FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3182299891> ?p ?o ?g. }

• W3182299891 endingPage "112576" @default.
• W3182299891 startingPage "112576" @default.
• W3182299891 abstract "Crop type maps were created without the traditional need for in-season training data across the Corn Belt and Great Plains regions of the United States. This was accomplished through machine learning of historical land cover information, paired with a time-series of multi-spectral satellite imagery composites spanning the growing season, to develop rulesets, which were used for real-time prediction in the current year. Specifically, a decade's worth of annual 30 m resolution crop specific maps, known as the Cropland Data Layer (CDL), provided the foundation, and prior and current year's satellite imagery from Landsat 7 and 8 and Sentinel-2a and -2b built upon it. Four modeling scenarios, all using random forests, were performed to understand the crop mapping abilities of the datasets independently and combined. They were 1) use of CDLs only (i.e. prediction based solely on crop rotation history) 2) use of Landsat 7 and 8 bottom-of-atmosphere surface reflectance imagery only, 3) use of Sentinel-2a and -2b top-of-atmosphere imagery only, and 4) integration of the CDL, Landsat, and Sentinel-2 information together in a unified effort. Furthermore, the model runs were generated monthly, beginning in April, through the growing season to provide understanding of classification performance as a function of time. The 2020 crop year, relatively normal in terms of planting and weather, was used for the test. Accuracy statistics were generated by randomly sampling 50 counties and comparing those classification outputs to the actual 2020 CDL. Pixel-level results showed that prediction by midsummer using only the CDL information provided a crop type map with corn and soybean consumer and producer agreement above 70% and winter wheat just below 50%. The early season imagery-based classifications were markedly worse. However, as Landsat or Sentinel-2 imagery accumulated through July, those classifications became significantly better than those reliant on the use of the CDL information only. Ultimately, the very best crop maps resulted from integrating the CDLs with a full season's worth of Landsat and Sentinel-2 imagery. At that point in late September, the corn and soybean agreements were around 85% and winter wheat near 70%. All analysis was performed within Google Earth Engine cloud-based public imagery repository and high-performance computing system. The classification outputs provide practitioners with US crop type maps in near real-time." @default.
• W3182299891 created "2021-07-19" @default.
• W3182299891 creator A5034644894 @default.
• W3182299891 creator A5077318052 @default.
• W3182299891 date "2021-10-01" @default.
• W3182299891 modified "2023-10-11" @default.
• W3182299891 title "Pre- and within-season crop type classification trained with archival land cover information" @default.
• W3182299891 cites W1957257429 @default.
• W3182299891 cites W1966845328 @default.
• W3182299891 cites W1970797271 @default.
• W3182299891 cites W1981213426 @default.
• W3182299891 cites W1998281138 @default.
• W3182299891 cites W1999110225 @default.
• W3182299891 cites W2008085934 @default.
• W3182299891 cites W2013973135 @default.
• W3182299891 cites W2032109992 @default.
• W3182299891 cites W2035549557 @default.
• W3182299891 cites W2038951852 @default.
• W3182299891 cites W2039431454 @default.
• W3182299891 cites W2047548450 @default.
• W3182299891 cites W2050076538 @default.
• W3182299891 cites W2068094410 @default.
• W3182299891 cites W2078332587 @default.
• W3182299891 cites W2102932370 @default.
• W3182299891 cites W2118899651 @default.
• W3182299891 cites W2136754539 @default.
• W3182299891 cites W2157675604 @default.
• W3182299891 cites W2179721300 @default.
• W3182299891 cites W2199031689 @default.
• W3182299891 cites W2290326488 @default.
• W3182299891 cites W2331071973 @default.
• W3182299891 cites W2344328155 @default.
• W3182299891 cites W2414117070 @default.
• W3182299891 cites W2552805558 @default.
• W3182299891 cites W2560167313 @default.
• W3182299891 cites W2578830027 @default.
• W3182299891 cites W2585282541 @default.
• W3182299891 cites W2591129009 @default.
• W3182299891 cites W2607245364 @default.
• W3182299891 cites W2610947800 @default.
• W3182299891 cites W2725897987 @default.
• W3182299891 cites W2736036091 @default.
• W3182299891 cites W2751786729 @default.
• W3182299891 cites W2766727660 @default.
• W3182299891 cites W2810242891 @default.
• W3182299891 cites W2883026662 @default.
• W3182299891 cites W2897285410 @default.
• W3182299891 cites W2897391101 @default.
• W3182299891 cites W2909543378 @default.
• W3182299891 cites W2911964244 @default.
• W3182299891 cites W2912036566 @default.
• W3182299891 cites W2920930972 @default.
• W3182299891 cites W2935991947 @default.
• W3182299891 cites W2938102982 @default.
• W3182299891 cites W2943472941 @default.
• W3182299891 cites W2955639914 @default.
• W3182299891 cites W2955666723 @default.
• W3182299891 cites W2970979186 @default.
• W3182299891 cites W2999712229 @default.
• W3182299891 cites W3003421670 @default.
• W3182299891 cites W3004794698 @default.
• W3182299891 cites W3087578562 @default.
• W3182299891 cites W3144755632 @default.
• W3182299891 cites W561088580 @default.
• W3182299891 doi "https://doi.org/10.1016/j.rse.2021.112576" @default.
• W3182299891 hasPublicationYear "2021" @default.
• W3182299891 type Work @default.
• W3182299891 sameAs 3182299891 @default.
• W3182299891 citedByCount "45" @default.
• W3182299891 countsByYear W31822998912021 @default.
• W3182299891 countsByYear W31822998912022 @default.
• W3182299891 countsByYear W31822998912023 @default.
• W3182299891 crossrefType "journal-article" @default.
• W3182299891 hasAuthorship W3182299891A5034644894 @default.
• W3182299891 hasAuthorship W3182299891A5077318052 @default.
• W3182299891 hasBestOaLocation W31822998911 @default.
• W3182299891 hasConcept C106131492 @default.
• W3182299891 hasConcept C127413603 @default.
• W3182299891 hasConcept C137580998 @default.
• W3182299891 hasConcept C137660486 @default.
• W3182299891 hasConcept C140779682 @default.
• W3182299891 hasConcept C146978453 @default.
• W3182299891 hasConcept C147176958 @default.
• W3182299891 hasConcept C153294291 @default.
• W3182299891 hasConcept C154945302 @default.
• W3182299891 hasConcept C160633673 @default.
• W3182299891 hasConcept C19269812 @default.
• W3182299891 hasConcept C205649164 @default.
• W3182299891 hasConcept C2778102629 @default.
• W3182299891 hasConcept C2780648208 @default.
• W3182299891 hasConcept C31972630 @default.
• W3182299891 hasConcept C39432304 @default.
• W3182299891 hasConcept C41008148 @default.
• W3182299891 hasConcept C4792198 @default.
• W3182299891 hasConcept C62649853 @default.
• W3182299891 hasConcept C6557445 @default.
• W3182299891 hasConcept C86803240 @default.
• W3182299891 hasConcept C97137747 @default.

• next