Kussul Nataliia, Shelestov Andrii,Skakun Sergii

Space Research Institute of NAS of Ukraine and SSA of UkraineKyivNational University of Environmental and Life Science

WGISS-37,14-28 April 2014, Cocoa-Beach, Florida, USA

SSAU: JECAM & GEOGLAM activities – big data challenge

Cocoa-Beach 2014

JECAM-test sites in UkraineJECAM-test sites in Ukraine

• Test sites (officially established in 2011)

– Kyiv oblast (SRI)• Crop area estimation• In-situ measurements

– crop types, along the roads and segment surveys

– Lviv oblast (The State Science-Technological Centre of Soil Fertility Protective)

• Crop rotation identification

– Pshenychne (National University of Life and Environmental Sciences of Ukraine)

• Biopar parameters retrieval and crop growth model calibration

• In-situ measurements– crop types, crop height, LAI, fCover, soil parameters

Administrative map of Ukraine and location of Kyiv and Lviv region

Map of intensive observation sub-site

Relationship between humus and satellite-derived biomass

Cocoa-Beach 2014

Crop mapping: UkraineCrop mapping: Ukraine

• Crop mixture (winter,spring,summer), important minor crops• Uneven crop proportions distribution• Large territory – big data problem

Cocoa-Beach 2014

Ground data availabilityGround data availability

Crop type~5500 fields:• May 2013 (2000 fields);• August 2013 (3000 fields);• March 2014 (500 fields)

• Crop state (DHP)– ~20 fields

– 30 ESU, 3 times per season

– LAI, fAPAR, fCover

• Crop damage– ~50 fields

Cocoa-Beach 2014

ESA Sentinel-2 for ESA Sentinel-2 for AgricultureAgriculture, 2013, 2013• Satellite data

– SPOT4 (17 images, 8 cloud&snow free)

– RapidEye (29, images, 7 cloud&snow free)

– RADARSAT2 (12 images)

– Landsat -8 (4 images)

• Ground data (June)– 320 fields (crop type)– Maize (30%)– Wheat (20%),

Soya(20%)– Sunflower (10%)– Rapeseed (2.5%)– Barley (2%)

Cocoa-Beach 2014

Preprocessing for classificationPreprocessing for classification

1. Conversion to top-of-atmosphere (TOA) reflectance.

2. Atmospheric corrections (from TOA to surface reflectance (SR)) using the SMAC model

3. Clouds and shadows identification

4. Filling in missing data due to cloud and shadow areas (restoration) .

Cocoa-Beach 2014

Crop map for Kyiv oblast (2013),Crop map for Kyiv oblast (2013),overall accuracy 86%overall accuracy 86%

Cocoa-Beach 2014

Validation of global productsValidation of global products

• For JECAM test site in Ukraine

• Within FP7 ImagineS• (Dr. Roselyne Lacaze

HYGEOS,FRANCE) • Follows ESA VALERI

protocol (30 ESU – elementary sampling units)• compliant with CEOS Land

Product Validation (LPV) guidelines

• CAN-EYE software• Biophysical parameters:

• LAI, FAPAR, FCover

Cocoa-Beach 2014

LAI maps derived from Landsat-LAI maps derived from Landsat-8, SPOT-4 and RapidEye8, SPOT-4 and RapidEye

RapidEye, 07.05.2013 Landsat8, 17.05.2013SPOT4, 12.05.2013

Landsat8, 09.06.2013 SPOT4, 11.06.2013 RapidEye 11.06.2013

Cocoa-Beach 2014

LAI (RapidEye, NDVI) vs. LAI LAI (RapidEye, NDVI) vs. LAI (SPOT5, transfer function)(SPOT5, transfer function)

RapidEye, 11.06.2013

SP

OT

5, 1

5.06

.201

3

These values are correspondent to village and road

Ejection MaskEjection Mask

Cocoa-Beach 2014

PlansPlans

– Winter & summer crop maps• PROBA-V, MODIS• Sentinel-1 and Landsat-7,8, Sentinel-2• 3 oblasts (NUTS2) validated, whole Ukraine –

provisional– More research in 2014 on the assessment of

integration of SAR and optical images for crop mapping in Ukraine.

– Special attention will be paid to sequential crop mapping, i.e. producing crops as satellite images become available.

Cocoa-Beach 2014

Thank you!