Evaluation of Sentinel-2A Surface Reflectance Derived Using Sen2Cor in North America

Li, Yingjie; Chen, Jing; Ma, Qingmiao; Zhang, Hankui K.; Liu, Jane

doi:10.1109/jstars.2018.2835823

Cited by 54 publications

(34 citation statements)

References 52 publications

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“…Nadir reflectances of snow and white ice may exceed 1.0 in the blue part of the electromagnetic spectrum (e.g., Perovich, 1994;Goyens et al, 2018) but Sen2Cor BOA reflectances exceed 1.0 even in bands 3 and 4 (560 and 665 nm, respectively). This may result from anisotropic effects due to snow parameters or floe topography and matches observations of Li et al (2018) who found that Sen2Cor overestimates BOA reflectance; ACOLITE, ATCOR, and iCOR BOA reflectance values, however, are <1.0 in all bands.…”

Section: Sentinel-2a Datasupporting

confidence: 86%

“…A further test with a fixed visibility of 120 km, corresponding to an AOT of ∼0.0780, as proposed by Pflug et al (2016) as a good practice for clear air and low AOT over Antarctica, did not improve the results for BOA reflectance and R rs significantly. The increased BOA reflectance in comparison to the other processors fit findings of Li et al (2018) who report that surface reflectance derived with Sen2Cor is generally overestimated, in particular for bright pixels. The high values over water may be attributed to the lacking correction of skyglint and adjacency effects or the provided LUTs.…”

Section: Ac Processorssupporting

confidence: 84%

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Application of Sentinel-2 MSI in Arctic Research: Evaluating the Performance of Atmospheric Correction Approaches Over Arctic Sea Ice

2019

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Multispectral remote sensing may be a powerful tool for areal retrieval of biogeophysical parameters in the Arctic sea ice. The MultiSpectral Instrument on board the Sentinel-2 (S-2) satellites of the European Space Agency offers new possibilities for Arctic research; S-2A and S-2B provide 13 spectral bands between 443 and 2,202 nm and spatial resolutions between 10 and 60 m, which may enable the monitoring of large areas of Arctic sea ice. For an accurate retrieval of parameters such as surface albedo, the elimination of atmospheric influences in the data is essential. We therefore provide an evaluation of five currently available atmospheric correction processors for S-2 (ACOLITE, ATCOR, iCOR, Polymer, and Sen2Cor). We evaluate the results of the different processors using in situ spectral measurements of ice and snow and open water gathered north of Svalbard during RV Polarstern cruise PS106.1 in summer 2017. We used spectral shapes to assess performance for ice and snow surfaces. For open water, we additionally evaluated intensities. ACOLITE, ATCOR, and iCOR performed well over sea ice and Polymer generated the best results over open water. ATCOR, iCOR and Sen2Cor failed in the image-based retrieval of atmospheric parameters (aerosol optical thickness, water vapor). ACOLITE estimated AOT within the uncertainty range of AERONET measurements. Parameterization based on external data, therefore, was necessary to obtain reliable results. To illustrate consequences of processor selection on secondary products we computed average surface reflectance of six bands and normalized difference melt index (NDMI) on an image subset. Medians of average reflectance and NDMI range from 0.80-0.97 to 0.12-0.18 while medians for TOA are 0.75 and 0.06, respectively.

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Section: Sentinel-2a Datasupporting

confidence: 86%

Section: Ac Processorssupporting

confidence: 84%

Application of Sentinel-2 MSI in Arctic Research: Evaluating the Performance of Atmospheric Correction Approaches Over Arctic Sea Ice

2019

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“…Derivation of surface reflectance is achieved from the atmospheric inversion of a set of look-up tables generated with the libRadtran atmospheric RTM [47]. Sen2Cor achieves uncertainties around 0.03 for the AOT 550 and 0.3 g•cm −2 for the CWV, which are propagated to absolute errors of <0.05 in surface reflectance [48][49][50]. These errors, nevertheless, should not hamper the retrieval of biophysical variables from L2A reflectance data, e.g., as successfully demonstrated by [51,52].…”

Section: Imaging Principlementioning

confidence: 99%

“…Retrieval performance results against 5'000 LUT reference datasets for biophysical variables retrieval from surface reflectance (TOC), TOA radiance (TOA) and surface reflectance with noise levels after atmospheric correction (ATM). For the TOC and TOA retrieval datasets 2% Gaussian noise was added, while for the TOC-ATM retrieval datasets noises are added according to [50]. An easy way to gain insight into the functioning of the GPR models at TOC and TOA scale is by means of inspecting the sigmas (σ b ), i.e., the band relevance, of the trained GPR models.…”

