## SAT-IRR: Satellite for Irrigation Scheduling

=>

Following the irrigation scheduling experiment in Morocco during the SPOT4-Take5 experiment (Le Page et al, 2014), a Web tool owing to help the irrigation decision making is under development (http://osr-cesbio.ups-tlse.fr/Satirr). The tool is functional on three Landsat8 tiles: Marrakech in Morocco, Kairouan in Tunisia, Toulouse, France.

As the tool is addressing irrigators, the idea is to set an irrigated plot of the simplest and fastest possible way. After drawing his plot on a base map, the user answers to 4 questions. He chose his culture among 7 options currently parameterized (corn, wheat, olive ...), its soil among the 12 USDA typical soils, the sowing date and irrigation method (flooding, sprinkler or drip). This rough initialization is adequate launch the service although, at any time, the user can change the plot contours or refine parameterization if he knows the soils, the peculiarities of its crop, etc.

Screenshot from SAT-IRR web interface. The four icons allow modifying the plot parameters and contours, input irrigation, and consulting the results as graphs or tables. The graph results show a small Openlayers window with the last NDVI image, the sequence of NDVI thumbnail images, and 4 graphics: The “atmospheric part (rainfall, Reference Evapotranspiration and actual evapotranspiration), the second graph shows the status of the soil water content separated in three layers, the third graph shows the evolution of Basal Crop Coefficient and Fraction Cover, and the last graph is NDVI. The blue square at the right of the graphs are projections for the next month, including the green bars which are irrigation recommendations

Initially the server makes an approximation of an average behavior of the plant. For this, a monthly climatology is compiled (multi-annual average of weather parameters) and then interpolated to daily values, while the average behavior of the plant is extracted from FAO-56 Tables "FAO Irrigation and Drainage No. 56 : Guidelines for Computing Crop Water Requirements" (Allen et al, 1998). In a second step, the satellite images already on the server are examined and the relationship between NDVI and Basal Crop coefficient (Kcb) and percent of ground covered by vegetation (Fraction cover, Fc) are determined at each date by averaging on the plot.
Past weather is populated by measurements on the nearest synoptic station of the World Meteorological Organization and synthesized in the form of daily reference evapotranspiration (ET0) and rainfall. Forecasts are obtained by the API of the Norwegian Meteorological Institute.
Finally, a water balance very close to the one described in the FAO-56 is calculated by combining typical crop behavior and climate, satellite imagery, weather data and forecasts and projection into the future of crop development. The goal is to propose a date and dose of irrigation.

In addition to updating the weather (measures and forecasts), the server will check every day for the availability of new images (only Landsat8 for the time being). If there is availability, the tile is downloaded, it is then corrected for atmospheric effects using the information provided by the nearest photometer from the Aeronet network using the SMAC code (Rahman & Dedieu, 1994), then a cloud mask is created and NDVI is calculated. This image is stored as the original file is discarded to not overload the server.
All parameterization / measures / prediction are stored in a postgres / postgis database that links with a web client interface. The user can view the results in tables or graphs, and add its own irrigation in another dedicated interface.
While the interface is still a little rough, we are essentially considering developments on the server side:

• Use of Sentinel-1: In the current state, the well-performance of water balance is based on the actual information of irrigation provided by the user. We plan to test the use of S1 images to determine the irrigation dates.
• Access to local agro-weather stations: As part of the development of the Environmental Information System in Cesbio, telemetry of several weather stations has been settled up (eg, see http://trema.ucam.ac .ma) (Jarlan et al, 2015), we have to make these stations accessible through a standardized web service like Sensor Web.
• Introduction of collective irrigation network. The PhD work of Kharrou (2013) and Belaqziz (2013, 2014) have shown that remote sensing can be used to optimize water rotations of an irrigated command. We plan to offer the possibility of introducing a set of plots to associate it with a distribution network and ultimately offer an optimized arrangement of the water rotation. However, at present, this goal is more into the order of a challenge!
• We are currently working on a procedure to introduce wheat yield using remote sensing data (J. Toumi PhD Thesis) and further expect to input an early wheat yield prediction into the tool.

