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Rapid crop cover mapping for the conterminous United States

June 14, 2018

Timely crop cover maps with sufficient resolution are important components to various environmental planning and research applications. Through the modification and use of a previously developed crop classification model (CCM), which was originally developed to generate historical annual crop cover maps, we hypothesized that such crop cover maps could be generated rapidly during the growing season. Through a process of incrementally removing weekly and monthly independent variables from the CCM and implementing a ‘two model mapping’ approach, we found it viable to generate conterminous United States-wide rapid crop cover maps at a resolution of 250 m for the current year by the month of September. In this approach, we divided the CCM model into one ‘crop type model’ to handle the classification of nine specific crops and a second, binary model to classify the presence or absence of ‘other’ crops. Under the two model mapping approach, the training errors were 0.8% and 1.5% for the crop type and binary model, respectively, while test errors were 5.5% and 6.4%, respectively. With spatial mapping accuracies for annual maps reaching upwards of 70%, this approach demonstrated a strong potential for generating rapid crop cover maps by the 1st of September.

Publication Year 2018
Title Rapid crop cover mapping for the conterminous United States
DOI 10.1038/s41598-018-26284-w
Authors Devendra Dahal, Bruce K. Wylie, Daniel Howard
Publication Type Article
Publication Subtype Journal Article
Series Title Scientific Reports
Index ID 70197646
Record Source USGS Publications Warehouse
USGS Organization Earth Resources Observation and Science (EROS) Center