File Download
Links for fulltext
(May Require Subscription)
- Publisher Website: 10.3390/rs10121927
- Scopus: eid_2-s2.0-85058894821
- WOS: WOS:000455637600069
Supplementary
- Citations:
- Appears in Collections:
Article: Assessment of Sentinel-2 MSI spectral band reflectances for estimating fractional vegetation cover
Title | Assessment of Sentinel-2 MSI spectral band reflectances for estimating fractional vegetation cover |
---|---|
Authors | |
Keywords | Fractional vegetation cover Random forest regression Sentinel-2 satellites Variable selection |
Issue Date | 2018 |
Citation | Remote Sensing, 2018, v. 10, n. 12, article no. 1927 How to Cite? |
Abstract | Fractional 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 Identifier | http://hdl.handle.net/10722/321827 |
ISI Accession Number ID |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Wang, Bing | - |
dc.contributor.author | Jia, Kun | - |
dc.contributor.author | Liang, Shunlin | - |
dc.contributor.author | Xie, Xianhong | - |
dc.contributor.author | Wei, Xiangqin | - |
dc.contributor.author | Zhao, Xiang | - |
dc.contributor.author | Yao, Yunjun | - |
dc.contributor.author | Zhang, Xiaotong | - |
dc.date.accessioned | 2022-11-03T02:21:43Z | - |
dc.date.available | 2022-11-03T02:21:43Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | Remote Sensing, 2018, v. 10, n. 12, article no. 1927 | - |
dc.identifier.uri | http://hdl.handle.net/10722/321827 | - |
dc.description.abstract | Fractional 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.language | eng | - |
dc.relation.ispartof | Remote Sensing | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.subject | Fractional vegetation cover | - |
dc.subject | Random forest regression | - |
dc.subject | Sentinel-2 satellites | - |
dc.subject | Variable selection | - |
dc.title | Assessment of Sentinel-2 MSI spectral band reflectances for estimating fractional vegetation cover | - |
dc.type | Article | - |
dc.description.nature | published_or_final_version | - |
dc.identifier.doi | 10.3390/rs10121927 | - |
dc.identifier.scopus | eid_2-s2.0-85058894821 | - |
dc.identifier.volume | 10 | - |
dc.identifier.issue | 12 | - |
dc.identifier.spage | article no. 1927 | - |
dc.identifier.epage | article no. 1927 | - |
dc.identifier.eissn | 2072-4292 | - |
dc.identifier.isi | WOS:000455637600069 | - |