Rapid corn and soybean mapping in US Corn Belt and neighboring areas.
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.
Department of Environmental Science, Policy and Management, University of California, Berkeley, California, USA.
Liheng
L
Zhong
Ministry of Education Key Laboratory for Earth System Modeling, Center for Earth System Science, Tsinghua University, Beijing, 100084, China.|Joint Center for Global Change Studies, Beijing 100875, China.
Le
L
Yu
Department of Geological &Atmospheric Sciences, Iowa State University, Ames, Iowa, 50011, USA.
Xuecao
X
Li
Department of Sociology, Tsinghua University, Beijing 100084, China.
Lina
L
Hu
Ministry of Education Key Laboratory for Earth System Modeling, Center for Earth System Science, Tsinghua University, Beijing, 100084, China.|Joint Center for Global Change Studies, Beijing 100875, China.
Peng
P
Gong
D000383:Agriculture / Q000379:methods / Q000706:statistics & numerical data; D000465:Algorithms; D018556:Crops, Agricultural / Q000254:growth & development*; D008377:Maps as Topic; D013025:Glycine max / Q000254:growth & development*; D018531:Spacecraft; D013997:Time Factors; D014481:United States; D016885:United States Department of Agriculture; D003313:Zea mays / Q000254:growth & development*