Pardhasaradhi Teluguntla
Biography
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.
Science and Products
Global Crop Water Productivity and Savings through waterSMART (GCWP)
The waterSMART (Sustain and Manage America’s Resources for Tomorrow) project places technical information and tools in the hands of stakeholders that allow them to answer pertinent questions regarding water availability. Two goals of waterSMART are to 1) establish water availability and its use based on an understanding of the past and present water users and to 2) project water availability...
Global Hyperspectral Imaging Spectroscopy of Agricultural-Crops & Vegetation (GHISA)
This webpage showcases the key research advances made in hyperspectral remote sensing of agricultural crops and vegetation over the last 50 years. There are three focus areas:
Global Food Security-Support Analysis Data at 30 m (GFSAD)
The GFSAD30 is a NASA funded project to provide high resolution global cropland data and their water use that contributes towards global food security in the twenty-first century. The GFSAD30 products are derived through multi-sensor remote sensing data (e.g., Landsat, MODIS, AVHRR), secondary data, and field-plot data and aims at documenting cropland dynamics from 1990 to 2017.
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
The South Asia (India, Pakistan, Bangladesh, Nepal, Sri Lanka and Bhutan) has a staggering 900 million people (~43% of the population) who face food insecurity or severe food insecurity as per United Nations, Food and Agriculture Organization’s (FAO) the Food Insecurity Experience Scale (FIES). The existing coarse-resolution (>250-m) cropland...
Murali Krishna Gumma; Thenkabail, Prasad; Pardhasaradhi Teluguntla; Oliphant, Adam A meta-analysis of global crop water productivity of three leading world crops (wheat, corn, and rice) in the irrigated areas over three decades
The overarching goal of this study was to perform a comprehensive meta-analysis of irrigated agricultural Crop Water Productivity (CWP) of the world’s three leading crops: wheat, corn, and rice based on three decades of remote sensing and non-remote sensing-based studies. Overall, CWP data from 148 crop growing study sites (60 wheat, 43 corn, and...
Daniel J. Foley; Thenkabail, Prasad; Aneece, Itiya; Pardhasaradhi Teluguntla; Oliphant, Adam 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
Cropland extent maps are useful components for assessing food security. Ideally, such products are a useful addition to countrywide agricultural statistics since they are not politically biased and can be used to calculate cropland area for any spatial unit from an individual farm to various administrative unites (e.g., state, county, district)...
Oliphant, Adam; Thenkabail, Prasad S.; Teluguntla, Pardhasaradhi; Xiong, Jun; Gumma, Murali Krishna; Russell G. Congalton; Kamini Yadav 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
A satellite-derived cropland extent map at high spatial resolution (30-m or better) is a must for food and water security analysis. Precise and accurate global cropland extent maps, indicating cropland and non-cropland areas, are starting points to develop higher-level products such as crop watering methods (irrigated or rainfed), cropping...
Xiong, Jun N.; Thenkabail, Prasad S.; James C. Tilton; Gumma, Murali Krishna; Teluguntla, Pardhasaradhi G.; Oliphant, Adam; Congalton, Russell G.; Yadav, Kamini; Gorelick, Noel 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
Mapping high resolution (30-m or better) cropland extent over very large areas such as continents or large countries or regions accurately, precisely, repeatedly, and rapidly is of great importance for addressing the global food and water security challenges. Such cropland extent products capture individual farm fields, small or large, and are...
Teluguntla, Pardhasaradhi; Thenkabail, Prasad S.; Oliphant, Adam; Xiong, Jun N.; Gumma, Murali Krishna; Congalton, Russell G.; Yadav, Kamini; Huete, Alfredo Mapping cropland fallow areas in myanmar to scale up sustainable intensification of pulse crops in the farming system
Cropland fallows are the next best-bet for intensification and extensification, leading to increased food production and adding to the nutritional basket. The agronomical suitability of these lands can decide the extent of usage of these lands. Myanmar’s agricultural land (over 13.8 Mha) has the potential to expand by another 50% into additional...
Gumma, Murali Krishna; Thenkabail, Prasad S.; Deevi, Kumara Charyulu; Mohammed, Irshad A.; Teluguntla, Pardhasaradhi; Oliphant, Adam; Xiong, Jun N.; Aye, Tin; Whittbread, Anthony M. Spectral matching techniques (SMTs) and automated cropland classification algorithms (ACCAs) for mapping croplands of Australia using MODIS 250-m time-series (2000–2015) data
Mapping croplands, including fallow areas, are an important measure to determine the quantity of food that is produced, where they are produced, and when they are produced (e.g. seasonality). Furthermore, croplands are known as water guzzlers by consuming anywhere between 70% and 90% of all human water use globally. Given these facts and the...
Teluguntla, Pardhasaradhi G.; Thenkabail, Prasad S.; Xiong, Jun N.; Gumma, Murali Krishna; Congalton, Russell G.; Oliphant, Adam; Poehnelt, Justin; Yadav, Kamini; Rao, Mahesh N.; Massey, Richard Automated cropland mapping of continental Africa using Google Earth Engine cloud computing
The automation of agricultural mapping using satellite-derived remotely sensed data remains a challenge in Africa because of the heterogeneous and fragmental landscape, complex crop cycles, and limited access to local knowledge. Currently, consistent, continent-wide routine cropland mapping of Africa does not exist, with most studies focused...
Xiong, Jun N.; Thenkabail, Prasad S.; Gumma, Murali Krishna; Teluguntla, Pardhasaradhi G.; Poehnelt, Justin; Congalton, Russell G.; Yadav, Kamini; Thau, David Mapping rice-fallow cropland areas for short-season grain legumes intensification in South Asia using MODIS 250 m time-series data
The goal of this study was to map rainfed and irrigated rice-fallow cropland areas across South Asia, using MODIS 250 m time-series data and identify where the farming system may be intensified by the inclusion of a short-season crop during the fallow period. Rice-fallow cropland areas are those areas where rice is grown during the kharif growing...
Gumma, Murali Krishna; Thenkabail, Prasad S.; Teluguntla, Pardhasaradhi G.; Rao, Mahesh N.; Mohammed, Irshad A.; Whitbread, Anthony M. Inland valley wetland cultivation and preservation for africa’s green and blue revolution using multi-sensor remote sensing
Thenkabail, Prasad S.; Teluguntla, Pardhasaradhi G. Global Cropland Area Database (GCAD) derived from Remote Sensing in Support of Food Security in the Twenty-first Century: Current Achievements and Future Possibilities
The precise estimation of the global agricultural cropland- extents, areas, geographic locations, crop types, cropping intensities, and their watering methods (irrigated or rainfed; type of irrigation) provides a critical scientific basis for the development of water and food security policies (Thenkabail et al., 2012, 2011, 2010). By year 2100,...
Teluguntla, Pardhasaradhi G.; Thenkabail, Prasad S.; Xiong, Jun N.; Gumma, Murali Krishna; Giri, Chandra; Milesi, Cristina; Ozdogan, Mutlu; Congalton, Russ; Tilton, James; Sankey, Temuulen Tsagaan; Massey, Richard; Phalke, Aparna; Yadav, Kamini Hyperspectral remote sensing for terrestrial applications
Remote sensing data are considered hyperspectral when the data are gathered from numerous wavebands, contiguously over an entire range of the spectrum (e.g., 400–2500 nm). Goetz (1992) defines hyperspectral remote sensing as “The acquisition of images in hundreds of registered, contiguous spectral bands such that for each picture element of an...
Thenkabail, Prasad S.; Teluguntla, Pardhasaradhi G.; Murali Krishna Gumma; Venkateswarlu Dheeravath