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Automated cloud and cloud shadow detection for Landsats 4-8 images

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MFmask

IMPORTANT: Fmask 4.0 beta software on Windows (~1G) is ready to use now! It can be downloaded at this [link] (https://drive.google.com/drive/folders/1SXBnEBDJ1Kbv7IQ9qIgqloYHZfdP6O1O). This version has substantial better cloud, cloud shadow, and snow detection results for Sentinel 2 and better results (compared to the 3.3 version that is currently being used by USGS as the Collection 1 QA Band) for Landsats 4-8 . This one software can be used for Landsats 4-8 Collection 1 product and Sentinel 2 Level-1C product at the same time. NOTE this new version (including all features of MFmask) also works better in mountainous area than MFmask 1.1.

Further information about the Fmask 4.0 tool can be found at https://github.com/GERSL/Fmask.

Great news! MFmask 1.1 beta has been publicly released. This new package can FULL AUTOMATEDLY work!

The software called MFmask (Matlab package) is used for automated clouds, cloud shadows, and snow masking for Landsat 4-8 images. The MFmask is developed by integrating Digital Elevation Models (DEMs) into the existing Fmask algorithm (Version 3.3; https://github.com/prs021/fmask). It is specifically designed for Landsat images acquired from mountainous area well (thereafter we call this algorithm MFmask, in which the letter “M” refers to mountainous), and also applicative for Landsat images acquired in non-mountainous areas.

Now MFmask can AUTOMATEDLY download, project, and resample DEMs to Landsat's resolution and extent, thanking for TopoToolbox (https://topotoolbox.wordpress.com)! What we need to do just keep your network workable and run the MFmask package at Landsat folder. Note that Matlab version MUST be higher than R2014b, OR the corresponding DEM data for each Landsat image still needs to be manually downloaded, mosaicked, projected, and resampled to Landsat's resolution and extent.

If you HAVE Matlab higher than R2014b and workable network, just run the MFmask package at Landsat folder.

If you HAVE NOT Matlab higher than R2014b or workable network, but you still want to use DEMs to enhance the cloud and cloud shadow detection, please manually download, project, and resample to Landsat's resolution and extent, and resaved the DEM data as a TIFF file named with end of ‘_dem.TIF’. Available DEMs can be found as follows, ASTER 30m (1 arc-second) DEM data: http://dx.doi.org/10.5067/aster/astgtm.002. SRTM 30m (1 arc second) DEM data: https://doi.org/10.5067/measures/srtm/srtmgl1.003. Note that the corresponding DEM data for each Landsat image needs to bemanually downloaded, mosaicked, projected, and resampled to Landsat's resolution andextent. DEM derivatives (e.g., slope and aspect) are also calculated by using other software, such as ENVI, ERDAS, and ArcMap.

If you CANNOT automatedly or manually obtain DEMs, MFmask still works well. It will directly come back to the routine of Fmask, but also improve the cloud shadow location prediction with aid of neighboring clouds. That means MFmask will also generate similar cloud masks and better cloud shadow masks without DEMs, compared with original Fmask.

Please contact Shi Qiu (qsly09@hotmail.com) at School of Resources and Environment, University of Electronic Science and Technology of China if you have any questions. Please cite the following papers:

paper 1: Qiu, S., He, B., Zhu, Z., Liao, Z., and Quan, X. (2017). Improving Fmask cloud and cloud shadow detection in mountainous area for Landsats 4-8 images, Remote Sensing of Environment, 199, 107-119. doi:10.1016/j.rse.2017.07.002 (paper for MFmask version 1.0.).

paper 2: Zhu, Z., Wang, S., and Woodcock, C. E. (2015). Improvement and expansion of the fmask algorithm: cloud, cloud shadow, and snow detection for landsats 4–7, 8, and sentinel 2 images. Remote Sensing of Environment, 159, 269-277. doi:10.1016/j.rse.2014.12.014 (paper for Fmask version 3.2.).

paper 3: Zhu, Z. and Woodcock, C. E. (2012). Object-based cloud and cloud shadow detection in landsat imagery. Remote Sensing of Environment, 118(6), 83-94. doi:10.1016/j.rse.2011.10.028 (paper for Fmask version 1.6.).

After running MFmask, there will be an image called XXXMFmask that can be opened by ENVI. The image values are presenting the following classes:

0 => clear land pixel

1 => clear water pixel

2 => cloud shadow

3 => snow

4 => cloud

255 => no observation

One sample data can be download from the following links:

Google drive: https://drive.google.com/drive/folders/0B1UcOl384wK-S1hNU0g3UlpGQ2s

or

Baidu drive:https://pan.baidu.com/s/1bYENpk

Table 1. Validation data for MFmask. Note that * indicates the reference image with both manual cloud and cloud shadow mask. All of them are availble at the following link: https://landsat.usgs.gov/landsat-7-cloud-cover-assessment-validation-data.

