1. Crop area estimation in Geoland 2 Ispra, 14-15/05/2012 I. Ukraine region II. North China Plain

2. I. Crop area estimates over Ukraine region

3. ZH KH K Area km2% crop land Kyivska (K)28 00036.5% Khmelnitska (KH)20 00039% Zhytomyrska (ZH)29 90022% Test area: 3 oblasts around Kiev Crop typeKKHZHTotal Oilseed rape612 68 Sugar beet66 76 Soybean62 24 vegetables32 32 Sunflower31 12 Crop typeKKHZHTotal Winter wheat3230 3532 Spring barley1826 1921 Potato12 1813 maize1410 12 Crop distribution: % of crop area over main cropland area (2007 stats)

4. 2. Hard classifications Ground survey + high resolution imagery  land cover maps Ground survey – Segments & along the road HR imagery – AWIFS – Landsat 5 – TM – IRS LISS 3 – RapidEye (RE) – Problem: Heavy cloud conditions in spring 10 Classes: Artificial-urban, winter (winter & spring wheat, rapeseed), spring (winter & spring barley), summer (maize, potatoes, sugar beet, sunflower, soybean, vegetables), family gardens, other crops, woodland, permanent grassland, bare land, water & wetland For the sub-pixel approach  merged winter & spring

5. 3. Choice of high resolution LC map 1Artificial 2'winter' 3'spring' 4'summer' 5Family garden 6Other crops 7woodland 8Perm grassland 9Bare land 10Water-wetland 75 scenes from 04 to 09 Overall accuracy MLP: 63% Stripes at the overlapping areas of TM-frames  Masked Landsat 5-TM (Kiev Oblast)

6. Hard classifcation map Creation of AFIs Reference AF for each LR pixel derived from hard classification INPUT data MODIS/VGT NDVI time series [april-september] - 11 images OUTPUT data Estimated AFIs (Area Fraction Images) NEURAL NETWORK Crop area % = f(NDVI) Training pixels sampled every 8 rows & columns TRAINING DATA LR soft classification: Principle grassland Other crops winter crops summer crops forest Family gardens

7. 4. Neural network: choice of the number of hidden nodes Evolution of the determination coefficient of the regression, at pixel level, between the reference area fraction (derived from HR hard classification) and the soft classification area faction for all classes pooled together as a function of the number of hidden nodes Model chosen: 4 hidden nodes in Ukraine, 11 MODIS NDVI, 8 classes -> 88 weights Ukraine 2010

8. 5. Assessment of crop area fractions at pixel level For Kiev oblast Winter & spring crops merged All pixels considered for the correlation

9. 6. Assessment at district level Correlation of class area fractions aggregated at district level for the Kiev oblast district

10. 7. Use of soft classification with AFS Predicting crop % from ground survey from class (winter crops, summer crops) % from soft classification Area fractions aggregated per segment Winter wheat Maize cropR2 maize0.55 Winter wheat0.44 soybean0.31 sunflower0.10 Sugar beet0.05 rapeseed0.03 potato0.01

11. II. North China Plain

12. 1. Objective Problem: – difficulty of acquiring HR imagery at optimal timing – ground survey = cost and time consuming – Estimation crop areas for ongoing season Solution ? – Use the sub-pixel classification approach. – Spatial and temporal extrapolation of a Neural Network

13. 2. Temporal extrapolation 20052006200720082009201020112012… 1. Perform a hard classification (ground survey, collection of high resolution data, classify) for a certain year. 2.Use the hard classification and moderate resolution data of the reference year to train a neural network. 3. Apply the neural network on moderate resolution data for the consecutive years. Condition : Interannual variation in temporal NDVI response is minor and has little effect on Neural Network performance (recognizing crop specific NDVI profiles). 1. Perform a hard classification (ground survey, collection of high resolution data, classify) for a certain year. 2.Use the hard classification and moderate resolution data of the reference year to train a neural network. 3. Apply the neural network on moderate resolution data for the consecutive years. Condition : Interannual variation in temporal NDVI response is minor and has little effect on Neural Network performance (recognizing crop specific NDVI profiles). Reference year

14. 3. Spatial extrapolation Training area 1. Perform a hard classification (ground survey, collection of high resolution data, classify) on a reference area. 2.Use the hard classification to train a neural network. 3. Apply the neural network on moderate resolution data for a wider area. Condition : Phenological differences over the region of interest is minor and has little effect on Neural Network performance (recognizing crop specific NDVI profiles). 1. Perform a hard classification (ground survey, collection of high resolution data, classify) on a reference area. 2.Use the hard classification to train a neural network. 3. Apply the neural network on moderate resolution data for a wider area. Condition : Phenological differences over the region of interest is minor and has little effect on Neural Network performance (recognizing crop specific NDVI profiles).

