Comparison of Different Multispectral Sensors for Photosynthetic and Non-Photosynthetic Vegetation-Fraction Retrieval
1
Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
2
School of Civil Engineering, Chongqing Jiaotong University, Chongqing 400074, China
3
State Key Laboratory of Desertification and Aeolian Sand Disaster Combating, Gansu Desert Control Research Institute, Lanzhou 730070, China
4
School of Geography and Environmental Science, Northwest Normal University, Lanzhou 730070, China
5
Earth Atmosphere and Environment, Science Faculty, Monash University, Clayton, VIC 3800, Australia
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(1), 115; https://doi.org/10.3390/rs12010115
Received: 3 November 2019
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Revised: 16 December 2019
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Accepted: 24 December 2019
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Published: 1 January 2020
(This article belongs to the Special Issue Remote Sensing of Dryland Environment)
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.
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Keywords:
Sentinel-2A MSI; GF1 WFV; Landsat-8 OLI; photosynthetic vegetation; non-photosynthetic vegetation; linear and nonlinear spectral-mixture analysis
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MDPI and ACS Style
Ji, C.; Li, X.; Wei, H.; Li, S. Comparison of Different Multispectral Sensors for Photosynthetic and Non-Photosynthetic Vegetation-Fraction Retrieval. Remote Sens. 2020, 12, 115. https://doi.org/10.3390/rs12010115
AMA Style
Ji C, Li X, Wei H, Li S. Comparison of Different Multispectral Sensors for Photosynthetic and Non-Photosynthetic Vegetation-Fraction Retrieval. Remote Sensing. 2020; 12(1):115. https://doi.org/10.3390/rs12010115
Chicago/Turabian StyleJi, Cuicui, Xiaosong Li, Huaidong Wei, and Sike Li. 2020. "Comparison of Different Multispectral Sensors for Photosynthetic and Non-Photosynthetic Vegetation-Fraction Retrieval" Remote Sensing 12, no. 1: 115. https://doi.org/10.3390/rs12010115
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