1. AGRICULTURAL MONITORING IN UKRAINE: STATE OF THE ART AND PROSPECTS WITHIN COPERNICUS PROGRAM
Prof. Nataliia Kussul, Space Research Institute NASU-SSAU
Tatiana Adamenko, Hydrometeorological Centre, Ukraine

2. Agrometeorological observations
Moisture reserves (mm) in the 0-20 sm soil layer
Crop conditions visual assessment  
5 - Excellent 4 - Good 3 - Satisfactory 2 - Poor 1 - Death
October Winter Wheat (norm mm)

3. Agromonitoring with CGMS
Adopted for Ukraine CGMS system (since 2011)
Meteorological observations:
Plant phenology - daily;
Assessment of the state of the crops - daily;
Plant height (daily);
Density of crops (phase dependent);
Damage by pests and diseases;
A crops survey over large areas;
Monitoring of wintering conditions crops - daily;
Determination of the available moisture in the soil (1 per decade)
Weather stations location

4. AgroMeteo data details
164 weather stations // 120 agrometeorological stations;
Main crops observed:
Wheat, rye, barley, rapeseed, maize, oat, peas, sunflower, soybeans, sugar beet, millet and buckwheat, perennial grasses
Observations on the test fields (> 1 ha) located on <5 km from station
Pros:
High frequency of agrometeorological observations;
Cons:
Coarse data - 1 station per approx 5000 km2

5. Satellite monitoring – crop mapping/area
1. No-data pixels restoration (clouds and shadows) using self-organized Kohonen maps
2. Universal machine learning time series classification for the regional level based on neural networks ensemble
3. Map filtration (voting and weighted voting approaches with division parcels into the fields)

6. Validation: JECAM experiments
Ukrainian JECAM site
Cropland mask
Kyiv oblast
Info on methods and site location – non clear

7. Previous results (2013) OA, % pixel based Satellite Kyiv oblast
L-8 + R-2
90.10
Landsat-8
86.01
Radarsat-2
84.07
Pixel-based
Parcel-based
Radarsat-2
OA для слияния L-8 и R-2 – оба метода
Landsat-8: 6 scenes
Radarsat-2: 12 scenes
Landsat-8 + Radarsat-2

8. Radarsat-2 vs Sentinel-1
Kyiv oblast
Radarsat-2:
Nominal Scene Size - 50x50 km2
Resolution - 8 m
Repeat cycle - 24 days
Cost - more than 4000$ per scene
Sentinel-1:
Nominal Scene Size - 250x250 km2
Resolution - 10 m
Repeat cycle - 12 days
More than 500 scenes in 2015 for Ukraine territory
Cost - free
Sentinel-1
Radarsat-2

9. Multi mission crop classification (2015)
Kyiv oblast (2015)
Satellite
OA, %
pixel based
L-8 + S-1
92.7
Sentinel-1
91.4
Landsat-8
85.4
Что добавить?
Satellite data:
4 Landsat-8 scenes
15 Sentinel-1 scenes
Ground data:
547 ground samples (train and test sets)

10. Filtration results (Kyiv oblast, 2013)
A majority voting scheme
Pixel-based classification map
Method that divides parcel into the fields

11. References
N. Kussul, S. Skakun, A. Shelestov, M. Lavreniuk, B. Yailymov, O. Kussul “Regional scale crop mapping using multi-temporal satellite imagery“ // International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences. – P
A. Kolotii, N. Kussul, A. Shelestov, S. Skakun, B. Yailymov, R. Basarab, M. Lavreniuk, T.Oliinyk “Comparison of biophysical and satellite predictors for wheat yield forecasting in Ukraine” International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences. – P
Kogan, F., Kussul, N., Adamenko, T., Skakun, S., Kravchenko, O., Kryvobok, O., Shelestov, A., Kolotii, A., Kussul, O., Lavrenyuk, A. “Winter wheat yield forecasting in Ukraine based on Earth observation, meteorological data and biophysical models”// International Journal of Applied Earth Observation and Geoinformation, 2013, 23, pp. 192–203.

12. DISCUSSION AND CONCLUSIONS
No satellite data in classical agromonitoring in Ukraine (with agrometeorological observations – very coarse resolution) – new possibilities with modern satellite data.
Efficient method for crop classification and area estimation using time series of optical and SAR images at regional level (NUTS2) in Ukraine has been developed.
Use of multi-temporal multi-polarization SAR data in combination with multi-temporal optical images allows to increase the accuracy of crop classification up to 7%.
Sentinel-1 has high temporal resolution, good coverage & is free. Copernicus program is promising for operational crop monitoring.

13. Thank you!