## Improvement of high cloud detection in MAJA for Sentinel-2 images.

=>

The research to improve the performances of our MAJA atmospheric correction processor relies on a very small team within CESBIO, with two persons, Bastien Rouquié, who is working at improving aerosol retrievals, and myself, who spend most of the time in meetings, or writing blog posts, and sometimes doing both simultaneously . This is why improvements take time.

Atmospheric absorption : in blue, the surface reflectance of a vegetation pixel, as a function of wavelength. In red, the reflectance of the same pixel at the top of atmosphere. For a wavelength of 1.38 µm, water vapour totally absorbs the light that comes from the earth surface at sea level.

Anyway, I recently found some time to work on improving cloud masks above water. And these last days, i worked at improving the detection of high cloud using the cirrus band, which is available on Sentinel-2 as well as Landsat 8. As explained in this post, the "cirrus" band is locates in a strong absorption band of water vapour, strong enough to prevent photons from going from the sun to the satellite via the earth surface at sea level. As most of the water vapour lies in the low layers of the atmosphere, the clouds situated higher in the atmosphere have more chances to reflect light to the satellite. As a result, this channel allows us to detect the high clouds.

The detection method looks simple : just use a threshold on the cirrus band. Well that's not that easy !

## Amélioration de la détection des nuages hauts dans MAJA pour les images de Sentinel-2

=>

La recherche pour l'amélioration des performances des méthodes de MAJA repose actuellement sur une toute petite équipe au CESBIO, composée de deux personnes, Bastien Rouquié, qui travaille sur l'amélioration des estimations d'aérosols, et moi, qui passe presque tout mon temps en réunions, ou a écrire des articles de blog (et parfois les deux en même temps). C'est pour cette raison que de nombreux points que nous aimerions améliorer sur MAJA ne s'améliorent que lentement.

Absorption atmosphérique. En bleu, la réflectance de surface pour un pixel couvert de végétation, en fonction de la longueur d'onde, en rouge la réflectance au sommet de l'atmosphère pour ce même pixel. A 1.38 µm, la vapeur d'eau absorbe totalement la lumière provenant de la surface au niveau de la mer.

J'ai pu récemment consacrer un peu de temps à l'amélioration de la détection des nuages, notamment au dessus de l'eau, et plus récemment, à améliorer la détection des nuages hauts, en utilisant la  bande "cirrus" située à 1.38 µm, qui est disponible sur Sentinel-2 et Landsat 8. Comme précisé dans ce post, la bande "cirrus" est située dans une forte bande d'absorption de la vapeur d'eau, telle qu'au niveau de la mer, les photons ont très peu de chances de faire le parcours du soleil au satellite en passant par la surface de la terre sans être absorbés. Comme la majorité de la vapeur d'eau est située dans les basses couches de l'atmosphère, les nuages situés plus haut dans l'atmosphère ont davantage de chances de renvoyer de la lumière jusqu'au satellite. C'est donc un moyen de détecter les nuages hauts.

La méthode de détection parait donc très simple, il suffit de faire un seuillage sur la réflectance dans la bande 1.38 µm.  Malheureusement, c'est plus compliqué que ça.

## Another validation of CESBIO's 2016 France land-cover map

In this post, a validation of the land-cover map of France produced by CESBIO for the 2016 period was presented. This validation used independent data (that is data collected by different teams and using different procedures than the data used for the classifier training), but the validation procedure consisted in applying classical machine learning metrics which, as described in this other post, have some limitations.

A fully independent validation following a sound protocol is costly and needs skills and expertise that are very specific. SIRS is a company which is specialised in the production of geographic data from satellite or aerial images. Among other things, they are the producers of Corine Land Cover for France and they are also responsible for quality control and validation of other Copernicus Land products.

SIRS has recently performed a validation of the 2016 France land-cover map. The executive summary of the report reads as follows:

This report provides the evaluation results of the CESBIO OSO 2016 10m layer and the CESBIO OSO 2016 20m layer.

The thematic accuracy assessment was conducted in a two-stage process:

1. An initial blind interpretation in which the validation team did not have knowledge of the product’s thematic classes.
2. A plausibility analysis was performed on all sample units in disagreement with the production data to consider the following cases:
• Uncertain code, both producer and operator codes are plausible. Final validation code used is producer code.
• Error from first validation interpretation. Final validation used is producer code
• Error from producer. Final validation code used is from first validation interpretation
• Producer and operator are both wrong. Final Validation code used is a new code from this second interpretation.

