P. Teluguntla

发表

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

R. Congalton, P. Thenkabail, M. Gumma, 2017 .

Advances in Hyperspectral Remote Sensing of Vegetation and Agricultural Crops

Irshad A. Mohammed, P. Thenkabail, M. Gumma, 2014, Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for Vegetation.

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

A. Huete, R. Congalton, P. Thenkabail, 2018, ISPRS Journal of Photogrammetry and Remote Sensing.

Global Food Security Support Analysis Data at Nominal 1 km (GFSAD1km) Derived from Remote Sensing in Support of Food Security in the Twenty-First Century: Current Achievements and Future Possibilities

C. Milesi, R. Congalton, P. Thenkabail, 2015, Remote Sensing Handbook - Three Volume Set.

NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Food Security Support Analysis Data (GFSAD) Crop Dominance 2010 Global 1 km V001

Cristina Milesi, Russell G. Congalton, James C. Tilton, 2016 .

NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Food Security-support Analysis Data (GFSAD) Cropland Extent 2015 Southeast Asia 30 m V001

Russell G. Congalton, Prasad S. Thenkabail, Kamini Yadav, 2017 .

NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Food Security-support Analysis Data (GFSAD) Cropland Extent 2015 Australia, New Zealand, China, Mongolia 30 m V001

R. Congalton, P. Thenkabail, M. Gumma, 2017 .

Monitoring of Spatiotemporal Dynamics of Rabi Rice Fallows in South Asia Using Remote Sensing

P. Thenkabail, M. Gumma, A. Whitbread, 2018 .

Mapping cropland fallow areas in myanmar to scale up sustainable intensification of pulse crops in the farming system

Irshad A. Mohammed, Pardhasaradhi Teluguntla, Murali Krishna Gumma, 2018, GIScience & Remote Sensing.

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

P. Thenkabail, M. Gumma, P. Teluguntla, 2022, GIScience & Remote Sensing.

New Generation Hyperspectral Data From DESIS Compared to High Spatial Resolution PlanetScope Data for Crop Type Classification

P. Thenkabail, P. Teluguntla, A. Oliphant, 2022, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

Impact of flooded rice paddy on remotely sensed evapotranspiration in the Krishna River basin, India

D. Ryu, B. George, P. Teluguntla, 2020, Hydrological Processes.

Relating Trends in Streamflow to Anthropogenic Influences: A Case Study of Himayat Sagar Catchment, India

A. Western, B. George, P. Teluguntla, 2012, Water Resources Management.

Inland valley wetland cultivation and preservation for africa’s green and blue revolution using multi-sensor remote sensing

Irshad A. Mohammed, P. Thenkabail, M. Gumma, 2015 .

Mapping Flooded Rice Paddies Using Time Series of MODIS Imagery in the Krishna River Basin, India

Jeffrey P. Walker, Hector M. Malano, Dongryeol Ryu, 2015, Remote. Sens..

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

Russell G. Congalton, Jun Xiong, James C. Tilton, 2017, Remote. Sens..

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

Prasad S. Thenkabail, Chandra Giri, Murali Krishna Gumma, 2019, GIScience & Remote Sensing.

Analysis of Spectral Measurements in Paddy Rice Field: Implications for Land Use Classification

A. Radha, D. Ryu, B. Nawarathna, 2011 .

Machine Learning Approaches and Sentinel-2 Data in Crop Type Mapping

M. Gumma, P. Teluguntla, P. Panjala, 2021, Studies in Big Data.

Spectral matching techniques (SMTs) and automated cropland classification algorithms (ACCAs) for mapping croplands of Australia using MODIS 250-m time-series (2000–2015) data

Russell G. Congalton, Jun Xiong, Prasad S. Thenkabail, 2017, Int. J. Digit. Earth.

Global Cropland Area Database (GCAD) derived from Remote Sensing in Support of Food Security in the Twenty-first Century: Current Achievements and Future Possibilities

Cristina Milesi, James C. Tilton, Prasad S. Thenkabail, 2015 .

Multidecadal Trend of Basin‐Scale Evapotranspiration Estimated Using AVHRR Data in the Krishna River Basin, India

J. Walker, D. Ryu, B. George, 2013 .

Indo-Ganges River Basin Land Use/Land Cover (LULC) and Irrigated Area Mapping

P. Thenkabail, M. Gumma, A. Whitbread, 2019, Indus River Basin.

Hyperspectral Narrowband Data Propel Gigantic Leap in the Earth Remote Sensing

P. Thenkabail, P. Teluguntla, A. Oliphant, 2021, Photogrammetric Engineering & Remote Sensing.

Handbook of Remote Sensing, Global Food Security Support Analysis Data at Nominal 1 km (GFSAD1km) Derived from Remote Sensing in Support of Food Security in the Twenty-First Century: Current Achievements and Future Possibilities

Prasad S. Thenkabail, Murali K. Gumma, Chandra Giri, 2015 .

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

A. Huete, R. Congalton, P. Thenkabail, 2018, 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

C. Milesi, R. Congalton, P. Thenkabail, 2021, Professional Paper.

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

A. Huete, R. Congalton, P. Thenkabail, 2018, ISPRS Journal of Photogrammetry and Remote Sensing.

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

Russell G. Congalton, Jun Xiong, Prasad S. Thenkabail, 2019, Int. J. Appl. Earth Obs. Geoinformation.

A meta-analysis of global crop water productivity of three leading world crops (wheat, corn, and rice) in the irrigated areas over three decades

Prasad S. Thenkabail, Daniel J. Foley, A Oliphant, 2020, Int. J. Digit. Earth.

Global Cropland Area Database (GCAD) derived from Remote Sensing in Support of Food Security in the Twenty-first Century: Current Achievements and Future Possibilities

P. Thenkabail, M. Gumma, M. Ozdogan, 2015 .

Crop Water Productivity from Cloud-Based Landsat Helps Assess California's Water Savings

P. Thenkabail, P. Teluguntla, A. Oliphant, 2023, Remote. Sens..