Pardhasaradhi Teluguntla

Author

Also Known As

P Teluguntla, P. Teluguntla, Pardharsadhi Teluguntla, Pardhasaradhi G. Teluguntla, Pardhasaradhi Teluguntla, Teluguntla, Pardhasaradhi

Publications

2.6K

Citations

H-Index

Concepts

All Affiliations

United States Geological Survey Astrogeology Science Center Bay Area Environmental Research Institute International Crops Research Institute for the Semi-Arid Tropics University of New Hampshire

About

Pardhasaradhi Teluguntla is an author at United States Geological Survey specializing in engineering, remote sensing, and precision agriculture.

Top Concepts

#	Concept	H-Index	Publications	Citations
1	Engineering	9	9	1.3K
2	Geography	8	8	1.2K
3	Social Sciences	4	4	1K
4	Public Health	3	3	194
5	Food Systems	1	1	23

Pardhasaradhi Teluguntla

Publications

2.2K

Citations

H-Index

Page 1

	Year	Citations
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, Prasad S. Thenkabail, Adam Oliphant, ISPRS Journal of Photogrammetry and Remote Sensing Precision AgricultureEnvironmental MonitoringEngineeringHigh ResolutionLand Use	2018	521
Automated cropland mapping of continental Africa using Google Earth Engine cloud computing Jun Xiong, Prasad S. Thenkabail, Murali Krishna Gumma, ISPRS Journal of Photogrammetry and Remote Sensing	2017	482
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 Xiong, Prasad S. Thenkabail, James C. Tilton, Remote Sensing Precision AgricultureEnvironmental MonitoringEngineeringLand UseAgricultural Economics	2017	391
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 Adam Oliphant, Prasad S. Thenkabail, Pardhasaradhi Teluguntla, International Journal of Applied Earth Observation and Geoinformation	2019	213
Agricultural cropland extent and areas of South Asia derived using Landsat satellite 30-m time-series big-data using random forest machine learning algorithms on the Google Earth Engine cloud Murali Krishna Gumma, Prasad S. Thenkabail, Pardhasaradhi Teluguntla, GIScience & Remote Sensing Earth ObservationPrecision AgricultureEngineeringLand UseRandom Forest Machine	2019	151
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 Prasad S. Thenkabail, Pardhasaradhi Teluguntla, Jun Xiong, USGS professional paper Earth ObservationGlobal Cropland-extent ProductPrecision AgricultureEngineeringMachine Learning	2021	82
A meta-analysis of global crop water productivity of three leading world crops (wheat, corn, and rice) in the irrigated areas over three decades Daniel J. Foley, Prasad S. Thenkabail, Itiya Aneece, International Journal of Digital Earth	2019	79
Spectral matching techniques (SMTs) and automated cropland classification algorithms (ACCAs) for mapping croplands of Australia using MODIS 250-m time-series (2000–2015) data Pardhasaradhi Teluguntla, Prasad S. Thenkabail, Jun Xiong, International Journal of Digital Earth	2017	60
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 Murali Krishna Gumma, Prasad S. Thenkabail, Pranay Panjala, GIScience & Remote Sensing	2022	59
Mapping cropland fallow areas in myanmar to scale up sustainable intensification of pulse crops in the farming system Murali Krishna Gumma, Prasad S. Thenkabail, Kumara Charyulu Deevi, GIScience & Remote Sensing	2018	44

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