15. 2005 2006 2007 2009 2005: 2 TM classif 2006: 1 LISS classif 2007: 3 TM-classif 2 AWiFS classif 2009: 1 TM classif 1 AWiFS classif name = YYYYwt_sen YYYY = year wt = winter wheat sen = sensor 2005: 2 TM classif 2006: 1 LISS classif 2007: 3 TM-classif 2 AWiFS classif 2009: 1 TM classif 1 AWiFS classif name = YYYYwt_sen YYYY = year wt = winter wheat sen = sensor 4. Collection of hard classifications Work on winter wheat estimations

16. 2005wt_tmb + SPOT-VGT 2005 NEURAL NETWORK (05_tmh_NN) 2005wt_tmh + SPOT-VGT 2005 NEURAL NETWORK (05_tmb_NN) 2006wt_li + SPOT-VGT 2006 NEURAL NETWORK (06_li_NN) 2007wt_awa + SPOT-VGT 2007 NEURAL NETWORK (07_awa_NN) 2007wt_awg + SPOT-VGT 2007 NEURAL NETWORK (07_awg_NN) 2007wt_tm + SPOT-VGT 2007 NEURAL NETWORK (07_tm_NN) 2007wt_tmb + SPOT-VGT 2007 NEURAL NETWORK (07_tmb_NN) 2007wt_tmh + SPOT-VGT 2007 NEURAL NETWORK (07_tmh_NN) 2009wt_aw + SPOT-VGT 2009 NEURAL NETWORK (09_aw_NN) 2009wt_tm + SPOT-VGT 2009 NEURAL NETWORK (09_tm_NN) 5. CALIBRATION 10 dekadal SPOT-VGT images [11 feb – 31 may] Training pixel sampling: every 8th row/column Use the same year as the hard classification

17. 6. APPLICATION NEURAL NETWORK NEURAL NETWORK INPUT = SPOT VGT data [11 feb – 31 may] 200520062007200820092010 OUTPUT = 6 Estimated Area Fraction images for winter wheat, one for each season 20052006 20072008 2009 2010 4 hidden nodes

18. 1.Visual inspection crop patterns – Temporal and spatial consitency check 2.Compare with official statistics – Collection of official statistics for the 60 districts – [1994-2009] 7. VALIDATION

19. 7. VALIDATION – visual inspection crop patterns Unstable crop pattern – 05_tmb_NN Stable crop pattern – 07_awg_NN 2005200620072009 2010

20. For the 60 districts: – Collection of official statistics [1994-2009] – Caclulate the estimated crop areas for every sub- pixel classification per district 7. VALIDATION – Compare with official statistics

21. Example: Comparisson Estimated Area Fraction for 2005 with official statistics for 2005 For the Neural Network 05_tmb_NN Example: Comparisson Estimated Area Fraction for 2005 with official statistics for 2005 For the Neural Network 05_tmb_NN 1. Spatial2. Scatterplot

22. 7. VALIDATION – Compare with official statistics 07_awa_NN, 07awg_NN, 09tm_NN  stable

23. 7. VALIDATION – Compare with official statistics Neural Network: 05_tmb_NN R 2 = 0.13R 2 = 0.07 R 2 = 0.0 R 2 = 0.21R 2 = 0.10 Neural Network: 09_tm_NN R 2 = 0.64R 2 = 0.77R 2 = 0.68 R 2 = 0.74R 2 = 0.75 LOW PERFORMANCE HIGH PERFORMANCE

24. Heterogeneous reference dataset single TM-frame HR classifications are not capable to act as an input for the sub-pixel approach over the North China Plain mountain areas are known by strong underestimations – Winter wheat area is less important – Impact on results – Remove from further analysis 8. DISCUSSION

25. 8. DISCUSSION – phenology Phenological differences – spatialy – North China Plain N-Z extent > 1000 km – Different agro-ecological zones – Reflected in NDVI-profile – Impact on crop specific Neural Network spectral profile recognition – divide North China Plain a priori in agro-ecological zones Zhoukou Shi Liaocheng Shi Baoding Shi Time window used in sub-pixel approach

26. Phenological differences – temporaly – Climatological conditions differ from year to year 8. DISCUSSION – phenology

27. Spatial and temporal extrapolation of the sub-pixel approach is limited to hard classifications with a sufficient high coverage of the region of interest AWIFS classification of 2007 and TM classification of 2009 provide the best training data The extent of the North China Plain is too large to use a single high resolution image as reference for the sub- pixel approach for the whole region Divide the North China Plain in smaller agro-ecological zones or provinces Focus on wheat dense areas 9. Conclusion

28. THANK YOU