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JECAM | Joint Experiment for Crop Assessment and Monitoring

Belgium

Project Overview

  1. Crop identification and Crop Area Estimation Cropped Land
  2. Crop Condition/Stress 
  3. Soil Moisture
  4. Evapotranspiration

Mapping resolution: 

     5-10 m for the field estimates 

Timeliness (with regards to growing season):
     
Growing season (March – August).

The current project phase is research and operational pilot.


Project Reports

2017 Site Progress Report

2015 Site Progress Report

2014 Site Progress Report

 

  1. Matton, Nicolas ; Sepulcre Canto, Guadalupe ; Waldner, François ; Valero, Silvia ; Morin, David ; Inglada, Jordi ; Arias, Marcela ; Bontemps, Sophie ; Koetz, Benjamin ; Defourny, Pierre, An Automated Method for Annual Cropland Mapping along the Season for Various Globally-Distributed Agrosystems Using High Spatial and Temporal Resolution Time Series, 2015, Remote Sensing, Vol. 7, no.10, p. 13208-13232
     
  2. Valero, Silvia ; Morin, David ; Inglada, Jordi ; Sepulcre Canto, Guadalupe ; Arias, Marcela ; Hagolle, Olivier ; Dedieu, Gérard ; Bontemps, Sophie ; Defourny, Pierre ; Koetz, Benjamin, Production of a Dynamic Cropland Mask by Processing Remote Sensing Image Series at High Temporal and Spatial Resolutions, 2016, Remote Sensing, Vol. 8, no.55, p. 1-21
     
  3. Inglada, Jordi ; Arias, Marcela ; Tardy, Benjamin ; Hagolle, Olivier ; Valero, Silvia ; Morin, David ; Dedieu, Gérard ; Sepulcre Canto, Guadalupe ; Bontemps, Sophie ; Defourny, Pierre ; Koetz, Benjamin, Assessment of an Operational System for Crop Type Map Production Using High Temporal and Spatial Resolution Satellite Optical Imagery, 2015, Remote Sensing, Vol. 7, no.9, p. 12356-12379
     
  4. Chomé, Guillaume ; Baret, Philippe ; Defourny, Pierre, Mapping farming practices in Belgian intensive cropping systems from Sentinel-1 SAR times-series, 2016, Living Planet Symposium 2016, Prague, Czech Republic, in: Proceedings of Living Planet Symposium 2016.
     
  5. Radoux, Julien ; Chomé, Guillaume ; Jacques, Damien ; Waldner, François ; Bellemans, Nicolas ; Matton, Nicolas ; Lamarche, Céline ; d'Andrimont, Raphaël ; Defourny, Pierre, Sentinel-2’s Potential for Sub-Pixel Landscape Feature Detection, 2016, Remote Sensing, Vol. 8, no.6, p. 488.
     
  6. Delloye, Cindy ; Weiss, Marie ; Baret, Frédéric ; Morin, David ; Defourny, Pierre, Accuracy assessment of GAI retrieval from SPOT5 Take5 according to crop type and crop development (BELCAM), Living Planet Symposium 2016 Prague, Czech Republic.

Implementation Plans

Crop identification and Crop Area Estimation Cropped Land:

Crop Condition/Stress :

Soil Moisture:

Estimation of biophysical variables (LAI):

Investigation of the options to scaling down evapotranspiration data coming from Meteosat Seconde Génération every 30 minutes.

The current project phase is research and operational pilot.


Site Description

Locations

Belgium / France
Site Extent   Centroid: 50.65, 5
Top left: 51, 4.5 Bottom Right: 49.6, 5.8

Figure 1: Belgium JECAM site. Background is a 10-m sen2agri L3A Cloud Free false-color Composite from September 2016 (Sentinel-2 and Landsat-8 -bands NIR, RED, GREEN).

 

Location:

     Belgium

Topography:

     The landscape topography is flatlands and hills.

Soils:

     The main soil texture is loam, with some sandy soils.

Drainage class/ irrigation:

     Soil drainage class is moderately well-drained.

Crop calendar:

Field size:

     Typical field size ranges from 2 to 15 ha. Average: 3 ha.

Climate and Weather:

     The climatic zone is temperate. Climate at site is moderately humid and cool, with annual rainfall of about 780 mm, which is relatively well distributed over the year. Year average temperature is approximately 11°C.

Agricultural methods used:

     Mainly rainfed, mechanized and intensive cropping systems, with typical field size ranges from 3 to 15 ha.

     Irrigation infrastructure is not frequent.

Figure 2: DHP acquired in winter wheat field between March and July 2016. (BELCAM project)


Specific Project Objectives & Deliverables

Cropland and crop type mapping

In the frame of the Sen2Agri ESA project, the JECAM site was selected as one of the demonstration site to demonstrate the Sen2Agri system in Near-Real-Time. The full growing season time series of Sentinel-2 and Landsat 8 images covering the JECAM site was automatically downloaded and processed to generate (1) a suite of Dynamic Cropland Mask and (2) Crop Type map.

The Dynamic Cropland Mask consists in a binary map separating annual cropland areas and other areas, thus corresponding to a mask over annually cultivated areas. The annual cropland is defined as a piece of land with a minimum area of 0.25 ha, actually sowed/planted and harvestable at least once within the year following the sowing date. This binary map is produced along the agricultural season on a monthly basis, to serve for instance as a mask for monitoring crop growing conditions, as basis for sampling stratification and for agricultural extension. Its accuracy is expected to increase as long as additional images are integrated into the development process. 

 

The Crop Type product is a map of the main crop types or groups in a given region. The top main crop types are considered per region. The main crop types are defined as those covering a minimum area of 5% of the annual cropland in the region representing a cumulated area higher than 75% of the latter. For Belgium, the main crop types are Winter Wheat, Maize, Barley, Potatoes, Sugar beet and rapeseed. The crop types are identified over the previously generated crop mask. 

