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

Zhuoting Wu; Prasad S. Thenkabail; Rick Mueller; Audra Zakzeski; Forrest Melton; Lee Johnson; Carolyn Rosevelt; John Dwyer; Jeanine Jones; James P. Verdin

doi:10.1117/1.JRS.8.083685

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

Journal of Applied Remote Sensing

By: Zhuoting Wu, Prasad S. Thenkabail, Rick Mueller, Audra Zakzeski, Forrest Melton, Lee Johnson, Carolyn Rosevelt, John Dwyer, Jeanine Jones, and James P. Verdin

https://doi.org/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	2014
Language	English
Publisher	SPIE
Contributing office(s)	Earth Resources Observation and Science (EROS) Center, Western Geographic Science Center
Description	Article 083685; 17 p.
Country	United States
State	California
Google Analytic Metrics	Metrics page