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Environmental Sciences

D-Index
70
Citations
19018
World Ranking
1667
National Ranking
701

Overview

Prasad S. Thenkabail is affiliated with the United States Geological Survey in the United States. Their research focuses primarily on environmental science with an emphasis on remote sensing applications in agriculture and land use. The scientist works extensively on combining satellite data with machine learning techniques to improve crop classification and mapping.

The primary fields of study for Thenkabail include Environmental Science, with significant contributions in subfields such as Ecology, Global and Planetary Change, Atmospheric Science, Media Technology, and Plant Science.

The main research topics include:

  • Remote Sensing in Agriculture
  • Remote Sensing and Land Use
  • Remote-Sensing Image Classification
  • Plant Water Relations and Carbon Dynamics
  • Smart Agriculture and AI
  • Solar Radiation and Photovoltaics
  • Land Use and Ecosystem Services

Recent papers include:

  • "Mapping croplands of Europe, Middle East, Russia, and Central Asia using Landsat, Random Forest, and Google Earth Engine," 2020, ISPRS Journal of Photogrammetry and Remote Sensing
  • "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," 2021, USGS professional paper
  • "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," 2022, GIScience & Remote Sensing
  • "Classifying Crop Types Using Two Generations of Hyperspectral Sensors (Hyperion and DESIS) with Machine Learning on the Cloud," 2021, Remote Sensing
  • "New Generation Hyperspectral Sensors DESIS and PRISMA Provide Improved Agricultural Crop Classifications," 2022, Photogrammetric Engineering & Remote Sensing

Thenkabail frequently publishes in prominent venues such as:

  • Photogrammetric Engineering & Remote Sensing
  • Remote Sensing
  • ISPRS Journal of Photogrammetry and Remote Sensing
  • GIScience & Remote Sensing
  • IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

They have collaborated frequently with several co-authors, including:

  • Pardhasaradhi Teluguntla
  • Itiya Aneece
  • Adam Oliphant
  • Daniel J. Foley
  • Richard L. McCormick

The research of Prasad S. Thenkabail integrates satellite remote sensing technologies such as Landsat and hyperspectral sensors with machine learning approaches implemented on cloud platforms like Google Earth Engine. This work supports food and water security by improving the accuracy of cropland classification and monitoring on regional and global scales.

Best Publications

  • Hyperspectral Vegetation Indices and Their Relationships with Agricultural Crop Characteristics

    Prasad S Thenkabail;Ronald B Smith;Eddy De Pauw

  • Free access to Landsat imagery.

    Curtis E. Woodcock;Richard Allen;Martha Anderson;Alan Belward

  • Hyperspectral Remote Sensing of Vegetation

    Prasad Srinivasa Thenkabail;John Grimson Lyon;Alfredo Huete

  • Accuracy assessments of hyperspectral waveband performance for vegetation analysis applications

    Prasad S. Thenkabail;Eden A. Enclona;Mark S. Ashton;Bauke Van Der Meer

  • A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform

    Pardhasaradhi Teluguntla;Pardhasaradhi Teluguntla;Prasad S. Thenkabail;Adam Oliphant;Jun N. Xiong

  • Global irrigated area map (GIAM), derived from remote sensing, for the end of the last millennium

    Prasad S. Thenkabail;Chandrashekhar M. Biradar;Praveen Noojipady;Venkateswarlu Dheeravath

  • Automated cropland mapping of continental Africa using Google Earth Engine cloud computing

    Jun N. Xiong;Prasad S. Thenkabail;Murali Krishna Gumma;Pardhasaradhi G. Teluguntla

  • Hyperion, IKONOS, ALI, and ETM+ sensors in the study of African rainforests

    Prasad S Thenkabail;Eden A Enclona;Mark S Ashton;Christopher Legg

  • Nominal 30-M Cropland Extent Map of Continental Africa by Integrating Pixel-Based and Object-Based Algorithms Using Sentinel-2 and Landsat-8 Data on Google Earth Engine

    Jun N. Xiong;Prasad S. Thenkabail;James C. Tilton;Murali Krishna Gumma

  • Evaluation of Narrowband and Broadband Vegetation Indices for Determining Optimal Hyperspectral Wavebands for Agricultural Crop Characterization

    P. S. Thenkabail

  • Remote Sensing Sensors and Applications in Environmental Resources Mapping and Modelling.

    Assefa M Melesse;Qihao Weng;Prasad S Thenkabail;Gabriel B Senay

  • A support vector machine to identify irrigated crop types using time-series Landsat NDVI data

    Baojuan Zheng;Soe W. Myint;Prasad S. Thenkabail;Rimjhim M. Aggarwal

  • Global Land Cover Mapping: A Review and Uncertainty Analysis

    Russell G. Congalton;Jianyu Gu;Kamini Yadav;Prasad S. Thenkabail

  • Ganges and Indus river basin land use/land cover (LULC) and irrigated area mapping using continuous streams of MODIS data

    Prasad S. Thenkabail;Mitchell Schull;Hugh Turral

  • Mapping rice areas of South Asia using MODIS multitemporal data

    Murali Krishna Gumma;Andrew Nelson;Prasad S. Thenkabail;Amrendra N. Singh

  • Selection of Hyperspectral Narrowbands (HNBs) and Composition of Hyperspectral Twoband Vegetation Indices (HVIs) for Biophysical Characterization and Discrimination of Crop Types Using Field Reflectance and Hyperion/EO-1 Data

    P. S. Thenkabail;I. Mariotto;M. K. Gumma;E. M. Middleton

  • Biomass estimations and carbon stock calculations in the oil palm plantations of African derived savannas using IKONOS data

    Prasad S. Thenkabail;N. Stucky;B. W. Griscom;M. S. Ashton

  • A global map of rainfed cropland areas (GMRCA) at the end of last millennium using remote sensing

    Chandrashekhar M. Biradar;Prasad S. Thenkabail;Praveen Noojipady;Yuanjie Li

  • Mapping seasonal rice cropland extent and area in the high cropping intensity environment of Bangladesh using MODIS 500 m data for the year 2010

    Murali Krishna Gumma;Murali Krishna Gumma;Prasad S. Thenkabail;Aileen Maunahan;Saidul Islam;Saidul Islam

  • Hyperspectral versus multispectral crop-productivity modeling and type discrimination for the HyspIRI mission

    Isabella Mariotto;Isabella Mariotto;Prasad S. Thenkabail;Alfredo Huete;E. Terrence Slonecker

Frequent Co-Authors

Murali Krishna Gumma
Murali Krishna Gumma International Crops Research Institute for the Semi-Arid Tropics
Chandrashekhar Biradar
Chandrashekhar Biradar International Center for Agricultural Research in the Dry Areas, Egypt
Russell G. Congalton
Russell G. Congalton University of New Hampshire
Hugh Turral
Hugh Turral International Water Management Institute
Alfredo Huete
Alfredo Huete University of Technology Sydney
Mutlu Ozdogan
Mutlu Ozdogan University of Wisconsin–Madison
Andrew Nelson
Andrew Nelson University of Twente
Trent W. Biggs
Trent W. Biggs San Diego State University
Mark S. Ashton
Mark S. Ashton Yale University
Jerry W. Knox
Jerry W. Knox Cranfield University

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