Earth Engine platform

Precise knowledge of crop water consumption is essential to better manage agricultural water use, particularly in regions where most countries struggle with increasing water and food insecurity. Approaches such as cloud computing and remote sensing (RS) have facilitated access, process, and visualization of big geospatial data to map and monitor crop water requirements. To find the most reliable V
Authors
Neda Abbasi, Hamideh Nouri, Kamel Didan, Armando Barreto-Muñoz, Sattar Chavoshi Borujeni, Christian Opp, Pamela L. Nagler, Prasad Thenkabail, Stefan Siebert
By
Ecosystems Mission Area, Southwest Biological Science Center, Western Geographic Science Center

New generation hyperspectral sensors DESIS and PRISMA provide improved agricultural crop classifications

Using new remote sensing technology to study agricultural crops will support advances in food and water security. The recently launched, new generation spaceborne hyperspectral sensors, German DLR Earth Sensing Imaging Spectrometer (DESIS) and Italian PRecursore IperSpettrale della Missione Applicativa (PRISMA), provide unprecedented data in hundreds of narrow spectral bands for the study of the E
Authors
Itiya Aneece, Prasad Thenkabail
By
Western Geographic Science Center

New generation hyperspectral data From DESIS compared to high spatial resolution PlanetScope data for crop type classification

Thoroughly investigating the characteristics of new generation hyperspectral and high spatial resolution spaceborne sensors will advance the study of agricultural crops. Therefore, we compared the performances of hyperspectral Deutsches Zentrum fur Luftund Raumfahrt- (DLR) Earth Sensing Imaging Spectrometer (DESIS) and high spatial resolution PlanetScope in classifying eight crop types in Californ
Authors
Itiya Aneece, Daniel Foley, Prasad Thenkabail, Adam Oliphant, Pardhasaradhi G. Teluguntla
By
Western Geographic Science Center

Multiple agricultural cropland products of South Asia developed using Landsat-8 30 m and MODIS 250 m data using machine learning on the Google Earth Engine (GEE) cloud and spectral matching techniques (SMTs) in support of food and water security

Cropland products are of great importance in water and food security assessments, especially in South Asia, which is home to nearly 2 billion people and 230 million hectares of net cropland area. In South Asia, croplands account for about 90% of all human water use. Cropland extent, cropping intensity, crop watering methods, and crop types are important factors that have a bearing on the quantity,
Authors
Murali Krishna Gumma, Prasad Thenkabail, Pranay Panjala, Pardhasaradhi Teluguntla, Takashi Yamano, Ismail Mohammad
By
Western Geographic Science Center

Global food-security-support-analysis data at 30-m resolution (GFSAD30) cropland-extent products—Download Analysis

IntroductionThe global food-security-support-analysis data at 30-meter resolution (GFSAD30) cropland-extent product is a project to provide high-resolution global cropland-extent data relating to water use. It is the first global-land-cover map focusing exclusively on agriculture with a 30-meter spatial resolution. The overarching goal of the GFSAD30 project is to produce consistent and unbiased e
Authors
Adam Oliphant, Prasad Thenkabail, Pardhasaradhi Teluguntla
By
Western Geographic Science Center

Classifying crop types using two generations of hyperspectral sensors (Hyperion and DESIS) with machine learning on the cloud

Advances in spaceborne hyperspectral (HS) remote sensing, cloud-computing, and machine learning can help measure, model, map and monitor agricultural crops to address global food and water security issues, such as by providing accurate estimates of crop area and yield to model agricultural productivity. Leveraging these advances, we used the Earth Observing-1 (EO-1) Hyperion historical archive and
Authors
Itiya Aneece, Prasad Thenkabail
By
Western Geographic Science Center

Global cropland-extent product at 30-m resolution (GCEP30) derived from Landsat satellite time-series data for the year 2015 using multiple machine-learning algorithms on Google Earth Engine cloud

Executive SummaryGlobal food and water security analysis and management require precise and accurate global cropland-extent maps. Existing maps have limitations, in that they are (1) mapped using coarse-resolution remote-sensing data, resulting in the lack of precise mapping location of croplands and their accuracies; (2) derived by collecting and collating national statistical data that are often
Authors
Prasad S. Thenkabail, Pardhasaradhi G. Teluguntla, Jun Xiong, Adam Oliphant, Russell G. Congalton, Mutlu Ozdogan, Murali Krishna Gumma, James C. Tilton, Chandra Giri, Cristina Milesi, Aparna Phalke, Richard Massey, Kamini Yadav, Temuulen Sankey, Ying Zhong, Itiya Aneece, Daniel Foley
By
Western Geographic Science Center, International Programs

Hyperspectral narrowband data propel gigantic leap in the earth remote sensing

Hyperspectral narrowbands (HNBs) capture data as nearly continuous “spectral signatures” rather than a “few spectral data points” along the electromagnetic spectrum as with multispectral broadbands (MBBs). Almost all of satellite remote sensing of the Earth in the twentieth century was conducted using MBB data from sensors such as the Landsat-series, Advanced Very High-Resolution Radiometer (AVHRR
Authors
Prasad Thenkabail, Itiya Aneece, Pardhasaradhi Teluguntla, Adam Oliphant
By
Western Geographic Science Center

Mapping croplands of Europe, Middle East, Russia, and Central Asia using Landsat 30-m data, machine learning algorithms and Google Earth Engine

Accurate and timely information on croplands is important for environmental, food security, and policy studies. Spatially explicit cropland datasets are also required to derive information on crop type, crop yield, cropping intensity, as well as irrigated areas. Large area defined as continental to global cropland mapping is challenging due to differential manifestation of croplands, wide range
Authors
Aparna Phalke, Mutlu Ozdogan, Prasad Thenkabail, Tyler Erickson, Noel Gorelick
By
Western Geographic Science Center

Led the release of the world’s first Landsat 30-m derived global cropland extent product.The data is already widely used worldwide and is downloadable from the NASA\USGS LP DAAC site:

https://lpdaac.usgs.gov/news/release-of-gfsad-30-meter-cropland-extent-products/

The GFSAD30 project accomplished their goal!

The GFSAD30 project accomplished their goal!

The global croplands mapping project has succesfully mapped croplands across the entire planet in order to understand the security of food world wide...

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New Map of Worldwide Croplands Supports Food and Water Security

New Map of Worldwide Croplands Supports Food and Water Security

India has the highest net cropland area while South Asia and Europe are considered agricultural capitals of the world.

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New Remote Sensing Handbook Published

New Remote Sensing Handbook Published

A newly published, three-volume “Remote Sensing Handbook” is a comprehensive coverage of all remote sensing topics written by over 300 leading global...
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*Disclaimer: Listing outside positions with professional scientific organizations on this Staff Profile are for informational purposes only and do not constitute an endorsement of those professional scientific organizations or their activities by the USGS, Department of the Interior, or U.S. Government

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