Section: Biophysical Variables Retrievalmentioning

confidence: 99%

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Global Sensitivity Analysis of Leaf-Canopy-Atmosphere RTMs: Implications for Biophysical Variables Retrieval from Top-of-Atmosphere Radiance Data

et al. 2019

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Knowledge of key variables driving the top of the atmosphere (TOA) radiance over a vegetated surface is an important step to derive biophysical variables from TOA radiance data, e.g., as observed by an optical satellite. Coupled leaf-canopy-atmosphere Radiative Transfer Models (RTMs) allow linking vegetation variables directly to the at-sensor TOA radiance measured. Global Sensitivity Analysis (GSA) of RTMs enables the computation of the total contribution of each input variable to the output variance. We determined the impacts of the leaf-canopy-atmosphere variables into TOA radiance using the GSA to gain insights into retrievable variables. The leaf and canopy RTM PROSAIL was coupled with the atmospheric RTM MODTRAN5. Because of MODTRAN’s computational burden and GSA’s demand for many simulations, we first developed a surrogate statistical learning model, i.e., an emulator, that allows approximating RTM outputs through a machine learning algorithm with low computation time. A Gaussian process regression (GPR) emulator was used to reproduce lookup tables of TOA radiance as a function of 12 input variables with relative errors of 2.4%. GSA total sensitivity results quantified the driving variables of emulated TOA radiance along the 400–2500 nm spectral range at 15 cm − 1 (between 0.3–9 nm); overall, the vegetation variables play a more dominant role than atmospheric variables. This suggests the possibility to retrieve biophysical variables directly from at-sensor TOA radiance data. Particularly promising are leaf chlorophyll content, leaf water thickness and leaf area index, as these variables are the most important drivers in governing TOA radiance outside the water absorption regions. A software framework was developed to facilitate the development of retrieval models from at-sensor TOA radiance data. As a proof of concept, maps of these biophysical variables have been generated for both TOA (L1C) and bottom-of-atmosphere (L2A) Sentinel-2 data by means of a hybrid retrieval scheme, i.e., training GPR retrieval algorithms using the RTM simulations. Obtained maps from L1C vs L2A data are consistent, suggesting that vegetation properties can be directly retrieved from TOA radiance data given a cloud-free sky, thus without the need of an atmospheric correction.

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Predicting catchment-scale methane fluxes with multi-source remote sensing

et al. 2021

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Context Spatial patterns of CH4 fluxes can be modeled with remotely sensed data representing land cover, soil moisture and topography. Spatially extensive CH4 flux measurements conducted with portable analyzers have not been previously upscaled with remote sensing. Objectives How well can the CH4 fluxes be predicted with plot-based vegetation measures and remote sensing? How does the predictive skill of the model change when using different combinations of predictor variables? Methods We measured CH4 fluxes in 279 plots in a 12.4 km2 peatland-forest-mosaic landscape in Pallas area, northern Finland in July 2019. We compared 20 different CH4 flux maps produced with vegetation field data and remote sensing data including Sentinel-1, Sentinel-2 and digital terrain model (DTM). Results The landscape acted as a net source of CH4 (253–502 µg m−2 h−1) and the proportion of source areas varied considerably between maps (12–50%). The amount of explained variance was high in CH4 regressions (59–76%, nRMSE 8–10%). Regressions including remote sensing predictors had better performance than regressions with plot-based vegetation predictors. The most important remote sensing predictors included VH-polarized Sentinel-1 features together with topographic wetness index and other DTM features. Spatial patterns were most accurately predicted when the landscape was divided into sinks and sources with remote sensing-based classifications, and the fluxes were modeled for sinks and sources separately. Conclusions CH4 fluxes can be predicted accurately with multi-source remote sensing in northern boreal peatland landscapes. High spatial resolution remote sensing-based maps constrain uncertainties related to CH4 fluxes and their spatial patterns.

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Evaluation of Sentinel-2A Surface Reflectance Derived Using Sen2Cor in North America

Cited by 54 publications

References 52 publications

Application of Sentinel-2 MSI in Arctic Research: Evaluating the Performance of Atmospheric Correction Approaches Over Arctic Sea Ice

Application of Sentinel-2 MSI in Arctic Research: Evaluating the Performance of Atmospheric Correction Approaches Over Arctic Sea Ice

Global Sensitivity Analysis of Leaf-Canopy-Atmosphere RTMs: Implications for Biophysical Variables Retrieval from Top-of-Atmosphere Radiance Data

Predicting catchment-scale methane fluxes with multi-source remote sensing

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