If you want to try it out, be my guest, it's free. If you want to try it out on other regions, please contact me!

References:

1. Le Page M., J. Toumi, S. Khabba, O. Hagolle, A. Tavernier, M. Kharrou, S. Er-Raki, M. Huc, M. Kasbani, A. Moutamanni, M. Yousfi, and L. Jarlan, “A Life-Size and Near Real-Time Test of Irrigation Scheduling with a Sentinel-2 Like Time Series (SPOT4-Take5) in Morocco,” Remote Sens., vol. 6, no. 11, pp. 11182–11203, Nov. 2014.
2. Allen R., L. Pereira, D. Raes, and M. Smith, FAO Irrigation and Drainage n°56: Guidelines for Computing Crop Water Requirements, no. 56. FAO, 1998, pp. 273–282.
3. Rahman H. and G. Dedieu, “SMAC: a simplified method for the atmospheric correction of satellite measurements in the solar spectrum,” Int. J. Remote Sens., vol. 15, no. 1, pp. 123–143, 1994.
4. Kharrou M.H., M. Le Page, A. Chehbouni, V. Simonneaux, S. Er-Raki, L. Jarlan, L. Ouzine, S. Khabba, and A. Chehbouni, “Assessment of Equity and Adequacy of Water Delivery in Irrigation Systems Using Remote Sensing-Based Indicators in Semi-Arid Region, Morocco,” Water Resour. Manag., vol. 27, no. 13, pp. 4697–4714, Sep. 2013.
5. Belaqziz S., S. Mangiarotti, M. Le Page, S. Khabba, S. Er-Raki, T. Agouti, L. Drapeau, M. H. Kharrou, M. El Adnani, and L. Jarlan, “Irrigation scheduling of a classical gravity network based on the Covariance Matrix Adaptation – Evolutionary Strategy algorithm,” Comput. Electron. Agric., vol. 102, pp. 64–72, Mar. 2014.
6. Belaqziz S., S. Khabba, S. Er-Raki, L. Jarlan, M. Le Page, M. H. Kharrou, M. El Adnani, and A. Chehbouni, “A new irrigation priority index based on remote sensing data for assessing the networks irrigation scheduling,” Agric. Water Manag., vol. 119, pp. 1–9, Mar. 2013.
7. Jarlan L., S. Khabba, S. Er-raki, M. Le Page et al, “Remote sensing of water resources in semi-arid Mediterranean basins: The Joint International Laboratory TREMA,” Int. J. Remote Sens., vol. (under review), 2015.

## Feedback on the irrigation scheduling experiment using remote sensing images

CESBIO contributes to an international joint laboratory in Morocco, called TREMA, "Télédétection et Ressources en Eau en Méditerranée semi-Aride", which means "Remote Sensing and Water Resources in Semi-Arid Mediterranean". This year, this laboratory has embarked on an ambitious experiment of irrigation scheduling by satellite imagery, on a wheat plot near Marrakech. This experiment was already described in March, and it gave very promising results.

The main objective of the experiment was to see if  the logistics of irrigation scheduling by water balance model were feasible in real conditions. For this, a farmer accepted to play our game on two four hectares plots of wheat: Irrigation of the reference plot was driven by the farmer in the usual way. The test plot irrigations was driven by our tool SAMIR (FAO-56 model forced by satellite imagery).

Since the sowing late December to the harvest in early June, a weather station installed on a reference culture has given us the daily reference evapo-transpiration measurements. On the other hand, to control a posteriori the quality of our estimates of water requirements for irrigation, two flux measuring stations were set up. We also acquired a series of images SPOT5 early in the season to compensate for the slightly late start of SPOT4 experience (TAKE5) which began in February.

In addition to a clear weather throughout the season, we were able to benefit from the excellent work of the SPOT4 (TAKE5) team which provided us with the georeferenced images very quickly. The NDVI evolutions were thus available in a relatively short time. As an end user, the Office of Agricultural Haouz allowed us to perform the irrigation of the test plot in the best conditions while being subjected to the constraints of the canal system.

