A Supplementary Module to Improve Accuracy of the Quality Assessment Band in Landsat Cloud Images

Ruyin Cao,Jin Chen,Ji Zhou,Yan Feng

doi:10.3390/rs13234947

Abstract

Cloud contamination is a serious obstacle for the application of Landsat data. To popularize the applications of Landsat data, each Landsat image includes the corresponding Quality Assessment (QA) band, in which cloud and cloud shadow pixels have been flagged. However, previous studies suggested that Landsat QA band still needs to be modified to fulfill the requirement of Landsat data applications. In this study, we developed a Supplementary Module to improve the original QA band (called QA_SM). On one hand, QA_SM extracts spectral and geometrical features in the target Landsat cloud image from the original QA band. On the other, QA_SM incorporates the temporal change characteristics of clouds and cloud shadows between the target and reference images. We tested the new method at four local sites with different land covers and the Landsat-8 cloud cover validation dataset (“L8_Biome”). The experimental results show that QA_SM performs better than the original QA band and the multi-temporal method ATSA (Automatic Time-Series Analyses). QA_SM decreases omission errors of clouds and shadows in the original QA band effectively but meanwhile does not increase commission errors. Besides, the better performance of QA_SM is less affected by the selections of reference images because QA_SM considers the temporal change of land surface reflectance that is not caused by cloud contamination. By further designing a quantitative assessment experiment, we found that the QA band generated by QA_SM improves cloud-removal performance on Landsat cloud images, suggesting the benefits of the new method to advance the applications of Landsat data.

Highlights

Landsats provide the longest freely available time-series images with a medium spatial resolution, which have been widely used in many applications
The first category is the single-image methods, which use the multispectral features in the individual cloud image by assuming that clouds are generally brighter than other land cover types at given bands whereas cloud shadows are darker [5,6,7,8,9,10,11]
Band generated by QA_SM has higher accuracy than those generated by other methods; Second, we took the cloud-removal application as the example to investigate whether the improved Quality Assessment (QA) band by QA_SM can really benefit the application of Landsat cloud images; Third, since QA_SM uses a cloud-free reference image acquired at another time, we investigated whether and to what extent the performance of QA_SM is affected by the different selections of reference images

Summary

Introduction

Landsats provide the longest freely available time-series images with a medium spatial resolution, which have been widely used in many applications (e.g., land cover mapping, [1]). The first category is the single-image methods, which use the multispectral features in the individual cloud image by assuming that clouds are generally brighter than other land cover types at given bands whereas cloud shadows are darker [5,6,7,8,9,10,11]. Some methods further include more other features, such as the lower temperature of clouds estimated from the thermal infrared bands and the geometric relationship between clouds and cloud shadows. Instead of using these well-defined cloud features, some recent

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