At short wavelengths, aerosols impact the surface reflectance observed by a satellite sensor. In order to accurately estimate the terrestrial surface dynamics, the surface reflectances should be corrected for the influence of aerosol perturbation. The atmospheric correction applied to the standard PROBA-V data (Sterckx et al. 2014) uses the Simplified Model for Atmospheric Correction (SMAC) v4.2 (Rahman and Dedieu 1994, Berthelot and Dedieu, 1997). The aerosol optical thickness (AOT) is estimated from the images with an optimization method that utilises a relation between TOA NDVI and the observed SWIR / BLUE reflectance ratio (Maisongrande et al. 2001).
The objective of this activity is to investigate the added value of a joint surface-aerosol retrieval algorithm to improve land surface reflectance characterization derived from PROBA-V observations. The suggested method will take advantage of previous work (Govaerts et al. 2010) using an Optimal Estimation method and will assess the possibility to apply such an approach to PROBA-V observations.
Improvements with respect to the approach currently implemented at VITO is documented and discussed in the light of user requirements. This development accounts for past and/or ongoing ESA-related activities, such as the Aerosol Climate Change Initiative (CCI) and GlobAlbedo, keeping as final objective the possibility to derive the related Essential Climate Variables (ECVs), providing recommendations to apply such a method operationally on PROBA-V or Sentinel-3 missions.
The proposed method primarily targets to fulfill the ECV generation requirements, which is the most interesting for the processing of the entire SPOT-VGT/PROBA-V time series. Given that the PROBA-V data characteristics are very similar to those of SPOT-VGT, the same method could be applied to that data set in the future. Therefore, the work focuses on the 1 km resolution PROBA-V data.
A 1-year joint surface reflectance and aerosol retrieval time series will be evaluated with MODIS surface reflectance and Aerosol Robotic Network (AERONET) Aerosol Optical Thickness (AOT) observations, respectively, and its added value compared to the currently used method will be assessed.