1
Method for automated classification with
INSPIRE data and Sentinel-2 satellite
imagery: case remote crop monitoring
www.spatineo.com
Joona Laine / Spatineo
INSPIRE CONFERENCE 2018 Antwerp

Background and Introduction
● EU is supporting farmers with Common Agricultural Policy
(CAP), including direct subsidy payments for farmers
● EU member countries have to control the validity of CAP
subsidy applications
● Crop type identification is one of the tasks
● According to EC1
, Crop monitoring could be carried out using
remote sensing imagery, such as Sentinel-2
● The overall accuracy (OA) should be >95%2
1. European Comission (2014). COMMISSION IMPLEMENTING REGULATION (EU) No 809/2014
2. JRC TECHNICAL REPORTS: 1st draft of the technical guidance on the decision to go for substitution of OTSC by monitoring

Objective
The main objectives of our work:
● to generate method for automated classification with various
kind of vector or raster spatial data
● to investigate whether it was possible to reliably identify the
crop growing in land parcels by using machine learning
methods and Sentinel-2 satellite imagery in Finland

Datasets
● Sentinel-2 products
○ For preprocessing: L1C images and cloud mask, L2A snow mask
○ For classification: L2A images for 10m and 20m bands
○ All available L2A products during the thermal growing season of
from the area of whole Finland covering land parcels
● Agricultural land parcels obtained from Finnish Agency for
Rural Affairs (INSPIRE land cover)
○ Land parcels from CAP subsidy applications 2017 and 2018
○ Supervised land parcels, ~5% of the CAP application parcels
○ Formed 10 crop type classes according to suggestion of Finnish
Agency for Rural Affairs

Challenges: weather
● Challenging weather conditions for optical sensors
● In Finland partly cloudy images have to be used as well

Challenges: class imbalance
● Class distribution highly
imbalanced
● Two dominating classes
● Possible solutions:
○ Resampling
○ Model class weighting

Challenges: class imbalance
● Class distribution highly
imbalanced
● Two dominating classes
● Possible solutions:
○ Resampling
○ Model class weighting

Crop monitoring: overview

Masks
● 4 masks used:
○ Sentinel-2 cloud mask
○ Generated cloud mask3
○ Sentinel-2 snow mask
○ Generated cloud shadow
mask4
● Masks filter out non-clear
pixels from the images
3. S2cloudless algorithm https://medium.com/sentinel-hub/improving-cloud-detection-with-machine-learning-c09dc5d7cf13
4. Algorithm presented at https://github.com/samsammurphy/cloud-masking-sentinel2/blob/master/cloud-masking-sentinel2.ipynb
Basemap by National Land Survey of Finland

Extracting and Preprocessing
● Calculating the bandwise statistical features of the parcels
from each available image during set time period
● Temporal interpolation of the extracted values
● Filtering out parcels with insufficient data
● Selection and calculation of the variables that produce highest
accuracy
=> Make the data usable for machine learning algorithms

Classification algorithm selection
● Multiple different ML
algorithms tested
● MLP and SVM produced
some of the best results
● New methods tested and
constantly
Multilayer Perceptron (MLP)5
Support Vector Machines (SVM)6
5. Gardner, M.W and S.R Dorling (1998). “Artifcial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences”
6. Mountrakis, Giorgos, Jungho Im, and Caesar Ogole (2011). “Support vector machines in remote sensing: A review”

Results for 2017
● OA: 89%, K: 0.80
● Supervised parcels used
for training
● Time period: 1st of May to
1st of September
1:Broad bean, 2:Pea, 3:Beet, 4:Fallow, 5:Spring rapeseed, 6:Spring cereal, 7:Grass, 8:Potato, 9:Turnip rape, 10:Winter cereal

Results for 2018
● OA: 93%, K: 0.88
● Two models:
○ One for labels 5 and 6
○ One for other labels
● Trained and evaluated with
CAP subsidy application
parcels using 8-fold cross
validation
● Time period: 1st of May to 1st
of August

Results for 2018: calibrated
● OA: 95%, K: 0.90
● Models calibrated with conf.
level 0.807
● 97.9% (913126 out of 932920)
parcels classified after
calibration
7. Schmedtmann, J. and M. L. Campagnolo (2015). “Reliable crop identification with satellite imagery in the context of Common Agriculture Policy subsidy control”

Conclusions
● Developed method works even under challenging conditions
● Imbalance of class distribution is major problem but it can be
solved
● Crop classes should be grouped based on biological and
phenological similarities if possible => policy needs to take
this into account
● No method will be perfect
○ Not all parcels can be classified
○ Timely results are required to allow farmers to react to false
negatives
○ No ground truth available

Future directions
● Modifying the workflow to further meet the EC technical
guidance suggestions8
● Classifying with more ML algorithms
● Chaining multiple different ML algorithms
● Classifying with different crop class division and class
formation
● Using other remote sensing sources, such as Sentinel-1
● Utilizing the method for other applications with INSPIRE data
8. JRC TECHNICAL REPORTS: 1st draft of the technical guidance on the decision to go for substitution of OTSC by monitoring

17www.spatineo.com
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