A cloud detection method based on a time series of MODIS surface reflectance images

Figures & data

Table 1. Important components of MOD09A1.

Band	Bandwidth (nm)	Original resolution (m)	Description
1	620–670	250	Resampled to 500-m resolution
2	841–876	250	Resampled to 500-m resolution
3	459–479	500
4	545–565	500
5	1230–1250	500
6	1628–1652	500
7	2105–2155	500
State		1000

Figure 1. The general flowchart of TSCD algorithm.

Figure 2. One time series (March of 2001 to the end of 2004) of the Changbaishan subset, x-coordinate represents about 180 dates (each per 8 days), y-coordinate represents the band 3 values, MODCM and TSCDCM. ‘+’ at value 0.1 represents MODCM, and ‘x’ at value 0.05 represents TSCDCM.

Figure 3. Time series (March of 2001 to the end of 2004) of the Changbaishan subset.

Figure 4. Time series (March of 2001 to the end of 2004) of the Beijing subset.

Figure 5. A subset of h27v06 from 009 day to 361 day for 2007, size 200×200.

Figure 6. A subset of h11v04 form 009 day to 361 day for 2005, size 200×200.

Figure 7. A subset of h27v06 for date 129 in 2007, size 200×200.

Figure 8. A subset of h27v06 for date 193 in 2007, size 200×200.

Figure 9. Two subsets of h22v07 for date 145 (left) and 193 (right) in 2008 size 200×200, the two RGB images have a different normalization for helping visual distinction between snow and Clouds.

Figure 10. A subset of h13v09 for date 201 in 2008, size 400×400.