Resulting to this two-stage approach, it should be noticed that the plausibility analysis exhibit better results than the blind analysis.

The thematic accuracy assessment was carried out over 1,428 sample units covering France and Corsica.
The final results show that the CESBIO OSO product meet the usually accepted thematic validation requirement, i.e. 85 % in both blind interpretation and plausibility analysis. Indeed, the overall accuracies obtained are 81.4 +/- 3.68% for the blind analysis and 91.7 +/- 1.25% for the plausibility analysis on the CESBIO OSO 10m layer. The analysis on the 20m layer shows us that the overall accuracy for the blind approach is 81.1 +/-3.65% and 88.2 +/-3.15% for the plausibility approach.
Quality checks of the validation points have been made by French experts. It should be noticed that for the blind analysis, the methodology of control was based mostly on Google Earth imagery, no additional thematic source of information that could provide further context was used such as forest stand maps, peatland maps, etc.

These results are very good news for us and for our users. The report also contains interesting recommendations that will help us to improve our algorithms. The full report is available for download.

## Theia produit au niveau 2A toute la bande côtière du Maghreb

Les tuiles en bleu viennent d'être ajoutées aux zones où Theia fournit des produits de niveau 2A.

=>

Depuis la découverte des récents bugs et la mise en place de moyens de les contourner, le rythme de  production de MUSCATE s'est accru, et nous avons donc pu étendre un peu les zones où nous fournissons des données Sentinel-2 (A&B) corrigées des effets atmosphériques, avec un bon masque de nuages: il s'agit de produits de niveau 2A fournis par la chaîne MAJA.

Nous venons de mettre en ligne toutes les données acquises sur la zone côtière du Maghreb, du Maroc à la frontière entre Algérie et Tunisie. Quelques tuiles manquantes ont également été rajoutées au sud du Maroc, et sur le Cap Bon, en Tunisie. Avec cet ajout de 50 tuiles, toutes les terres qui bordent la méditerranée occidentale sont donc suivies pat les produits de Theia (enfin, il manque les Baléares).

## [MUSCATE] Theia releases Sentinel-2 L2A products on the whole Maghreb coastal region

The blue tiles were just added to the zones where Theia provides level 2A products with MAJA.

=>

Since we understood the bugs which were slowing MUSCATE, and we found ways to mitigate them, the production rhythm of MUSCATE improved and we were able to extend the zones where we provide Sentinel-2 (A&B) L2A products. L2A products provide surface reflectances after correction of atmospheric effects and with a high quality cloud mask. The products we deliver are provided by MAJA processor.

We just released all the data acquired on the Maghreb coastal zone, from Morocco to Tunisia. A few missing tiles have also been added in South Morocco, and on Cap Bon in Tunisia. With these new tiles, we now monitor all the lands that surround the occidental part of Mediterranean sea, adding 50 tiles to those already processed.

## [Muscate news] Distribution server operational again

MUSCATE distribution server is back to operations. Hundreds of products are currently being uploaded, which should take several hours.

Le serveur de distribution de MUSCATE est de nouveau opérationnel. Quelques heures seront toutefois nécessaires pour importer toutes les données produites pendant la semaine écoulée.

## [MUSCATE news] Distribution server in maintenance

New update: Muscate distribution server is back but you should use the following address until Monday: https://theia.cnes.fr/atdistrib/rocket/#/home . New products will be uploaded starting from Monday too. And since the week-end is there, it is not compulsary to downloard our L2A images until then (even if you can...)

Update : the server will also be down of Friday . Sorry for that

In order to solve the issue described in this post, MUSCATE distribution server is in maintenance. The distribution of new products should resume  tomorrow (Wednesday) or Thursday. Meanwhile, the production is going on. Sorry for the inconvenience.

Le serveur de distribution de MUSCATE est en maintenance pour résoudre le problème décrit dans cet article. La distribution de nouveaux produits devrait reprendre demain (mercredi) ou jeudi. Pendant les travaux, la production continue. Veuillez nous excuser pour ces perturbations.

