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RSS FeedsRemote Sensing, Vol. 11, Pages 2424: Global Sensitivity Analysis of the SCOPE Model in Sentinel-3 Bands: Thermal Domain Focus (Remote Sensing)

 
 

18 october 2019 17:00:40

 
Remote Sensing, Vol. 11, Pages 2424: Global Sensitivity Analysis of the SCOPE Model in Sentinel-3 Bands: Thermal Domain Focus (Remote Sensing)
 


Sentinel-3 satellite has provided simultaneous observations in the optical (visible, near infrared (NIR), shortwave infrared (SWIR)) and thermal infrared (TIR) domains since 2016, with a revisit time of 1–2 days. The high temporal resolution and spectral coverage make the data of this mission attractive for vegetation monitoring. This study explores the possibilities of using the Soil Canopy Observation, Photochemistry and Energy fluxes (SCOPE) model together with Sentinel-3 to exploit the two sensors onboard of Sentinel-3 (the ocean and land color instrument (OLCI) and sea and land surface temperature radiometer (SLSTR)) in synergy. Sobol’ variance based global sensitivity analysis (GSA) of top of atmosphere (TOA) radiance produced with a coupled SCOPE-6S model was conducted for optical bands of OLCI and SLSTR, while another GSA of SCOPE was conducted for the land surface temperature (LST) product of SLSTR. The results show that in addition to ESA level-2 Sentinel-3 products, SCOPE is able to retrieve leaf area index (LAI), leaf chlorophyll content (Cab), leaf water content (Cw), leaf senescent material (Cs), leaf inclination distribution (LAD). Leaf dry matter content (Cdm) and soil brightness, despite being important, were not confidently retrieved in some cases. GSA of LST in TIR domain showed that plant biochemical parameters—maximum carboxylation rate (Vcmax) and stomata conductance-photosynthesis slope (Ball-Berry m)—can be constrained if prior information on near-surface weather conditions is available. We conclude that the combination of optical and thermal domains facilitates the constraint of the land surface energy balance using SCOPE.


 
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Remote Sensing, Vol. 11, Pages 2413: Project-Based Learning Applied to Unmanned Aerial Systems and Remote Sensing (Remote Sensing)
Remote Sensing, Vol. 11, Pages 2423: Measuring Sand Dune Migration Rates with COSI-Corr and Landsat: Opportunities and Challenges (Remote Sensing)
 
 
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