Refinement of Cropland Data Layer Using Machine Learning

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.

Preserving local biodiversity through crop diversification

“…With regard to the food crop related variables, all except food crop yield are derived from the CDL data, which are based on satellite imagery and extensive agricultural ground truth data. Importantly the CDL has been shown to be up to 95% accuracy for identifying the major crop types, namely corn, soybean, and wheat (Zhang et al, 2020). Nevertheless, the degree of accuracy for other food crops is still unknown, and thus there may be measurement error in our measure of crop diversity.…”

HarvestGRID: High-resolution harvested crop areas of the United States from 1981 to 2019

“…Additionally, any uncertainties associated with spatial distribution of gridded data products are also present in our data product. Remotely sensed data products, relying on spectral signatures to distinguish crops exhibit varying accuracy based on factors such as crop type, geographic location, quality and quantity of satellite imagery available [25]. Furthermore, since crop-specific gridded dataset before 2008 was unavailable, we assumed that the distribution of crops prior to 2008 resembled the average crop distribution post-2008.…”

Surface Water Mapping and Flood Monitoring in the Mekong Delta Using Sentinel-1 SAR Time Series and Otsu Threshold

“…The water is shallow (5-15 cm) and contaminated with soil in the sowing period (brown color in the Sentinel-2 FBR images), while it is deeper and clarified in the flood period (dark blue color in the Sentinel-2 FBR images). Moreover, machine-learning-based (support vector machine, decision tree, and random forest) or deep-learning-based (deep neural networks, convolutional neural networks, and recurrent neural network) supervised classification methods are widely used for LULC classification and water detection [62,63,[109][110][111][112]. One of the most important advantages of these methods is the ability to deal with uncertainties in the input data and cope with multiple input data sources in order to improve the accuracy of the classification or regression results [76,113].…”