## 4 Current Status and Identified Needs
### Level 2 Land Surface Reflectances
#### 4.2.1 SR Assessment within ACIX Initiative  
The lack of a global network of ground-based surface reflectance measurements over land is currently considered the most urgent data gap for assessing the radiometric accuracy of current and future ESA optical land imaging sensors at the BOA level, as currently pointed out in a recent workshop [3]. In the absence of such ground-based infrastructure, the common approach is an indirect validation, as developed in the frame \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**ID** & **Status and Needs** & **Recommendations** \\ \hline \multirow{4}{*}{IDN-04} & There is currently a need for providing per-pixel & Space Agencies should work towards the provision of uncertainty information at pixel-level in Level 1 \\  & uncertainty estimates at Level 1. This is a critical input for consistently assimilating satellite observations into geophysical and weather forecast models. & Space Agencies should work towards the provision of uncertainty information at pixel-level in Level 1 \\ \cline{1-1}  & PICS are invaluable targets for assessing radiometric accuracy and temporal stability and for cross-calibration. Though, used RTMs should be improved in order to meet the accuracy requirement set by the climate community (1\%). & \\ \hline \multirow{4}{*}{IDN-06} & The Moon is the best vicarious target for optical sensor stability monitoring and inter-calibration. However, the accuracy of the current (ROLO) lunar irradiance model still prevents its use for absolute calibration. New observations are urgently required for using the Moon as an absolute radiometric target. & Space Agencies, in coordination with GSICS and CEOS-WGCV, should support improvements in Lunar irradiance models and inter-comparison exercises with the target of using the Moon as absolute target for radiometric calibration, reaching the target of 1\% in radiometric accuracy. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._of the joint ESA-NASA Atmospheric Correction Inter-Comparison Exercise (ACIX) with focus on the Sentinel-2 and Landsat-8 (L8) missions [118]. The validation of SR within ACIX is performed over a set of AERONET stations [52], globally spread to be representative of different surface and climatological conditions. The AERONET network provides a reliable, globally representative and consistent dataset of atmospheric variables that allows for the validation of the performance of an atmospheric correction processor using common statistical metrics. In the frame of ACIX, a \"ground-truth\" dataset of synthetic SR was computed with the 6 SV RTM [119] using as input data the Aerosol Optical Thickness (AOT), the aerosol micro-physical properties and Total Column Water Vapor (TCWV) provided by AERONET retrievals. A protocol was consolidated within ACIX allowing to quantitatively estimate the uncertainty budget in SR products in terms of Accuracy, Precision and Uncertainty (APU) metrics [49] and to compare this estimate against mission requirements. The first ACIX initiative, completed in 2018, was successful in identifying drawbacks and limitations of current AC codes for S2 and L8 missions, helping the algorithm providers to improve their methods. Typical limitations include the use of Dark Dense Vegetation (DDV) approaches for AOT estimation that fail over non-vegetated scenes or the choice of predefined aerosol models. Owing to this success, a new ACIX-II initiative was designed, building on the lessons learnt gathered during the first exercise [120]. Within ACIX-II, a clear distinction was made, and different inter-comparisons were run for land and aquatic reflectances and cloud masks. In the land domain, the ensemble of the considered AERONET stations was significantly increased, with the objective to verify the accuracy of the AC algorithms at a global scale. Furthermore, the ground-based measurements were included for the direct validation of both the land and water reflectances. Over land, these observations were taken from the RadCalNet network [107].