Abstract
The goal of this study was to promptly map the extent of corn and soybeans early in the growing season. A classification experiment was conducted for the US Corn Belt and neighboring states, which is the most important production area of corn and soybeans in the world. To improve the timeliness of the classification algorithm, training was completely based on reference data and images from other years, circumventing the need to finish reference data collection in the current season. To account for interannual variability in crop development in the cross-year classification scenario, several innovative strategies were used. A random forest classifier was used in all tests, and MODIS surface reflectance products from the years 2008–2014 were used for training and cross-year validation. It is concluded that the fuzzy classification approach is necessary to achieve satisfactory results with R-squared ~0.9 (compared with the USDA Cropland Data Layer). The year of training data is an important factor, and it is recommended to select a year with similar crop phenology as the mapping year. With this phenology-based and cross-year-training method, in 2015 we mapped the cropping proportion of corn and soybeans around mid-August, when the two crops just reached peak growth.
Highlights
The goal of this study was to promptly map the extent of corn and soybeans early in the growing season
A random forest classifier was used in all tests, and MODerate-resolution Imaging Spectroradiometer (MODIS) surface reflectance products from the years 2008–2014 were used for training and cross-year validation
All these states are characterized by a certain level of agricultural development with corn and/or soybean production, which is treated in USDA weekly reports of crop progress
Summary
The goal of this study was to promptly map the extent of corn and soybeans early in the growing season. The year of training data is an important factor, and it is recommended to select a year with similar crop phenology as the mapping year. For this area, numerous studies and intensive networks have been established to make production forecasts based on surveys, census, fieldwork and statistical analysis by governmental and private sectors. Compared with existing fast-response, survey-based approaches, it is challenging for remote sensing studies to meet practical requirements on a timely basis, owing to limitations of image and reference data availability, processing time, and other factors. For estimates of harvested area, image classification is important to mapping the extent of corn and soybean fields.
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