 

        
a)                                                         b)

        
c)                                                         d)

Figure 5: Composites and crop type classification outputs. (a) L3A September 2016 false-color cloud free composite (NIR,RED,GREEN), 
b) crop type classification output on (a) area,  c) L3A  September 2016 false-color cloud free composite (NIR,RED,GREEN), d) croptype classification output on (c) area

 

LAI estimation with optical images

The two years field campaign of DHP acquisition allows to validate the LAI retrieved with S-2A images for the winter wheat. The results show the added value of S2-A new spectral bands to estimate the LAI comparing to SPOT5 Take5 which tend to a systematic underestimation of the LAI value retrieved from 3.5. The new S2-A bands have two main impacts: (i) overcome the saturation effect around a LAI of 4 and (ii) decrease the spreading of the point around the 1:1 axis; these two effects allow the decreasing of the RMSE from 1 to 0.7.


Figure 6: LAI estimate over a farmer fields, estimation available in near real time during the all growing season.

 

Tillage detection

The image processing has been realized in three main steps. First, the preprocessing of S1A SLC images has been realized using SNAP with S1 Toolbox. Second, the hourly rain data of 29 meteorological stations has been gridded at 10km over the whole study area. Third, the pixels of the images which received a rain exceeding 1mm within 3 hours before the acquisition have been masked. The preprocessing chain consisted in: applying the precise S1A orbits, calibrating, removing thermal noise, debursting, multilooking (5x1) and terrain correcting with SRTM 1sec. Since the analysis is performed on an object base, the images have not been filtered. In total, 17 images were used for the orbit 110 (descending) and 16 images were used for the orbit 161 (ascending). The time series ranges from 04/07/2015 to 17/02/2016.

The classifications achieved accuracies of 87% for field use, 86% and above 94% for tillage detection in winter wheat and cover crop respectively. However, those accuracies are relying on the choice of the good discrimination periods. This requires crop calendar and crop growth conditions knowledge to account for timing particularities in the farmers’ behaviour. Furthermore, the global overall accuracy of tillage detection is the product of the field use OA and of the tillage detection OA. This product gives a global OA of 78-80% (depending on the field use). To further improve this discrimination, one could test fields’ stratification between agro-meteorological areas in order to decrease the variability in soils conditions, farming calendar and the way the tillage is realized.

 


Figure 7: Tillage occurrence detection in Wallonia with S1A - SAR time series. R: Sigma0 VV, G: Sigma0 VH, B : ratio


In Situ Observations

  1. Parameter: Tillage Occurrence Detection
    Data Collection Protocol:

    An intensive field data collection has been set up in order to retrieve information on 535 parcels among which 107 are fully described in terms of practices occurrence for 2015. This detailed characterization of the practices occurrence has been achieved through farmers’ interviews. The condition of the parcels in February have been reported for the remaining 428 fields through field observations (bare soil, crop types, visible practices, previous crop from residues). All the fields have been manually delineated based on very high spatial resolution orthoimages. Hence, this dataset contains three main information: the polygon of the parcel, the occurrence date of the different practices (tillage, shallow ploughing...), the field condition on the 18th of February.

    Frequency:
  2. Parameter: Crop Type
    Data Collection Protocol:

    An intensive field data collection has been carried out in July 2016 in order to collect crop type information in approximately 1800 fields spread over Walloon region (Southern part of Belgium).  

    Frequency:
  3. Parameter: Digital Hemispherical Pictures (DHP)
    Data Collection Protocol:

    DHP have been acquired in 2015 and 2016 on three crops (winter wheat, maize and potato) all along the growing season in a field sample spread over Belgium. Those DHP were acquired for validation purposes of the Biophysical Variables (BV) retrieved from satellite imagery (SPOT5Take5 in 2015 and S2-A in 2016) with a radiative transfer model: fCover, fAPAR and LAI. 

    Frequency:

EO Data Requirements

Approximate Start Date of Acquisition: February/ March 1
Approximate End Date of Acquisition: August 1
Spatial Resolution: 20 m - 5 m
Temporal Frequency: Every 15 days (weekly preferred)
Latency of Data Delivery: Every 15 days, Weekly (preferred)
Wavelengths Required: G, R, NIR, SWIR
Across Swath: 25 km
Along Track: 30km

SAR Data Requirements

Approximate Start Date of Acquisition: March 1
Approximate End Date of Acquisition: August 1 (September 1 preferred)
Spatial Resolution: 10 m
Temporal Frequency: Every 2 weeks (weekly preferred)
Latency of Data Delivery: Every 2 weeks (weekly preferred)
Wavelengths Required: C and X band
Polarization VV, HH, VH, HV
Incidence Angle Restrictions: /
Across Track: 25 km
Along Track: 30 km

Locations

Belgium / France

Centroid
Latitude: 50.65
Longitude: 5

Site Extent
Top left
Latitude: 51
Longitude: 5
Bottom Right
Latitude: 49.6
Longitude: 5.8


Optical Sensors

RapidEye
Imaging Mode:
Spatial Resolution: 5m
Acquisition Frequency: Weekly
Pre-Processing Level:
Application:

Landsat 8
Imaging Mode:
Spatial Resolution: 5m
Acquisition Frequency:
Pre-Processing Level:
Application:

JECAM | Joint Experiment for Crop Assessment and Monitoring | Group on Earth Observation

©2013 Joint Experiment for Crop Assessment and Monitoring © HER MAJESTY THE QUEEN IN RIGHT OF CANADA SA MAJESTE LA REINE DU CHEF DU CANADA (2012)