## 4 Discussion  
### Soil Village Detection  
Our main goal was detecting autumn tillage and vegetation cover from earth observations for large-scale agricultural monitoring. Several previous studies such as [26; 28; 36] on tillage detection with SAR imagery alone or fusion of SAR and MSI have concentrated on tillage intensity classification. However, few studies have detected off-season land cover classes on broader scale including also vegetation covered land cover types [37; 51; 52]. Shortage of studies on higher granularity of winter-time land cover classes indicates that the task is difficult. We observed significant and consistent improvement in classification accuracy by combining SAR and MSI data. The result is well in line with those obtained both in crop tillage classification [36; 37] as well as in other EO tasks [34; 48; 53; 54; 55]. Since data fusion is easy with the proposed object representations, only requiring georeferencing and simple early fusion, we strongly recommend routinely using both sources for this task. When using a single image capture method, MSI was here clearly more accurate than SAR, but this observation needs to be interpreted with care because our experiment was carried out on images with at most 10% occlusion. During a normal year, the time window for making observations on tillage operations is short and typically cloudy across Southern Finland, and MSI alone could not be trusted. Somewhat low classification accuracy for the classes _conservation tillage, autumn crop_ and _stubble with companion crop_ is explained by three main reasons: (a) the amount of data for these classes is smaller compared to the other three, (b) under certain conditions some of the classes are virtually indistinguishable, and (c) the ground truth data is imperfect due to mislabeling. Regarding the difficulty of the problem, the _autumn crop, stubble_ and _stubble with companion crop_ all have variable amounts of plant growth that makes the classes highly similar in terms of all EO sources. Also, _ploughed_ and _conservation tillage_ may resemble each other after snow melt in April on certain soil types, especially where stalks have been highly decomposed. Regarding mislabeling, the reference data were prepared with automatic rule-based labeling, which is inherently error-prone. Whereas contradictory examples (duplicates) can be removed, mislabeling remains a practical challenge due to inevitable simplifications when building the rules. Imperfections in the underlying information on agricultural  
Figure 11: Normalized confusion matrix for classification of fused SAR + MSI image objects for the full set of annotated classes. practices imply that each class membership has different reliability. For example, planting of autumn crop is explicitly declared by the farmer, thus having really high reliability, whereas ploughing is merely inferred by applying a long classifying set of rules to the information. _Conservation tillage_ is an example of low reliability. Farmers explicitly declare to apply conservation tillage in October as it is subsidized, but if weather conditions are not suitable for filling after the declaration date, fields may remain covered by vegetation. As vegetation cover is considered the more sustainable option, \"no-till\" is not subject to a penalty. As a result, probability of _stubble_ field samples mislabeled as _conservation tillage_ is high. For improving the quality of the reference data, one could consider unsupervised clustering techniques as in [56] to discover structure and compare with supervised techniques and the assumed labels. During data exploration we performed an initial trial with spectral and K-nearest neighbor clustering on the density representation of the objects, the results of which did suggest some internal structure within the given classes of the dataset. Additionally, specific geospatial properties such as latitude can be significant in Finland with varying microclimates affecting vegetation cover and could be used as additional features to improve the accuracy.