Biological soil crusts, commonly referred to as biocrusts (including lichens), play a vital ecological role in arid environments by stabilizing the soil, reducing erosion, and influencing nutrient and water cycles. Remote sensing has become an indispensable tool for mapping biocrusts because it can efficiently analyze extensive and inaccessible areas. Furthermore, spectral analysis of biocrusts provides insight into their type and characteristics. This study focuses on detecting and mapping biocrusts, specifically lichens, in the hyper-arid Namib Desert. It utilizes a combination of spectral indices that highlight their key biophysical components: phycobilin, chlorophyll, and cellulose. The study encompasses laboratory hyperspectral analyses, wetting experiments, and statistical methods, including PLS-DA and PLS-R, as well as Sentinel-2 image processing, to examine reflection and absorption features in the visible, near-infrared, and shortwave-infrared (SWIR) regions. Alongside the well-established Crust Index (CI) and Normalized Difference Vegetation Index (NDVI), the novel Satellite Cellulose Absorption Index (SCAI) was introduced. The latter index utilizes the SWIR1 and SWIR2 bands to identify lichens on a landscape scale, showing significant differentiation between lichens and bare substrate under dry conditions. Examinations by the PLS methods confirm that phycobilin reflection in the blue band, chlorophyll absorption in the red, and cellulose absorption in SWIR2 are the primary biological indicators of biocrusts, and combining them provides complementary insights. The satellite-based indices exhibit a declining spatial trend in lichen density from the coast inland, correlating with fog and moisture availability. These findings underscore the potential of remote sensing tools to assess biocrust distribution and dynamics on a landscape scale. The proposed SCAI, which is sensitive to moisture, provides a scalable solution for mapping biocrusts, offering new opportunities for monitoring arid ecosystems under the impending changing climate. Keywords: Spectral indices; Remote sensing; Satellite Cellulose Absorption Index (SCAI); Partial least squares.