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Teluguntla, P and Thenkabail, P S and Xiong, J and Oliphant, A and Gumma, M K and Congalton, R and Yadav, K and Massey, R and Phalke, A and Tilton, J and Smith, C (2017) Mapping cropland extent and areas of Australia at 30-m resolution using multi-year time-series Landsat data and Random Forest machine learning algorithm through Google Earth Engine (GEE) Cloud Computing. In: 20th William T. Pecora Memorial Remote Sensing Symposium. Pecora 20 – “Observing a Changing Earth: Science for Decisions…Monitoring, Assessment, and Projection”, November 14-16, 2017, Sioux Falls, South Dakota, USA.
Teluguntla, P and Thenkabail, P S and Xiong, J and Gumma, M K and Congalton, R G and Oliphant, A and Poehnelt, J and Yadav, K and Rao, M and Massey, R (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. International Journal of Digital Earth. pp. 1-34. ISSN 1753-8947
Gumma, M K and Thenkabail, P S and Teluguntla, P and Oliphant, A and Xiong, J and Congalton, R G and Yadav, K and Smith, C (2017) NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Food Security-support Analysis Data (GFSAD) Cropland Extent 2015 South Asia, Afghanistan, Iran 30 m V001. Monograph. NASA EOSDIS Land Processes DAAC, South Dakota, USA.
Xiong, J and Thenkabail, P S and Tilton, J and Gumma, M K and Teluguntla, P and Oliphant, A and Congalton, R and Yadav, K and Gorelick, N (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 Sensing, 9(10) (1065). pp. 1-27. ISSN 2072-4292
Xiong, J and Thenkabail, P S and Teluguntla, P and Oliphant, A and Congalton, R and Gumma, M K and Yadav, K and Massey, R and Tilton, J and Smith, C (2017) An Automated Crop Intensity Algorithm (ACIA) for global cropland intensity mapping at 30-m using multi-source time-series data and Google Earth Engine. In: 20th William T. Pecora Memorial Remote Sensing Symposium. Pecora 20 – “Observing a Changing Earth: Science for Decisions…Monitoring, Assessment, and Projection”, November 13-16, 2017, Sioux Falls, South Dakota, USA.
Xiong, J and Thenkabail, P S and Congalton, R and Yadav, K and Teluguntla, P and Oliphant, A and Gumma, M K and Massey, R and Smith, C (2017) CropRef: Reference Datasets and techniques to improve global cropland mapping. In: 20th William T. Pecora Memorial Remote Sensing Symposium. Pecora 20 – “Observing a Changing Earth: Science for Decisions…Monitoring, Assessment, and Projection”., November 13-16, 2017, Sioux Falls, South Dakota, USA.
Oliphant, A and Thenkabail, P S and Teluguntla, P and Congalton, R and Yadav, K and Gumma, M K and Xiong, J and Massey, R and Smith, C (2017) Mapping Croplands of Southeast Asia, Japan, and North and South Korea using Landsat 30-m time-series, random forest algorithm. In: 20th William T. Pecora Memorial Remote Sensing Symposium. Pecora 20 – “Observing a Changing Earth: Science for Decisions…Monitoring, Assessment, and Projection”., November 13-16, 2017, Sioux Falls, South Dakota, USA.
Gumma, M K and Thenkabail, P S and Kumara Charyulu, D and Mohammed, I A and Teluguntla, P and Oliphant, A and Xiong, J and Aye, T and Whitbread, A M (2018) Mapping cropland fallow areas in Myanmar to scale up sustainable intensification of pulse crops in the farming system. GIScience & Remote Sensing (TSI), 55 (6). pp. 926-949. ISSN 1548-1603
Teluguntla, P and Thenkabail, P S and Oliphant, A and Xiong, J and Gumma, M K and Congalton, R G and Yadav, K and Huete, A (2018) 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. ISPRS Journal of Photogrammetry and Remote Sensing (TSI), 144. pp. 325-340. ISSN 09242716
Gumma, M K and Thenkabail, P S and Teluguntla, P G and Oliphant, A and Xiong, J and Giri, C and Pyla, V and Dixit, S and Whitbread, A M (2019) 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. GIScience & Remote Sensing (TSI). pp. 1-21. ISSN 1548-1603