Type Name Date Sun Elevation(°) True Cloud Cover (%) Elevation Difference (m)
  austral p227_r98 2001/11/3 41.19 45.10 1564
austral p228_r94 2001/12/12 50.72 95.92 702
austral p228_r97 2001/11/26 46.90 67.89 630
austral p228_r98 2001/1/26 40.91 99.50 1553
austral p229_r97 2001/12/3 47.55 42.05 900
* austral p230_r92 2001/12/26 51.62 2.32 885
* austral p230_r94 2001/12/26 49.91 12.46 1585
austral p230_r95 2001/12/10 49.79 55.40 1830
austral p231_r96 2001/1/31 41.64 87.10 2799
austral p74_r92 2001/11/3 47.98 20.21 1066
austral p75_r92 2001/11/26 51.98 43.45 1583
austral p76_r92 2001/1/17 48.38 68.32 1460
  boreal p139_r24 2001/8/7 51.06 7.53 1621
boreal p143_r21_2 2001/5/31 53.82 45.59 625
* boreal p143_r21_3 2001/8/3 48.95 14.64 627
* boreal p195_r26 2001/5/11 55.42 7.18 854
* boreal p49_r22 2001/6/13 55.96 9.32 1009
  boreal p54_r19 2001/6/16 52.84 60.98 1420
mid-latitude_N p111_r29 2001/4/29 55.09 62.87 977
mid-latitude_N p139_r33 2001/5/19 63.07 0.00 3204
  mid-latitude_N p147_r35 2001/5/11 63.03 6.75 3678
mid-latitude_N p154_r34 2001/7/31 60.87 0.00 1968
mid-latitude_N p184_r37 2001/6/15 66.50 0.00 558
mid-latitude_N p186_r32_1 2001/2/21 33.88 33.46 1714
mid-latitude_N p186_r32_2 2001/5/12 61.10 7.71 1703
mid-latitude_N p186_r32_4 2001/10/3 41.91 0.49 1703
mid-latitude_N p196_r35 2001/4/16 56.59 0.00 1339
mid-latitude_N p33_r37 2001/4/26 61.07 2.95 1592
mid-latitude_N p36_r37 2001/5/1 62.29 0.00 1698
  mid-latitude_N p46_r32 2001/6/8 64.33 56.84 1530
* mid-latitude_S p171_r82 2001/11/11 58.60 6.76 1267
* mid-latitude_S p71_r87 2001/10/29 51.87 8.14 1003
mid-latitude_S p72_r88 2001/1/5 53.58 14.63 1215
mid-latitude_S p72_r89 2001/2/6 46.53 2.09 645
mid-latitude_S p73_r89 2001/12/30 53.43 1.77 1852
mid-latitude_S p73_r90 2001/12/14 54.11 29.38 1424
* mid-latitude_S p74_r91 2001/11/3 49.04 31.53 1705
mid-latitude_S p89_r82 2001/11/12 58.80 0.99 1337
mid-latitude_S p92_r86 2001/10/16 49.22 63.17 1454
polar_north p197_r14 2001/6/26 47.06 18.97 1149
polar_north p61_r2 2000/6/14 32.24 1.41 1700
* subtropical_N p118_r40 2001/7/3 65.82 18.11 987
  subtropical_N p131_r46 2001/9/16 59.49 52.04 1358
subtropical_N p142_r48 2001/4/22 63.88 0.74 1041
subtropical_N p148_r42 2001/5/2 64.64 0.00 534
* subtropical_N p189_r47 2001/8/5 62.92 12.07 1106
* subtropical_N p26_r46 2001/3/24 57.00 8.48 3200
* subtropical_N p31_r43 2001/6/15 66.29 16.93 2667
* subtropical_N p35_r42 2001/8/14 62.20 3.31 858
* subtropical_S p1_r75 2001/2/5 55.37 4.24 4845
subtropical_S p1_r76 2001/11/20 62.13 1.41 3106
subtropical_S p113_r75 2001/1/5 58.62 22.07 880
subtropical_S p158_r72_1 2001/3/21 52.39 30.76 1259
* subtropical_S p158_r72_2 2001/4/22 46.98 10.48 1260
subtropical_S p158_r72_3 2001/9/13 52.54 55.38 1260
subtropical_S p158_r72_4 2001/11/16 62.79 43.00 1260
  subtropical_S p177_r80 2001/10/20 55.92 7.66 1000
subtropical_S p179_r75 2001/2/20 53.84 0.00 1430
subtropical_S p216_r74 2001/12/8 61.24 42.64 1471
subtropical_S p230_r79 2001/2/9 53.15 5.94 998
subtropical_S p232_r79 2001/11/6 59.99 0.49 4934
  tropical p11_r55 2001/6/3 59.05 95.63 1105
tropical p116_r50 2001/4/16 63.01 13.67 983
tropical p184_r55 2001/7/17 57.73 92.08 1117
tropical p184_r63 2001/12/8 56.94 89.23 607
tropical p190_r54 2001/9/29 61.57 34.97 527
tropical p4_r70 2001/2/26 55.53 87.75 2706

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