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.
Highlights
Fractional vegetation cover (FVC), defined as the fraction of green vegetation as seen from the nadir of the total statistical area [1,2,3], is an important parameter to characterize the status of land surface vegetation and is required as a pivotal parameter for many models applied to climate change monitoring, weather prediction, desertification evaluation, soil erosion monitoring, hydrological simulation and drought monitoring [4,5,6]
The objective of this study was to assess the performance of the Sentinel-2 multi-spectral instrument (MSI) band reflectances for estimating FVC and to explore if the three RE band reflectances are significant for improving FVC estimation accuracy, as well as determining which bands are more important for FVC estimation
These results demonstrate that the dataset generated by PROSAIL model has a lot of variability and can adapt to most conditions of land surface
Summary
Fractional vegetation cover (FVC), defined as the fraction of green vegetation as seen from the nadir of the total statistical area [1,2,3], is an important parameter to characterize the status of land surface vegetation and is required as a pivotal parameter for many models applied to climate change monitoring, weather prediction, desertification evaluation, soil erosion monitoring, hydrological simulation and drought monitoring [4,5,6]. Zribi et al used ERS2/SAR data to estimate FVC in semi-arid regions, where they proposed a model describing the relationship between FVC and radar backscattering coefficient. They used supervised classification to estimate FVC, and their FVC estimation accuracy was greater than 85% [12]. Ding et al compared six dimidiate pixel models based on different VIs and four look-up table (LUT) methods to estimate FVC from Landsat 8 OLI data in the grassland and agricultural fields, and results indicate that the accuracies of LUT methods were slightly lower than those of dimidiate pixel models [14]. Comparing the four types of remote sensing data, multi-spectral data seem to be the ideal data to estimate FVC over large area
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