Item talk:Q50669
From geokb
{
"USGS Staff Profile": {
"@context": "https://schema.org",
"@type": "Person",
"affiliation": [
{
"@type": "Organization",
"name": "Editor-in-Chief, Remote Sensing Open Access Journal; 2011-present."
},
{
"@type": "Organization",
"name": "Associate Editor, American Society of Photogrammetric Engineering and Remote Sensing (PE&RS), a Journal of the Imaging and Geospatial Information Society (ASPRS)."
},
{
"@type": "Organization",
"name": "Editorial Advisory Board, International Society of Photogrammetry and Remote Sensing (ISPRS) Journal of Photo. & Remote Sensing, 2014-present."
},
{
"@type": "Organization",
"name": "Editorial Board Member, Remote Sensing of Environment (2007-2016)"
},
{
"@type": "Organization",
"name": "Core member, NASA South/Southeast Asia Research Initiative (SARI): 2014-present"
},
{
"@type": "Organization",
"name": "Member, American Society of Photogrammetry and Remote Sensing (1988-present)"
},
{
"@type": "Organization",
"name": "Chair: International Society of Photogrammetry and Remote Sensing (ISPRS) Working Group WG VIII/7: Land cover and its dynamics, including Agricultural & Urban Land Use (2013-2016)"
},
{
"@type": "Organization",
"name": "Global Coordinator, Committee for Earth Observing Systems Agriculture Societal Beneficial Areas (CEOA SBA) (2010-2013)"
},
{
"@type": "Organization",
"name": "Co-lead, IEEE \u201cWater for the World\u201d (2007-2011)"
},
{
"@type": "Organization",
"name": "Member, Landsat Science Team (2007-2011)"
}
],
"award": [
"2023 Fellow, American Society of Photogrammetric Engineering and Remote Sensing (ASPRS)",
"2023 Talbert Abrahms Grand Award, highest paper award from American Society of Photogrammetric Enginering and Remote Sensing (ASPRS).",
"2022 - PESEP Scholar. The NASA-ISRO Professional Engineer and Scientist Exchange Program (PESEP). USA (NASA) and India (ISRO) scientific exchange scientist for 2022-2023.",
"2020 - Proposal evaluation panel for Israeli Ministry of Science and Technology, to their bi-national Italy-Israel joint laboratory in Precision Agriculture.",
"2019 - Advisory Board member, Taylor and Francis Inc., online library collection to support the United Nations\u2019 Sustainable Development Goals (UN SDGs).",
"2019 - USGS STAR award for supervision",
"2019 - Member, NASA Surface Biology Geology (SBG)-Applications. For the SBG hyperspectral remote sensing mission (replacing former HyspIRI program).",
"2019 - Member, NASA Calibration and Validation Working Group. For the SBG hyperspectral remote sensing mission (replacing former HyspIRI program).",
"2019 - USGS 10-year service recognition",
"2018 - The Excellent Reviewer of Remote Sensing of Environment",
"2018 - Honored by the Arabian Gulf University, Bahrain and the Dubai-based International Center for Biosaline Agriculture (ICBA) for giving the keynote lecture.",
"2016 - NASA Group Achievement Award, 2016. (Member of Team) Fallowed Area Map",
"2015 - ASPRS Best Scientific Paper Award, 2015: ASPRS ERDAS award for best scientific paper in remote sensing (given annually for the papers published in American Society of Photogrammetry",
"2015 - Task Force Member NASA, SARI, 2015-present. South Asia Regional Initiative (SARI), A response to regional needs in Land Cover/Land Use Change (LCLUC) Science and Education (NASA)",
"2015 - Innovations Inventory, PARIS21, 2015: 'Remote Sensing Data for Drought Assessment and Monitoring' monograph authors (as first author) is in the PARIS21.",
"2013 - Panel chair, 2013, USGS RGE. For the Spring 2013 GIS and Remote Sensing USGS Research Grade Evaluation (RGE) panel.",
"2008 - ASPRS President\u2019s award for practical papers: American Society of Photogrammetry and Remote Sensing (ASPRS) John I. Davidson President\u2019s Award for practical papers, 2008.",
"2007 - Special achievement in GIS award from ESRI, awarded by ESRI President Mr. Jack Dangermond during the 2007 annual ESRI conference in San Diego.",
"2006 - Best team award for my remote sensing and GIS team @ the International Water Management Institute (IWMI) during Institute\u2019s Annual Research Meeting 2006.",
"2005 - Best paper award (5 best paper awards given) by International Water Management Institute (IWMI) during Institute\u2019s Annual Research Meeting 2005.",
"2004 - Best paper award (5 best paper awards given) by International Water Management Institute (IWMI) during Institute\u2019s Annual Research Meeting 2004.",
"2001 - Member, Scientific Advisory Board, Rapideye, a Private German Satellite Company.",
"1994 - Autometric award for outstanding paper by American Society of Photogrammetry and Remote Sensing (ASPRS)."
