Comparison of Different Multispectral Sensors for Photosynthetic and Non-Photosynthetic Vegetation-Fraction Retrieval
| Autor: | Sike Li, Huaidong Wei, Cuicui Ji, Xiaosong Li |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Endmember
010504 meteorology & atmospheric sciences Pixel photosynthetic vegetation linear and nonlinear spectral-mixture analysis non-photosynthetic vegetation Multispectral image 0211 other engineering and technologies Red edge 02 engineering and technology Vegetation 01 natural sciences GF1 WFV Landsat-8 OLI Shadow Spatial ecology General Earth and Planetary Sciences Environmental science lcsh:Q Sentinel-2A MSI lcsh:Science Image resolution 021101 geological & geomatics engineering 0105 earth and related environmental sciences Remote sensing |
| Zdroj: | Remote Sensing Volume 12 Issue 1 Pages: 115 Remote Sensing, Vol 12, Iss 1, p 115 (2020) |
| ISSN: | 2072-4292 |
| DOI: | 10.3390/rs12010115 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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