The Empirical Line Method (ELM) is a widely applied technique of achieving absolute atmospheric correction assuming a linear relationship between the raw Digital Numbers (DNs) or at-sensor radiance and surface reflectance measurements collected in-situ. The ELM measures reference targets of known reflectance in an image. Labsphere has created an automated vicarious calibration system using the SPecular Array Radiometric Calibration (SPARC) mirror technology in the new Field Line-of-sight Automated Radiance Exposure (FLARE) network. In the FLARE system the known reflectance targets are convex mirrors - because of that it is titled Mirror based Empirical Line Method (MELM). In this context, the objective of this work is to present the initial results of the MELM using one the FLARE network system. The FLARE system evaluated in this work is the Alpha Node located at Arlington, SD. Initially, the data collected in 2020 and 2021 with the Alpha FLARE concomitant with the OLI sensor overpass on-board the Landsat-8 satellite were used in the assessment. In summary, the surface reflectance image product available to download for OLI sensor were compared directly with the surface reflectance image resulting from the MELM method. The preliminary results showed the mean absolute error data between the surface reflectance from the OLI Level-2 product image and the surface reflectance from the MELM was lower than 0.01 for the Blue, Green, Red and SWIR-1 bands; lower than 0.03 for the for the NIR and SWIR-2 spectral bands; and around 0.05 for Coastal Aerosol band (all in reflectance units). These results suggest the MELM technique using FLARE has great potential for reflectance surface evaluation of orbital sensors.
Accurate radiometric cross calibration is critical for guaranteeing the consistency of measurements from different Earth observation sensors, and fully using the combined data in quantitative applications. It becomes even more indispensable with the rapid increase of remote sensing data availability from numerous sensors. The assessment of the Spectral Band Adjustment Factor (SBAF) is a key component of the cross-calibration method. The SBAF compensates for intrinsic differences in sensor response caused by Spectral Response Function (SRF) mismatches. Currently, Sentinel and Landsat data represent the most widely accessible medium spatial resolution multispectral satellite data. Hence, in this study, the SBAF of the Multi-Spectral Imager (MSI) on-board Sentinel-2 and the Operational Land Imager (OLI) on-board Landsat-8 was estimated over pseudo-invariant calibration sites (PICS) located in North Africa. The SBAF depends on the hyperspectral profile of the target and the sensor SRF. Here, the hyperspectral profile was derived from the Hyperion hyperspectral imager on-board the EO-1. Finally, it is important to highlight that an estimate of the SBAF is incomplete unless accompanied with its uncertainty. The uncertainty analysis of the SBAF was implemented using Monte Carlo simulation. The results obtained in this study can be utilized by any user who needs the SBAF of the OLI and MS1 over North Africa Desert sites.
First launched in 1972, the Landsat satellite sensors have provided the longest continuous record of high quality images of the Earth’s surface that are used in both civilian and military applications. Extraction of quantitative information (e.g., surface reflectance) from the Landsat image data is only possible through an accurate absolute radiometric calibration. Typically, this calibration has been performed as a radiance-based cross-calibration between sensors. However, to convert radiance to reflectance, an accurate estimate of solar exoatmospheric irradiance is critical; and there are several solar models currently available which estimate exoatmospheric irradiance with varying levels of accuracy. Because of these inconsistencies in solar models, a TOA reflectance-based approach, independent of exoatmospheric irradiance, has been developed to provide a consistent cross-calibration of the Landsat series (from Landsat 8 OLI to Landsat 4 MSS), based on analysis of coincident and near-coincident scene pairs acquired with each sensor. The methodology uses Landsat-8 OLI reflectance measurements as the starting point (reference), as they are estimated with a 3% uncertainty (compared to the 5% uncertainty associated with radiance measurements). A set of radiometric calibration coefficients has been estimated based on the equations presented in this paper, which allows direct conversion of the digital numbers from the image data to TOA reflectance. The results obtained from application of these coefficients show significant improvement in consistency of reflectance measurements among the Landsat sensors.
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