On the ground, everything did not work as well as we planned. Following a misunderstanding with the farmer, we completely missed the second irrigation and the fertilizer application was not timely. Indeed, the study plot is installed on a heavy clay soil that forms a crust. We were not aware that, a few days after sowing, a specific irrigation is needed to ease the emergence of plants. On the other hand, the farmer applied nitrogen fertilizer on two plots just after irrigation of the reference parcel and relatively far from the irrigation of the test plot. Under these conditions the nitrogen is relatively less soluble, and our test plot lacked fertilizers.

Our experiment has been seriously hampered by the misunderstandings with the farmer. But despite the bad start, the experiment was pursued to its end.

This plot shows the changes throughout the course of the experiment of the water supply from rainfall and irrigation, the evapo-transpiration ETobs measured in the field and the Evapo-Transpiration ET estimated by SAMIR model, using the vegetation status from SPOT4 (Take5) images. On this plot, the dates of irrigation were suggested by the model.

To our surprise, the results are extremely promising. Indeed, despite a 20% lower biomass compared to the plot driven by the farmer, we got a equivalent performance in grain yield. This can be explained by the fact that, although the average number of wheat blades was much lower on the test plot, it is very likely that the reference plot, irrigated by the traditional method, has suffered water stress in late March limiting the filling of grain.

This full-scale experiment finally turned out to be very instructive. First,  imaging/weather/irrigation logistics worked great : the weather data transmission, the reception and the geometric and radiometric correction of images, the model runs and  irrigation decision were largely automated. The SPOT4 (Take5) data, that prefigure those of Sentinel-2, proved perfectly suited to this application. Unfortunately, the clay crust has severely limited the emergence of culture. Yet this phenomenon, well-known to our farmer, taught us to cultivate humility , and we will consider the introduction of the risk in a decision support system. Finally, the functional constraints of the gravity irrigation system have taught us that our tool should be more flexible to recommend an irrigation period instead of a single date, and that we should link the service to weather forecasts.

Following this experiment, we started developing a Web service (SAT-IRR) that should shortly provide the essential functions of an irrigation decision support with a simplified interface.

## =>

#### Land cover and land use maps

Although different, the terms land use and land cover are often used as synonymous. From Wikipedia Land cover is the physical material at the surface of the earth. Land covers include grass, asphalt, trees, bare ground, water, etc. There are two primary methods for capturing information on land cover: field survey and analysis of remotely sensed imagery. and Land use is the human use of land. Land use involves the management and modification of natural environment or wilderness into built environment such as fields, pastures, and settlements. It also has been defined as "the arrangements, activities and inputs people undertake in a certain land cover type to produce, change or maintain it" (FAO, 1997a; FAO/UNEP, 1999).

A precise knowledge of land use and land cover is crucial for many scientific studies and for many operational applications. This accurate knowledge needs frequent information updates, but may also need to be able to go back in time in order to perform trend analysis and to suggest evolution scenarios.

Satellite remote sensing offers the possibility to have a global point of view over large regions with frequent updates, and therefore it is a very valuable tool for land cover map production.

However, for those maps to be available in a timely manner and with a good quality, robust, reliable and automatic methods are needed for the exploitation of the available data.

#### Classical production approaches

The automatic approaches to land cover map production using remote sensing imagery are often based on image classification methods.

This classification can be:

• supervised: areas for which the land cover is known are used as learning examples;
• unsupervised: the image pixels are grouped by similarity and the classes are identified afterwards.

Supervised classification often yields better results, but it needs reference data which are difficult or costly to obtain (field campaigns, photo-interpretation, etc.).

#### What time series bring

Until recently, fine scale land cover maps have been nearly exclusively produced using a small number of acquisition dates due to the fact that dense image time series were not available.

The focus was therefore on the use of spectral richness in order to distinguish the different land cover classes. However, this approach is not able to differentiate classes which may have a similar spectral signature at the acquisition time, but that would have a different spectral behaviour at another point in time (bare soils which will become different crops, for instance). In order to overcome this problem, several acquisition dates can be used, but this needs a specific date selection depending on the map nomenclature.

