## The adjacency effects, how they work.

As explained in the post about atmospheric effects, the scattering of light by molecules and aerosols in the atmosphere brings about several effects : scattering adds some haze on the images (the atmospheric reflectance), lessens the signal from the surface (the atmospheric transmission), and blurs the images (the adjacency effects). This post is about the adjacency effects, the other aspects have already been quickly explained in the above post.

The figure on the right shows the types of paths that light can follow before getting to the satellite. Path 1 corresponds to the atmospheric reflectance, path 2 is path that interacts with the target, it is the one which is useful to determine the surface reflectance, paths 3 and 4 contribute to the total reflectance but interact with the surface away from the target. These paths are thus the cause of adjacency effects and they blur the images.

If not corrected, adjacency effects may cause large errors. Let's take the case of a fully developed irrigated field surrounded by bare soil. For such a case, the second figure on the right shows the relative percentage of errors for reflectances and NDVI as a function of aerosol optical thickness, if adjacency effect is not corrected.

An approximate correction can be applied, but it thus requires to know the aerosol optical thickness. In our MACCS processor, here is how it works :

1. We first correct the images under the assumption that the Landscape is uniform. We obtain a surface reflectance under uniform absorption which is noted $\rho_{s,unif}$ .
2. We compute the neighbourhood reflectance ( $\rho_{s,adj}$ ) using a convolution filter with a 2km radius, that computes the average neighborhood reflectance weighted by the distance to the target. To be fully rigorous, this filter should depend on the optical thickness and on the viewing and sun angle (The less aerosols, the larger radius), but as we did not work on an accurate model, we used a constant radius.
3. We correct for the contribution of paths 3 and 4 using :

$\rho_{s}=\frac{\rho_{s,unif}.T^{\uparrow}.\frac{1-\rho_{s,unif}.s}{1-\rho_{s,adj}.s}-\rho_{s,adj}. T_{dif}^{\uparrow}}{T_{dir}^{\uparrow}}$

• where $T^{\uparrow}=T_{dif}^{\uparrow}+T_{dir}^{\uparrow}$ is the total upward transmission, sum of diffuse and direct upward transmissions, and s is the atmosphere spheric albedo. These quantities depend on the wavelength, on the aerosol model and on the AOT. They are computed using Look up Tables based on radiative transfer calculations.

As this processing uses convolution with a large radius, it takes quite a large part of the atmospheric processing time.

##### Result Exemples

The images below show 3 stages of the atmospheric processing, for 2 Formosat-2 images obtained over Montreal (Canada) with a 2 days interval. The first image was acquired on a hazy day (aerosol optical thickness (AOT) of 0.47 according to MACCS estimate); and the second one on a clear day (AOT=0.1).

• The first line corresponds to the Top Of Atmosphere images, without atmospheric correction. The left image is obviously blurred compared to the right image.
• The second line corresponds to the atmospheric correction under uniform landscape assumption (as in step 1). The left image is still obviously blurred compared to the right image.
• the third line show the same images after adjacency effect correction. In that case, the left image is not blurred any more, it is even maybe a little over corrected as it seems somewhat sharper that the right image.

TOA Images (On the left, the hazy image)

Surface reflectance under uniform landscape assumption (on the left, the hazy image)

Surface reflectance after adjacency effect correction (on the left, the hazy image)

The pixel wise comparison of reflectances is also a way to show the enhancement due to the adjacency effect correction. The plot below compares the images of both dates corrected under the uniform landscape assumption (on the left), and after adjacency effect correction (on the right). You may observe that the dots are closer the the black diagonal on the right. On the hazy image (May 27th), the high reflectances are a little too low, while the low reflectances are a little too high, which is the symptom of a loss of contrast.

## Le Pôle Thématique Surfaces Continentales THEIA

(English Version)

Le "Pôle Thématique Surfaces Continentales" THEIA est une structure nationale inter-organismes destinée à valoriser les données satellitaires, en premier lieu au service de la recherche environnementale sur les terres émergées, et en second lieu des politiques publiques de suivi et de gestion des ressources environnementales. Son objectif est de faciliter la mesure de l’impact des pressions anthropiques et du climat sur les écosystèmes et les territoires, observer, quantifier et modéliser les cycles de l’eau et du carbone, de suivre les évolutions des sociétés et de leurs activités, notamment de leurs pratiques agricoles, et de comprendre les dynamiques de la biodiversité.

Au sein de ce Pôle Thématique, le CNES met en place un centre de production MUlti Satellite, multi-CApteurs, pour des données multi-TEmporelles (MUSCATE). Ce centre a pour but de mettre à disposition des utilisateurs des produits prêts à l'emploi issus de séries temporelles d'images acquises sur de grands territoires. La mission Sentinel-2 sera bien sûr le fer de lance de ce centre de production, mais avant le lancement de la constellation, MUSCATE a d'ores et déjà produit les données issues de l'expérience SPOT4 (Take 5). En même temps, le centre de traitement prépare aussi l'exploitation de toutes les données LANDSAT acquises au dessus de la France continentale, de 2009 à 2011.

Le centre de production MUSCATE existe déjà sous la forme d'un prototype développé au CNES avec un fort soutien de la société CAP GEMINI. Ce prototype est déjà capable de traiter les données des satellites LANDSAT, SPOT, Formosat-2, Venµs et Sentinel-2, à partir de chaînes développées au CNES pour le traitement géométrique [1], au CESBIO pour la détection des nuages [2] et pour la correction des effets atmosphériques [3]. En parallèle, le développement d'un centre de production opérationnel est en phase de spécification.

