Ukraine. Space Research Institute

Transcription

1 Ukraine Demonstration objectives Space Research Institute Todeliver consistent information at field scalefor Ukraine using Sen2Agri system: Crop mask; Crop type; Crop status; Early crop area indicators, detection of crop anomalies To validate the products of Sen2Agri system To introduce the products to the authoritiesand to be prepared for preoperational use of the system

2 Use case (additional) Winter wheat crop productionestimation based on crop area estimation and crop state monitoring with LAI and NDVI

3 Site features Location: Ukraine Intensive agriculture area. Main crop types: winter wheat, winter rapeseed, spring barley, maize, soybeans, sunflower, sugar beet, and vegetables Field size: from 30 to 250 ha Crop calendar: Winter: September July; Summer: April October Cloud coverage can be very frequent during the growing season Topography: mostly flat, slope: 0% to 2% Soils: different kinds of chernozems Soil drainage is ranging from poor to well-drained. Irrigation infrastructure is limited Climate and weather: humid continental

4 EO and in-situ dataset -1 S2 acquisitions along the season up to 1800 scenes (March October, 2016) About 15% of S2 images cloudfree

5 EO and in-situ dataset -1 S1 acquisitions along the season up to 1000 scenes (March October, 2016)

6 EO and in-situ dataset -1 Field campaign and in-situ dataset (LC/LU and crop types) Train set 5536 parcels Test set 2153 parcels

7 EO and in-situ dataset -1 Field campaign and in-situ dataset (LAI validation) Indirect method for biopar estimation (DHP imagery, CAN-EYE); Total amount 121 samples April July 2016; 9 expeditions; JECAM test site (Pshenichne); 3 main crops: Winter wheat (42 ESU); Maize (37 ESU); Soy beans (42 ESU)

8 Sen2-Agri products assessment L3B

9 Sen2-Agri products assessment L4A Mykolaiv region, 2016

10 Sen2-Agri products assessment L4A

11 Sen2-Agri Winter Crop Map Crop\Are a (10 3 ha) Official statistics* Sen2- Agri SRI Winter wheat Winter rapeseed *without AR_Crimea

12 GEE Winter Crop Map (derived by SRI) Crop\Are a (10 3 ha) Official statistics* Sen2- Agri SRI Winter wheat Winter rapeseed *without AR_Crimea

13 Comparison Sen2-Agri & SRI winter crop maps Sen2-Agri Winter Crop Map Sentinel-2, Natural Color 01/05/2016 GEE Winter Crop Map (SRI) Comparison Sen2-Agri & SRI

14 Comparison of winter wheat areas Winter Wheat 800 Statistic Sen2Agri SRI area, 10 3 ha Regions

15 Comparison of winter rapeseed areas Winter rapeseed area, 10 3 ha Statistic Sen2Agri SRI Regions

16 Winter wheat production, Sample use case Winter wheat area estimates based on classification map vs statistics Winter wheat yield estimates based on time series of VI and official statistics Winter wheat production

17 Maize production, Sample use case Maize area estimates based on classification map vs statistics Maize yield estimates based on Hydrometcenrte forecast and official statistics

18 Comparison Sen2-Agri & SRI crop maps Sen2-Agri crop map 2016 (Kiev Region) Winter wheat Spring cereals Maize Sunflower Soybeans Sen2-Agri crop map 2016 (Kiev Region) Crop, % PA UA Winter wheat Spring cereals Maize Sunflower Soybeans OA 93.6

19 Comparison Sen2-Agri & SRI crop maps SRI crop map 2016 (Kiev Region) Winter wheat Spring cereals Maize Sunflower Soybeans Sen2-Agri crop map 2016 (Kiev Region) Crop, % PA UA Winter wheat Spring cereals Maize Sunflower Soybeans OA 93.6 SRI crop map 2016 (Kiev Region) Crop, % PA UA Winter wheat Spring cereals Maize Sunflower Soybeans OA 94

20 Comparison Sen2-Agri & SRI crop maps Agreed part of two maps Winter wheat Spring cereals Maize Sunflower Soybeans Sen2-Agri crop map 2016 (Kiev Region) Crop, % PA UA Winter wheat Spring cereals Maize Sunflower Soybeans OA 93.6 SRI crop map 2016 (Kiev Region) Agreed part of two maps Crop, % PA UA Winter wheat Spring cereals Maize Sunflower Soybeans OA 94 Crop, % PA UA Winter wheat Spring cereals Maize Sunflower Soybeans OA 97.6

