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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, 2018-10, Vol.144, p.325-340

2018

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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 (Basel, Switzerland), 2017-10, Vol.9 (10), p.1065

2017

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Classifying Crop Types Using Two Generations of Hyperspectral Sensors (Hyperion and DESIS) with Machine Learning on the Cloud

Remote sensing (Basel, Switzerland), 2021-11, Vol.13 (22), p.4704

2021

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Remote Sensing Open Access Journal of MDPI: Current Progress and Future Vision

Remote sensing (Basel, Switzerland), 2020-08, Vol.12 (15), p.2442

Thenkabail, Prasad S.

2020

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Remote Sensing 10th Anniversary Best Paper Award

Remote sensing (Basel, Switzerland), 2019-08, Vol.11 (15), p.1790

Thenkabail, Prasad S.

2019

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A Bibliometric Profile of the Remote Sensing Open Access Journal Published by MDPI between 2009 and 2018

Remote sensing (Basel, Switzerland), 2019-01, Vol.11 (1), p.91

2019

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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

GIScience and remote sensing, 2022-12, Vol.59 (1), p.1048-1077

2022

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An Automated Cropland Classification Algorithm (ACCA) for Tajikistan by Combining Landsat, MODIS, and Secondary Data

Remote sensing (Basel, Switzerland), 2012-10, Vol.4 (10), p.2890-2918

2012

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Global Croplands and their Importance for Water and Food Security in the Twenty-first Century: Towards an Ever Green Revolution that Combines a Second Green Revolution with a Blue Revolution

Remote sensing (Basel, Switzerland), 2010-09, Vol.2 (9), p.2305-2312

Thenkabail, Prasad S.

2010

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Hyperspectral remote sensing of vegetation.: (Fundamentals, sensor systems, spectral libraries, and data mining for vegetation)

Second edition., 2018

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A meta-analysis of global crop water productivity of three leading world crops (wheat, corn, and rice) in the irrigated areas over three decades

International journal of digital earth, 2020-08, Vol.13 (8), p.939-975

2020

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Accuracy assessments of hyperspectral waveband performance for vegetation analysis applications

Remote sensing of environment, 2004-06, Vol.91 (3), p.354-376

2004

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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, 2016-10, Vol.9 (10), p.981-1003

2016

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Remote Sensing Sensors and Applications in Environmental Resources Mapping and Modelling

Sensors (Basel, Switzerland), 2007-12, Vol.7 (12), p.3209-3241

2007

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Mapping Vegetation Index-Derived Actual Evapotranspiration across Croplands Using the Google Earth Engine Platform

Remote sensing (Basel, Switzerland), 2023-02, Vol.15 (4), p.1017

2023

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Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for Vegetation

2, 2018

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Remote sensing of water resources, disasters, and urban studies

Remote sensing handbook ; v. 3, 2016, Vol.III

Thenkabail, Prasad S

1st edition, 2016

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Advanced Applications in Remote Sensing of Agricultural Crops and Natural Vegetation

Second edition., 2018

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Land Resources Monitoring, Modeling, and Mapping with Remote Sensing

1, 2015

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Mapping cropland fallow areas in myanmar to scale up sustainable intensification of pulse crops in the farming system

GIScience and remote sensing, 2018-11, Vol.55 (6), p.926-949

2018

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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

Remote sensing (Basel, Switzerland), 2010-01, Vol.2 (1), p.211-261

2010

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Mapping Irrigated Areas of Ghana Using Fusion of 30 m and 250 m Resolution Remote-Sensing Data

Remote sensing (Basel, Switzerland), 2011-04, Vol.3 (4), p.816-835

2011

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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, 2017-09, Vol.10 (9), p.944-977

2017

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Hyperspectral versus multispectral crop-productivity modeling and type discrimination for the HyspIRI mission

Remote sensing of environment, 2013-12, Vol.139, p.291-305

2013

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Biophysical and Biochemical Characterization and Plant Species Studies

Second edition., 2019

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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

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

Classifying Crop Types Using Two Generations of Hyperspectral Sensors (Hyperion and DESIS) with Machine Learning on the Cloud

Remote Sensing Open Access Journal of MDPI: Current Progress and Future Vision

Remote Sensing 10th Anniversary Best Paper Award

A Bibliometric Profile of the Remote Sensing Open Access Journal Published by MDPI between 2009 and 2018

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

An Automated Cropland Classification Algorithm (ACCA) for Tajikistan by Combining Landsat, MODIS, and Secondary Data

Global Croplands and their Importance for Water and Food Security in the Twenty-first Century: Towards an Ever Green Revolution that Combines a Second Green Revolution with a Blue Revolution

Hyperspectral remote sensing of vegetation.: (Fundamentals, sensor systems, spectral libraries, and data mining for vegetation)

A meta-analysis of global crop water productivity of three leading world crops (wheat, corn, and rice) in the irrigated areas over three decades

Accuracy assessments of hyperspectral waveband performance for vegetation analysis applications

Mapping rice-fallow cropland areas for short-season grain legumes intensification in South Asia using MODIS 250 m time-series data

Remote Sensing Sensors and Applications in Environmental Resources Mapping and Modelling

Mapping Vegetation Index-Derived Actual Evapotranspiration across Croplands Using the Google Earth Engine Platform

Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for Vegetation

Remote sensing of water resources, disasters, and urban studies

Advanced Applications in Remote Sensing of Agricultural Crops and Natural Vegetation

Land Resources Monitoring, Modeling, and Mapping with Remote Sensing

Mapping cropland fallow areas in myanmar to scale up sustainable intensification of pulse crops in the farming system

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

Mapping Irrigated Areas of Ghana Using Fusion of 30 m and 250 m Resolution Remote-Sensing Data

Spectral matching techniques (SMTs) and automated cropland classification algorithms (ACCAs) for mapping croplands of Australia using MODIS 250-m time-series (2000-2015) data

Hyperspectral versus multispectral crop-productivity modeling and type discrimination for the HyspIRI mission

Biophysical and Biochemical Characterization and Plant Species Studies

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