For instance, in the left image, which is acquired in May, it is very difficult to tell where the rapeseed fields are since they are very similar to the wheat ones. On the right image, acquired in April, blooming rapeseed fields are very easy to spot.

May image. Light green fields are winter crops, mainly wheat and rapeseed. But which are the rapeseed ones?

April image. Blooming rapeseed fields are easily distinguished in yellow while wheat is in dark green.

If one wants to build generic (independent from the geographic sites and therefore also from the target nomenclatures) and operational systems, regular and frequent image acquisitions have to be ensured. This will soon be made possible by the Sentinel-2 mission, and it is right now already the case with demonstration data provided by Formosat-2 and SPOT4 (Take 5). Furthermore, it can be shown that having a high temporal resolution is more interesting than having a high spectral diversity. For instance, the following figure shows the classification performance results (in terms of $\kappa$ index, the higher the better) as a function of the number of images used. Formosat-2 images (4 spectral bands) and simulated Sentinel-2 (13 bands) and Venµs (12 bands) data have been used. It can be seen that, once enough acquisitions are available, the spectral richness is caught up by a fine description of the temporal evolution.

#### What we can expect from Sentinel-2

Sentinel-2 has unique capabilities in the Earth observation systems landscape:

• 290 km. swath;
• 10 to 60 m. spatial resolution depending on the bands;
• 5-day revisit cycle with 2 satellites;
• 13 spectral bands.

Systems with similar spatial resolution (SPOT or Landsat) have longer revisit periods and fewer and larger spectral bands. Systems with similar temporal revisit have either a lower spatial resolution (MODIS) or narrower swaths (Formosat-2).

The kind of data provided by Sentinel-2 allows to foresee the development of land cover map production systems which should be able to update the information monthly at a global scale. The temporal dimension will allow to distinguish classes whose spectral signatures are very similar during long periods of the year. The increased spatial resolution will make possible to work with smaller minimum mapping units.

However, the operational implementation of such systems will require a particular attention to the validation procedures of the produced maps and also to the huge data volumes. Indeed, the land cover maps will have to be validated at the regional or even at the global scale. Also, since the reference data (i.e. ground truth) will be only available in limited amounts, supervised methods will have to be avoided as much as possible. One possibility consists of integrating prior knowledge (about the physics of the observed processes, or via expert rules) into the processing chains.

Last but not least, even if the acquisition capabilities of these new systems will be increased, there will always be temporal and spatial data holes (clouds, for instance). Processing chains will have to be robust to this kind of artefacts.

#### Ongoing work at CESBIO

Danielle Ducrot, Antoine Masse and a few CESBIO interns have recently produced a a large land cover map over the Pyrenees using 30 m. resolution multi-temporal Landsat images. This map, which is real craftsmanship, contains 70 different classes. It is made of 3 different parts using nearly cloud-free images acquired in 2010.

70-class land cover map obtained from multi-temporal Landsat data.

In his PhD work, Antoine works on methods allowing to select the best dates in order to perform a classification. At the same time, Isabel Rodes is looking into techniques enabling the use of all available acquisitions over very large areas by dealing with both missing data (clouds, shadows) and the fact that all pixels are not acquired at the same dates.

These 2 approaches are complementary: one allows to target very detailed nomenclatures, but needs some human intervention, and the other is fully automatic, but less ambitious in terms of nomenclature.

A third approach is being investigated at CESBIO in the PhD work of Julien Osman: the use of prior knowledge both quantitative (from historical records) and qualitative (expert knowledge) in order to guide the automatic classification systems.

We will give you more detailed information about all those approaches in coming posts on this blog.

## The atmospheric effects : how they work.

=>

Earth surface observations by space-borne optical instruments are disrupted by the atmosphere. Two atmospheric effects combine to alter the images :

• the light absorption by air molecules
• the light scattering by molecules and aerosols

Here are two SPOT4 (Take5) images, acquired with a time gap of 5 days above Morocco. Because of atmospheric effects, the second image has less contrast and is"hazier" than the first one.

