Adam Oliphant
Biography
Adam Oliphant is a geographer with the USGS based in Flagstaff Arizona. He is part of the Western Geographic Science Center and specializes in using remote sensing to map vegetation over countries and continents. Adam recently finished mapping cropland extent across all of Southeast and Northeast Asia using Landsat 7&8 as part of the Global Food Security-support Analysis Data at 30m (GFSAD30) project, see croplands.org and https://lpdaac.usgs.gov/node/1281
Current research focused on mapping crop type and cropland fallows in the United States and integrating NASA/USGS satellite sensors with satellite systems from ESA including Sentinel 1&2. Adam has an interest in Citizen Science and community participation in the collection, analysis, and dissemination of scientific data and projects.
Adam earned a master’s degree in Forestry with an emphasis in Remote Sensing from Virginia Tech, where he researched the spatial distribution of autumn olive (Elaeagnus umbellate) on reclaimed surface coal mines in Appalachia. He also has an interest in surface water quality and quantity monitoring and using consumer grade electronics to collect scientifically useful information.
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. Fog water collection effectiveness: Mesh intercomparisons
To explore fog water harvesting potential in California, we conducted long-term measurements involving three types of mesh using standard fog collectors (SFC). Volumetric fog water measurements from SFCs and wind data were collected and recorded in 15-minute intervals over three summertime fog seasons (2014–2016) at four California sites. SFCs...
Fernandez, Daniel; Torregrosa, Alicia; Weiss-Penzias, Peter; Zhang, Bong June; Sorensen, Deckard; Cohen, Robert; McKinley, Gareth; Kleingartner, Justin; Oliphant, Andrew; Bowman, Matthew 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
The GFSAD30 project accomplished their goal!
The global croplands mapping project has succesfully mapped croplands across the entire planet in order to understand the security of food world wide. Below are many press anouncments and interviews with the project leader Prasad Thenkabail.