Accuracy of Rapid Crop Cover Map of Conterminous United States for 2010

Metadata Updated: October 9, 2019

Spatially accurate annual crop cover maps are an important component to various planning and research applications; however, the importance of these maps varies significantly with the timing of their availability. Utilizing a previously developed crop classification model (CCM), which was used to generate historical annual crop cover maps (classifying nine major crops: corn, cotton, sorghum, soybeans, spring wheat, winter wheat, alfalfa, other hay/non alfalfa, fallow/idle cropland, and ‘other’ as one class for remaining crops), we hypothesized that such crop cover maps could be generated in near real time (NRT). The CCM was trained on 14 temporal and 15 static geospatial datasets, known as predictor variables, and the National Agricultural Statistics Service (NASS) Cropland Data Layers (CDL) was used as the dependent variable. We were able to generate a NRT crop cover map by the first day of September through a process of incrementally removing weekly and monthly data from the CCM and comparing the subsequent map results with the original maps and NASS CDLs. Initially, our NRT results revealed training error of 1.4% and test error of 8.3%, as compared to 1.0% and 7.6%, respectively for the original CCM. Through the implementation of a new ‘two-mapping model’ approach, we were able to substantially improve the results of the NRT crop cover model. We divided the NRT model into one ‘crop type model’ to handle the classification of the nine specific crops and a second, binary model to classify crops as presence or absence of the ‘other’ crop. Under the two-mapping model 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% for crop type and binary model, respectively. With overall mapping accuracy for the map reaching 67.18 percent, this approach shows strong potential for generating crop type maps of current year in September.

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License: No license information was provided. If this work was prepared by an officer or employee of the United States government as part of that person's official duties it is considered a U.S. Government Work.

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Metadata Date December 11, 2017
Metadata Created Date October 9, 2019
Metadata Updated Date October 9, 2019
Reference Date(s) January 1, 2018 (publication)
Frequency Of Update notPlanned

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Resource Type Dataset
Metadata Date December 11, 2017
Metadata Created Date October 9, 2019
Metadata Updated Date October 9, 2019
Reference Date(s) January 1, 2018 (publication)
Responsible Party U.S. Geological Survey, CLIMATE & LAND-USE (Point of Contact)
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Bbox East Long -66.09375000000001
Bbox North Lat 49.49667452747045
Bbox South Lat 24.04646399966658
Bbox West Long -125.85937500000001
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Frequency Of Update notPlanned
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Harvest Object Id b846cc35-2ccb-416e-b52d-e9e04d995cad
Harvest Source Id 34ce571b-cb98-4e0b-979f-30f9ecc452c5
Harvest Source Title DOI CKAN Harvest Source
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Metadata Type geospatial
Progress completed
Spatial Data Service Type
Spatial Reference System
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