Prasad S. Thenkabail
Orcid: 0000-0002-2182-8822
According to our database1,
Prasad S. Thenkabail
authored at least 34 papers
between 2009 and 2023.
Collaborative distances:
Collaborative distances:
Timeline
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Online presence:
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on orkg.org
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on orcid.org
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on id.loc.gov
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on d-nb.info
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Bibliography
2023
Crop Water Productivity from Cloud-Based Landsat Helps Assess California's Water Savings.
Remote. Sens., October, 2023
Mapping Vegetation Index-Derived Actual Evapotranspiration across Croplands Using the Google Earth Engine Platform.
Remote. Sens., February, 2023
2022
New Generation Hyperspectral Data From DESIS Compared to High Spatial Resolution PlanetScope Data for Crop Type Classification.
IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2022
New Generation and Old Generation Hyperspectral Remote Sensing Data and their Comparisons with Multispectral Data in the Study of Global Agriculture and Vegetation.
Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 2022
2021
Classifying Crop Types Using Two Generations of Hyperspectral Sensors (Hyperion and DESIS) with Machine Learning on the Cloud.
Remote. Sens., 2021
DESIS and PRISMA: A study of a new generation of spaceborne hyperspectral sensors in the study of world crops.
Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 2021
2020
Remote. Sens., 2020
A meta-analysis of global crop water productivity of three leading world crops (wheat, corn, and rice) in the irrigated areas over three decades.
Int. J. Digit. Earth, 2020
2019
A Bibliometric Profile of the <i>Remote Sensing Open Access Journal</i> Published by MDPI between 2009 and 2018.
Remote. Sens., 2019
Mapping cropland extent of Southeast and Northeast Asia using multi-year time-series Landsat 30-m data using a random forest classifier on the Google Earth Engine Cloud.
Int. J. Appl. Earth Obs. Geoinformation, 2019
2018
Accuracies Achieved in Classifying Five Leading World Crop Types and their Growth Stages Using Optimal Earth Observing-1 Hyperion Hyperspectral Narrowbands on Google Earth Engine.
Remote. Sens., 2018
2017
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.
Remote. Sens., 2017
Spectral matching techniques (SMTs) and automated cropland classification algorithms (ACCAs) for mapping croplands of Australia using MODIS 250-m time-series (2000-2015) data.
Int. J. Digit. Earth, 2017
2016
Mapping rice-fallow cropland areas for short-season grain legumes intensification in South Asia using MODIS 250 m time-series data.
Int. J. Digit. Earth, 2016
2015
Developing<i> in situ</i> Non-Destructive Estimates of Crop Biomass to Address Issues of Scale in Remote Sensing.
Remote. Sens., 2015
A support vector machine to identify irrigated crop types using time-series Landsat NDVI data.
Int. J. Appl. Earth Obs. Geoinformation, 2015
2014
<i>Remote Sensing</i> Open Access Journal: Increasing Impact through Quality Publications.
Remote. Sens., 2014
2013
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.
IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2013
2012
An Automated Cropland Classification Algorithm (ACCA) for Tajikistan by Combining Landsat, MODIS, and Secondary Data.
Remote. Sens., 2012
2011
Remote. Sens., 2011
Mapping Irrigated Areas of Ghana Using Fusion of 30 m and 250 m Resolution Remote-Sensing Data.
Remote. Sens., 2011
Improving Water Productivity for Agriculture - Predicting and Preventing Crisis in Irrigated Water Use in a Changing Climate.
Proceedings of the IEEE Global Humanitarian Technology Conference, 2011
Earth Observing Data and Methods for Advancing Water Harvesting Technologies in the Semi-arid Rain-Fed Environments of India.
Proceedings of the IEEE Global Humanitarian Technology Conference, 2011
2010
A Holistic View of Global Croplands and Their Water Use for Ensuring Global Food Security in the 21st Century through Advanced Remote Sensing and Non-remote Sensing Approaches.
Remote. Sens., 2010
Global Croplands and their Importance for Water and Food Security in the Twenty-first Century: Towards an Ever Green Revolution that Combines a Second Green Revolution with a Blue Revolution.
Remote. Sens., 2010
2009
Irrigated Area Maps and Statistics of India Using Remote Sensing and National Statistics.
Remote. Sens., 2009
A global map of rainfed cropland areas (GMRCA) at the end of last millennium using remote sensing.
Int. J. Appl. Earth Obs. Geoinformation, 2009