{"accessLevel": "public", "bureauCode": ["010:12"], "contactPoint": {"@type": "vcard:Contact", "fn": "Devendra Dahal (CTR)", "hasEmail": "mailto:devendra.dahal.ctr@usgs.gov"}, "description": "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 \u2018other\u2019 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 \u2018two-mapping model\u2019 approach, we were able to substantially improve the results of the NRT crop cover model. We divided the NRT model into one \u2018crop type model\u2019 to handle the classification of the nine specific crops and a second, binary model to classify crops as presence or absence of the \u2018other\u2019 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 66.31 percent, this approach shows strong potential for generating crop type maps of current year in September.", "distribution": [{"@type": "dcat:Distribution", "accessURL": "https://doi.org/10.5066/F7B27TG8", "description": "Landing page for access to the data", "format": "XML", "mediaType": "application/http", "title": "Digital Data"}, {"@type": "dcat:Distribution", "description": "The metadata original format", "downloadURL": "https://data.usgs.gov/datacatalog/metadata/USGS.5a870105e4b00f54eb3a2b0a.xml", "format": "XML", "mediaType": "text/xml", "title": "Original Metadata"}], "identifier": "http://datainventory.doi.gov/id/dataset/USGS_5a870105e4b00f54eb3a2b0a", "keyword": ["USGS:5a870105e4b00f54eb3a2b0a", "conus", "crop cover mapping", "modelling", "near real time"], "modified": "2020-08-18T00:00:00Z", "publisher": {"@type": "org:Organization", "name": "U.S. Geological Survey"}, "spatial": "-125.85937500000001, 24.04646399966658, -66.09375000000001, 49.49667452747045", "theme": ["geospatial"], "title": "Accuracy of Rapid Crop Cover Map of Conterminous United States for 2011"}