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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


� 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


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Product Technical Specifications


� 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


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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


Satellite Imagery


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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


� h

Landsat 7/8: multi-temporal composites

Mar2015

Apr2015-Jun2015

Jul2015-Sep2015


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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


Sentinel-1: monthly composite (Jan 2015)


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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


Classification & Validation 2015


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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


� 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


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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


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


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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


Optical and SAR comparison

� Optical based classification – internal validation sample

� SAR based classification – internal validation sample


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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


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


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Multiple crops on single LPIS polygon II


� 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


Multiple crops on single LPIS polygon III


� Multiple crops vs. crop anomaly

� „Difficult“ to detect example

winter rapeseed

winter cereals

spring cereals

sugar beet

potatoes

maize

fodder crops

other annual crops


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Classification & Validation 2015/2016


� 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


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� Sentinel-2 composite, SVM classification, aggregation into LPIS database

Classification

winter rapeseed

winter cereals

fodder crops

no vegetation


Validation

� Internal validation - done by Gisat, sample size: 137 LPIS polygons� Validation data collected during dedicated filed campaign


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� Winter rapeseed misdetection - different pheno-phase, will be removed

using subsequent image acquisition

Early classification


Conclusions & Next steps


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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


� 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


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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


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

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