## 1 Introduction
Although it is largely ignored by large portions of the RS community, the unequivocal true fact that inherently ill-posed CV \(\supset\) EO-IU algorithms for radiometric _Cal_ of DNs into SURF or surface albedo values do require EO image classification to be performed in advance is implicitly confirmed by existing open source or commercial software toolboxes for EO image enhancement (pre-processing), critically reviewed hereafter. Supported by NASA, the baseline of the U.S. Landsat ARD format [139; 140; 141; 142; 143] is atmospheric effect removal by the open source Landsat-4/5/7 Ecosystem Disturbance Adaptive Processing System (LEDAPS). In LEDAPS, exclusion masks for water, cloud, shadow and snow surface types were detected by an over-simplistic set of prior knowledge-based spectral decision rules applied per pixel [146]. Quantitative analyses of LEDAPS products led by its authors revealed that these exclusion masks were prone to errors, to be corrected in future LEDAPS releases [146]. The same considerations hold for the Landsat 8 OLI/TIRS-specific Landsat Surface Reflectance Code (LaSRC) [160] adopted by the U.S. Landsat ARD format [139; 140; 141; 142; 143]. Unfortunately, suitable for testing or _Val_ purposes, a multi-level image consisting of exclusion masks has never been generated as standard output by either LEDAPS or LaSRC. To detect quality layers cloud and cloud-shadow, in addition to snow/ice pixels, recent versions of LEDAPS and LaSRC adopted the open source C Function of Mask (CFMask) algorithm [139; 140]. CFMask was derived from the open source Function of Mask (FMask) algorithm [58; 59], translated into the C programming language to facilitate its implementation in a production environment. Unfortunately, to date, in a recent comparison of cloud and cloud-shadow detectors, those implemented in LEDAPS scored low among alternative solutions [62]. By the way, potential users of U.S. Landsat ARD imagery are informed by USGS in advance about typical CFMask artifacts [63]. Like other cloud detection algorithms [61; 62], CFMask may have difficulties over bright surface types such as building tops, beaches, snow/ice, sand dunes, and salt lakes. Optically thin clouds will always be challenging to identify and have a higher probability of being omitted by the U.S. Landsat ARD algorithm. In addition, the algorithm performance has only been validated for cloud detection, and to a lesser extent for cloud shadows. No rigorous evaluation of the snow/ice detection has ever been performed [63]. Transcoded into CFMask by the U.S. Landsat ARD processor, the open source Frmask algorithm for cloud, cloud-shadow and snow/ice detection was originally developed for single-date 30 m resolution 7-band (from visible blue, B, to thermal InfraRed, TIR) Landsat-5/7/8 MS imagery, which includes a thermal band as key input data requirement [58]. In recent years, FMask was extended to 10 m/20 m resolution Sentinel-2 MS imagery [59], featuring no thermal band, and to Landsat image time-series (multiTemporal Mask, TMask) [60]. For more details about the FMask software design and implementation, refer to the further Section 3. Figure 6.3 As in [74], courtesy of [PERSON], ReSe Applications Schläpfer. A complete (“augmented”) hybrid (combined deductive and inductive) inference workflow for multi-spectral (MS) image correction from atmospheric, adjacency and topographic effects. It combines a standard Atmospheric/Topographic Correction for Satellite Imagery (ATCOR) commercial software workflow [71; 72], with a bidirectional reflectance distribution function (BRDF) effect correction, which requires as input an image time-series of the same surface area acquired with different combinations of the sun and sensor positions. Processing blocks are represented as circles and output products as rectangles.