##### Light Absorption :
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.

The air molecules absorb the light within thin absorption bands. Within these absorption bands, the reflectance measured by the satellite is lessened, and in some cases, the light may be completely absorbed and the apparent reflectance at the top of atmosphere (TOA) is zero.  (for instance, at 1.4µm, in the figure on the right. Such a property is used to detect high clouds with Sentinel-2 or Landsat-8).

Thankfully, the satellite designers usually choose to locate the spectral bands away from strong absorption bands (but beware of satellite designers ). Within the satellite channels, the absorption is generally sufficiently low so that an approximate knowledge of the absorbent abundance is enough to obtain an accurate correction of absorption. Information on absorbing gases (ozone, water vapour) concentration may be found in weather analyses.

##### Light scattering

The air molecules scatter the light. A photon that passes close to a molecule will be deflected in another direction. As the air molecules are very small compared to visible light wavelengths, they will mainly scatter short wavelengths (in the blue range). The blue sky results from the scattering of sun light by air molecules, since the blue light in the sun spectrum is much scattered while the other wavelengths are mainly transmitted to the ground. A cloud also scatters the light, but its large particles (droplets, crystals) scatter all wavelengths, which explain its white colour.

Apart from clouds and air molecules, scattering may be due to aerosols. Aerosols are particles of diverse nature (sulphates, soot, dust...), suspended in the atmosphere. Their abundance, type and size are extremely variable, and their effect on light is also variable. Small aerosols will mostly scatter blue light, while larger aerosols will scatter all wavelengths. Some aerosols may also absorb light. All this variability makes the correction of their effect very tricky.

The above video, provided by NASA, gives an idea of the way aerosol properties may change from one day to the other, within a two years period. The colour gives an idea of aerosol types, while the colour intensity provides the aerosol optical thickness.

##### Simplified model :

In a very simplified way, atmospheric effects may be modelled as follows :

ρTOA= Tgatm +Td ρsurf)

where :

• ρTOA is the Top of Atmosphere reflectance
• ρsurf is the earth surface reflectance
• ρatm is the atmospheric reflectance
• Tg is the air molecules (gazeous) transmission (Tg<1)
• Td is the transmission due to scattering (Td<1)

When aerosol quantity increases, ρatm increases while Td decreases. These two variables also depend on view and sun angles. The closer to vertical, the lower value of ρatm and the higher value of Td .

The above model should only be applied to a uniform landscape. But above a standard landscape, a heavy loaded atmosphere will also blur the images. This is explained in another post.

##### Models, corrections.

Several models may be used to perform atmospheric corrections. For, approximate corrections, the SMAC model should be one of the simplest. SMAC be downloaded from the CESBIO site. The difficulty in using any atmospheric correction model lies in providing the necessary information on aerosol properties. We will talk about that in another post.

Other more accurate models may be used. In our case, in the MACCS processor, we pre-compute "Look-up Tables " using an accurate radiative transfer code (Successive Orders of Scattering), that simulates the light propagation through the atmosphere. But the use of a complex model is only justified if it is possible to obtain an accurate knowledge of the aerosol optical properties.

## SPOT4(Take5) : Cloud statistics after one month

=>

We have now received all the L1A images of the SPOT4(Take5) experiment taken between January the 31st and March the 10th, for which at least some part of the surface is visible. We ortho-rectify these images to obtain level 1C products, but sometimes, the cloud cover is still too high to process the image. We can use all these productions to derive some statistics about cloud cover.

Proportion of images processed at Level 1A and Level 1C for the sites selected by each agency.
Institution Images acquired L1A processed L1C processed % L1A % L1C
CNES 324 184 157 56 % 49 %
JRC 54 29 27 53 % 50 %
ESA 84 41 34 49 % 40 %
NASA 48 26 26 54% 54%
CCRS 6 1 1 17 % 17 %

Between 40% and 50% of the images taken are sufficiently clear so that the ortho-rectification is feasible. When the production of all cloud masks (level2A) is finished, we will be able to compute the number of cloud free observations for each pixel.

