The cloud detection : how it works.

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Clouds are white, bright, rather high in the sky. Their temperature is generally lower than that of the surface. They move and change appearance, and they cast shadows on the ground.

All these properties can be used to automatically detect clouds.

 

Standard detection

The basic technique consists in thresholding the image of a spectral band in the short wavelength range (preferably a blue band). Pixels whose reflectance is above the threshold are declared cloudy. This method is not very subtle and often does not detect thin clouds, it also makes many false detections. We can also check that the cloud is white, but the contribution of this verification is not really effective, because thin clouds are not perfectly white, while many bright pixels are white, in cities for example.

 

Multi-temporal detection

The clouds detected by the multi-temporal methodon this FORMOSAT-2 image are outlined by white contours. Note that some agricultural plots are brighter than some clouds, with nearly no confusion. Click on the image to view animation

A multi-temporal detection may be applied when time series of satellite images are available, if they are acquired with a roughly constant viewing angle, as in the case of SPOT4 (Take5), Venμs, LANDSAT, and Sentinel-2.

Usually, reflectances of land surfaces change slowly, but when a cloud appears, the reflectance increases sharply. So, by comparing the processed image with a previous image, the pixels for which the reflectance in the blue increased significantly can be classified as clouds, provided the detected pixels have a whiter spectrum than in the previous image. This method greatly improves the discrimination between cloudy and clear pixels.

However, this detection method has a cost, because it requires to process the data in chronological order and therefore prevents processing the image independently. It complicates the processing center and also affects the parallelization of processing. However, this method is implemented in MUSCATE center, to process SPOT4 (Take5), LANDSAT, Venμs and Sentinel-2 time series.

For more details on this method, used in MACCS Level 2A processor :

Hagolle, O., Huc, M., Villa Pascual D., & Dedieu, G. (2010). A multi-temporal method for cloud detection, applied to FORMOSAT-2, VENµS, LANDSAT and SENTINEL-2 images. Remote Sensing of Environment, 114(8), 1747-1755.
Detection of high clouds using an absorption band

Plane contrails will be much easier to detect with the new 1380nm band available on Landsat 8 and Sentinel-2.

On Landsat 8 and Sentinel-2,  a new spectral band is available, at 1380 nm. This spectral band corresponds to a strong water vapour absorption band. At this wavelength, the solar radiation is totally absorbed in his back and forth between the top of the atmosphere and the surface. In contrast, the radiation reflected by a cloud above 3000 meters is not totally absorbed as water vapor is mainly located in the lower layers of the atmosphere. Therefore, a simple threshold on the reflectance of this band enables to detect high clouds. Cirrus clouds are usually very difficult to detect, it will not be the case with this method which is also used within MUSCATE for LANDSAT-8 and Sentinel-2 satellites.

 

Thermal Infrared detection

High clouds are usually colder than the surface, the presence of a thermal band on Landsat satellites enables to use this property as a detection criterion. However, the thermal variations of the earth surface from a day to another are large, and prevent from detecting low clouds which have a temperature close to that of earth surface. We have not used this method as it applies only to LANDSAT.

 

3D detection

The Venµs satellite has two identical bands that observe scenes from two slightly different angles. This couple of bands makes it possible to see the terrain in 3D, with a moderate accuracy, but sufficient to tell the clouds from the surface. We use this method to detect clouds on Venμs data, in addition to the multi-temporal method. It should detect clouds  more than 500 meters high, and most importantly, knowing the cloud altitude will help detecting shadows.

 

Shadow detection

To be continued

First Level 2A time series of SPOT4 (Take5) images

(aerosol images have been added at the end of the post)
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The verification of the various steps of our SPOT4(take5) processing scheme is going on. On Thursday, we received our first time series, I orthorectified them on Friday, and we were then able to start testing our level 2A processor with the first time series. The one displayed below was obtained on the CESBIO site in Tensift valley : Marrakech is near the center of the image, while the Atlas mountains are in the South East part of the image.

The images on the left column are ortho-rectified, and expressed in Top of Atmosphere reflectance (Level 1C product), while the right column displays the same images after atmospheric correction and cloud detection (Level 2A products), produced by Mireille Huc (CESBIO).

We quickly figured out that the cloud detection would be easy on these very clear images, even if on the February 10th, several diffuse plane contrails can be hardly seen but are partially detected, and some of their shadows as well (clouds are circled by red lines, while shadows are circled by a black line). No false cloud detection is visible. Water bodies and snow are also correctly detected for this first try (circled in blue and purple respectively)

The atmospheric correction, based on a multi-temporal method that detects the aerosols, enabled to detect that the image of February the 5th was hazier than the images of January 31st and February 10th.The February 5th image (left column) has a subtle blueish haze compared to the other dates. On the right column, the tint is roughly constant from one image to the other, which means that the aerosol detection and the atmospheric correction are working well. The aerosol images provided below are also very consistent, with the Atlas mountains playing their role of physical barrier blocking the aerosols on either side of the images. There is an aerosol measurement station on this site but it broke down at the end of January, just for the start of the experiment : Murphy's law...

So, we have reviewed and tested all the steps of the processing, but we still have to check that our methods are sufficiently robust to handle correctly the very diverse situations offered by the 42 sites. How do you say, in English "ce n'est pas une mince affaire" ?

Level 1C products expressed in reflectance at the top of atmosphere.
(c) CNES, processing : CESBIO
Level 2A products expressed in surface reflectance after atmospheric correction
(c) CNES, processing : CESBIO

Aerosol optical thickness images are displayed below. One can note that the image of the February 5th is consitent with a lot of aerosols in the North of the Atlas, and nearly no aerosols in the South. The mountains often act as barriers for the aerosols witch usually stay at a low altitude. The orange dots correspond to the snow mask whereas the red ones correspond to the cloud mask. The brighter spots on the last image may be artifacts.