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.
Highlights
Arid regions occupy over 30% of the global land surface, and desertification is especially severe in arid and semiarid zones, affecting more than two billion people
Non-photosynthetic vegetation (NPV) is plant material lacking chlorophyll, and it occupies a great part of natural vegetation in arid and semiarid regions [1,2]
The average spectral value of each endmember was taken as the adopted Photosynthetic vegetation (PV)/NPV/BS/shadow spectra by way of removing the effect of endmember variability concerning temporal and spatial data (Figure 6)
Summary
Arid regions occupy over 30% of the global land surface, and desertification is especially severe in arid and semiarid zones, affecting more than two billion people. Photosynthetic vegetation (PV) is defined as plant material including chlorophyll (e.g., green leaves and flowers), which is a significant plant factor in arid and semiarid regions. Non-photosynthetic vegetation (NPV) is plant material lacking chlorophyll (e.g., senescent plants, branches, and plant stubble), and it occupies a great part of natural vegetation in arid and semiarid regions [1,2]. Acquiring fractional cover of PV (f PV) and NPV (f NPV) data synchronicity and quantification is very significant for vegetation productivity and the monitoring of desertification. It provides important factors for different ecological and hydrological models
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