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Article: Assessment of Sentinel-2 MSI spectral band reflectances for estimating fractional vegetation cover

TitleAssessment of Sentinel-2 MSI spectral band reflectances for estimating fractional vegetation cover
Authors
KeywordsFractional vegetation cover
Random forest regression
Sentinel-2 satellites
Variable selection
Issue Date2018
Citation
Remote Sensing, 2018, v. 10, n. 12, article no. 1927 How to Cite?
AbstractFractional vegetation cover (FVC) is an essential parameter for characterizing the land surface vegetation conditions and plays an important role in earth surface process simulations and global change studies. The Sentinel-2 missions carrying multi-spectral instrument (MSI) sensors with 13 multispectral bands are potentially useful for estimating FVC. However, the performance of these bands for FVC estimation is unclear. Therefore, the objective of this study was to assess the performance of Sentinel-2 MSI spectral band reflectances on FVC estimation. The samples, including the Sentinel-2 MSI canopy reflectances and corresponding FVC values, were simulated using the PROSPECT + SAIL radiative transfer model under different conditions, and random forest regression (RFR) method was then used to develop FVC estimation models and assess the performance of various band reflectances for FVC estimation. These models were finally evaluated using field survey data. The results indicate that the three most important bands of Sentinel-2 MSI data for FVC estimation are band 4 (Red), band 12 (SWIR2) and band 8a (NIR2). FVC estimation using these bands has a comparable accuracy (root mean square error (RMSE) = 0.085) with that using all bands (RMSE = 0.090). The results also demonstrate that band 12 had a better performance for FVC estimation than the green band (RMSE = 0.097). However, the newly added red-edge bands, with low scores in the RFR model, have little significance for improving FVC estimation accuracy compared with the Red, NIR2 and SWIR2 bands.
Persistent Identifierhttp://hdl.handle.net/10722/321827
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorWang, Bing-
dc.contributor.authorJia, Kun-
dc.contributor.authorLiang, Shunlin-
dc.contributor.authorXie, Xianhong-
dc.contributor.authorWei, Xiangqin-
dc.contributor.authorZhao, Xiang-
dc.contributor.authorYao, Yunjun-
dc.contributor.authorZhang, Xiaotong-
dc.date.accessioned2022-11-03T02:21:43Z-
dc.date.available2022-11-03T02:21:43Z-
dc.date.issued2018-
dc.identifier.citationRemote Sensing, 2018, v. 10, n. 12, article no. 1927-
dc.identifier.urihttp://hdl.handle.net/10722/321827-
dc.description.abstractFractional vegetation cover (FVC) is an essential parameter for characterizing the land surface vegetation conditions and plays an important role in earth surface process simulations and global change studies. The Sentinel-2 missions carrying multi-spectral instrument (MSI) sensors with 13 multispectral bands are potentially useful for estimating FVC. However, the performance of these bands for FVC estimation is unclear. Therefore, the objective of this study was to assess the performance of Sentinel-2 MSI spectral band reflectances on FVC estimation. The samples, including the Sentinel-2 MSI canopy reflectances and corresponding FVC values, were simulated using the PROSPECT + SAIL radiative transfer model under different conditions, and random forest regression (RFR) method was then used to develop FVC estimation models and assess the performance of various band reflectances for FVC estimation. These models were finally evaluated using field survey data. The results indicate that the three most important bands of Sentinel-2 MSI data for FVC estimation are band 4 (Red), band 12 (SWIR2) and band 8a (NIR2). FVC estimation using these bands has a comparable accuracy (root mean square error (RMSE) = 0.085) with that using all bands (RMSE = 0.090). The results also demonstrate that band 12 had a better performance for FVC estimation than the green band (RMSE = 0.097). However, the newly added red-edge bands, with low scores in the RFR model, have little significance for improving FVC estimation accuracy compared with the Red, NIR2 and SWIR2 bands.-
dc.languageeng-
dc.relation.ispartofRemote Sensing-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectFractional vegetation cover-
dc.subjectRandom forest regression-
dc.subjectSentinel-2 satellites-
dc.subjectVariable selection-
dc.titleAssessment of Sentinel-2 MSI spectral band reflectances for estimating fractional vegetation cover-
dc.typeArticle-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.3390/rs10121927-
dc.identifier.scopuseid_2-s2.0-85058894821-
dc.identifier.volume10-
dc.identifier.issue12-
dc.identifier.spagearticle no. 1927-
dc.identifier.epagearticle no. 1927-
dc.identifier.eissn2072-4292-
dc.identifier.isiWOS:000455637600069-

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