Validation Examples of ATCOR Haze Removal of Rapid-Eye Images

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

Copernicus Cal/Val synergy among current and future optical missions

Operational Calibration and Validation (Cal/Val) is required to ensure the quality of and build confidence in Copernicus data. However, current Cal/Val activities are limited and insufficiently harmonized between different missions. The objective of the Copernicus H2020 Cal/Val Solution (CCVS) project is to define a holistic solution for all Copernicus Sentinel missions to overcome current limitations both for current and upcoming Sentinel-missions. This includes improved calibration of currently operational or planned Copernicus Sentinel sensors and the validation of Copernicus core products generated by the payload ground segment. CCVS started with an overview of existing calibration and validation sources and means, identified gaps in the current cal/val practise and is proposing long-term solutions to address the currently existing constraints in the Cal/Val domain. An objective is also to exploit existing synergies between the missions. The analysis performed within the CCVS project is based on experience from many experts in the Cal/Val domain and on feedback from different working groups gathering European Space Agencies, Copernicus Services, measurement networks and International partners. This presentation will give an overall assessment of Copernicus Cal/Val maturity in the optical mission component both for sensor calibration and characterization and for product quality. Required developments in terms of technologies and instrumentation, Cal/Val methods, instrumented sites and dissemination service are addressed. One of our findings is the need for ground-based hyperspectral reference measurements in particular to prepare the validation of CHIME

DESIS and Copernicus Sentinel-2 Surface Reflectance, AOT and WV Products compared to measurements on ground

PACO atmospheric correction software is implemented in the DESIS L2A processor providing Bottom-Of-Atmosphere (BOA) ground reflectance spectral image cube together with aerosol optical thickness (AOT) and integrated water vapour (WV) maps. PACO can also be applied to Sentinel-2 data providing equivalent outputs. This presentation will rely on reference measurements of SR, AOT and WV which had been performed on ground in parallel to DESIS acquisitions in August 2019 and 2020. Microtops photometers are used for measurements of the atmospheric parameters and SR measurements on ground used a hyperspectral SVC spectroradiometer covering the spectral range from 380 nm to 2.5 µm. There are Sentinel-2 overpasses over the same area at the same day. Both DESIS and Sentinel-2 data were be processed with PACO and then compared to the available reference measurements

Validation of DESIS Surface Reflectance Product with Measurements on Ground

The hyperspectral instrument "DLR Earth Sensing Imaging Spectrometer" (DESIS) was developed within a collaboration between the US company Teledyne Brown Engineering (TBE) and the German Aerospace Center (DLR). DESIS is operating onboard of the International Space Station (ISS) since August 2018 and is in operational phase since October 2019. The validation of the L2A products in remote sensing is performed by different approaches. One of them is the comparison of the ground surface reflectance as delivered by the L2A-remote sensing products with in-situ measurements. Those in-situ measurements are performed with spectroradiometers at the same wavelength of the remote sensor and at the time of a satellite overpass. The presentation provides recent validation results for the DESIS BOA reflectance product on basis of ground-based reference measurements. Accuracy represents the mean difference of BOA-reflectance retrieval to a reference value and uncertainty gives the rms around the reference. Accuracy and uncertainty of surface reflectance retrieval from DESIS data is benchmarked by comparison with surface reflectance retrieval from Sentinel-2 data

Validation of a new atmospheric correction Software using AERONET reference data

Atmospheric correction of satellite images based on radiative transfer calculations is a prerequisite for many applications. The program ATCOR, developed at the German Aerospace Center (DLR), is a rather versatile atmospheric correction software, capable of processing data acquired by different optical satellite sensors. A Python-based version of this code is currently being developed to process L2A products of Sentinel-2, Landsat-8 and of new space-based hyper-spectral sensors such as DESIS and EnMAP. In this contribution we will present the first validation results of this software, comparing L2A products generated from Sentinel-2 L1C images with in-situ (AERONET) data

Copernicus Cal/Val Solution - D2.4 - Systematic Ground-Based Measurements

This document aims to map different existing ground-based and air-borne instrumented Cal/Val sites and networks acquiring measurements in a systematic manner, in Europe and worldwide. It does not include all available Cal/Val networks but only those that we interviewed or had enough information available online to include in this report. To meet the needs of satellite Cal/Val, measurements one must adhere to the definition for a Fiducial Reference Measurement (FRM)(Giuseppe Zibordi et al. 2014) and to the principles of the Quality Assurance framework for Earth Observation (QA4EO 2010). The scope of this document is not to evaluate the quality or maturity of the networks/sites that were being interviewed. It only maps the current situation and serves as an input for a later stage of the project. The completed questionnaires that we used to collect the data assembled in this report are not added directly to the document but will be available for project partners for next stage analyses

Evaluation of SEN2COR surface reflectance products over land surface with reference measurements on ground

Sen2Cor is the atmospheric correction processor selected by ESA for operational, systematic processing of Copernicus Sentinel-2 mission data. It is used for generating the Level 2A products distributed to users by the Copernicus SciHub. Accurate atmospheric correction of Sentinel-2 data and knowledge of its uncertainties are preconditions for high quality downstream applications. In this work we present the comparison of Sentinel-2 Bottom-of-Atmosphere products with measurements of surface reflectance on ground. Source of reference measurements are both surface reflectance data from RadCalNet and from dedicated field campaigns. The analysis shows, that the uncertainty of SR-retrieval with Sen2Cor is better than about 7% for bright surfaces and about 17% for darker. In addition to this performance evaluation, the data are also applied to compare the use of reference data coming from permanent operating bright RadCalNet sites and from ad-hoc field campaigns at darker sites