About: Prasad S. Thenkabail

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An Entity of Type : wikibase:Item, within Data Space : wikidata.demo.openlinksw.com associated with source document(s)

researcher

Attributes	Values
rdf:type	Item
description	forskare (sv) ikertzailea (eu) researcher (en) 研究者 (ja) araştırmacı (tr) wetenschapper (nl) дослідник (uk) वैज्ञानिक (hi) изилдөөчү (ky)
rdfs:label	Prasad S. Thenkabail (en) Prasad Thenkabail (es) Prasad Thenkabail (nl)
skos:prefLabel	Prasad S. Thenkabail (en) Prasad Thenkabail (es) Prasad Thenkabail (nl)
name	Prasad S. Thenkabail (en) Prasad Thenkabail (es) Prasad Thenkabail (nl)
instance of	wds:Q58458414-44B8474D-C4D0-4CB1-988E-116A9255704B
instance of	Homo sapiens
skos:altLabel	P S Thenkabail (en) P. S. Thenkabail (en) P.S. Thenkabail (en) Prasad S Thenkabail (en) Prasad Thenkabail (en)
occupation	wds:Q58458414-ADE8525E-73E1-4E9D-8298-2C51A1E44CDF
NUKAT ID	wds:Q58458414-3F5A1916-EC85-4136-A1F6-0427EFF4B73E
sex or gender	wds:Q58458414-A7BE2732-D6B0-44FC-8C0A-A47EE3103300
ISNI	wds:Q58458414-248395A6-D9DC-4272-A352-B0D7FEA94DFD
VIAF ID	wds:Q58458414-ABCE5BAC-38B9-4BEB-B1C0-D99392019E51
GND ID	wds:Q58458414-c568c7e6-0d4c-4880-a55c-4f98c3a7b26e
Library of Congress authority ID	wds:Q58458414-1FDA0DC7-316F-4A30-805A-20481CCB8CA9
DBLP author ID	wds:Q58458414-276BDEA5-30F3-4E3F-AB57-91437F8A14B3
IdRef ID	wds:Q58458414-2F1DE87A-85D3-4868-9008-AAAB1A9CA41F
date of birth	wds:Q58458414-82250682-2A29-458E-A7E2-44A5421A096B
WorldCat Identities ID (superseded)	wds:Q58458414-E1131A20-1A19-4CE9-850A-6386075F5DD7
National Library of Israel J9U ID	wds:Q58458414-DC8FBB72-8DEC-4536-B30B-A1B723FD5EFA
VIAF ID	http://viaf.org/viaf/63598947
GND ID	https://d-nb.info/gnd/138800952
Library of Congress authority ID	http://id.loc.gov/authorities/names/no96011721
DBLP author ID	https://dblp.org/pid/87/10426.rdf
IdRef ID	http://www.idref.fr/158971337/id
occupation	researcher
NUKAT ID	n2012070705
sex or gender	male
ISNI	0000000066884233
VIAF ID	63598947
GND ID	138800952
Library of Congress authority ID	no96011721
DBLP author ID	87/10426
IdRef ID	158971337
date of birth	1958-01-01 00:00:00Z (xsd:dateTime)
WorldCat Identities ID (superseded)	lccn-no96011721
National Library of Israel J9U ID	987007522485505171
ORCID iD	wds:Q58458414-1FE86F6F-FEDD-43E3-AEDB-ADFA9FD25DD7
ORCID iD	https://orcid.org/0000-0002-2182-8822
ORCID iD	0000-0002-2182-8822
ORKG ID	wds:Q58458414-207f1d78-5915-42dc-9ec6-85f017504be1
ORKG ID	R189509
is about of	https://www.wikidata.org/wiki/Special:EntityData/Q58458414
is author of	Remote Sensing Sensors and Applications in Environmental Resources Mapping and Modelling Free Access to Landsat Imagery Spectral matching techniques (SMTs) and automated cropland classification algorithms (ACCAs) for mapping croplands of Australia using MODIS 250-m time-series (2000–2015) data Automated cropland mapping of continental Africa using Google Earth Engine cloud computing 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 Mapping rice-fallow cropland areas for short-season grain legumes intensification in South Asia using MODIS 250 m time-series data Mapping seasonal rice cropland extent and area in the high cropping intensity environment of Bangladesh using MODIS 500m data for the year 2010 Changes in agricultural cropland areas between a water-surplus year and a water-deficit year impacting food security, determined using MODIS 250 m time-series data and spectral matching techniques, in the Krishna River basin (India) Mapping Irrigated Areas of Ghana Using Fusion of 30 m and 250 m Resolution Remote-Sensing Data A Holistic View of Global Croplands and Their Water Use for Ensuring Global Food Security in the 21st Century through Advanced Remote Sensing and Non-remote Sensing Approaches Irrigated areas of India derived using MODIS 500 m time series for the years 2001–2003 A global map of rainfed cropland areas (GMRCA) at the end of last millennium using remote sensing Spatial models for selecting the most suitable areas of rice cultivation in the Inland Valley Wetlands of Ghana using remote sensing and geographic information systems Irrigated area mapping in heterogeneous landscapes with MODIS time series, ground truth and census data, Krishna Basin, India Hyperspectral versus multispectral crop-productivity modeling and type discrimination for the HyspIRI mission Seasonal cultivated and fallow cropland mapping using MODIS-based automated cropland classification algorithm Global irrigated area map (GIAM), derived from remote sensing, for the end of the last millennium Selection of Hyperspectral Narrowbands (HNBs) and Composition of Hyperspectral Twoband Vegetation Indices (HVIs) for Biophysical Characterization and Discrimination of Crop Types Using Field Reflectance and Hyperion/EO-1 Data Decadal Variations in NDVI and Food Production in India A meta-analysis of global crop water productivity of three leading world crops (wheat, corn, and rice) in the irrigated areas over three decades 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 Mapping Irrigated Areas Using MODIS 250 Meter Time-Series Data: A Study on Krishna River Basin (India) Global Irrigated Area Maps (GIAM) and Statistics Using Remote Sensing Detecting floristic structure and pattern across topographic and moisture gradients in a mixed species Central African forest using IKONOS and Landsat-7 ETM+ images 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 Irrigated Area Maps and Statistics of India Using Remote Sensing and National Statistics Classifying Crop Types Using Two Generations of Hyperspectral Sensors (Hyperion and DESIS) with Machine Learning on the Cloud 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 Mapping Vegetation Index-Derived Actual Evapotranspiration across Croplands Using the Google Earth Engine Platform A sweet deal? Sugarcane, water and agricultural transformation in Sub-Saharan Africa Mapping cropland fallow areas in myanmar to scale up sustainable intensification of pulse crops in the farming system Global Map of Rainfed Cropland Areas (GMRCA) and Statistics Using Remote Sensing Irrigated Areas of India Derived from Satellite Sensors and National Statistics International Water Management Institute's Data Storehouse Pathway (IWMIDSP): a unique data and knowledge gateway of spatial data with emphasis on river basins MODIS phenology-derived, multi-year distribution of conterminous U.S. crop types Mapping croplands of Europe, Middle East, Russia, and Central Asia using Landsat, Random Forest, and Google Earth Engine Remote Sensing 10th Anniversary Best Paper Award A History Of Irrigated Areas Of The World Water productivity mapping using remote sensing data of various resolutions to support "more crop per drop"

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