03 Sen2-Agri 3rdUW DemonstrationFeedback Ukraine · OA 93.6 Winter wheat Spring cereals Maize...

3rd Sen2Agri User Workshop - Rome, 28-29 June 2017

Space Research InstituteDemonstration objectives

� To deliver consistent information at field scale for 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 authorities and to be

prepared for preoperational use of the system

Ukraine


Use case (additional)

Winter wheat crop production estimation

based on crop area estimation and crop

state monitoring with LAI and NDVI


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


EO and in-situ dataset -1

• S2 acquisitions along the season – up to 1800 scenes (March – October, 2016)

About 15% of S2 images cloudfree



• S1 acquisitions along the season – up to 1000 scenes (March – October, 2016)



• Field campaign and in-situ dataset (LC/LU and crop

types)

Train set – 5536 parcels

Test set – 2153 parcels



• 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)


Sen2-Agri products assessment – L3B


Sen2-Agri products assessment L4A Mykolaiv region, 2016


Sen2-Agri products assessment L4A


Sen2-Agri Winter Crop Map

Crop\Are

a (103 ha)

Official

statistics*

Sen2-

AgriSRI

Winter

wheat5367.2 7027.3 6481.9

Winter

rapeseed344 451.3 448.8

*without AR_Crimea


GEE Winter Crop Map (derived by SRI)

Crop\Are

a (103 ha)

Official

statistics*

Sen2-

AgriSRI

Winter

wheat5367.2 7027.3 6481.9

Winter

rapeseed344 451.3 448.8

*without AR_Crimea


Comparison Sen2-Agri & SRI winter crop maps

Sen2-Agri Winter Crop Map Sentinel-2, Natural Color 01/05/2016

Comparison Sen2-Agri & SRIGEE Winter Crop Map (SRI)


Comparison of winter wheat areas

0

100

200

300

400

500

600

700

800

are

a,

10

3h

a

Regions

Winter Wheat

Statistic Sen2Agri SRI


Comparison of winter rapeseed areas

0

5

10

15

20

25

30

35

40

45

50

are

a,

10

3h

a

Regions

Winter rapeseed

Statistic Sen2Agri SRI


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


Maize production, Sample use case

Maize yield estimates based on Hydrometcenrteforecast and official statistics

Maize area estimates based on classification map vs statistics


Comparison Sen2-Agri & SRI crop maps

Sen2-Agri crop map 2016 (Kiev Region)

Crop, % PA UA

Winter wheat 96.4 98

Spring cereals 94.2 84.9

Maize 89 95.1

Sunflower 98.9 92.7

Soybeans 90.6 90

OA 93.6

Winter wheat

Spring cereals

Maize

Sunflower

Soybeans




SRI crop map 2016 (Kiev Region)

Crop, % PA UA



Maize 89 95.1

Sunflower 98.9 92.7

Soybeans 90.6 90

OA 93.6

Crop, % PA UA

Winter wheat 93.7 95.9


Maize 95.2 93.6

Sunflower 97.3 97.9

Soybeans 92.2 94.3

OA 94

Winter wheat

Spring cereals

Maize

Sunflower

Soybeans





Agreed part of two maps

Crop, % PA UA



Maize 89 95.1

Sunflower 98.9 92.7

Soybeans 90.6 90

OA 93.6

Crop, % PA UA

Winter wheat 93.7 95.9


Maize 95.2 93.6

Sunflower 97.3 97.9

Soybeans 92.2 94.3

OA 94

Crop, % PA UA

Winter wheat97.1 98.7

Spring cereals 94 87.2

Maize 99 96.4

Sunflower 99.8 99.6

Soybeans 94.8 98.8

OA 97.6

Winter wheat

Spring cereals

Maize

Sunflower

Soybeans



Agreed part of two maps



Winter wheat Spring cereals Maize Sunflower Soybeans

Statistics 197,2 79,1 268 161,2 173,3

Sen2-Agri 254,63817 22,28391 306,07209 217,55205 190,82718

SRI 189,15012 56,55195 318,64464 234,94455 226,81557

0

50

100

150

200

250

300

350

Are

a (

10

3h

a)

Crop Areas 2016 (Kiev Region)

Statistics Sen2-Agri SRI


• We compared classification results for different

algorithms and input data:

– Atmospheric correction (Sen2Cor, MACCS, TOA)

– Band combinations

– Best sensors and bands combination

– Classifier

Experiments on algorithms


Best atmospheric correction method

Satellite data:� 11 Sentinel-2 scenesGround data:� 563 ground samples

(train and test sets)

TOA (OA=80.7%) Sen2cor (OA=80.6%) MACCS (OA=82.7%)

Training setTest set


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

L8 74.6

S2 10m(warp 20) 71.1

S2 10m(warp 20) + band 5 75.9

S2 10m(warp 20) + band 6 72.6

S2 10m(warp 20) + band 7 71.5

S2 10m(warp 20) + band 8A 71.4

S2 10m(warp 20) + band 11 74.6

S2 10m(warp 20) + band 12 73.8

S2 20m 76.3

S2 10m(warp 20) + S2 20m 78.2


Best sensors and bands combination

Satellite data:� 4 Sentinel-2 scenes� 21 Sentinel-1 scenes� 2 Landsat-8 scenesGround data:� 728 ground samples

(train and test sets)

S1 (OA=77%) S1+S2 (OA=79.4%) S1+S2+L8 (OA=79.9%)

Combination Overall Accuracy, %

S2 + L8 78.6

S2 + L8 without blue bands 76.9

S1 10m 77

S1 10m + S2 10m 79.4

S1 + S2 79.5

S1 + S2 + L8 79.9


Best classifier

# Class ENN RF CNN

UA,% PA,% UA,% PA,% UA,% PA,%

1 Artificial 74.4 95 53.9 85.2 75.3 51.5

2 Winter wheat 80.2 81.6 73.2 69.6 78.9 86.5

3 Winter rapeseed 95.2 18.9 96.8 73.2 94.7 62.8

4 Spring crops 45.1 32 5.6 4.6 22.7 8.7

5 Maize 93.4 94.6 90 94.7 92.1 94.6

6 Sugar beet 74.7 100 72 99.9 71.6 100

7 Sunflower 88.5 94.9 90.4 88.6 87.7 93.1

8 Soybeans 95.5 79.3 96.8 73.1 94.4 76.7

10 Forest 99.1 99.7 99.2 98.2 98.3 99.7

11 Grassland 87.9 78.4 86.8 80.4 88.5 83.5

13 Water - - 95 100 80.3 98.1

14 Wetland 74.9 66.4 52.5 73.6 70 65.6

15 Winter barley 23.1 31.5 10.7 28.6 28.1 45.5

18 Buckwheat 18 68.5 75.4 99.9 21.8 39.7

OA,% / Kappa 79.9 / 0.77 79.5 / 0.77 82.3 / 0.8


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).


• 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 storage for 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)

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




Sites

S2A productsL3B (LAI products)


Cropland mask and crop type maps (35UPQ tile sample) – April – July, 2016

Sample Products on SRI premices

L4A

L4B

Non-maskedL4B

masked

L4B

Consorcium


Sample products from the system installed in SRI

• Vegetation indices (NDVI, LAI)

NDVI (17/07/2016) LAI (17/07/2016)


• 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

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03 Sen2-Agri 3rdUW DemonstrationFeedback Ukraine · OA 93.6 Winter wheat Spring cereals Maize...

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