## Improvements of MAJA cloud masks in production

We are continuously working on improving the Sentinel-2 L2A products delivered by Theia. Since the 2nd of February, a new parametrization was put in production in order to improve two points of the cloud masks. There is a page which summarizes the variations of Theia product versions. Because of some error within Muscate production centre, we are not able to change the product version when we change a parameter. You have to look at the production date to know which version was used. This inconvenience will be corrected soon.

### Clouds above water

Although MAJA is optimized for cloud detection and atmospheric correction over land, it is important to detect clouds well over water. For instance to detect the shadows of these clouds that are cast over land. But until now, the clouds mask was missing quite a lot of clouds over water. This was not a surprise as we had passed only a few hours determining the thresholds to apply. We recently found some more time to obtain a better tuning of the detection thresholds above water.

Thresholds in the SWIR have been halved (from 0.08 to 0.04), in the absence of sunglint. When sunglint is likely, due to the geometry of acquisition, the threshold is still higher (0.016, but it was 0.25 before).This means that when the sunglint flag is raised, the accuracy of cloud detection is reduced.

Impact of new cloud detection threshold over water : left, old threshold, right, new threshold.

### Cloud dilation

The plane contrail illustrates the difference in observation angles of the spectral bands of Sentinel-2. The cumulus clouds above has the same effect, although lower because the cloud is at a lower altitude.

As it may be seen on the image above, the cloud limits are fuzzy, and except for the big and nice cumulus clouds, the pixel next to the cloud mask is also affected by clouds. Even if the cloud has sharp limits, it also changes the illumination of neighbouring pixels, and, in the case of Sentinel-2, as all the bands do not observe exactly in the same direction, there is a "parallax effect" which results in different cloud positions depending on the spectral band. For all these reasons, we need to dilate the cloud mask (by the way, this is an identified drawback of Sen2cor, which does not dilates its clouds).

Our dilation buffer was originally 480m. Following complaints from some users working in very cloudy countries, we had reduced the buffer to 240m in may 2017. But we recently figured out that the reduced dilation was degrading our estimates of  aerosols, as undetected clouds are considered as aerosols.  Due to this, the new version which runs since the2nd of February has once again a dilation of 480m.

Left, aerosol validation results with a dilation of 240m, right with a dilation of 480m. The new result brings a significant improvement (Merci à Bastien Rouquié pour ce résultat)

## La neige de Pyeongchang

Connaissiez-vous ce dicton coréen "On n'est jamais trop prudent" ?

Voici une série d'images Sentinel-2 près du site olympique de Pyeongchang. On voit de la neige artificielle apparaître dès le mois de novembre !

Les images suivantes montrent qu'au cours du mois de janvier de la neige naturelle a finalement recouvert le site...

## Using NDVI with atmospherically corrected data

### NDVI

NDVI is by far the most commonly used vegetation index. NDVI was developed in the early seventies (Rouse 1973, Tucker 1979), and widely used with remote sensing in the nineties until now. It is computed from the surface reflectance in the red and near infra-red channels on each side of the red-edge.

$NDVI=\frac{\rho(NIR)-\rho(RED)}{\rho(NIR)+\rho(RED)}$

where $\rho(NIR)$ and $\rho(RED)$ are reflectances in the NIR and RED. Although several users still use top-of atmosphere reflectances (TOA), surface reflectances should be used to reduce sensitivity to variations of aerosol atmospheric content.

A time profile of surface reflectance from Sentinel2 satellite for the blue, green, red and NIR spectral bands for a summer crop in South East France. The observation under constant viewing angles minimizes directional effects.One can also notice that reflectance variations related to vegetation status are greater in the near infra-red, while the noise is usually lower. As a result, a vegetation index should rely more on the NIR than on the red.

I think NDVI is mainly used for the following reasons (but feel free to comment and add your reasons) :

• it has the large advantage of qualifying the vegetation status with only one dimension, instead of N dimensions if we consider the reflectances of each channel. Of course, by replacing N dimensions by only one, a lot of information is lost.
• it  enables to reduce the temporal noise due to directional effects. But with the Landsat, Sentinel-2 or Venµs satellites, which observe under constant viewing angles, the directional effects have been considerably reduced.

I therefore tend to tell students that if NDVI is convenient, it is not the only way to monitor vegetation.