The training session will last around 1h30
The slides will be available on the Sen2-Agri website in the coming 24hrs(http://www.esa-sen2agri.org/)
Presenters:
Sophie Bontemps and Nicolas Bellemans from UCLouvain
Members of the consortium available to answer your questions by chat:
Cosmin Cara, Cosmin Udroiu, Laurentiu Nicola from Cs-Romania
Welcome to your 1st
Sen2-agri online training
• What is Sen2-Agri ?
– Gobal overview
– Methods
– Sen2-Agri Processing System
• System presentation and hands-on training using the system web interface
Overview of the session
S-1 A/B/C/D
S-1 A/B 2nd Generation
S-2 A/B/C/D
S-2 A/B 2nd Generation
S-3 A/B/C/D
S-3 A/B 2nd Generation
S-4 A/B (on MTG)
S-5 Precursor
S-5 A/B/C (on MetOp-SG)
S-6 A/B
Access to Contributing Missions
Copernicus Space Component
Long term continuity with free and open access
Sentinel-2 for operational ag.
monitoring at 10 m resolution ?
In the big data era providing dense and systematic time series and cloud computing facilities, the key question is :
can we exploit in near real time the EO data flows at parcel level over large areas dealing with the cropping systems diversity ?
EARLY AREA INDICATOR
EARLY AREA INDICATOR
Binary map identifying annually cultivated land at 10m updated every month
Crop type map at 10 m for the main regional
crops including irrigated/rainfed
discrimination
Vegetation status map at 10 m delivered every
week (NDVI, LAI, pheno index)
Monthly cloud free surface reflectance composite at 10-20 m
Top priority : open source system to deliver
4 Sen2-Agri products along the season
in line with the GEOGLAM core products
Methods
Monthly cloud-free composite
Benchmarking conclusions
• Weighted average approach
• Compositing period can vary between 30 to 50 days window
• Implements a directional correction for seamless composites
• Recurrent implementation : L3A product updated with each new L2A product
To limit the data volume to keep on-line in Sen2Agri system
Juillet 2016
Août 2016
Septembre 2016
Octobre 2016
Cape Town
Western Cape Province monitored by Sentinel 2 in 2016
June July August September October November
Winter grain production region (South Africa)
November 201650 days window
5/10/2016-25/11/2016
First cloud free composite series over Mali
at 10m resolution from Sentinel-2 and Landsat 8
Dynamic cropland mask2 chains implemented to deal with presence/absence of in-situ data
Benchmarking conclusions• Surface reflectance preparation
• Linear interpolation of the refldata for the in situ data version
• Gap filling using Whittaker smoothing if absence of in situ data
• RF supervised algorithm on a set temporal feature
• Trimming to clean the reference map
• A posteriori smoothing based on a per-object approach
(Matton et al., RS2015)
(Valero S. et al., RS2016)
Sen2-Agri 10 m cropland map for Ukraine
September 2016
Overall accuracy : 98 %
F-score cropland : 97 %
Non-Cropland
Cropland
November 2016
Overall accuracy : 98 %
F-score cropland : 99 %
Overall accuracy: 94 %
F-score cropland: 80 %
System validation:
2016 Cropland mask at 10m resolution for Mali
from Sentinel-2 and Landsat 8
Cropland product for local site in Northern China
without in situ data (trained from global CCI LC map)
Cropland mask, obtained without in-situ data but using the ESA CCI Land Cover map to extract training dataset(http://maps.elie.ucl.ac.be/CCI/viewer/)
Cropland
Non cropland
Crop type map
Benchmarking conclusions• Surface reflectance preparation
• Linear interpolation of the refl data for the in situ data version
• Based on the crop mask previouslygenerated
• Random forest classifier• Classifier applied on temporal features :
• Surface reflectance• NDVI• NDWI• Brightness
(Inglada et al., RS2015)
Sen2-Agri 10 m main crop types map for Ukraine (July 2016)
Overall accuracy : 82,7 %
Sen2-Agri 10 m products : field level
Sen2-Agri 10 m main crop types map for South AfricaSummer grain production area – Northern provinces
Maize
Soyabeans
Sunflower
Fodder crops
Other crops
Free State
Main Crops F1-Score
Maize 94%
Soyabean 83%
Sunflower 82 %
Fodder crops 90%
Overall Accuracy 90,1 %EO data: 6 months of S2 and
L8 acquisitions(Oct 2016 – March 2017)
In situ data: ~12000 samples(~451000 ha)
3 LAI products
Benchmarking conclusions• Non-linear regression model• Reflectance are simuated using
the ProSail model• 2 reprocessing options :
• Weighted average using the n last LAI value
• Fitting a phenologicalmodel on the full time series
• Mono-date LAI estimation
• Multi-date and fitted LAI reprocessing
Monitoring period
System operation for crop growth monitoring :Leaf Area Index (LAI) product
EoSSoS
Automatic EO data downloadfrom EO data providers
SoS
Before the monitoring period
t0
t2
t5 t10 t15
t18
t0t2
t10
t15
t18
System initialization
