Pardhasaradhi Teluguntla


Pardhasaradhi Teluguntla is currently a senior research scientist with Bay Area Environmental Research Institute (BAERI) and U.S. Geological Survey(USGS), Western Geographic Science Center (WGSC), in Flagstaff. He is working on global cropland mapping using multispectral and hyperspectral remote sensing to study global cropland dynamics for Global food and water security.

Prior to this, he was a scientist at the International Maize and Wheat Improvement Center (CIMMYT), an international scientific research organization operated under Consultative Group on International Agriculture Research (CGIAR). He worked on mapping maize production zones and stress-prone target ecologies in South Asia under the Abiotic stress Tolerant Maize for Asia (ATMA) project.  He also worked as a senior scientific officer for another CGIAR institution, the International Water Management Institute (IWMI), Hyderabad, India. Pardha was one of the key researchers in the Krishna River Basin studies funded by the Australian Centre for International Agriculture Research (ACIAR), Govt of Australia.

Pardha has 18+ years of experience in Remote Sensing and GIS applications relating to agriculture, water, and natural resource management. Pardha has worked extensively on land use/land cover (LULC) mapping, cropland area mapping (which includes both irrigated and rainfed croplands), rice paddy mapping and evapotranspiration (ET) mapping using multi-temporal and multi-sensor satellite remote sensing images.


  1. Teluguntla, P., Thenkabail, P.S., Oliphant, A., Xiong, J., Gumma, M.K., Congalton, R.G., Yadav, K., Huete, H. 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, 144: 325-340, ISSN 0924-2716.
  2. Teluguntla, P., Thenkabail, P.S., Xiong, J., Gumma, M.K., Congalton, R.G., Oliphant, A., Poehnelt, J., Yadav, K., Rao, M., & 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, 1-34
  3. Teluguntla, P., Thenkabail, P.S., Xiong, J., Gumma, M.K., Giri, C., Milesi, C., Ozdogan, M., Congalton, R., Tilton, J., Sankey, T.R., Massey, R., Phalke, A., and Yadav, K. 2015.  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. Chapter 6, Vol. II. Land Resources: Monitoring, Modelling, and Mapping, edited by Prasad S. Thenkabail
  4. Teluguntla, P., Ryu, D., George, B., Walker, J. P., and Malano H.M., 2015. Mapping flooded rice paddies using time series of MODIS imagery in the Krishna River Basin India, Remote Sensing, 7(7): 8858-8882.
  5. Teluguntla, P., Ryu, D., George, B., and Walker, J. P., 2013. Multi-decadal Trend of Basin-scale Evapotranspiration Estimated Using AVHRR Data in the Krishna River Basin India, Vadose Zone Journal, 12(3).
  6. Oliphant, A., Thenkabail, P.S., Teluguntla, P., Xiong, J., Gumma, M.K., Congalton, R., and Yadav, K. 2019. Mapping cropland extent of Southeast and Northeast Asia using multi-year time-series Landsat 30-m data using random forest classifier on Google Earth Engine. International Journal of Applied Earth Observation and Geoinformation. Accepted. In press.
  7. Xiong, J., Thenkabail, P., Tilton, J., Gumma, M., Teluguntla, P., Oliphant, A., Congalton, R., Yadav, K., & Gorelick, N. (2017a). 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, 1065
  8. Xiong, J., Thenkabail, P. S., Gumma, M. K., Teluguntla, P., Poehnelt, J., Congalton, R. G., Yadav, K., & Thau, D. (2017). Automated cropland mapping of continental Africa using Google Earth Engine cloud computing. ISPRS Journal of Photogrammetry and Remote Sensing126, 225-244.
  9. Gumma, M. K., Thenkabail, P. S., Teluguntla, P., Rao, M. N., Mohammed, I. A., & Whitbread, A. M. (2016). Mapping rice-fallow cropland areas for short-season grain legumes intensification in South Asia using MODIS 250 m time-series data. International Journal of Digital Earth, 01-23.
  10. Thenkabail, P.S., Teluguntla, P., Gumma, M.K., and Irshad A.M., 2014. Hyperspectral remote sensing of vegetation and agriculture crops. Photogrammetric Engineering and Remote Sensing. 80(8): 697-709.