Version 1
: Received: 7 September 2021 / Approved: 8 September 2021 / Online: 8 September 2021 (11:59:24 CEST)
How to cite:
Blessing, S.; Giering, R. Simultaneous Retrieval of Soil, Leaf, and Canopy Parameters from Sentinel-3 OLCI and SLSTR Multi-spectral Top-of-Canopy Reflectances. Preprints2021, 2021090147. https://doi.org/10.20944/preprints202109.0147.v1
Blessing, S.; Giering, R. Simultaneous Retrieval of Soil, Leaf, and Canopy Parameters from Sentinel-3 OLCI and SLSTR Multi-spectral Top-of-Canopy Reflectances. Preprints 2021, 2021090147. https://doi.org/10.20944/preprints202109.0147.v1
Blessing, S.; Giering, R. Simultaneous Retrieval of Soil, Leaf, and Canopy Parameters from Sentinel-3 OLCI and SLSTR Multi-spectral Top-of-Canopy Reflectances. Preprints2021, 2021090147. https://doi.org/10.20944/preprints202109.0147.v1
APA Style
Blessing, S., & Giering, R. (2021). Simultaneous Retrieval of Soil, Leaf, and Canopy Parameters from Sentinel-3 OLCI and SLSTR Multi-spectral Top-of-Canopy Reflectances. Preprints. https://doi.org/10.20944/preprints202109.0147.v1
Chicago/Turabian Style
Blessing, S. and Ralf Giering. 2021 "Simultaneous Retrieval of Soil, Leaf, and Canopy Parameters from Sentinel-3 OLCI and SLSTR Multi-spectral Top-of-Canopy Reflectances" Preprints. https://doi.org/10.20944/preprints202109.0147.v1
Abstract
Multi- and hyper-spectral, multi-angular top-of-canopy reflectance data call for an efficient retrieval system which can improve the retrieval of standard canopy parameters (as albedo, LAI, fAPAR), and exploit the information to retrieve additional parameters (e.g. leaf pigments). Furthermore consistency between the retrieved parameters and quantification of uncertainties are required for many applications. % (2) methods We present a retrieval system for canopy and sub-canopy parameters (OptiSAIL), which is based on a model comprising SAIL, PROSPECT-D (leaf properties), TARTES (snow properties), a soil model (BRDF, moisture), and a cloud contamination model. The inversion is gradient based and uses codes % created by Automatic Differentiation. The full per pixel covariance-matrix of the retrieved parameters is computed. For this demonstration, single observation data from the Sentinel-3 SY_2_SYN (synergy) product is used. The results are compared with the MODIS 4-day LAI/fPAR product and PhenoCam site photography. OptiSAIL produces generally consistent and credible results, at least matching the quality of the technically quite different MODIS product. For most of the sites, the PhenoCam images support the OptiSAIL retrievals. The system is computationally efficient with a rate of 150 pixel per second (7 millisecond per pixel) for a single thread on a current desktop CPU using observations on 26 bands. Not all of the model parameters are well determined in all situations. Significant correlations between the parameters are found, which can change sign and magnitude over time. OptiSAIL appears to meet the design goals, puts real-time processing with this kind of system into reach, seamlessly extends to hyper-spectral and multi-sensor retrievals, and promises to be a good platform for sensitivity studies. The incorporated cloud and snow detection adds to the robustness of the system.
Environmental and Earth Sciences, Environmental Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