After having looked at all the images in Europe or North Africa, we can confirm that all the pixels of these sites have been observed at least once without clouds, except for 3 sites : CAlsace, EBelgium and CTunisia (!). For the site in Alsace, we had to wait until the 4th of March, and until the 10th of March for the site in Tunisia. And up to now, only a little part of the site in Belgium has been observed, on the 8th of March.

Number of images acquired in February,
as a function of their cloud cover
Site Clouds < 10% 10% < Clouds < 50% 50% < Clouds < 80% 80% < Clouds
Alpes 2 0 2 2
Alsace 0 0 0 6
Ardèche 1 1 0 4
Loire 1 0 3 2
Bretagne 1 0 1 4
Languedoc 0 2 2 2
Provence 2 3 1 0
SudmipyO 1 1 1 3
SudmipyE 1 1 1 3
VersaillesE 2 0 1 3

In France, despite a very cloudy month of February, the 5 days repetitivity enabled to observe nearly each site at least once. But if SPOT4 had only imaged one out of two overpasses, only the sites in Versailles, Provence and the Alps would have been observed in any case.

This result confirms that it is absolutely necessary to launch both Sentinel-2 satellites with a short time interval, so enable the numerous operational applications that need to rely on a monthly clear observation. And it would be a pity if the recent GMES/Copernicus budget cuts resulted in delaying the Sentinel-2B satellite, reducing the repetitivity to only 10 days for several long years.

=>

Now that you know almost everything on our cloud detection method and on our shadow detection method, we can show you the first results obtained by Mireille Huc (CESBIO) with SPOT4(Take5) time series. As the method is multi-temporal, it needs an initialisation phase, and we had to wait until we had a sufficient number of images to produce the masks. These first results are not (yet) perfect, but are already quite presentable.

The images shown below are a series of 6 Level 1C images, expressed in Top of Atmosphere reflectance, with the contours of several masks orverlayed : the clouds are circled in green, their shadows in black, the water and snow mask are respectively circled in blue and pink. You may click twice on the images to see the details of the masks. These images were acquired in Provence (France), each of them is made from 4 (60x60 km²) SPOT Images obtained on the same day, ortho-rectified, then merged.

Most clouds are detected, including very thin clouds, while the number of false cloud detections is very low. Most large cloud shadow are also detected, even if a few of them were missed. The water mask is also quite accurate with nearly no false detections, taking into account it is produced at 200m resolution. The snow is well classified when the snow cover is high, but often, pixels with a moderate snow cover are classified as clouds. This is a classical difficulty with snow masks.

However, we know that your sharp eyes will have noticed some very thin clouds partly missed by our classification in the North East of the first image, a few false cloud detections on the 3rd and the 5th images (the ground dries and becomes brighter and whiter), some missed cloud shadows for some small clouds once in a while (we know why, it is an initialisation problem, but quite long to explain...). The cloud detection threshold for water pixels (the method is different from the cloud detection above land), is maybe a little to low, as some bright Camargue Lakes are wrongly classified as cloudy. But after all, for a first run, the result is not bad, and we will refine all the parameters when we have a sufficient number of images.

On the Fourth Image, only two of the 4 (60*60 km²) images are available, because the two others are too cloudy to be ortho-rectified, as we need to see the surface to take ground control points. In fact, the ortho-rectification step is the first of our cloud masking steps.

The clouds are circled in green, their shadows in black, the water and snow mask are respectively circled in blue and pink. You may click twice on the images to see the details of the masks.

## 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.

[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.

## Changing SPOT4 orbit : easy ?

(French Version)

When we submitted the SPOT4(Take 5) experiment to CNES, we knew that CNES would not accept it easily, since a similar proposal made by Gérard Dedieu before SPOT2 de-orbitation had been rejected. But we did not imagine the amount of work we were requesting from our colleagues at CNES. To show the project feasability, our CNES colleagues had to :

