Abstract
Abstract. As the most widely used crop-specific land use data, the Cropland Data Layer (CDL) product covers the entire Contiguous United States (CONUS) at 30-meter spatial resolution with very high accuracy up to 95% for major crop types (i.e., Corn, Soybean) in major crop area. However, the quality of early-year CDL products were not as good as the recent ones. There are many erroneous pixels in the early-year CDL product due to the cloud cover of the original Landsat images, which affect many follow-on researches and applications. To address this issue, we explore the feasibility of using machine learning technology to refine and correct misclassified pixels in the historical CDLs in this study. An end-to-end deep learning-based framework for restoration of misclassified pixels in CDL image is developed and tested. By feeding the CDL time series into the artificial neural network, a crop sequence model is trained and the misclassified pixels in an original CDL map can be restored. In the experiment with the 2005 CDL data of the State of Illinois, the misclassified pixels over Agricultural Statistics Districts (ASD) #1760 were corrected with a reasonable accuracy (> 85%). The findings suggest that the proposed method provides a low-cost and reliable way to refine the historical CDL data, which can be potentially scaled up to the entire CONUS.
Highlights
Since its first release of a full state wide data product in 1997, the Cropland Data Layer (CDL) product of the U.S Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) has been widely used by growers, agricultural industry, governments, educators and students, and researchers world-wide for crop production, agricultural production planning and management, government policy formulation and decision making, teaching, and various research activities (Liknes et al, 2009; Thompson, Prokopy; Hao et al, 2015; Lark et al, 2015; Di et al, 2017)
We present a machine learning-based crop sequence model to refine the historical CDL data
This study investigated the feasibility of using machine learning technology to refine CDL data
Summary
The CDL data covers the entire conterminous United States (CONUS) at 30-meter spatial resolution with a high accuracy up to 95% for classifying major crop types (i.e., Corn, Soybean, and Wheat). The quality of the early-year CDL products was not as good as recent years. There are many misclassified pixels in the CDL products because of cloud cover and lack of satellite images. Only a few states of CDL data were produced before 2008. The year 2000 CDL covers only Illinois, Indiana, Mississippi, North Dakota, and a part of Arkansas and Iowa. An effective method for refining and correcting the old CDL data is badly needed to improve the quality and accuracy of the historical CDL data
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