Since spring 2017, we have made the MAJA cloud screening and atmospheric correction processor available for commercial use. A bit later, end of June, the Sen2agri software package, which includes MAJA older version (named MACCS) , was also released to the public. We did not expect a large success, as these two packages are quite heavy ones, do not work on laptops, and require a specific linux system powerful computers (Red Hat or CentOS).
Anyway, the MAJA processor has had quite a large success, even if, I guess, it is far from the success of Sen2cor, which is much easier to install and use, even if the performances are not the same. The figures below correspond nearly to one download per day.
|Number of downloads of MAJA (stand alone version)
|Number of downloads of MACCS (Sen2Agri version)i
To celebrate this fact, we just published a brand new MAJA detailed description.
I have always wanted to provide an Algorithm Theoretical Basis Document related to MAJA, but never had time, because I always had more urgent things to do. Some papers had been published, allowing MAJA users to get a good idea or the methods we use, but the published articles did not cover all the features of MAJA.
But this time, due to a contractual engagement with ESA, it was the urgent thing to do. So, at last, after a few weeks of hard work, here it is.
If you have already read the papers from our team, you will recognize some text published quite a long time ago, but we updated all the text and added some parts which had not been explained yet in journal publications, and of course the new parts recently added to MAJA. This ATBD is now in line with version 2.0 of MAJA.
The main difficulty of the atmospheric correction comes from the determination of the aerosols optical properties: one has to know the optical properties of the aerosol type present in the atmosphere and determine their optical thickness. Using Sentinel-2 data to determine the aerosol type is very complicated, and our MAJA processor, used to generate Theia L2A products, only computes the aerosol optical thickness, while assuming a specific aerosol type.
The current operational version of the MAJA processor uses a constant aerosol type during the atmospheric correction, independently from the location and from the time of the year, thus affecting the quality of the atmospheric correction if the chosen aerosol type is not appropriate.
As an alternative, we tried to use the information from CAMS (Copernicus Atmosphere Monitoring Service), whichprovides forecasts of the Aerosol Optical Thickness (AOT, see figure below) of five different aerosol types: dust, black carbon, sea salt, sulfate and organic matter.
CAMS aerosol optical thickness (AOT) forecasts at 550 nm on 14 June 2016, 03:00 UTC: (top left) Dust, (top right) Sea Salt, (bottom left) Black Carbon, and (bottom right) Sulfate.
Version française par ici.
The land surface models simulate the water and energy fluxes between soil, land cover and atmosphere. Their scopes of application spread from numerical weather prevision to soil water modeling.
Energy and water budgets of the soil-plant continuum
However, these models are initially conceived to be applied on wide areas. Thus, they use low resolution cover parameters (>1km) derived from mid-resolution satellite observations (MODIS, VEGETATION). These parameters are mainly the Leaf Area Index (LAI), the vegetation type or the surface albedo. Yet the agricultural landscapes of Western Europe are characterized by a patchwork of plots smaller than one square kilometer. These plots have very different vegetation cycles, i.e. winter and summer crops, which could only be described at high resolution. The crop management practices like crop rotation or irrigation are also generally not taken into account.
The products of the Sentinel-2 space mission, with their high spatial and temporal resolution, could bring elements to fill in this missing information.
Criticizing is easy, and doing is hard, especially when trying to create a global map of croplands. Some collegues from CESBIO have worked on that subject within the Sen2Agri project, and obtained good resuts, but only at the local or country scale. Finding a method that works everywhere must clearly be much harder.
These days, I have received a lot of emails, tweets and posts about a new cropland global product at 30 m resolution, edited by USGS. I have no doubt it was a serious work from a serious team, done with appropriate terrain data and methods, validation, and of course a tremendous data processing.
But there it is, I checked it over a lot of places that I know very well, and it seems to me that the cropland mask, at least in South West France, is clearly overestimated. Is it the same in tour region ? Here are some examples :
Olivier pointed to me that ESA's ground segment, PEPS and MUSCATE were all in really good shape today... And the sky was clear yesterday at the time of the Sentinel-2A acquisition!
So I could download the Level-2A product from theia.cnes.fr, run our let-it-snow processor, start QGIS and here it is: the map of yesterday's snow cover area at 20 m resolution. If you know the region, you might notice that there is currently a big contrast in the snow cover extent between the French and the Spanish Pyrenees. This is due to the blocking of the moist air masses coming from the north.
