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Czech Agriculture National Demonstrator
(CzechAgri)
Kucera, Lubos; Vobora, Vaclav (GISAT, Czech Republic)
Savelkova, Lucie (SZIF, Czech Republic)
Defourny, Pierre (Université Catholique de Louvain, Belgium)
Koetz, Benjamin (ESA ESRIN, Italy)
Léo, Olivier; Lemoine, Guido (Joint Research Centre, Italy)
CzechAgri, Workshop on Control and Management of Agricultural Land in IACS, Baveno, 23.5.2016
Presentation outline
� Project Background
� Product Technical Specifications
� Satellite Imagery
� Classification & Validation 2015
� Classification & Validation 2015/2016
� Conclusions & Next steps
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Project Background
CzechAgri, Workshop on Control and Management of Agricultural Land in IACS, Baveno, 23.5.2016
� Jointly initiated in December 2015 by DG-JRC, ESA and SZIF
(Czech State Agricultural Intervention Fund)
� Run within the ESA Sentinel-2 for Agriculture project managed by
the Université Catholique de Louvain
� To demonstrate the capabilities of the Copernicus Sentinels for
EO based agriculture monitoring and management to Czech
stakeholders
� To demonstrate a proof of concept for national agricultural EO
mapping and monitoring products
CzechAgri context & objectives
CzechAgri, Workshop on Control and Management of Agricultural Land in IACS, Baveno, 23.5.2016
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Product Technical Specifications
CzechAgri, Workshop on Control and Management of Agricultural Land in IACS, Baveno, 23.5.2016
� Product Specifications
� Spatial coverage: Full country to regional
� Spatial resolution: 20 meters / LPIS polygons
� Temporal extent: Whole crop growing season
� Temporal frequency: 2-4 crop type maps per crop growing season
� Geometric accuracy: RMS < 20m
� Thematic accuracy: Overall accuracy > 80%
Individual crop accuracy > 60% (F1-score)
� Format: ArcInfo SHP
� Cartographic projection: Krovak / S-JTSK
� Input Data
� Satellite imagery
� Sentinel-1 & 2 time series
� Landsat 7 & 8 time series
� Crop parcel datasets
� Czech LPIS
� Crop in-situ data
� In-situ crop data, IACS (crop declaration) data
Czech National Crop Type Map
CzechAgri, Workshop on Control and Management of Agricultural Land in IACS, Baveno, 23.5.2016
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Crop Type Map 2015 & 2016
� Crop Type Map 2015
� Full country
� Based on Sentinel-1 & 2 and Landsat 7 & 8 time series
� Winter cereals, winter rapeseed, spring cereals, maize,
sugar beet, potatoes and fodder crops
� Temporal extent: Whole crop growing season
� Early Crop Type Map 2016
� Regional (eastern part of central Bohemia)
� Based on Sentinel-2 time series
� Winter cereals, winter rapeseed, fodder crops (includes
LAI map)
� Temporal extent: March 2016
� Crop Type Map 2016
� Full country
� Based on Sentinel-1 & 2 (complemented by Landsat 8)
time series
� To be produced
CzechAgri, Workshop on Control and Management of Agricultural Land in IACS, Baveno, 23.5.2016
Satellite Imagery
CzechAgri, Workshop on Control and Management of Agricultural Land in IACS, Baveno, 23.5.2016
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� Landsat-7 ETM+, Landsat-8 OLI - Oct2014 - Dec2015
� L7 from USGS repository, L8 from ESA archive
� 12 paths/rows over CZ, 675 scenes (357 L7 ~ 80GB, 318 L8 ~ 300GB)
� Conversion DN to TOA, atmosphere correction (6S), band subset (blue,
green, red, nir, swir1, swir2)
� Cloud/shadow/snow detection using Fmask
� Multi-temporal composites: cloud free seamless, period 1-3 months -
Mar2015, Apr2015 - Jun2015, Jul2015 - Sep2015
Number of clear-sky values (green = 1, red > 10)
Landsat 7/8: available data & pre-processing
CzechAgri, Workshop on Control and Management of Agricultural Land in IACS, Baveno, 23.5.2016
� h
Landsat 7/8: multi-temporal composites
Mar2015
Apr2015-Jun2015
Jul2015-Sep2015
CzechAgri, Workshop on Control and Management of Agricultural Land in IACS, Baveno, 23.5.2016
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� Sentinel-1 GRD IW, Oct2014-Dec2015, operational SciHub, 763 scenes ~ 885GB
� SAR data pre-processing (S1 and SNAP toolboxes)
Sentinel-1: available data & pre-processing
� Calibration to sigma-naught
� Terrain correction and ortho-
rectification
� Speckle filtering (Lee Speckle Filter)
� Conversion to dB scale
� Selection of suitable imagery based
on meteorological data and visual
inspection
� Monthly composites using SNAP
CzechAgri, Workshop on Control and Management of Agricultural Land in IACS, Baveno, 23.5.2016
Sentinel-1: monthly composite (Jan 2015)
CzechAgri, Workshop on Control and Management of Agricultural Land in IACS, Baveno, 23.5.2016