],
"description": [
{
"@type": "TextObject",
"abstract": "Senior Scientist (ST) with the Western Geographic Science Center",
"additionalType": "short description"
},
{
"@type": "TextObject",
"abstract": "Dr. Prasad S. Thenkabail, Senior Scientist (ST), United States Geological Survey (USGS), is a world-recognized expert in remote sensing science with major contributions in the field sustained for nearly 40 years. Dr. Thenkabail\u2019s career scientific achievements can be gauged by successfully making the list of world\u2019s top 1% of scientists across fields (22 scientific fields and 176 sub-fields).",
"additionalType": "staff profile page introductory statement"
},
{
"@type": "TextObject",
"abstract": "Dr. Thenkabail has conductedpioneering researchinhyperspectral remote sensing of vegetationand in that ofglobal croplands and their water use for food security. Inhyperspectral remote sensinghe has done cutting-edge research with wide implications in advancing remote sensing science in application to agriculture and vegetation. This body of work led to more than ten peer-reviewed research publications with high impact. For example, a single paper entitled \u201cHyperspectral vegetation indices and their relationships with agricultural crop characteristics\u201d has received 1500 citations (3/14/23). In studies ofglobal croplands for food and water security, he has led the release of the world\u2019s first Landsat-derived: 1. global cropland extent product @ 30m (GCEP30), and 2. global rainfed and irrigated area product @ 30m (LGRIP30). This work demonstrates a \u201cparadigm shift\u201d in how remote sensing science is conducted. As per Google Scholar, the papers Dr. Thenkabail's research are cited 14,235 times. His h-index is 58 and i10-index is 113.Dr. Thenkabail\u2019s contributions to series of leading edited books on remote sensing science places him as a world leader in remote sensing science advances. He editedthree-volume bookentitledRemote Sensing Handbookpublished by Taylor and Francis, with 82 chapters and more than 2000 pages, widely considered a \u201cmagnus opus\u201d encyclopedic standard reference for students, scholars, practitioners, and major experts in remote sensing science. He has recently completed editingHyperspectral Remote Sensing of Vegetationpublishedbooks by Taylor and Francis in four volumes with 50 chapters.This is the second edition that is currently in press and is a follow-up on the earlier single-volumeHyperspectral Remote Sensing of Vegetation. He has also edited a book onRemote Sensing of Global Croplands for Food Security.He obtained his PhD from the Ohio State University in 1992 and has 168 publications including 9 books, 146 peer-reviewed journal articles, and 13 major data releases. Dr. Thenkabail is at the center ofrendering scientific service to the world\u2019s remote sensing communityin roles that includeEditor-in-Chief (2011-present)of Remote Sensing Open Access Journal andAssociate Editor (2017-present)of American Society\u2019s Journal Photogrammetric Engineering and Remote Sensing. Dr. Thenkabail was recognized as Fellow of the American Society of Photogrammetry and Remote Sensing (ASPRS) in 2023. His scientific papers have won several awards over the years demonstrating world class highest quality research. These include: 2023 Talbert Abrams Grand Award, the highest scientific paper award of the ASPRS, 2015 ASPRS ERDAS award for best scientific paper in remote sensing, and 1994 Autometric Award for the outstanding paper in remote sensing. He was a Landsat Science Team Member (2007-2011).",
"additionalType": "personal statement"
},
{
"@type": "TextObject",
"abstract": "As a result of Dr. Thenkabail\u2019s scientific accomplishments, standing, and stature, he is a highly sought-after speaker. Since 2011, he has given 117 talks (averaging ~12 per year) of which 40% (47/117) were invited. He has been invited as a speaker in Bahrain, Brazil, Canada, China, Egypt, Germany, India, Indonesia, Israel, Myanmar, Thailand, Vietnam, and various places in USA (e.g., Purdue, OSU).",
"additionalType": "staff profile page abstract"
}
],
"email": "pthenkabail@usgs.gov",
"hasCredential": [
{
"@type": "EducationalOccupationalCredential",
"name": "1992 - Doctor of Philosophy (PhD) in Agricultural Engineering, The Ohio State University, Columbus, Ohio, USA."
