We are about to finish a R&D project, where we developed and tested crop classification methodology specifically suited for Estonian agricultural, ecological and climatic conditions. We relied mostly on Sentinel-1 and -2 data and used neural network machine learning approach to distinguish 28 different crop types. Results are promising and the methodology is ready for operational service to automate another part of agricultural monitoring.
Using machine learning in crop type classification is not new, and definitely not a revolutionary breakthrough - already for decades different classifiers (Support Vector Machine, Decision Trees, Random Forest and many more) have been used in land cover classification. Recently also neural networks, the wunderkind of machine learning and image recognition, are widely used in crop discrimination. Satellite data, as the main input to classification models, has no serious alternatives, since our aim is to implement it on worldwide scale and in applications, which run near real time. So, why even get excited about another crop type classification study, which exploits same methods and datasets as tens of previous studies?
I can give you one reason. Estonia has been very successful in following European Commission (EC) guidelines and rules in modernizing the EU Common Agricultural Policy. In 2018 EC adopted new rules that allow to completely replace physical checks on farms with a system of automated checks based on analysis of Earth observation data. The same year Estonian Agricultural Registers And Information Board (ARIB) launched the first nation-wide fully automated mowing detection system, which uses Sentinel-1 and Sentinel-2 data and where the prediction model inside the system is developed by KappaZeta. The system has been running for 3 years, it has significantly reduced the amount of on-site checks and increased the detection of non-compliances. In short – saved Estonian and EU taxpayers’ money. Automated crop discrimination is the next step in pursuing the above-mentioned vision and will probably become the foundation of all agricultural monitoring. With proved and tested methodology, it’s highly likely that Estonia will take this next step in the very near future and launch it again on the nationwide level. This is definitely a perspective to be excited about.
Now, let’s see how we tackled “the good old” crop classification task.
Although algorithms and methods
are important to make a difference in prediction model performance, the
training data is the most valuable player in this game. In Estonia all farmers
who want to be eligible for subsidies need to declare crops online (field
geometry + crop type label). This open dataset
is freely accessible to everyone and has the permission to re-use and
redistribute both commercial and non-commercial purposes. Since the crop type
labels are defined by farmers and most of them are not double-checked by ARIB,
there can be mistakes (according to ARIB’s estimations, less than 5%). Therefore,
for additional validation we ran our own cluster analysis on time-series to
filter out obvious outliers in each class.
For Sentinel-1 images preprocessing we have developed our own processing chain to produce reliable time-series for several features. From the previous studies it’s known that features (channel values and indexes) from Sentinel-2 images combined with features from Sentinel-1 images (coherence, backscatter) give better classification results than any of these features separately.
We used data from 2018 and 2019 seasons (altogether more than 200 000 parcels) and aggregated all crop type labels into 28 classes which were defined by the need of the ARIB.Model architecture
Model architecture was rather simple – input layer, flatten layer, three fully connected dense layers (two of them followed by batch normalization layer) and output (Figure 3). Our experiment with adding 1D CNN layer after input didn’t improve results significantly. More complicated ResNet (residual neural network) architecture increased training time by approx. 30%, but results were similar to a linear neural network.
Classification resultsF1 score on validation set (9% of all dataset) was 0.85 and on test set (2% of all dataset) 0.84. In 10 classes the recall was more than 0.9 and in 16 classes more than 0.8. See more from Figure 4 and 5.
Some features are more important than others
They are not. We found that the 5
most important features are Sentinel-1 backscatter (s1_s0vh, s1_s0vv),
NDVI, TC Vegetation and PSRI from Sentinel-2. To our
surprise, soil type and precipitation sum before satellite image acquisition
had low relevance.
If you are more interested about this study, our Sentinel-1 processing pipeline or machine learning expertise, then feel free to get in touch. We have the mentality to share not hide our experience and learn together on this exciting journey.