Snow cover area on 22 Nov 2017. blue: snow, grey: no snow, white: cloud.
Stay tuned! Theia should start to distribute these Sentinel-2 snow products in near real time very soon.
Since it became operationnal in December last year, MUSCATE has produced 50 000 level 2A products from Sentinel-2A. Let's recall what has been processed so far :
- For 550 tiles, we have processed all Sentinel-2A data acquired since December 2015.
- For 100 tiles, mainly in South America, and in Italy, we have processed all Sentinel-2A data from December 2016. We are currently catching up the backlog for Italy, and later on, for South American sites.
- For all these 650 tiles, we are producing all Sentinel-2 data (Sentinel-2A and Sentinel-2B) in near real time. I think THEIA is the only place where you can download Sentinel-2B L2A data so far. ESA has not started that production yet (nah, nah, nah )
- For all these 650 tiles, we have processed all Sentinel-2B data since beginning of October 2017. We will soon catch-up with the Sentinel-2B data acquired from July 2017.
See full screen
Map of the 650 tiles currently processed in near real time (in red). The blue tiles will be added beginning of next year.
All these products are available from https://theia.cnes.fr
Let's recall that MUSCATE uses the MAJA L2A processor, which uses multi-temporal criteria to perform a high quality cloud detection and atmospheric correction. Despite the recent installation of version 2.4, MUSCATE still regularly suffers from instability as soon as CNES High Performance Computer is overloaded. The problem does not lie in MAJA, but in the information exchanges between all the components of MUSCATE which need to respect an accurate timing (sorry, I am not able to explain better).
The exploitation team just installed a new version of MUSCATE (v 2.4.16.p2 (!)), which is expected to increase stability. But that's the theory, let's see if it works in the coming days and if we are able to increase our production rate.
The Cesbio contributes to the Pyrenees Climate Change Observatory (OPCC) through the analysis of the snow cover evolution using satellite imagery. We are working on three remote sensing products in the framework of the CLIM'PY project:
1. Daily cloud-free maps of the snow cover area in the Pyrenees at 500 m resolution since 2000 from MODIS ;
2. Maps of the snow cover area in the Pyrenees at 20 m and 30 m resolution since 2013 from Sentinel-2 and Landsat-8 ;
3. Maps of the annual peak snow depth in the Bassiès-Vicdessos region at 4 m resolution since 2015 (i.e., one map per year) from Pléiades stereo imagery .
Update : ESA is progressing fast. All lands overflown by Sentinel-2B were available in PEPS on November 12th, thanks to EDRS relay satellites.
The Sentinel-2 mission, made of two twin satellites S2A and S2B, has been stated "operational" since beginning of October, even if each satellite was only observing on one orbit out of two everywhere except in Europe and Africa. As a result, the 5 days revisit was only available in these two continents. But in the new Sentinel-2 mission status report, ESA announced this very good piece of news :
Sentinel-2A and -2B are together acquiring Europe, Africa and Greenland with 5 days revisit. The rest of the World is revisited every 10 days jointly by the two satellites. Full systematic 5-day revisit everywhere will be reached once EDRS downlink becomes available operationally, assumed during December 2017 for both S2A and S2B.
ESA declared Sentinel-2B operational at the beginning of October, although preliminary data were already available. Since this morning, MUSCATE is producing and distributing Sentinel-2B Level 2A products using the MAJA processor (the L2A products are expressed in surface reflectance after atmospheric correction and are provided with a good quality cloud mask). The current production starts from the first of October 2017 and will go on in real time, and we will progressively add the Sentinel-2B products acquired since July 2017.
First S2B products available for download from https://theia.cnes.fr
As always, the data can be freely downloaded from https://theia.cnes.fr
Here is a little example of time series of iimages acquired over Baotou, China, alternatively by Sentinel-2A and Sentinel-2B, here again, the images look the same, except where something has clearly changed on the ground, in the east-west irrigarted valley in the image center.
As MAJA processing is multi-temporal, its accuracy will benefit from the doubled repetitivity of acquisition, which should have an effect on our validation results. Using the production we did to check our parameters, we have already checked that the reflectances provided by both sensors are quite close, and agree well with in situ measurements obtained with CNES surface reflectance measuring station in La Crau. The results are provided below.