Item talk:Q234663
From geokb
{
"USGS Publications Warehouse": { "@context": "https://schema.org", "@type": "Article", "additionalType": "Journal Article", "name": "Developing in situ non-destructive estimates of crop biomass to address issues of scale in remote sensing", "identifier": [ { "@type": "PropertyValue", "propertyID": "USGS Publications Warehouse IndexID", "value": "70134261", "url": "https://pubs.usgs.gov/publication/70134261" }, { "@type": "PropertyValue", "propertyID": "USGS Publications Warehouse Internal ID", "value": 70134261 }, { "@type": "PropertyValue", "propertyID": "DOI", "value": "10.3390/rs70100808", "url": "https://doi.org/10.3390/rs70100808" } ], "journal": { "@type": "Periodical", "name": "Remote Sensing", "volumeNumber": "7", "issueNumber": "1" }, "inLanguage": "en", "isPartOf": [ { "@type": "CreativeWorkSeries", "name": "Remote Sensing" } ], "datePublished": "2015", "dateModified": "2016-07-08", "abstract": "Ground-based estimates of aboveground wet (fresh) biomass (AWB) are an important input for crop growth models. In this study, we developed empirical equations of AWB for rice, maize, cotton, and alfalfa, by combining several\u00a0in situ\u00a0non-spectral and spectral predictors. The non-spectral predictors included: crop height (H), fraction of absorbed photosynthetically active radiation (FAPAR), leaf area index (LAI), and fraction of vegetation cover (FVC). The spectral predictors included 196 hyperspectral narrowbands (HNBs) from 350 to 2500 nm. The models for rice, maize, cotton, and alfalfa included H and HNBs in the near infrared (NIR); H, FAPAR, and HNBs in the NIR; H and HNBs in the visible and NIR; and FVC and HNBs in the visible; respectively. In each case, the non-spectral predictors were the most important, while the HNBs explained additional and statistically significant predictors, but with lower variance. The final models selected for validation yielded an\u00a0R2\u00a0of 0.84, 0.59, 0.91, and 0.86 for rice, maize, cotton, and alfalfa, which when compared to models using HNBs alone from a previous study using the same spectral data, explained an additional 12%, 29%, 14%, and 6% in AWB variance. These integrated models will be used in an up-coming study to extrapolate AWB over 60 \u00d7 60 m transects to evaluate spaceborne multispectral broad bands and hyperspectral narrowbands.", "description": "28 p.", "publisher": { "@type": "Organization", "name": "MDPI" }, "author": [ { "@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" } ] }, { "@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" } ] } ], "funder": [ { "@type": "Organization", "name": "Western Geographic Science Center", "url": "https://www.usgs.gov/centers/western-geographic-science-center" } ] }, "OpenAlex": { "abstract_inverted_index": { "Ground-based": [ 0 ], "estimates": [ 1 ], "of": [ 2, 23, 48, 60, 149 ], "aboveground": [ 3 ], "wet": [ 4 ], "(fresh)": [ 5 ], "biomass": [ 6 ], "(AWB)": [ 7 ], "are": [ 8 ], "an": [ 9, 147, 179, 196 ], "important": [ 10 ], "input": [ 11 ], "for": [ 12, 25, 79, 144, 155 ], "crop": [ 13, 44 ], "growth": [ 14 ], "models.": [ 15 ], "In": [ 16, 117 ], "this": [ 17 ], "study,": [ 18 ], "we": [ 19 ], "developed": [ 20 ], "empirical": [ 21 ], "equations": [ 22 ], "AWB": [ 24, 187, 201 ], "rice,": [ 26, 80, 156 ], "maize,": [ 27, 81, 157 ], "cotton,": [ 28, 82, 158 ], "and": [ 29, 37, 58, 83, 87, 96, 102, 107, 109, 111, 132, 153, 159, 184, 214 ], "alfalfa,": [ 30, 160 ], "by": [ 31 ], "combining": [ 32 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