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 L1 B 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.