},
{
"@type": "EducationalOccupationalCredential",
"name": "1983 - Master of Engineering (M.E.) in Hydraulics and Water Resources Engineering, Mysore University (India)."
},
{
"@type": "EducationalOccupationalCredential",
"name": "1981 - Bachelor of Civil Engineering (B.E.), Mysore University (India)."
}
],
"hasOccupation": [
{
"@type": "OrganizationalRole",
"affiliatedOrganization": {
"@type": "Organization",
"name": "Western Geographic Science Center",
"url": "https://www.usgs.gov/centers/western-geographic-science-center"
},
"roleName": "Senior Scientist (ST)",
"startDate": "2024-05-10T21:01:12.566368"
},
{
"@type": "Occupation",
"additionalType": "self-claimed professional experience",
"name": "2022 - present - Senior Scientist (ST), United States Geological Survey (USGS)"
},
{
"@type": "Occupation",
"additionalType": "self-claimed professional experience",
"name": "Oct. 2008-2022 - USGS: Supervisory Research Geographer-15 (2017-present), Research Geographer-15 (2011-2017), Research Geographer-14 (2008-2011), United States Geological Survey (USGS), Flagstaff, AZ.USA."
},
{
"@type": "Occupation",
"additionalType": "self-claimed professional experience",
"name": "March 2003-Sept. 2008 - IWMI: Principal Researcher, Global Research Division group and Head of Remote Sensing and GIS Unit, International Water Management Institute (IWMI), Colombo, Sri Lanka."
},
{
"@type": "Occupation",
"additionalType": "self-claimed professional experience",
"name": "April 1997-March 2003 - Yale University: Associate Research Scientist, Center for Earth Observation, Yale University, New Haven, CT,USA."
},
{
"@type": "Occupation",
"additionalType": "self-claimed professional experience",
"name": "Nov. 1995-March 1997 - ICIMOD: Remote Sensing Specialist, International Center for Integrated Mountain Development (ICIMOD), Kathmandu, Nepal."
},
{
"@type": "Occupation",
"additionalType": "self-claimed professional experience",
"name": "July 1992-Nov. 1995 - IITA: Remote Sensing Specialist, International Institute of Tropical Agriculture, Ibadan, Nigeria."
},
{
"@type": "Occupation",
"additionalType": "self-claimed professional experience",
"name": "Sept. 1998-June 1992 - OSU: Graduate Research Assistant, The Ohio State University, Columbus, Ohio."
},
{
"@type": "Occupation",
"additionalType": "self-claimed professional experience",
"name": "Dec. 1984-Nov. 1986 - Mysore and Bangalore University: Teaching hydraulics and water resources, India."
},
{
"@type": "Occupation",
"additionalType": "self-claimed professional experience",
"name": "The countries he has worked in include China, Cambodia, Indonesia, Israel, Syria, United States, Canada, Brazil, Uzbekistan, Bangladesh, India, Myanmar, Nepal, Sri Lanka, Republic of Benin, Burkina Faso, Cameroon, Central African Republic, Cote d'Ivoire, Gambia, Ghana, Mali, Nigeria, Senegal, Togo, Mozambique, and South Africa."
