Item talk:Q234475
From geokb
{
"USGS Publications Warehouse": { "@context": "https://schema.org", "@type": "Article", "additionalType": "Journal Article", "name": "Advantage of hyperspectral EO-1 Hyperion over multispectral IKONOS, GeoEye-1, WorldView-2, Landsat ETM+, and MODIS vegetation indices in crop biomass estimation", "identifier": [ { "@type": "PropertyValue", "propertyID": "USGS Publications Warehouse IndexID", "value": "70160007", "url": "https://pubs.usgs.gov/publication/70160007" }, { "@type": "PropertyValue", "propertyID": "USGS Publications Warehouse Internal ID", "value": 70160007 }, { "@type": "PropertyValue", "propertyID": "DOI", "value": "10.1016/j.isprsjprs.2015.08.001", "url": "https://doi.org/10.1016/j.isprsjprs.2015.08.001" } ], "journal": { "@type": "Periodical", "name": "ISPRS Journal of Photogrammetry and Remote Sensing", "volumeNumber": "108", "issueNumber": null }, "inLanguage": "en", "isPartOf": [ { "@type": "CreativeWorkSeries", "name": "ISPRS Journal of Photogrammetry and Remote Sensing" } ], "datePublished": "2015", "dateModified": "2016-01-06", "abstract": "Crop biomass is increasingly being measured with surface reflectance data derived from multispectral broadband (MSBB) and hyperspectral narrowband (HNB) space-borne remotely sensed data to increase the accuracy and efficiency of crop yield models used in a wide array of agricultural applications. However, few studies compare the ability of MSBBs versus HNBs to capture crop biomass variability. Therefore, we used standard data mining techniques to identify a set of MSBB data from the IKONOS, GeoEye-1, Landsat ETM+, MODIS, WorldView-2 sensors and compared their performance with HNB data from the EO-1 Hyperion sensor in explaining crop biomass variability of four important field crops (rice, alfalfa, cotton, maize). The analysis employed two-band (ratio) vegetation indices (TBVIs) and multiband (additive) vegetation indices (MBVIs) derived from Singular Value Decomposition (SVD) and stepwise regression. Results demonstrated that HNB-derived TBVIs and MBVIs performed better than MSBB-derived TBVIs and MBVIs on a per crop basis and for the pooled data: overall, HNB TBVIs explained 5\u201331% greater variability when compared with various MSBB TBVIs; and HNB MBVIs explained 3\u201333% greater variability when compared with various MSBB MBVIs. The performance of MSBB MBVIs and TBVIs improved mildly, by combining spectral information across multiple sensors involving IKONOS, GeoEye-1, Landsat ETM+, MODIS, and WorldView-2. A number of HNBs that advance crop biomass modeling were determined. Based on the highest factor loadings on the first component of the SVD, the \u201cred-edge\u201d spectral range (700\u2013740 nm) centered at 722 nm (bandwidth = 10 nm) stood out prominently, while five additional and distinct portions of the recorded spectral range (400\u20132500 nm) centered at 539 nm, 758 nm, 914 nm, 1130 nm, 1320 nm (bandwidth = 10 nm) were also important. The best HNB vegetation indices for crop biomass estimation involved 549 and 752 nm for rice (R2 = 0.91); 925 and 1104 nm for alfalfa (R2 = 0.81); 722 and 732 nm for cotton (R2 = 0.97); and 529 and 895 nm for maize (R2 = 0.94). The higher spectral resolution of the EO-1 Hyperion hyperspectral sensor and the ability of users to choose distinct HNBs for improved crop biomass estimation outweigh the benefits that come with higher spatial resolution of MSBBs.", "description": "14 p.", "publisher": { "@type": "Organization", "name": "Elsevier" }, "author": [ { "@type": "Person", "name": "Marshall, Michael T. mmarshall@usgs.gov", "givenName": "Michael T.", "familyName": "Marshall", "email": "mmarshall@usgs.gov", "affiliation": [ { "@type": "Organization", "name": "Western Geographic Science Center", "url": "https://www.usgs.gov/centers/western-geographic-science-center" } ] }, { "@type": "Person", "name": "Thenkabail, Prasad S. pthenkabail@usgs.gov", "givenName": "Prasad S.", "familyName": "Thenkabail", "email": "pthenkabail@usgs.gov", "identifier": { "@type": "PropertyValue", "propertyID": "ORCID", "value": "0000-0002-2182-8822", "url": "https://orcid.org/0000-0002-2182-8822" }, "affiliation": [ { "@type": "Organization", "name": "Western Geographic Science Center", "url": "https://www.usgs.gov/centers/western-geographic-science-center" } ] } ], "funder": [ { "@type": "Organization", "name": "Western Geographic Science Center", "url": "https://www.usgs.gov/centers/western-geographic-science-center" } ], "spatialCoverage": [ { "@type": "Place", "additionalType": "country", "name": "United States", "url": "https://geonames.org/4074035" }, { "@type": "Place", "additionalType": "state", "name": "California" }, { "@type": "Place", "geo": [ { "@type": "GeoShape", "additionalProperty": { "@type": "PropertyValue", "name": "GeoJSON", "value": { "type": "FeatureCollection", "features": [ { "type": "Feature", "properties": {}, "geometry": { "type": "Polygon", "coordinates": [ [ [ -122.05810546875, 40.730608477796636 ], [ -122.82714843749999, 40.3130432088809 ], [ -122.56347656249999, 39.90973623453719 ], [ -122.62939453125001, 39.38526381099774 ], [ -122.23388671874999, 38.496593518947556 ], [ 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