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� Identified 5 relative orbits over CZ, re-processing of S2 data prior to end
of November, total 22 granules ~ 11 GB
� SEN2COR - atmospheric correction
� Recode internal cloud masks, all processing steps done by SNAP
� Summer composite: Aug-Sep 2015
Sentinel-2: available data & summer composite
CzechAgri, Workshop on Control and Management of Agricultural Land in IACS, Baveno, 23.5.2016
Classification & Validation 2015
CzechAgri, Workshop on Control and Management of Agricultural Land in IACS, Baveno, 23.5.2016
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� Provided by MoA
� Czech LPIS - based on farmer's block
� Version valid to 1.6.2015
� Total area of arable land: 2 488 892 ha
� Total no. of arable farmer's blocks: 242 748 polygons
� MMU - 1ha, at 20 m resolution corresponds 25 px
� LPIS data - arable land, farmer´s block area >= 1 ha
� Arable land mask derived from LPIS
� It represents 2 466 350 ha (99.1% of total arable land)
Ancillary Data: Czech LPIS
CzechAgri, Workshop on Control and Management of Agricultural Land in IACS, Baveno, 23.5.2016
� Provided by SZIF
� The sample parcels have been selected inside the CwRS 2015 sites
� The single crop is declared for the single farmer's block
� Declared area for the farmers block with a single crop is bigger than 2
hectares
� Declared crop was confirmed during the On-the-spot check (OTSC)
� Sample of parcels have been selected on arable land
� Stratified random sampling
� 10 crops
� 7 crops groups
� 4 491 declarations
� 2 294 provided to Gisat (2/3)
Ancillary Data: Crop declaration data
CzechAgri, Workshop on Control and Management of Agricultural Land in IACS, Baveno, 23.5.2016
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� Based on Support Vector Machine (SVM) classifier (pixel based)
� Classification performed under arable land mask derived from LPIS
� Integrate optical and SAR approaches
� fusion before classification (combine features)� simpler from the operational aspect
� fusion after classification (combine classes)� better for mixed classification results and more crops on single LPIS polygon
� Two independent classifications:
� Optical - based on Landsat 7/8 and Sentinel-2 multi-temporal composites
� SAR - based on Sentinel-1 monthly composites
� Integration - enhanced crop map to improve the maximum overall
accuracy - using maximum posterior probability within the LPIS polygon
� Aggregation - crop with largest area within the LPIS polygon
Classification approach
CzechAgri, Workshop on Control and Management of Agricultural Land in IACS, Baveno, 23.5.2016
Crop type maps
Optical
SAR
Integrated and
aggregated into LPIS
winter rapeseed
winter cereals
spring cereals
sugar beet
potatoes
maize
fodder crops
other annual crops
CzechAgri, Workshop on Control and Management of Agricultural Land in IACS, Baveno, 23.5.2016
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Internal and external validation
� Internal validation - done by Gisat, sample size: 866 LPIS polygons
� External validation - done by SZIF, sample size: 1485 LPIS polygons
CzechAgri, Workshop on Control and Management of Agricultural Land in IACS, Baveno, 23.5.2016
Optical and SAR comparison
� Optical based classification – internal validation sample
� SAR based classification – internal validation sample
CzechAgri, Workshop on Control and Management of Agricultural Land in IACS, Baveno, 23.5.2016
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No. crops vs. single LPIS polygon
� Single crop declared on single LPIS polygon for more than 80% of all polygons
� LPIS polygons with multiple crops need to be detected automatically (and need to be differentiated from LPIS polygons where more crops are incorrectly classified)
� Two step approach� Statistical analysis� Segmentation within LPIS blocks
� Initial test� Statistical analysis based on application of minimum parcel size and
individual crop ratios calculation� Applied for validation sample: 1481 LPIS polygons declared with multiple
crops, more than 90% identified using statistical approach� But still number of misidentified polygons to be corrected� Object based approach to be developed and tested
CzechAgri, Workshop on Control and Management of Agricultural Land in IACS, Baveno, 23.5.2016
Multiple crops on single LPIS polygon I
Mar 2015 Apr – Jun 2015 Aug – Sep 2015
� Multiple crops grown on single LPIS polygon
confirmed
� „Easy“ to detect examplewinter rapeseed
winter cereals
spring cereals
sugar beet
potatoes
maize
fodder crops
other annual crops
