Comparison of Different Multispectral Sensors for Photosynthetic and Non-Photosynthetic Vegetation-Fraction Retrieval

Abstract

It is very difficult and complex to acquire photosynthetic vegetation (PV) and non-PV (NPV) fractions (fPV and fNPV) using multispectral satellite sensors because estimations of fPV and fNPV are influenced by many factors, such as background-noise interference of pixel-, spatial-, and spectral-scale effects. In this study, comparisons between Sentinel-2A Multispectral Instrument (S2 MSI), Landsat-8 Operational Land Imager (L8 OLI), and GF1 Wide Field View (GF1 WFV) sensors for retrieving sparse photosynthetic and non-photosynthetic vegetation coverage are presented. The analysis employed a linear spectral-mixture model (LSMM) and nonlinear spectral-mixture model (NSMM) to unmix pixels with different spectral and spatial resolution images based on field endmembers; the estimated endmember fractions were later validated with reference to fraction measurements. The results demonstrated that: (1) with higher spatial and spectral resolution, the S2 MSI sensor had a clear advantage for retrieving PV and NPV fractions compared to L8 OLI and GF1 WFV sensors; (2) through incorporating more red edge (RE) and near-infrared (NIR) bands, the accuracy of NPV fraction estimation could be greatly improved; (3) nonlinear spectral mixing effects were not obvious on the 10–30 m spatial scale for desert vegetation; (4) in arid regions, a shadow endmember is a significant factor for sparse vegetation coverage estimated with remote-sensing data. The estimated NPV fractions were especially affected by the shadow effects and could increase root mean square by 50%. The utilized approaches in the study could effectively assess the performance of major multispectral sensors to extract fPV and fNPV through the novel method of spectral-mixture analysis.

Quantifying drought effects in Central European grasslands through regression-based unmixing of intra-annual Sentinel-2 time series

“…Our results confirm previous studies showing that multispectral data, and in particular Sentinel-2, are well suited for mapping the PV fraction accurately (e.g. Corbane et al, 2014;Guerschman et al, 2015;Ji et al, 2020). Using PV fractional cover reduces saturation effects (Gitelson et al, 2002) as well as sensitivity deficiencies under drought (Xu et al, 2014).…”

“…However, similar to Guerschman et al (2009), we found that NPV typically has a considerably lower reflectance than soil in the SWIR band centered on 2200 nm, increasing the spectral separability of NPV and soil for our study area. Second, results by Ji et al (2020) and Tian et al (2021) suggest, that the three red-edge bands and the relatively narrow NIR band of Sentinel-2 provide additional information for separating NPV from soil. NPV often exhibits a stronger reflectance increase in the red-edge wavelength region towards the NIR than soils.…”

Status of Phenological Research Using Sentinel-2 Data: A Review

“…PlanetScope and Sentinel-2 were both found to be spatially and temporally suitable to capture the trajectory of almond flower blooming and its critical stages through the use of EBI. Although it is not surprising that the very high-resolution ceres dataset provides the maximum capacity in detecting blooming signals early in the season, it would be interesting to see if the flowers and other parts of the vegetation can be unmixed from coarser resolution data as mentioned in Ji et al (2020) [75], who reported the capability of the visible bands and red-edge bands in discriminating the photosynthetic and non-photosynthetic fractions. Several studies have used spectral mixture analyses to identify photosynthetic and non-photosynthetic fractions [76][77][78] and vegetation indices to estimate concentration of plant pigments [79][80][81].…”

Comparison of Machine Learning Methods for Mapping the Stand Characteristics of Temperate Forests Using Multi-Spectral Sentinel-2 Data

“…Forest management explicitly necessitates the use of remotely sensed data owing to its cost effectiveness and regional availability. These data has been investigated in terms of data accuracy and reliability in many applications including those of vegetation, agriculture, and forestry [8][9][10]. Therefore, research focus is being increasingly centred on mapping the quantitative distribution of forest stand characteristics (e.g., diameter, height, location, basal area, potential volume of wood, and tree species) as a forest management strategy [1,6].…”