MSScvm (MSS clear-view-mask) is an automated cloud and cloud shadow identification system for Landsat MSS imagery. It is an important contribution to the field of large-volume Landsat image processing, as it provides an efficient means of identifying and removing cloud and cloud shadow pixels from inclusion in image mosaics, composites, and time series analysis.
Similar cloud and cloud shadow identification systems for Landsat TM, ETM+, and OLI imagery have been developed and have played a crucial role in facilitating the current trend of large-area and dense time series analysis.
MSScvm was developed to provide the same service to MSS data so that it could more easily be incorporated with TM, ETM+ and OLI data to produce 42+ year spectral chronologies representing the longest satellite-derived earth observation dataset. This extended record provides information on long-term Earth surface changes, including forest dynamics, desertification, urbanization, glacier recession, and coastal inundation. It also has the benefit of increasing the observation frequency of cyclic and sporadic events, such as drought, insect outbreaks, wildfire, and floods. Furthermore, utilization of the full 42+ years of Landsat imagery sets an example of effectual resource use and supports the need for continuity of Landsat missions to provide seamless spatial and temporal coverage into the future.
Clouds and their shadows block or reduce satellite sensors' view of Earth surface features, obscuring spectral information characteristic of clear-sky viewing. This spectral deviation from clear-sky view can cause false change in a change detection analysis and conceals true land cover, which can reduce the accuracy and information content of map products where cloud-free images are not available. As a result, cloud and cloud shadow identification and masking are important and often necessary pre-processing steps.
The algorithm is specific to the unique spectral characteristics of MSS data, relying on a simple, rule-based approach. Clouds are identified based on green band brightness and the normalized difference between the green and red bands, while cloud shadows are identified by near infrared band darkness and cloud projection. A digital elevation model is incorporated to correct for topography-induced illumination variation and aid in identifying water.
The MSScvm program is written in the R programming language and distributed as an R package for straightforward installation and easy use.
Besides creating cloud and cloud shadow masks, MSScvm will also prepare MSS images downloaded from EarthExplorer by decompressing, stacking, and converting DN values to TOA radiance and/or reflectance.