Validation Examples of ATCOR Haze Removal of Rapid-Eye Images

Kurzfassung

Atmospheric correction of satellite images is necessary for many applications of remote sensing. Among them are applications for agriculture, forestry, land cover and land cover change, urban mapping, emergency and inland water. ATCOR is a widely used atmospheric correction tool which can process data of many optical satellite sensors, for instance Landsat, Sentinel-2, SPOT and RapidEye. ATCOR includes a terrain and adjacency correction of satellite images and several Special algorithms like haze detection, haze correction, cirrus correction, de-shadowing and empirical methods for BRDF correction. The largest uncertainty in atmospheric correction arises out of spatial and temporal variation of Aerosol amount and type. Therefore validation of aerosol estimation is one important step in validation of atmospheric correction algorithms. Last year we presented validation results of aerosol retrieval by the widely used atmospheric correction tool ATCOR. We compared vertical column aerosol-optical thickness (AOT) spectra derived from Rapid-Eye data with in-situ sun-photometer measurements on the ground. Mean uncertainty was ΔAOT550 ≈ 0.04. The presentation will update these results including reference measurements on the ground in year 2015. Haze removal gives the chance to add more observation points in time series analysis. We started to investigate the accuracy of ATCOR haze removal by comparing haze-removed Rapid-Eye Images with atmospherically corrected images from nearby cloudless data takes. First results are shown in the proposed presentation.