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