Abstract
Landsat 8 images have been widely used for many applications, but cloud and cloud-shadow cover issues remain. In this study, multitemporal cloud masking (MCM), designed to detect cloud and cloud-shadow for Landsat 8 in tropical environments, was improved for application in sub-tropical environments, with the greatest improvement in cloud masking. We added a haze optimized transformation (HOT) test and thermal band in the previous MCM algorithm to improve the algorithm in the detection of haze, thin-cirrus cloud, and thick cloud. We also improved the previous MCM in the detection of cloud-shadow by adding a blue band. In the visual assessment, the algorithm can detect a thick cloud, haze, thin-cirrus cloud, and cloud-shadow accurately. In the statistical assessment, the average user’s accuracy and producer’s accuracy of cloud masking results across the different land cover in the selected area was 98.03% and 98.98%, respectively. On the other hand, the average user’s accuracy and producer’s accuracy of cloud-shadow masking results was 97.97% and 96.66%, respectively. Compared to the Landsat 8 cloud cover assessment (L8 CCA) algorithm, MCM has better accuracies, especially in cloud-shadow masking. Our preliminary tests showed that the new MCM algorithm can detect cloud and cloud-shadow for Landsat 8 in a variety of environments.
Highlights
Landsat 8 satellite was launched on 11 February 2013
Landsat 8 images have been widely used for many applications, such as land cover classification [3], agriculture [4,5,6], and disaster monitoring [7,8,9]
We focused on the limitation of the previous multitemporal cloud masking (MCM) algorithm
Summary
Landsat 8 satellite was launched on 11 February 2013. Landsat 8 images can be downloaded over the internet at no cost to users from United States Geological Survey (USGS). To support the monitoring of land cover change, the multi-temporal mask (Tmask) developed for automated masking of cloud, cloud-shadow, and snow for multi-temporal Landsat images [11]. In response to the limitations of these methods in tropical environments, cloud and cloud-shadow masking using a multi-temporal image approach named multi-temporal cloud masking (MCM) was developed [27]. This approach uses two images: The target image and reference image. The new MCM algorithm can be used to detect cloud and cloud-shadow in a variety of environments and the accuracies are expected to be significantly high
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