CzechAgri, Workshop on Control and Management of Agricultural Land in IACS, Baveno, 23.5.2016
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Multiple crops on single LPIS polygon II
Mar 2015 Apr – Jun 2015 Aug – Sep 2015
� Misclassification - multiple crops grown on
single LPIS polygon not confirmed
� „Difficult“ to detect examplewinter rapeseed
winter cereals
spring cereals
sugar beet
potatoes
maize
fodder crops
other annual crops
CzechAgri, Workshop on Control and Management of Agricultural Land in IACS, Baveno, 23.5.2016
Multiple crops on single LPIS polygon III
Mar 2015 Apr – Jun 2015 Aug – Sep 2015
� Multiple crops vs. crop anomaly
� „Difficult“ to detect example
winter rapeseed
winter cereals
spring cereals
sugar beet
potatoes
maize
fodder crops
other annual crops
CzechAgri, Workshop on Control and Management of Agricultural Land in IACS, Baveno, 23.5.2016
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Classification & Validation 2015/2016
CzechAgri, Workshop on Control and Management of Agricultural Land in IACS, Baveno, 23.5.2016
� Sentinel-2 only - composite based on two acquisitions (March 17/27)
� Regional sample product - eastern part of Central Bohemia
Winter crop classification 2015/2016
� Winter rapeseed, winter cereals, fodder
crops, no vegetation
� Mono-temporal classification, same
approach as for 2015 crop type map
� Training dataset based on visual
interpretation
� Validation dataset collected during field
campaign
CzechAgri, Workshop on Control and Management of Agricultural Land in IACS, Baveno, 23.5.2016
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� Sentinel-2 composite, SVM classification, aggregation into LPIS database
Classification
winter rapeseed
winter cereals
fodder crops
no vegetation
CzechAgri, Workshop on Control and Management of Agricultural Land in IACS, Baveno, 23.5.2016
Validation
� Internal validation - done by Gisat, sample size: 137 LPIS polygons� Validation data collected during dedicated filed campaign
CzechAgri, Workshop on Control and Management of Agricultural Land in IACS, Baveno, 23.5.2016
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� Winter rapeseed misdetection - different pheno-phase, will be removed
using subsequent image acquisition
Early classification
CzechAgri, Workshop on Control and Management of Agricultural Land in IACS, Baveno, 23.5.2016
Conclusions & Next steps
CzechAgri, Workshop on Control and Management of Agricultural Land in IACS, Baveno, 23.5.2016
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� Accuracy of Sentinel-1 based classification
� LPIS needs to be available
� Mostly automated processing (no manual post classification improvement applied)
� Huge amount of data to be processed (1.3 TB for single crop growing season – will increase for 2016)
� Early winter crop detection possible already in March with high accuracy (using optical imagery)
Main findings
CzechAgri, Workshop on Control and Management of Agricultural Land in IACS, Baveno, 23.5.2016
� Crop type classification 2016� Full Sentinel-2 time series� Individual crop accuracy vs. month of production � Simulation of iterative delivery during crop growing season
� Further analysis of 2015 and 2016 results (inter-annual)� Crop rotation� Crop area statistics� Crop diversification� …
� Automation� Detection of multiple crops on single LPIS polygon� Integration of optical and SAR based classification
Next steps
CzechAgri, Workshop on Control and Management of Agricultural Land in IACS, Baveno, 23.5.2016
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Acknowledgment
� ESA: CzechAgri initiative, funding through Sentinel-2 for Agriculture
project
� UCL: Project management, support to 2015/2016 crop classification
� DG-JRC: “Towards Future Copernicus Services Components for
Agriculture”
� SZIF: IACS data provision, external validation, consultations
� MoA: LPIS data provision
CzechAgri, Workshop on Control and Management of Agricultural Land in IACS, Baveno, 23.5.2016
Sentinel-1: Backscatter analysis
� Crop-backscatter signatures during crop growing season
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Sentinel-1: Separability analysis
� Crop-backscatter signatures
� Statistical Tests: Generalized linear models (GLMs), Analysis of Variance (ANOVA) and Tukey’s HSD to
identify which crops or crop groups have significantly different range of backscatter each month.
� Crop-backscatter signatures
� Statistical Tests: Generalized linear models (GLMs), Analysis of Variance (ANOVA) and Tukey’s HSD to
identify which crops or crop groups have significantly different range of backscatter each month
•Rapeseed •Rapeseed
•Sugar beat •Sugar beat