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Mapping cropland extent of Southeast and Northeast Asia using multi-year time-series Landsat 30-m data using a random forest classifier on the Google Earth Engine Cloud

OAR@ICRISAT

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Relation http://oar.icrisat.org/11209/
https://doi.org/10.1016/j.jag.2018.11.014
10.1016/j.jag.2018.11.014
 
Title Mapping cropland extent of Southeast and Northeast Asia using multi-year time-series Landsat 30-m data using a random forest classifier on the Google Earth Engine Cloud
 
Creator Oliphant, A J
Thenkabail, P S
Teluguntla, P
Xiong, J
Gumma, M K
Congalton, R G
Yadav, K
 
Subject GIS Techniques/Remote Sensing
Food Security
Asia
 
Description 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) within and across nations, which in turn can be used to estimate agricultural productivity as well as degree of disturbance on food security from natural disasters and political conflict. However, existing cropland extent maps over large areas (e.g., Country, region, continent, world) are derived from coarse resolution imagery (250 m to 1 km pixels) and have many limitations such as missing fragmented and\or small farms with mixed signatures from different crop types and\or farming practices that can be, confused with other land cover. As a result, the coarse resolution maps have limited useflness in areas where fields are small (
 
Publisher Elsevier
 
Date 2019-09
 
Type Article
PeerReviewed
 
Format application/pdf
 
Language en
 
Identifier http://oar.icrisat.org/11209/1/1-s2.0-S0303243418307414-main.pdf
Oliphant, A J and Thenkabail, P S and Teluguntla, P and Xiong, J and Gumma, M K and Congalton, R G and Yadav, K (2019) Mapping cropland extent of Southeast and Northeast Asia using multi-year time-series Landsat 30-m data using a random forest classifier on the Google Earth Engine Cloud. International Journal of Applied Earth Observation and Geoinformation (TSI), 81. pp. 110-124. ISSN 03032434