## 4 Current Status and Identified Needs
### Level 2 Land Surface Reflectances
#### 4.2.2 Cloud Mask Assessment within CMIX  
All AC codes in the optical domain typically work under the assumption of clear sky-conditions since the radiative impact of the clouds is not accounted for within the RTM. As a result, failure in accurately masking cloud-contaminated pixels will lead to uncertainties in the atmospherically corrected reflectances. Therefore, as part of ACIX-II, a dedicated initiative, named Cloud Mask Inter-Comparison Exercise (CMIX) [127], was defined to assess the accuracy of the currently used cloud detection schemes for the S2 and L8 missions. Despite not being an objective metric, the intercomparison of cloud masks is a widely used method to assess the accuracy of the cloud detection schemes, and it was the subject of various papers in recent years [128; 129]. The first CMIX exercise is now completed, and the outcomes are being summarised in a scientific paper. Some of the recommendations gathered as part of the CMIX, which are relevant for land applications, are here reported and discussed. While cloud contamination is generally easily detected for optically thick clouds, using the spectral information available within S2 and L8 bands, issues arise in the case of optically thin clouds (often referred to as semi-transparent) or for pixels nearby a cloud. In these cases, the performances of the different cloud mask algorithms strongly differ. Two conditions generally occur: on one side, clear-sky conservative algorithms overestimate the cloud cover; on the other side, cloud-conservative algorithms tend to underestimate the cloud presence. The choice between these two extremes is mostly driven by the needs of the subsequent application and the sensitivity of higher-level algorithms to residual cloud-contaminated pixels. As a rule of thumb, clear-sky conservative approaches are better suited for land and water applications, while for studying cloud optical properties, cloud-conservative approaches may be the preferred choice. With respect to pixels at the cloud edges, a practical method consists in dilating the cloud mask for a certain number of pixels to limit the impact of adjacency effects, i.e., increase of radiance for pixels nearby cloud boundaries, this is for instance the approach used by some of the CMIX algorithms [130; 131]. The problem of the cloud mask ultimately reduces to the basic question of defining what is a cloud, or rather, what is a cloud for the target application. This is a non-trivial question, and it has not been fully answered yet for optical land imaging sensors. In the meteorological community, a cloud is generally defined as: \"a visible aggregate of minute water droplets or ice particles in the atmosphere above the Earth's surface\" [132]. The issue remains however, that being visible depends on the observation conditions and on the remote sensing device being used. For instance, active Light Detection and Ranging (LiDAR) sensors provide enhanced capabilities for observing cloud optical and micro-physical properties not feasible with passive sensing techniques. In the absence of an agreed definition of a \"cloud\", CMIX adopted a pragmatic approach for validating and inter-comparing the different cloud masks using best practices and the reference data available in the literature. The approach was mostly based on the use of manually labelled S2 and L8 scenes, such as the Hollestein dataset for S2 [133] and the Biome dataset for L8 [134]. The labelling of the scenes into different classes (cloud, semi-transparent, clear pixel) was performed by experts. This labelling was considered to be the ground-truth against which all the cloud masks were compared. This approach clearly lacks an objective and physically based definition of cloud properties, since the manual labelling process is subjective and error-prone. Furthermore, inconsistencies were verified among the currently available labelled datasets for S2 and L8, owing to the intrinsic subjectivity of the photo-interpretation process.