}
],
"identifier": [
{
"@type": "PropertyValue",
"propertyID": "ORCID",
"value": "0000-0002-2182-8822"
},
{
"@type": "PropertyValue",
"propertyID": "GeoKB",
"value": "https://geokb.wikibase.cloud/entity/Q50669"
}
],
"jobTitle": "Senior Scientist (ST)",
"knowsAbout": [
{
"@type": "Thing",
"additionalType": "self-claimed expertise",
"name": "geospatial analysis"
},
{
"@type": "Thing",
"additionalType": "self-claimed expertise",
"name": "maps and atlases"
},
{
"@type": "Thing",
"additionalType": "self-claimed expertise",
"name": "remote sensing"
},
{
"@type": "Thing",
"additionalType": "self-claimed expertise",
"name": "spatial analysis"
},
{
"@type": "Thing",
"additionalType": "self-claimed expertise",
"name": "forest resources"
},
{
"@type": "Thing",
"additionalType": "self-claimed expertise",
"name": "natural resource management"
},
{
"@type": "Thing",
"additionalType": "self-claimed expertise",
"name": "water resources"
},
{
"@type": "Thing",
"additionalType": "self-claimed expertise",
"name": "ecosystems"
},
{
"@type": "Thing",
"additionalType": "self-claimed expertise",
"name": "environmental assessment"
},
{
"@type": "Thing",
"additionalType": "self-claimed expertise",
"name": "forest ecosystems"
},
{
"@type": "Thing",
"additionalType": "self-claimed expertise",
"name": "freshwater ecosystems"
},
{
"@type": "Thing",
"additionalType": "self-claimed expertise",
"name": "wetland ecosystems"
},
{
"@type": "Thing",
"additionalType": "self-claimed expertise",
"name": "climate change"
},
{
"@type": "Thing",
"additionalType": "self-claimed expertise",
"name": "droughts"
},
{
"@type": "Thing",
"additionalType": "self-claimed expertise",
"name": "global cropland mapping"
},
{
"@type": "Thing",
"additionalType": "self-claimed expertise",
"name": "hydrology"
},
{
"@type": "Thing",
"additionalType": "self-claimed expertise",
"name": "food security"
},
{
"@type": "Thing",
"additionalType": "self-claimed expertise",
"name": "floods"
},
{
"@type": "Thing",
"additionalType": "self-claimed expertise",
"name": "hyperspectral remote sensing"
},
{
"@type": "Thing",
"additionalType": "self-claimed expertise",
"name": "groundwater"
},
{
"@type": "Thing",
"additionalType": "self-claimed expertise",
"name": "irrigation"
},
{
"@type": "Thing",
"additionalType": "self-claimed expertise",
"name": "machine learning and cloud computing"
},
{
"@type": "Thing",
"additionalType": "self-claimed expertise",
"name": "water budget"
},
{
"@type": "Thing",
"additionalType": "self-claimed expertise",
"name": "crop water productivity"
},
{
"@type": "Thing",
"additionalType": "self-claimed expertise",
"name": "water use"
},
{
"@type": "Thing",
"additionalType": "self-claimed expertise",
"name": "water security"
},
{
"@type": "Thing",
"additionalType": "self-claimed expertise",
"name": "vegetation"
}
],
"memberOf": {
"@type": "OrganizationalRole",
"member": {
"@type": "Organization",
"name": "U.S. Geological Survey"
},
"name": "staff member",
"startDate": "2024-05-10T21:01:12.562814"
},
"name": "Prasad S. Thenkabail, PhD",
"url": "https://www.usgs.gov/staff-profiles/prasad-thenkabail"
},
"ORCID": {
"@context": "http://schema.org",
"@id": "https://orcid.org/0000-0002-2182-8822",
"@reverse": {
"creator": [
{
"@id": "https://doi.org/10.3390/rs15194894",
"@type": "CreativeWork",
"identifier": {
"@type": "PropertyValue",
"propertyID": "doi",
"value": "10.3390/rs15194894"
},
"name": "Crop Water Productivity from Cloud-Based Landsat Helps Assess California\u2019s Water Savings"
},
{
"@id": "https://doi.org/10.3390/rs15041017",
"@type": "CreativeWork",
"identifier": {
"@type": "PropertyValue",
"propertyID": "doi",
"value": "10.3390/rs15041017"
},
"name": "Mapping Vegetation Index-Derived Actual Evapotranspiration across Croplands Using the Google Earth Engine Platform"
},
{
"@id": "https://doi.org/10.1080/15481603.2022.2088651",