• find a project manager who coordinated the study: Sylvia Sylvander
• choose the new orbit (2 to 6 days repeat cycle) minimizing fuel consumption : We must keep enough fuel to be able to reduce the altitude of the satellite, so that it burns in the atmosphere after a period of less than 25 years. The finally chosen orbit provides a 5 days repeat cycle, resulting in a very low fuel consumption. This orbit also provides the exact repeatability of the two satellites Sentinel-2.
• choose the strategy change orbit. The date of optimal maneuver is January 29, but it corresponds to the end of a full moon, and it is prohibited to maneuver SPOT4 during the full moon. It is not due to superstition, but only because of a potential blinding of star sensors used to determine the orientation of the satellite. But a detailed analysis of recent full moons in the same period last year showed that the maneuver could still be executed on January 29 without any risk.
• check that the ground segment (designed 15 years ago) can handle the new orbit. The ground segment programs the satellite and the acquisitions, manages the old tape recorders, coordinates the data download to the receiving station, while avoiding interference with other satellites. Because the satellite is no longer on its nominal orbit, all the conditions of interference are to be recalculated.
• check that the ground segment is able to ingest and process products. The products are usually referenced by their orbit number, which will be different...
• test the system interfaces: a one-week trial on a simulator of the spacecraft and its system showed that everything should work fine
• the usual programming system at SpotImage will not work on this orbit, we will have to use CNES programming system, more flexible but less automated. It takes one hour and a half to program the 42 sites observed over 5 days, but someone will have to do it every 5 days.
• find internal and external staff (and budgets) to extend the life of SPOT4 for 5 months.
• negotiate with SpotImage (Astrium Geo), the cost of producing Level 1A products and maintaining the cloud notation
• prepare the MUSCATE production center that will provide level 1C and 2A users. This production facility will be implemented within the Land Data Centre.

Many thanks to Didier Roumiguières, Sylvia Sylvander, Laurence Houpert, Jean-Marc Walter, Jordane Sarda (CS-SI), Aurélie Moussy-Soffys, Frédéric Daniaud (CS-SI), Michel Moulin, Benoît Boissin,  Selma Cherchali, Françoise Schiavon, Marc Leroy, Jerôme Bijac (Astrium geo) and to all CNES and Astrium-Geo people who contributed to the acceptation SPOT4(Take5) experiment.

## Sentinel-2, Spot-4, Take-5 (English)

Paul Desmond's Take 5 jazz standard was written in an unusual 5 beat rythm.

(French version)

At the end of life of each satellite, CNES issues a call for ideas for short-term experiments taking place before de-orbiting the satellite. CESBIO took the opportunity to set up the Take 5 experiment at the end of SPOT4' life : this experiment will use SPOT4 as a simulator to give us a hint of the time series that ESA's Sentinel-2 mission will provide.

The first Sentinel-2 satellite should be launched within the next two years, and the second satellite should follow 18 months later. Together, these satellites will provide us every fifth day with high-resolution images of all land areas... or of the clouds that cover them. Despite these clouds, users will be guaranteed access to cloud-free data at least once per month. The arrival of these data should therefore cause a revolution in the use of remote sensing data.

In order to avoid wasting time when Sentinel-2 is launched, it is necessary to prepare today for the use of these data. However, at present, there are no suitable data to perfectly simulate the features of Sentinel-2:

• ESA is providing datasets simulating the spectral bands of the instrument, but these airborne data are not multi-temporal, and only cover small areas.
• -CNES and the Israel Space Agency are developing the VENµS project, whose goal is to provide high repeatability time series, but its launch is only scheduled for late 2014.
• CESBIO provided time series of FORMOSAT-2 images and LANDSAT, but in the first case, the data only cover areas of 20 * 20 km, whereas in the second case, the repeatability of the data is much lower than expected from Sentinel-2.

After 6 months of feasibility studies, CNES has just decided to launch Take 5 experiment. On January 29, the orbit will be lowered from SPOT4 by 3 kilometers to put it on a 5 day repeat cycle orbit, which means that the satellite will fly the same places on earth every 5 days. Spot4 will follow this orbit until the end of May 2013. During this period, 42 sites will be observed every 5 days, as in the case of Sentinel-2. The data will be processed and distributed by the "Pôle Thématique Surfaces Continentales" (PTSC) and distributed to users by the end of June 2013, they will be provided with the following two levels:

• Level 1C (data orthorectified reflectance at the top of the atmosphere)
• Level 2A (Data 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).