21 Comparison Sen2-Agri & SRI crop maps Crop Areas 2016 (Kiev Region) Statistics Sen2-Agri SRI Area (10 3 ha) Winter wheat Spring cereals Maize Sunflower Soybeans Statistics 197,2 79, ,2 173,3 Sen2-Agri 254, , , , ,82718 SRI 189, , , , ,81557

22 Experiments on algorithms We compared classification results for different algorithms and input data: Atmospheric correction (Sen2Cor, MACCS, TOA) Band combinations Best sensors and bands combination Classifier

23 Best atmospheric correction method Satellite data: 11 Sentinel-2 scenes Ground data: 563 ground samples (train and test sets) Training set Test set TOA (OA=80.7%) Sen2cor (OA=80.6%) MACCS (OA=82.7%)

24 Best bands combination for optical data Combination Overall Accuracy, % S2 10m (g,r,nir) 70.9 S2 10m (b,g,r,nir) 74.5 L8 (without blue band) 75.8 L S2 10m(warp 20) 71.1 S2 10m(warp 20) + band S2 10m(warp 20) + band S2 10m(warp 20) + band S2 10m(warp 20) + band 8A 71.4 S2 10m(warp 20) + band S2 10m(warp 20) + band S2 20m 76.3 S2 10m(warp 20) + S2 20m 78.2

25 Best sensors and bands combination Satellite data: 4 Sentinel-2 scenes 21 Sentinel-1 scenes 2 Landsat-8 scenes Ground data: 728 ground samples (train and test sets) Combination Overall Accuracy, % S2 + L S2 + L8 without blue bands 76.9 S1 10m 77 S1 10m + S2 10m 79.4 S1 + S S1 + S2 + L S1 (OA=77%) S1+S2 (OA=79.4%) S1+S2+L8 (OA=79.9%)

26 Best classifier # Class ENN RF CNN UA,% PA,% UA,% PA,% UA,% PA,% 1 Artificial Winter wheat Winter rapeseed Spring crops Maize Sugar beet Sunflower Soybeans Forest Grassland Water Wetland Winter barley Buckwheat OA,% / Kappa 79.9 / / / 0.8

27 Discussion 1. MACCS is the best method for atmospheric correction with gain + 2% for OA. 2. Using all available bands for optical data showed the best result compare to other bands combination (OA = 78.2%). 3. Combination of all satellites outperforms all other combination (OA = 79.9), but the obtained classification map has 30 m resolution. 4. CNN is the best classifier (OA = 82.3%) compare to common approaches (Random Forest and ensemble of neural networks).

28 Feedback on system and products Did you have the opportunity to operate the Sen2Agri system? Yes, we are operating Sen2-Agri system What is your experience? Positive, but system still non end-user friendly UI improvements for data search and jobs manipulations are necessary(below in details) Tricky to manage the system in case of some errors 20 Tb is too tiny storagefor Ukraine What are your recommendations for the future for the system? To add products filtration on dates and extent(starting from L2A) especially on Products tab and on Custom job tab To add understandable progress bar for L4A and L4B execution(with time estimates) To improve manipulations with Executing Jobs (workflow management tools with UI)

29 Feedback on system and products What are your recommendations for the future for the system (cont)? To add functionality for launch processing for sub-site(for example for Kiev region only when the whole Ukraine as site is specified) To optimize system performance -it takes too much time for the territory of Ukraine To improve system user guide especially for dealing with technical troubles To add automated checks of product integrity in case of power failure What are your recommendations for the future for the Sen2Agri products? To include SAR data (Sentinel-1) most of the images for 2016 were clouded To add product merging over specified administrative units(on vector boundaries) To consider percentage of cloudiness that Sen2-Agri system downloads - higher results acquisition

30 Feedback on system and products Sites L3B (LAI products) S2A products

31 Sample Products on SRI premices Cropland mask and crop type maps (35UPQ tile sample) April July, 2016 L4B L4BL4B Non-masked masked Consorcium L4A 3rd Sen2Agri User Workshop - Rome, June 2017

32 Sample products from the system installed in SRI Vegetation indices (NDVI, LAI) NDVI (17/07/2016) LAI (17/07/2016)

33 Recommendations Do you consider the demonstration phase relevant for testing the operational capabilities of the Sen2-Agri system? YES Do you consider the demonstration phase relevant with respect to your objectives? YES Which improvements do you expect in the future (priority ranking)? To add functionality mentioned in feedback section What are the top priority you would recommend to contribute to the system uptake by your team? Integration of SAR data