"@type": "CreativeWork",
"identifier": {
"@type": "PropertyValue",
"propertyID": "doi",
"value": "10.1080/15481603.2022.2088651"
},
"name": "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"
},
{
"@id": "https://doi.org/10.3133/ofr20221001",
"@type": "CreativeWork",
"identifier": {
"@type": "PropertyValue",
"propertyID": "doi",
"value": "10.3133/ofr20221001"
},
"name": "Global food-security-support-analysis data at 30-m resolution (GFSAD30) cropland-extent products\u2014Download Analysis"
},
{
"@id": "https://doi.org/10.3390/rs13224704",
"@type": "CreativeWork",
"identifier": {
"@type": "PropertyValue",
"propertyID": "doi",
"value": "10.3390/rs13224704"
},
"name": "Classifying Crop Types Using Two Generations of Hyperspectral Sensors (Hyperion and DESIS) with Machine Learning on the Cloud"
},
{
"@id": "https://doi.org/10.3133/pp1868",
"@type": "CreativeWork",
"identifier": {
"@type": "PropertyValue",
"propertyID": "doi",
"value": "10.3133/pp1868"
},
"name": "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"
},
{
"@id": "https://doi.org/10.1016/j.isprsjprs.2020.06.022",
"@type": "CreativeWork",
"identifier": {
"@type": "PropertyValue",
"propertyID": "doi",
"value": "10.1016/j.isprsjprs.2020.06.022"
},
"name": "Mapping croplands of Europe, Middle East, Russia, and Central Asia using Landsat, Random Forest, and Google Earth Engine"
},
{
"@id": "https://doi.org/10.1080/17538947.2019.1651912",
"@type": "CreativeWork",
"identifier": {
"@type": "PropertyValue",
"propertyID": "doi",
"value": "10.1080/17538947.2019.1651912"
},
"name": "A meta-analysis of global crop water productivity of three leading world crops (wheat, corn, and rice) in the irrigated areas over three decades"
},
{
"@id": "https://doi.org/10.1080/15481603.2019.1690780",
"@type": "CreativeWork",
"identifier": {
"@type": "PropertyValue",
"propertyID": "doi",
"value": "10.1080/15481603.2019.1690780"
},
"name": "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"
},
{
"@id": "https://doi.org/10.3390/rs11151790",
"@type": "CreativeWork",
"identifier": {
"@type": "PropertyValue",
"propertyID": "doi",
"value": "10.3390/rs11151790"
},
"name": "Remote Sensing 10th Anniversary Best Paper Award"
},
{
"@id": "https://doi.org/10.3390/rs11010091",
"@type": "CreativeWork",
"identifier": {
"@type": "PropertyValue",
"propertyID": "doi",
"value": "10.3390/rs11010091"
},
"name": "A Bibliometric Profile of the Remote Sensing Open Access Journal Published by MDPI between 2009 and 2018"
},
{
"@id": "https://doi.org/10.3390/rs10122027",
"@type": "CreativeWork",
"identifier": {
"@type": "PropertyValue",
"propertyID": "doi",
"value": "10.3390/rs10122027"
},
"name": "Accuracies Achieved in Classifying Five Leading World Crop Types and their Growth Stages Using Optimal Earth Observing-1 Hyperion Hyperspectral Narrowbands on Google Earth Engine"
},
{
"@id": "https://doi.org/10.1080/15481603.2018.1482855",
"@type": "CreativeWork",
"identifier": {
"@type": "PropertyValue",
"propertyID": "doi",
"value": "10.1080/15481603.2018.1482855"
},
"name": "Mapping cropland fallow areas in myanmar to scale up sustainable intensification of pulse crops in the farming system"
},
{
"@id": "https://doi.org/10.1080/17538947.2016.1267269",
"@type": "CreativeWork",
"identifier": {
"@type": "PropertyValue",
"propertyID": "doi",
"value": "10.1080/17538947.2016.1267269"
},
"name": "Spectral matching techniques (SMTs) and automated cropland classification algorithms (ACCAs) for mapping croplands of Australia using MODIS 250-m time-series (2000\u20132015) data"
},
{
"@id": "https://doi.org/10.3133/ofr20161038",
"@type": "CreativeWork",
"identifier": {
"@type": "PropertyValue",
"propertyID": "doi",
"value": "10.3133/ofr20161038"
},
"name": "A software tool for rapid flood inundation mapping"
}
]
},
"@type": "Person",
