## 2 The BEAM Toolbox and the Land Surface Temperature Processor
### The LST Processor as a BEAM Plug-In
#### 2.2.2 Description of the LST Processor in BEAM  
The LST processor is configured in BEAM using a two-step procedure, composed of several sub-processors. First, the surface directional reflectance (SDR) are retrieved using the synergy derivation approach mentioned in Section 2.2.1. Second, the land surface temperatures are calculated applying the LST algorithms available in the software, derived from the SEN4 LST project. Surface Directional Reflectance: The Synergy Derivation Approach  
Figure 2 shows a screen-shot of the SDR process to extract the reflectances used as input in the LST algorithm. The first sub-process consists of the collocation of the MERIS and AATSR images (Figure 3). As input for the cloud screening and atmospheric correction steps within the Surface Directional Reflectance (SDR) retrieval, a collocated MERIS/AATSR product is generated from a MERIS L1b product and a corresponding--overlapping in time and space--AATSR L1b product. This new collocation product contains a copy of all components of the master product, i.e., band data, tie-point grids, flag coding, bitmask definitions, and metadata. The MERIS L1b is treated as the \"master\" product, the AATSR L1b as the \"slave\" product. The non-overlapping areas of the master and slave products are cropped, the band data of the slave product are then re-sampled into the geographical raster of the master product, applying a nearest-neighbour re-sampling. Consequently, the collocation algorithm requires accurate geo-positioning information for both master and slave products. Since the MERIS L1b contains radiances, but the AATSR L1b contains Top Of Atmosphere (TOA) reflectances, a radiance-to-reflectance conversion is applied to the MERIS data. Figure 2: Screen-shot of the SDR module in the BEAM LST plug-in. This correction is done using the MERIS Radiometric Correction Tool available in BEAM, which uses a simple equation:  
\[\rho_{0}=\frac{\pi L_{0}}{F_{0}\cos(\theta_{s})}, \tag{4}\]  
Table 1 shows the variables of Equation (4):  
Both radiances and converted reflectances are stored in the collocated product. The tie points are taken from the MERIS (master) product as they are, whereas the AATSR tie points are resampled onto the MERIS grid and stored as regular bands. In the SEN4 LT processor realization, the collocation product serves as an intermediate result, and it is not written to disk. After the collocation follows the cloud screening, whose main objective is to accurately detect pixels contaminated by clouds and exclude these from LST retrieval. In summary, the cloud screening algorithm follows [26; 27]: first, a feature extraction and selection based on meaningful physical features is carried out (clouds are bright, white, high, etc.). Then, a supervised pixel-based feature classification, based on simulated TOA radiances and corresponding cloud optical thickness from radiative transfer models (RTMs) and on the real MERIS and AATSR data from a manually labeled training set, is applied to these features providing the pixel label (cloud or cloud free) and the cloud abundance. Finally, an image-based post-processing (such as removal of coastline artifacts) is applied to the classified image to refine the cloud products. The resulting product contains all bands and tie points copied from the collocated product, and an additional flag band indicating cloudy pixels. In the SEN4 LT processor realization, the cloud screening product serves as an intermediate result, and it is not written to disk. The atmospheric correction aims at making use of the angular and spectral sampling available from MERIS and AATSR in order to develop an improved algorithm for atmospheric correction (including aerosol retrieval) over land. Basically, the problem of land aerosol and SDR retrieval represents an optimization subject to multiple constraints.