## 4 Current Status and Identified Needs
### Level 2 Bio-Geophysical Land Products
\begin{table}
\begin{tabular}{c p{142.3 pt} p{142.3 pt}} \hline \hline
**ID** & **Status and Needs** & **Recommendations** \\ \hline \multirow{4}{*}{IDN-08} & Validation of cloud masks is currently based on the use of manually labelled reference ground-truth data with an obvious lack of an objective and a physically based approach. & Space Agencies, in coordination with CECOS-WGCV, should develop advanced methods for cloud mask validation using ground-based independent measurements, working towards the set-up of an autonomous globally representative network for both medium and coarse resolution sensors. \\ \hline \multirow{4}{*}{IDN-09} & There are inconsistencies among the publicly available manually labelled databases for S2 and L8 pixel classification, owing to the bias introduced by the photo-interpretation process. These inconsistencies are currently limiting their synergistic use for cloud mask validation. & Space Agencies, in coordination with CECOS-WGCV, should develop a reference of the manually labelled database specifically tailored to cloud mask validation. The database should include optically thick, semi-transparent and cloud shadow classes. \\ \hline \multirow{4}{*}{IDN-10} & There is a lack of unanimously agreed definition of cloud in the optical land community, notably for optically thin or semi-transparent clouds. Furthermore, this definition is generally application-dependent or related to the sensitivity of the downstream algorithm to residual cloudy-contaminated pixels. & Space Agencies, in coordination with CECOS-WGCV, should agree on a common and physically based definition of clouds, for easing inter-comparison of different cloud mask algorithms for optical sensors. \\ \hline \multirow{4}{*}{IDN-11} & The presence of undetected clouds or adjacency effects at the cloud edges can strongly impact accuracy of downstream products. On the other hand, there have been few theoretical studies to understand this problem and to quantitatively estimate the uncertainty associated to cloud screening in higher level land products. & Space Agencies, in coordination with the science community, should foster theoretical advances in order to rigorously estimate the uncertainty associated to the cloud mask, both for partially cloudy pixels and for clear pixels nearby a cloud. \\ \hline \multirow{4}{*}{IDN-12} & Terrestrial vegetated surfaces are generally highly anisotropic and heterogeneous at the medium resolution pixel scale. The two effects combine when assessing the quality of satellite-based SR products and it is difficult to disentangle the spatial and directional contribution. & Space Agencies, in coordination with the science community, should support theoretical advances, to fully understand the uncertainty associated to the validation of directional surface reflectance over typical land heterogenous vegetated surfaces. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._fAPAR currently differ in terms of the adopted definition (green, total, instantaneous, integrated), and this lack of a common definition is inducing discrepancies when inter-comparing products from different missions, although the largest source of differences is caused by the different underlying RTMs [143]. OLCI OGVI products (in the future to be renamed as GI-FAPAR), based on the legacy MERIS algorithm [21; 22], provide a measure of instantaneous green fAPAR in the plant canopy. * Leaf Area Index (LAI) is defined as one half of the total green (i.e., photosynthetically active) leaf area per unit horizontal ground surface area. Different definitions of LAI can be considered: the GAI (Green Area Index) has the same definition as LAI but includes only green elements, and the PAI (Plant Area Index) includes all elements, green and non-green. Similar to fAPAR the lack of consistent definition may cause discrepancies when inter-comparing satellite-based LAI products generated using different algorithms.