ACCA was not designed for precise spatial detection of clouds, but rather, to estimate the percentage of cloud cover in a given Landsat scene.

An early example is the automatic cloud cover assessment (ACCA), which uses a series of successive thresholds over bands and band combinations to define a hierarchical set of rules for clouds. Since clouds are bright and cold and cloud shadows are darker than the surrounding landscape, a common approach is to apply a threshold to the spectral values or some simple function of two or more spectral values. Some of these algorithms have then been adapted for use on Landsat data (see, for example, Oreopoulos et al., 2011 ).

Many algorithms for cloud and cloud shadow masking have been developed for other sensors, particularly for AVHRR and MODIS. Significant work has been devoted to cloud and cloud shadow identification. To illustrate this, we present an application that constructs obstruction-free composites of images acquired on different dates in support of a method for vegetation change detection. Additionally, SPARCS provides a measure of uncertainty in its classification that can be exploited by other algorithms that require clear sky pixels.

In a comparison with FMask, a high-quality cloud and cloud shadow classification algorithm currently available, SPARCS performs favorably, with substantially lower omission errors for cloud shadow (8.0% and 3.2%), only slightly higher omission errors for clouds (0.9% and 1.3%, respectively) and fewer errors of commission (2.6% and 0.3%). It then applies a series of spatial procedures to resolve pixels with ambiguous membership by using information, such as the membership values of neighboring pixels and an estimate of cloud shadow locations from cloud and solar geometry. The method uses a neural network approach to determine cloud, cloud shadow, water, snow/ice and clear sky classification memberships of each pixel in a Landsat scene. We develop a novel algorithm to identify and classify clouds and cloud shadow, SPARCS: Spatial Procedures for Automated Removal of Cloud and Shadow. The use of Landsat data to answer ecological questions is greatly increased by the effective removal of cloud and cloud shadow from satellite images.

Lessons from Shadow by Shadow Bregman