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Seasonal cultivated and fallow cropland mapping using MODIS-based automated cropland classification algorithm

Journal of Applied Remote Sensing

By:
, , , , , , , , ,
DOI: 10.1117/1.JRS.8.083685

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Abstract

Increasing drought occurrences and growing populations demand accurate, routine, and consistent cultivated and fallow cropland products to enable water and food security analysis. The overarching goal of this research was to develop and test automated cropland classification algorithm (ACCA) that provide accurate, consistent, and repeatable information on seasonal cultivated as well as seasonal fallow cropland extents and areas based on the Moderate Resolution Imaging Spectroradiometer remote sensing data. Seasonal ACCA development process involves writing series of iterative decision tree codes to separate cultivated and fallow croplands from noncroplands, aiming to accurately mirror reliable reference data sources. A pixel-by-pixel accuracy assessment when compared with the U.S. Department of Agriculture (USDA) cropland data showed, on average, a producer’s accuracy of 93% and a user’s accuracy of 85% across all months. Further, ACCA-derived cropland maps agreed well with the USDA Farm Service Agency crop acreage-reported data for both cultivated and fallow croplands with R-square values over 0.7 and field surveys with an accuracy of ≥95% for cultivated croplands and ≥76% for fallow croplands. Our results demonstrated the ability of ACCA to generate cropland products, such as cultivated and fallow cropland extents and areas, accurately, automatically, and repeatedly throughout the growing season.

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Additional Publication Details

Publication type:
Article
Publication Subtype:
Journal Article
Title:
Seasonal cultivated and fallow cropland mapping using MODIS-based automated cropland classification algorithm
Series title:
Journal of Applied Remote Sensing
DOI:
10.1117/1.JRS.8.083685
Volume
8
Issue:
1
Year Published:
2013
Language:
English
Publisher:
SPIE
Contributing office(s):
Western Geographic Science Center
Description:
17 p.
Larger Work Type:
Article
Larger Work Subtype:
Journal Article
Number of Pages:
17
Country:
United States
State:
California