Automated cloud, cloud shadow, and snow detection in multitemporal Landsat data: An algorithm designed specifically for monitoring land cover change

- A cloud, cloud shadow, and snow detection algorithm for multitemporal Landsat data
- A modified Norwegian Linear Reflectance-to-Snow-Cover algorithm is proposed.
- The results are more accurate than those of the single-date Fmask algorithm.
- This algorithm is suitable for preparing Landsat data for change detection.

We developed a new algorithm called Tmask (multiTemporal mask) for automated masking of cloud, cloud shadow, and snow for multitemporal Landsat images. This algorithm consists of two steps. The first step is based on a single-date algorithm called Fmask (Function of mask) that initially screens most of the clouds, cloud shadows, and snow. The second step benefits from the extra temporal information from the remaining “clear” pixels and further improves the cloud, cloud shadow, and snow mask. Three Top Of Atmosphere (TOA) reflectance bands (Bands 2, 4, and 5 - Landsat-7 band numbering) are used in a Robust Iteratively Reweighted Least Squares (RIRLS) method to estimate a time series model for each pixel. By comparing model estimates with Landsat observations for the three spectral bands, the Tmask algorithm is capable of detecting any remaining clouds, cloud shadows, and snow for an entire stack of Landsat images. Generally, this algorithm will not falsely identify land cover changes as clouds, cloud shadows, or snow, as it is capable of modeling land cover change. The multitemporal images also provide extra information for better discrimination of cloud and snow, which is difficult for single-date algorithm. A snow threshold is derived for Band 5 TOA reflectance for each pixel at each specific time based on a modified Norwegian Linear Reflectance-to-Snow-Cover (NLR) algorithm. By comparing the results of Tmask with a single-date algorithm (Fmask) for multitemporal Landsat images located at Path 12 Row 31, significant improvements are observed for identification of clouds, cloud shadows, and snow. The most significant improvement is observed for cloud shadow detection. Many of the errors in cloud, cloud shadow, and snow detection observed in Fmask are corrected by the Tmask algorithm. The goal is development of a cloud, cloud shadow, and snow detection algorithm that results in a multitemporal stack of images that is free of “noise” factors and thus suitable for detection of land cover change.