High spatial resolution missions as Sentinel-2 open opportunities to set-up operational Earth Observation (EO) services at local scale. However, due the frequent cloud coverage in Western Europe, combined with the lower revisit time of high spatial resolution sensors, operational services have often to combine data from different missions in order to have sufficiently frequent observations. A seamless combination of EO products coming from different missions is however not straightforward. Radiometric biases at TOA (Top-of-Atmosphere) level might exist. Even for a perfectly cross-calibrated constellation of instruments, intrinsic differences in the Relative Spectral Response Functions (RSRFs) of comparable bands might cause discrepancies in the final products. Furthermore biases in the products can also be introduced through the use of different processors and algorithms (e.g. for the atmospheric correction). Within the BELHARMONY project, a bottom-up approach is used in order to assess and to improve multi-mission high resolution time series consistency. First, vicarious radiometric calibration methods are used to assess the consistency at the L1 TOA level. This is done for the following sensors: Sentinel-2A/B MSI, Landsat-8 OLI, PROBA-V, and Deimos-1. The bias assessment is performed over targets with low as well as targets with medium radiance. The consistency evaluation over medium to high radiances is performed over 1) Land Equipped Sites (LES) through the use of the RadCalNet portal and 2) over the Libya-4 Pseudo-Invariant Calibration Site (PICS) site. The use of two different approaches allows for independent validation in order to reduce the uncertainty in the final results. The inter-calibration for low radiances targets is carried out over AERONET-OC stations. Next, to adjust the spectral response of one sensor to another, we model the difference that is related to the difference in the RSRFs , allowing to derive band- and/or index-dependent spectral adjustment functions. For this, we use simulated vegetation spectra derived from physically-based radiation transfer models that consider the leaf optical properties, the canopy structure, and the background reflectance. These simulations are completed by adding spectral libraries of non-vegetated surfaces. Finally a common processing chain for the processing of the L1 data up to L2 reflectance and L3 higher level products is used. In the common processing environment the iCOR atmospheric correction code is applied to the data of all the considered sensors.
Harmonization of multi-sensor high resolution time series : the BELHARMONY approach
Bassani C;
2019
Abstract
High spatial resolution missions as Sentinel-2 open opportunities to set-up operational Earth Observation (EO) services at local scale. However, due the frequent cloud coverage in Western Europe, combined with the lower revisit time of high spatial resolution sensors, operational services have often to combine data from different missions in order to have sufficiently frequent observations. A seamless combination of EO products coming from different missions is however not straightforward. Radiometric biases at TOA (Top-of-Atmosphere) level might exist. Even for a perfectly cross-calibrated constellation of instruments, intrinsic differences in the Relative Spectral Response Functions (RSRFs) of comparable bands might cause discrepancies in the final products. Furthermore biases in the products can also be introduced through the use of different processors and algorithms (e.g. for the atmospheric correction). Within the BELHARMONY project, a bottom-up approach is used in order to assess and to improve multi-mission high resolution time series consistency. First, vicarious radiometric calibration methods are used to assess the consistency at the L1 TOA level. This is done for the following sensors: Sentinel-2A/B MSI, Landsat-8 OLI, PROBA-V, and Deimos-1. The bias assessment is performed over targets with low as well as targets with medium radiance. The consistency evaluation over medium to high radiances is performed over 1) Land Equipped Sites (LES) through the use of the RadCalNet portal and 2) over the Libya-4 Pseudo-Invariant Calibration Site (PICS) site. The use of two different approaches allows for independent validation in order to reduce the uncertainty in the final results. The inter-calibration for low radiances targets is carried out over AERONET-OC stations. Next, to adjust the spectral response of one sensor to another, we model the difference that is related to the difference in the RSRFs , allowing to derive band- and/or index-dependent spectral adjustment functions. For this, we use simulated vegetation spectra derived from physically-based radiation transfer models that consider the leaf optical properties, the canopy structure, and the background reflectance. These simulations are completed by adding spectral libraries of non-vegetated surfaces. Finally a common processing chain for the processing of the L1 data up to L2 reflectance and L3 higher level products is used. In the common processing environment the iCOR atmospheric correction code is applied to the data of all the considered sensors.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
