Cloud detection under varied surfaces and atmospheric conditions with MODIS imagery

Abstract

Clouds cover approximately 70% of the Earth's surface. They can balance energy and water cycles, so they are considered one of the most important parameters in the Earth's surface system. Remote sensing provides a rapid yet efficient cloud detection approach, especially through moderate-resolution imaging spectro radiometer (MODIS) imagery that has been scanning the Earth's surface at a large scale, with a reasonable 0.25 km to 1km spatial resolution, more than once a day since 1999.The use of remote sensing for cloud detection has long been considered a simple issue because of the significant difference among cloud spectrums and other surfaces or atmospheric conditions. However, its instability is due to the complexity of cloud types, seasonal surface changes, and varied atmospheric conditions. Thus, this research aims (1) to compare typical cloud detection methods, and (2) propose and validate improved methods based on the previous ones. We selected an area in East China with complex surface and atmospheric conditions (e.g., aerosol pollution, Asian dust, and snow cover) as the study area, and obtained nine MODIS L1B products on typical seasons in the study areas for a cloud detection experiment. Typical spectral signatures of cloud, high reflectance in visible bands, and low-bright temperatures in infrared bands are commonly used for cloud detection; however, their results remain uncertain because of varied surfaces and atmospheric conditions. Thus, we compared three commonly used cloud detection methods, abandoned unstable infrared bands, and considered snow detection, from which we proposed two improved methods. The proposed methods improved the previous ones as results showed high and stable overall accuracy. One of our proposed methods obtained the best overall accuracy of 92.6±7%, and the average mapping accuracy of cloud area and no-cloud area was at 95.8% and 88.2%, respectively; the other method had a low overall accuracy of 82.9±13%, which was similar to the rest of the methods, but could detect almost all cloudy pixels. The proposed methods also widened the applicability of MODIS imagery under complex Earth surface and atmospheric conditions as results found that snow, air pollution, and Asian dust could be better distinguished from clouds by using the two methods based on MODIS data, except under some extremely heavy dust conditions. The two proposed cloud detection methods have different applicability in research. One obtained high and stable detection accuracy (92.6±7%); while the other achieved a relative low-detection accuracy, but detects most cloud cover information, which is suitable to remote sensing research with high sensitivity to cloud errors. Our proposed methods improved cloud detecting ability under complex ground and atmospheric conditions.