## 4 Brief Outline of Our Approach  
In general, our approach conformed to the CRISP-DM model, a de facto industry standard process for data mining, schematically depicted in Figure 3.  
Our earlier research reported in [15] represents one iteration of the CRISP-DM cycle, in which the target attributes were the long-term upper bounds of variables S, Sr, and g02, and the input attributes were the SHMU input attributes described in Section 3. Since  
Figure 3: Schematic depiction of data mining according to CRISP-DM.  
Figure 2: **(Left) Series of Sentinel 2 satellite photos of the same geographical area with one chosen pixel in the top right corner. (Right) Symbolic depiction of the time series of the reflectance values in one spectral band for the chosen pixel, with an abrupt change in the middle (indicated by the dashed arrow), which might signify an invasive human activity, such as plowing or harvesting.**  
this iteration was not successful, the work described here can be taken to represent the second iteration of the same CRISP-DM process, initiated by the return from the Evaluation phase back to the Data Understanding phase, in which satellite imagery was identified as a promising source of new and more informative input attributes, and the methodology was changed to estimating the field-measured values of target variables rather than their long-term upper bounds.  
The construction of satellite attributes described in the previous section, then, comprises the first part of the Data Preparation phase, which ends with the attribute selection described in the next section. The choice of suitable regression models and their subsequent training jointly represent the Modelling phase. Finally, Section 6 (Discussion) and Section 7 (Conclusions) present the contextualized evaluation of the results.  
The methodological shift to modelling the field-measured values of target variables rather than their long-term upper bounds helps to capture their seasonal variation. This raises an interesting question: how much of that variation is stable, i.e., could be reliably predicted purely on the basis of time, even without satellite imagery? In order to answer that question, we decided to pass to machine learning models the date of each six-week field measurement. In the text, we call it the measurement date, but in fact it is the Day of Year (1-365) referring to the day in the middle of the concerned six-week measurement period. We would like to point out that this attribute does not uniquely identify individual field measurements, because it has the same value for all 50 measurement locations in a given six-week period (since all of the field measurements were recorded synchronously). Therefore, there is no danger of overfitting regression models on the basis of this new attribute.  
For the sake of completeness, we note that we also passed to machine learning models the five SHMU input attributes, as well as geographical coordinates of each measurement location (latitude, longitude). Thus, the total number of input attributes rose to 2348. Our general hypothesis was that the attributes derived from satellite imagery would help to significantly improve the accuracy of models.  
In order to evaluate their accuracy, we combined n-fold cross validation with standard regression criteria, namely: the correlation coefficient, the relative absolute error, and the root relative squared error. Due to the limited number of available records, we used 40-fold cross validation, which ensured sufficient stability of the estimated model accuracy. One of the most serious drawbacks of this kind of validation is that it takes a lot of time, since the training process has to be repeated 40 times. On the other hand, resultant estimates represent the accuracy of trained models precisely, stably, and objectively.