Empirical line model for the atmospheric correction of sentinel-2A MSI images in the Caribbean Islands

Abstract

An Empirical Line Model (ELM) was tested to correct Sentinel-2A (MSI) images acquired in the tropical archipelago of San Andrés, Colombia. This approach uses a linear regression to model the relationship between the average ground reflectance and radiance on bands 2, 3, 4, and 8, for 32 spectrally homogeneous targets. The model was validated from eight targets measured on different land-covers trough the estimated coefficient of determination R 2. The result of the prediction equations observed was high, with a value of R 2 :0.91 performed at the 0.01 level of significance for the four wavebands, against the R 2 :0.77 of SEN2COR and R 2 :0.81 of ATCOR Correction Models. Complementary, a quantitative approach was proposed to determine the suitability of the ELM, based on the spectral response on six land-covers types for every band after correction. A separability index (M) was used from a set of independent targets to estimate the effectiveness of spectral classification of land-covers. The more evident results of the correction are on the vegetation cover in the NIR band (785-900 nm), where the ELM has 55% and 58% more separability than the SEN2COR and ATCOR models, respectively. Additionally, the absolute difference between the Top-of-Atmosphere (TOA) and Bottom-of-Atmosphere (BOA) images was calculated, finding the highest differences in the NIR band with 0.094 in the L1C-TOA reflectance image, and 0.013 in the ELM-BOA image. Finally, a sensitivity analysis on the Normalized Difference Vegetation Index (NDVI) to estimate the performance of the spectral response of ELM on vegetation cover was employed.

Satellite data reveal differential responses of Swiss forests to unprecedented 2018 drought

“…We applied an atmospheric correction to retrieve bottom-ofatmosphere reflectance (level-2A) using the Sen2Cor processor (Louis et al, 2016) of the Sentinel Application Platform (SNAP -ESA Sentinel Application Platform v6.0, http://step.esa.int). We also applied an empirical line-based radiometric co-registration of images (Ariza et al, 2018) to compensate possible inaccuracies in obtained reflectance values due to imperfect atmospheric characterizations over time and, thus, to further increase consistency of the time series. This procedure inter-calibrates image radiometric information of a specific image of the time series considering a pre-defined reference image (e.g., the first image of the time series).…”

Estimating Water Content and Grain Size of Intertidal Flat Sediments Using Visible to Shortwave-Infrared Reflectance and Sentinel 2A Data: A Case Study of the Red River Delta, Vietnam

“…To estimate surface WC and other physical properties, the atmospheric effects on optical sensor image data must be removed [47]. The empirical linear model (ELM) method [48] is the most accurate atmospheric correction method for S2A image data [49], [50]. Therefore, per-pixel reflectance at the top of atmosphere (TOA) of the images was transformed into the bottom of atmosphere (BOA) reflectance through ELM to allow a linear regression between the mean field reflectance at the ten field sample points and the TOA reflectance of the ten corresponding pixels of the S2A image.…”

Copernicus Sentinel-2 Calibration and Validation

“…S2VT work presented here mainly focused on Level-1C product type. However, certain authors (Gorroño et al, 2018;Ariza, Irizar, & Bayer, 2018;Keukelaere et al, 2018;Ressl & Pfeifer, 2018) also worked on other product levels (−1B, −2A). For the sake of clarity, a brief outline of these levels is as follows:…”