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

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...

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Bibliographic Details
Main Authors: Jun Xiong, Prasad S. Thenkabail, James C. Tilton, Murali K. Gumma, Pardhasaradhi Teluguntla, Adam Oliphant, Russell G. Congalton, Kamini Yadav, Noel Gorelick
Format: Article
Language:English
Published: MDPI AG 2017-10-01
Series:Remote Sensing
Subjects:
cropland mapping
cropland areas
30-m
Landsat-8
Sentinel-2
Random Forest
Support Vector Machines
segmentation
RHSeg
Google Earth Engine
Africa
Online Access:https://www.mdpi.com/2072-4292/9/10/1065

Internet

https://www.mdpi.com/2072-4292/9/10/1065

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