"familyName": "Thenkabail",
"givenName": "Prasad",
"mainEntityOfPage": "https://orcid.org/0000-0002-2182-8822"
},
"OpenAlex": {
"created_date": "2023-07-21",
"display_name": "Prasad S. Thenkabail",
"display_name_alternatives": [
"P. S. Thenkabail*",
"Prasad Srinavasa Thenkabail",
"P. Thenkabail",
"Prasad Thenkabail",
"Prasad S. Thenkabail",
"P. S. Thenkabail"
],
"ids": {
"openalex": "https://openalex.org/A5039070473",
"orcid": "https://orcid.org/0000-0002-2182-8822"
},
"last_known_institutions": [
{
"country_code": "US",
"display_name": "Astrogeology Science Center",
"id": "https://openalex.org/I4210111045",
"lineage": [
"https://openalex.org/I1286329397",
"https://openalex.org/I1335927249",
"https://openalex.org/I4210111045"
],
"ror": "https://ror.org/02623eb90",
"type": "facility"
},
{
"country_code": "US",
"display_name": "United States Geological Survey",
"id": "https://openalex.org/I1286329397",
"lineage": [
"https://openalex.org/I1286329397",
"https://openalex.org/I1335927249"
],
"ror": "https://ror.org/035a68863",
"type": "government"
}
],
"orcid": "https://orcid.org/0000-0002-2182-8822",
"topics": [
{
"count": 128,
"display_name": "Remote Sensing in Vegetation Monitoring and Phenology",
"domain": {
"display_name": "Physical Sciences",
"id": "https://openalex.org/domains/3"
},
"field": {
"display_name": "Environmental Science",
"id": "https://openalex.org/fields/23"
},
"id": "https://openalex.org/T10111",
"subfield": {
"display_name": "Ecology",
"id": "https://openalex.org/subfields/2303"
}
},
{
"count": 58,
"display_name": "Applications of Remote Sensing in Geoscience and Agriculture",
"domain": {
"display_name": "Physical Sciences",
"id": "https://openalex.org/domains/3"
},
"field": {
"display_name": "Earth and Planetary Sciences",
"id": "https://openalex.org/fields/19"
},
"id": "https://openalex.org/T13890",
"subfield": {
"display_name": "Atmospheric Science",
"id": "https://openalex.org/subfields/1902"
}
},
{
"count": 36,
"display_name": "Global Analysis of Ecosystem Services and Land Use",
"domain": {
"display_name": "Physical Sciences",
"id": "https://openalex.org/domains/3"
},
"field": {
"display_name": "Environmental Science",
"id": "https://openalex.org/fields/23"
},
"id": "https://openalex.org/T10226",
"subfield": {
"display_name": "Global and Planetary Change",
"id": "https://openalex.org/subfields/2306"
}
},
{
"count": 33,
"display_name": "Hyperspectral Image Analysis and Classification",
"domain": {
"display_name": "Physical Sciences",
"id": "https://openalex.org/domains/3"
},
"field": {
"display_name": "Engineering",
"id": "https://openalex.org/fields/22"
},
"id": "https://openalex.org/T10689",
"subfield": {
"display_name": "Media Technology",
"id": "https://openalex.org/subfields/2214"
}
},
{
"count": 23,
"display_name": "Mapping Forests with Lidar Remote Sensing",
"domain": {
"display_name": "Physical Sciences",
"id": "https://openalex.org/domains/3"
},
"field": {
"display_name": "Environmental Science",
"id": "https://openalex.org/fields/23"
},
"id": "https://openalex.org/T11164",
"subfield": {
"display_name": "Environmental Engineering",
"id": "https://openalex.org/subfields/2305"
}
},
{
"count": 17,
"display_name": "Optimal Operation of Water Resources Systems",
"domain": {
"display_name": "Physical Sciences",
"id": "https://openalex.org/domains/3"
},
"field": {
"display_name": "Engineering",
"id": "https://openalex.org/fields/22"
},
"id": "https://openalex.org/T10969",
"subfield": {
"display_name": "Ocean Engineering",
"id": "https://openalex.org/subfields/2212"
}
},
{
"count": 15,
"display_name": "Chemometrics in Analytical Chemistry and Food Technology",
"domain": {
"display_name": "Physical Sciences",
"id": "https://openalex.org/domains/3"
},
"field": {
"display_name": "Chemistry",
"id": "https://openalex.org/fields/16"
},