Les produits fournis par le centre MUSCATE sont les suivants :

Simulations des produits SPOT4(Take5) à partir d'images Formosat-2

• Niveau 1C (Données ortho-rectifiées en réflectance au sommet de l’atmosphère)
• Niveau 2A (Données ortho-rectifiées en réflectance de surface après correction atmosphérique,  avec un masque des nuages et de leurs ombres, ainsi qu'un masque des surfaces d’eau et de neige).
• Niveau 3A (Synthèses bi-mensuelles ou mensuelles de réflectances de surface, constituées de la moyenne pondérée des réflectances de surface des pixels non nuageux obtenus au cours de la période). Pour le moment, la chaîne de Niveau 3A n'existe que pour le satellite Venµs.

Les données produites par le centre MUSCATE seront autant que possible distribuées gratuitement aux laboratoires de recherche d'une part, et aux institutions publiques françaises d'autre part. Le PTSC disposera bien sûr, dans les mois qui viennent d'un serveur de distribution des données, dont la première version est en cours de finalisation.

[1]: Baillarin, S., P. Gigord, et O. Hagolle. 2008. « Automatic Registration of Optical Images, a Stake for Future Missions: Application to Ortho-Rectification, Time Series and Mosaic Products ». In Geoscience and Remote Sensing Symposium, 2008, 2:II‑1112‑II‑1115. doi:10.1109/IGARSS.2008.4779194.

[2]: Hagolle, Olivier, Mireille Huc, David Villa Pascual, et Gérard Dedieu. 2010. « A multi-temporal method for cloud detection, applied to FORMOSAT-2, VENµS, LANDSAT and SENTINEL-2 images ». Remote Sensing of Environment 114 (8) (août 16): 1747‑1755. doi:10.1016/j.rse.2010.03.002.

[3]: Hagolle, O, G Dedieu, B Mougenot, V Debaecker, B Duchemin, et A Meygret. 2008. « Correction of aerosol effects on multi-temporal images acquired with constant viewing angles: Application to Formosat-2 images ». REMOTE SENSING OF ENVIRONMENT 112 (4) (avril 15): 1689‑1701. doi:10.1016/j.rse.2007.08.016.

## THEIA : A new French Data Centre dedicated to Land Surfaces

The THEIA Land Data Centre is a French inter-agency initiative designed to promote the use of satellite data, primarily for environmental research on land surfaces but also for public policy monitoring and for management of environmental resources. Its objective is to foster the use of remote sensing data to measure the impact of human pressure and climate on ecosystems and local areas, to observe, quantify and model water and carbon cycles, to follow the evolution of societies and of their activities, including agricultural practices, and to understand the dynamics of biodiversity.

Within the Land Data Centre, CNES set up a production centre named MUSCATE. This centre aims are providing users with ready-to-use products derived from time series of images acquired over large areas. Sentinel-2 will of course be the spearhead of the production centre, but before the launch of the Sentinel-2, MUSCATE will already begin to produce data from the SPOT4 (Take 5) experiment. At the same time, the processing centre also prepares the production of all Landsat data acquired over mainland France from 2009 to 2011.

MUSCATE production centre already exists in the form of a prototype developed by CNES with strong support from Cap Gemini. This prototype is already able to handle LANDSAT, SPOT, FORMOSAT-2, Sentinel-2 and Venμs data, using processors developed by CNES for geometric processing [1], and developed by CESBIO for cloud detection [2] and for atmospheric correction [3]. Simultaneously, the development of an operational production facility is being specified.

Products provided by the MUSCATE Centre are:

Simulations of SPOT4(Take5) products from Formosat-2 data
• Level 1C (orthorectified reflectance at the top of the atmosphere)
• Level 2A (ortho-rectified surface reflectance after atmospheric correction, along with a mask of clouds and their shadows, as well as a mask of water and snow).
• Level 3A (bi-monthly or monthly composite products of surface reflectances, obtained as the weighted average surface reflectance of non-cloudy pixels obtained during the period). Up to now, Level 3A chain is only available for Venμs satellite.

The data produced by MUSCATE will be freely distributed to research laboratories on the one hand, and to the French public institutions on the other, they will be if possible distributed freely to a wider community. The Land Data Center is also building a distribution server to make all these data available.

Further reading about these products :

[1]: Baillarin, S., P. Gigord, et O. Hagolle. 2008. « Automatic Registration of Optical Images, a Stake for Future Missions: Application to Ortho-Rectification, Time Series and Mosaic Products ». In Geoscience and Remote Sensing Symposium, 2008, 2:II‑1112‑II‑1115. doi:10.1109/IGARSS.2008.4779194.

[2]: Hagolle, Olivier, Mireille Huc, David Villa Pascual, et Gérard Dedieu. 2010. « A multi-temporal method for cloud detection, applied to FORMOSAT-2, VENµS, LANDSAT and SENTINEL-2 images ». Remote Sensing of Environment 114 (8) (août 16): 1747‑1755. doi:10.1016/j.rse.2010.03.002.

[3]: Hagolle, O, G Dedieu, B Mougenot, V Debaecker, B Duchemin, et A Meygret. 2008. « Correction of aerosol effects on multi-temporal images acquired with constant viewing angles: Application to Formosat-2 images ». REMOTE SENSING OF ENVIRONMENT 112 (4) (avril 15): 1689‑1701. doi:10.1016/j.rse.2007.08.016.