(At last !) Automated download of Sentinel-2A Level 2A products from Theia


The production of Sentinel-2 L2A data is on-going at CNES THEIA, but it is still a little slower than expected. We had one fast day on which the exploitation team managed to process 600 tiles, but the production has often been slower as we needed to solve a few glitches, and as the whole CNES processing center had also its own issues.


Meanwhile, my colleagues at CNES MUSCATE Center, with the precious help of Dominique Clesse (CAP GEMINI) and Remi Mourembles (CAP GEMINI), have implemented the possibility to download the images via a script and no click. By the way, the shop cart, which did not work when we ordered more than 10 products has also been repaired.


The script is very easy to use, for instance, the following line downloads the SENTINEL-2 products above tile T31TCJ (Toulouse), acquired in September 2016 :

python ./theia_download.py -t 'T31TCJ' -c SENTINEL2 -a config_theia.cfg -d 2016-09-01 -f 2016-10-01

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Sentinel-2 to monitor forest fires in Siberia?

At the beginning of summer, a colleague in Igarka (Maxime Deschuyteneer ?) informed me on 20th of July that a forest fire up to the city was responsible, according to him, of “a small greenhouse effect that make you cough”…Indeed, forest fires are recurring problems in Central Siberia, mainly during June and July because of a sharp increase of temperatures. These fires have widely increased last years and the year 2016 would be the most “blazed” of history since, according to GreenPeace Russia, 3.5 million ha of forests have been burnt1, as big as seven French departments! On 24th September, NASA has also published an Aqua MODIS scene from 18th September 2016 showing huge plumes moving towards North East of Russia as well as zones (in red) where the satellite has detected unusual warm temperatures associated with fire2. The extent of MODIS image on the map of concentrations of aerosols allows presenting the scale of the phenomenon.


(at the top)- NASA’s Aqua satellite scene (MODIS) showing huge plumes2; (at the bottom) – Map of concentration of aerosols2

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Sentinel-2 captures the coastal ground uplift after Kaikoura earthquake in New Zealand

On Monday Nov 14 New Zealand was hit by an earthquake of magnitude 7.8. The epicenter was located near Kaikoura on the east coast of the South Island.


Yesterday, the NZ Herald published aerial photographs showing tectonic uplift of the seabed of between 2 and 2.5 metres north of Kaikoura [1]. These photos were taken by @TonkinTaylor who posted them on Twitter.


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Radiometric quantities : irradiance, radiance, reflectance


Beware, this document contains equations

Radiometric quantities are numerous and may seem rather complex at first sight. Here is a little guide of the various quantities you might stumble upon when using optical remote sensing data. It might be a little boring, it is full-up with integral formulas, but well, to really understand what images mean, it might be useful. Come on, let's go, and cheers !

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Monthly cloud free syntheses merging Sentinel-2 and Landsat 8

To compute a cloud free synthesis of surface reflectances every month, a good repetitivity of observations is necessary. The weighted average method we developed at CESBIO, and which will be part of ESA's sen2agri system was coded by Cosmin Udroiu at CS Romania. It was meant to work with both Sentinel-2 sensors and an observation every fifth day. As we are still waiting for the launch of Sentinel-2B, the monthly syntheses obtained with Sentinel-2A alone really lack cloud free data.


On the left, the Sentinel-2A monthly synthesis, above Odessa (Ukraine) in May, and on the right its flag, with, in black, the areas flagged as cloud or cloud shadow. When a pixel is flagged as cloud or cloud shadow, the monthly synthesis provides the minimum blue reflectance, which tends to avoid clouds (if possible), but often selects cloud shadows.


Fortunately, the Sen2agri L3A processor is designed to work with LANDSAT 8 too, as both satellites have similar spectral bands, and as the MACCS atmospheric correction used to produce the L2A input products works for both sensors. Of course LANDSAT 8 geometric resolution is not that of Sentinel-2, so to avoid degrading Sentinel-2 imagery when LANDSAT8 data are available, we give Landsat 8 a very low weight in the weighted average. As a result, Landsat is really taken into account only when no cloud free Sentinel-2 was available during the synthesis period.


Same result as above, but including LANDSAT 8 data. A cloud free date at least is now found for every pixel. The water mask obtained from Level 2A product is a little wrong on the Landsat 8 image due to the presence of turbid waters and thin clouds. A solution for this problem will be implemented in next MACCS L2A version. Note that the monthly synthesis of both Sentinel-2 and LANDSAT-8 leaves nearly no visible artifacts on the lands.

For a better comparison of both versions, here is a little animation of composites with and without Landsat 8.

The Sen2Agri system is still in validation phase and should be released as open source next May, 6 months from now. The L3A synthesis processor will be also implemented within Theia and monthly L3A products will be distributed by Theia as it is already the case for L2A products.

3D views of Aru Co avalanches from Pléiades stereo imagery

A Pléiades stereo pair has been acquired on 2016-Oct-01 just a few days after the second glacier collapse in the Aru mountains. The panchromatic band has 0.5 m resolution, which allowed us to generate a post-event digital elevation model of the area. From this digital elevation model and the Pléiades 2 m multispectral imagery, Etienne Berthier generated these stunning 3D views of the aftermath...

Preliminary estimates of the volume detached from the glaciers are 66 Mm3 (first, north one) 83 Mm3 (second, southern one).

High spatial and temporal resolution optical remote sensing data to estimate maize biomass and yield


Climate variability has a strong impact on maize yield. For example, the strong drought that occurred in 2016 led to lower yields across France, even for irrigated fields. Yield estimates have a significant strategic and economic importance. High spatial and temporal resolution remote sensing data are a valuable tool for providing yield estimates at a large scale.


In a recent study (Battude et al. 2016) based on optical image time series (combination of Formosat-2, Landsat-8, SPOT4-Take5 and Deimos-1, about two images per month), CESBIO researchers have developed a new method for the estimation of maize yield. A new formulation of SAFY agro-meteorological model taking into account of the observed seasonal variation of the specific leaf area (SLA) and the effective light use efficiency (ELUE) was proposed.


Results show that these modifications improve biomass estimates at local scale.


Comparison of measured and simulated Dry Aboveground Mass (DAM) with the original version of SAFY (left) and the new model version (right)

Yield estimates are compared to annual statistical values (Agreste) on two departments in the southwest of France : the Gers and the Haute-Garonne. Results show that the model reproduces well yields (R = 0.96; RRMSE = 4.6%), even if it sometimes overestimates the values for rainfed fields.


Comparison of simulated yield and Agreste values [t.ha-1] for the Gers and Haute-Garonne departments in 2013 (left) and 2014 (right), with the distinction between irrigated and rainfed fields. Standard errors associated to simulated values are reported.

GAI thus seems to be a good indicator for estimating the irrigated maize yield at regional scale. For rainfed fields, coupling SAFY with a water balance module simulating the soil water content  may improve yield estimates. Sentinel-2 mission offers new perspectives and its data should improve the model estimates.


Reference : Battude M., Al Bitar A., Morin D., Cros J., Huc M., Marais Sicre C., Le Dantec V., Demarez V. (2016) Estimating maize biomass and yield over large area using high spatial and temporal resolution Sentinel-2 like remote sensing data. Remote Sensing of Environment 184, 668-681 DOI: 10.1016/j.rse.2016.07.030