"id": "https://openalex.org/T10640",
"subfield": {
"display_name": "Analytical Chemistry",
"id": "https://openalex.org/subfields/1602"
}
},
{
"count": 12,
"display_name": "Land-Use Suitability Assessment Using GIS",
"domain": {
"display_name": "Physical Sciences",
"id": "https://openalex.org/domains/3"
},
"field": {
"display_name": "Environmental Science",
"id": "https://openalex.org/fields/23"
},
"id": "https://openalex.org/T13058",
"subfield": {
"display_name": "Management, Monitoring, Policy and Law",
"id": "https://openalex.org/subfields/2308"
}
},
{
"count": 11,
"display_name": "Adaptation to Climate Change in Agriculture",
"domain": {
"display_name": "Life Sciences",
"id": "https://openalex.org/domains/1"
},
"field": {
"display_name": "Agricultural and Biological Sciences",
"id": "https://openalex.org/fields/11"
},
"id": "https://openalex.org/T10439",
"subfield": {
"display_name": "Ecology, Evolution, Behavior and Systematics",
"id": "https://openalex.org/subfields/1105"
}
},
{
"count": 10,
"display_name": "Hydrological Modeling and Water Resource Management",
"domain": {
"display_name": "Physical Sciences",
"id": "https://openalex.org/domains/3"
},
"field": {
"display_name": "Environmental Science",
"id": "https://openalex.org/fields/23"
},
"id": "https://openalex.org/T10330",
"subfield": {
"display_name": "Water Science and Technology",
"id": "https://openalex.org/subfields/2312"
}
},
{
"count": 10,
"display_name": "Non-destructive Leaf Area Estimation Methods",
"domain": {
"display_name": "Life Sciences",
"id": "https://openalex.org/domains/1"
},
"field": {
"display_name": "Agricultural and Biological Sciences",
"id": "https://openalex.org/fields/11"
},
"id": "https://openalex.org/T14365",
"subfield": {
"display_name": "Plant Science",
"id": "https://openalex.org/subfields/1110"
}
},
{
"count": 8,
"display_name": "Global Forest Drought Response and Climate Change",
"domain": {
"display_name": "Physical Sciences",
"id": "https://openalex.org/domains/3"
},
"field": {
"display_name": "Environmental Science",
"id": "https://openalex.org/fields/23"
},
"id": "https://openalex.org/T10266",
"subfield": {
"display_name": "Global and Planetary Change",
"id": "https://openalex.org/subfields/2306"
}
},
{
"count": 8,
"display_name": "Rice Water Management and Productivity Enhancement",
"domain": {
"display_name": "Life Sciences",
"id": "https://openalex.org/domains/1"
},
"field": {
"display_name": "Agricultural and Biological Sciences",
"id": "https://openalex.org/fields/11"
},
"id": "https://openalex.org/T12045",
"subfield": {
"display_name": "Plant Science",
"id": "https://openalex.org/subfields/1110"
}
},
{
"count": 8,
"display_name": "Agricultural Development and Policy in South Asia",
"domain": {
"display_name": "Life Sciences",
"id": "https://openalex.org/domains/1"
},
"field": {
"display_name": "Agricultural and Biological Sciences",
"id": "https://openalex.org/fields/11"
},
"id": "https://openalex.org/T13508",
"subfield": {
"display_name": "General Agricultural and Biological Sciences",
"id": "https://openalex.org/subfields/1100"
}
},
{
"count": 7,
"display_name": "Digital Soil Mapping Techniques",
"domain": {
"display_name": "Physical Sciences",
"id": "https://openalex.org/domains/3"
},
"field": {
"display_name": "Environmental Science",
"id": "https://openalex.org/fields/23"
},
"id": "https://openalex.org/T10770",
"subfield": {
"display_name": "Environmental Engineering",
"id": "https://openalex.org/subfields/2305"
}
},
{
"count": 7,
"display_name": "Integrated Management of Water, Energy, and Food Resources",
"domain": {
"display_name": "Physical Sciences",
"id": "https://openalex.org/domains/3"
},
"field": {
"display_name": "Environmental Science",
"id": "https://openalex.org/fields/23"
},
"id": "https://openalex.org/T12724",
"subfield": {
