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Pardhasaradhi Teluguntla
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- affiliation: US Geological Survey, Flagstaff, AZ, USA
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2020 – today
- 2024
- [j10]Murali Krishna Gumma, Pranay Panjala, Pardhasaradhi Teluguntla:
Dryland cropping in different Land uses of Senegal using Sentinel-2 and hybrid ML method. Int. J. Digit. Earth 17(1) (2024) - [j9]Adam J. Oliphant, Prasad S. Thenkabail, Pardhasaradhi Teluguntla, Itiya P. Aneece, Daniel J. Foley, Richard L. McCormick:
Automated Cropland Fallow Algorithm (ACFA) for the Northern Great Plains of USA. Int. J. Digit. Earth 17(1) (2024) - 2023
- [j8]Daniel J. Foley
, Prasad S. Thenkabail
Crop Water Productivity from Cloud-Based Landsat Helps Assess California's Water Savings. Remote. Sens. 15(19): 4894 (2023) - 2022
- [j7]Itiya P. Aneece
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. 15: 7846-7858 (2022) - [c1]Prasad S. Thenkabail, Itiya P. Aneece, Pardhasaradhi Teluguntla, Adam J. Oliphant, Daniel J. Foley:
New Generation and Old Generation Hyperspectral Remote Sensing Data and their Comparisons with Multispectral Data in the Study of Global Agriculture and Vegetation. IGARSS 2022: 5744-5745 - 2020
- [j6]Daniel J. Foley
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 13(8): 939-975 (2020)
2010 – 2019
- 2019
- [j5]Adam J. Oliphant
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 81: 110-124 (2019) - 2017
- [j4]Pardhasaradhi Teluguntla
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 10(9): 944-977 (2017) - [j3]Jun Xiong, Prasad S. Thenkabail, James C. Tilton, Murali K. Gumma
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. 9(10): 1065 (2017) - 2016
- [j2]Murali K. Gumma
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 9(10): 981-1003 (2016) - 2015
- [j1]Pardhasaradhi Teluguntla
Mapping Flooded Rice Paddies Using Time Series of MODIS Imagery in the Krishna River Basin, India. Remote. Sens. 7(7): 8858-8882 (2015)
Coauthor Index
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