ArticleScientific reports2018
Rapid Crop Cover Mapping for the Conterminous United States.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The trial behind it
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Who cites it
4 citing papers in PubMed.
- A multi-scale approach for identification of potential pesticide use sites impacting vernal pool critical habitat in California.The Science of the total environment · 2023Article
- A review of domestic land use change attributable to U.S. biofuel policy.Renewable & sustainable energy reviews · 2022Article
- Crop identity and memory effects on aboveground arthropods in a long-term crop rotation experiment.Ecology and evolution · 2019Article
- Utilizing Collocated Crop Growth Model Simulations to Train Agronomic Satellite Retrieval Algorithms.Remote sensing · 2018Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 1
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Registered trials
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