"display_name": "Water Science and Technology",
"id": "https://openalex.org/subfields/2312"
}
},
{
"count": 6,
"display_name": "Species Distribution Modeling and Climate Change Impacts",
"domain": {
"display_name": "Physical Sciences",
"id": "https://openalex.org/domains/3"
},
"field": {
"display_name": "Environmental Science",
"id": "https://openalex.org/fields/23"
},
"id": "https://openalex.org/T10895",
"subfield": {
"display_name": "Ecological Modeling",
"id": "https://openalex.org/subfields/2302"
}
},
{
"count": 6,
"display_name": "Machine Learning Methods for Solar Radiation Forecasting",
"domain": {
"display_name": "Physical Sciences",
"id": "https://openalex.org/domains/3"
},
"field": {
"display_name": "Computer Science",
"id": "https://openalex.org/fields/17"
},
"id": "https://openalex.org/T11276",
"subfield": {
"display_name": "Artificial Intelligence",
"id": "https://openalex.org/subfields/1702"
}
},
{
"count": 5,
"display_name": "Precision Agriculture Technologies",
"domain": {
"display_name": "Life Sciences",
"id": "https://openalex.org/domains/1"
},
"field": {
"display_name": "Agricultural and Biological Sciences",
"id": "https://openalex.org/fields/11"
},
"id": "https://openalex.org/T10616",
"subfield": {
"display_name": "Plant Science",
"id": "https://openalex.org/subfields/1110"
}
},
{
"count": 5,
"display_name": "Hydrological Effects and Water Resource Management in China",
"domain": {
"display_name": "Physical Sciences",
"id": "https://openalex.org/domains/3"
},
"field": {
"display_name": "Environmental Science",
"id": "https://openalex.org/fields/23"
},
"id": "https://openalex.org/T14157",
"subfield": {
"display_name": "Water Science and Technology",
"id": "https://openalex.org/subfields/2312"
}
},
{
"count": 5,
"display_name": "Machine Learning for Mineral Prospectivity Mapping",
"domain": {
"display_name": "Physical Sciences",
"id": "https://openalex.org/domains/3"
},
"field": {
"display_name": "Computer Science",
"id": "https://openalex.org/fields/17"
},
"id": "https://openalex.org/T12157",
"subfield": {
"display_name": "Artificial Intelligence",
"id": "https://openalex.org/subfields/1702"
}
},
{
"count": 5,
"display_name": "Global Methane Emissions and Impacts",
"domain": {
"display_name": "Physical Sciences",
"id": "https://openalex.org/domains/3"
},
"field": {
"display_name": "Environmental Science",
"id": "https://openalex.org/fields/23"
},
"id": "https://openalex.org/T11588",
"subfield": {
"display_name": "Global and Planetary Change",
"id": "https://openalex.org/subfields/2306"
}
},
{
"count": 5,
"display_name": "Global Drought Monitoring and Assessment",
"domain": {
"display_name": "Physical Sciences",
"id": "https://openalex.org/domains/3"
},
"field": {
"display_name": "Environmental Science",
"id": "https://openalex.org/fields/23"
},
"id": "https://openalex.org/T11186",
"subfield": {
"display_name": "Global and Planetary Change",
"id": "https://openalex.org/subfields/2306"
}
},
{
"count": 4,
"display_name": "Volunteered Geographic Information and Geospatial Crowdsourcing",
"domain": {
"display_name": "Social Sciences",
"id": "https://openalex.org/domains/2"
},
"field": {
"display_name": "Social Sciences",
"id": "https://openalex.org/fields/33"
},
"id": "https://openalex.org/T10757",
"subfield": {
"display_name": "Geography, Planning and Development",
"id": "https://openalex.org/subfields/3305"
}
},
{
"count": 4,
"display_name": "Factors Affecting Sagebrush Ecosystems and Wildlife Conservation",
"domain": {
"display_name": "Physical Sciences",
"id": "https://openalex.org/domains/3"
},
"field": {
"display_name": "Environmental Science",
"id": "https://openalex.org/fields/23"
},
"id": "https://openalex.org/T13388",
"subfield": {
"display_name": "Ecology",
"id": "https://openalex.org/subfields/2303"
}
}
],
"updated_date": "2024-05-20T17:09:20.985891"
}
}
