## 3 Available Data
We used the B10 spectral band (cirrus cloud detection) from Level 1 images, and B1, B2, B3, B4, B6, B8, B11, and AOT spectral bands from Level 2 images [18]. We also used several normalized difference indices to indicate the prevalence of bare soils (NDSI), flourishing vegetation (NDVI), water (NDWI), and moisture (NDMI) in a given pixel [19]. (An overview of these spectral bands is provided in Table 1). Since the low number of our field measurements (data records) did not permit the application of deep learning (which could directly process raw images), we had to construct the satellite input attributes manually. This first exploratory experiment considers the reflectance values of pixels no further away than 500 m from each of the fifty chosen measurement locations. Each image pixel in such a circular measurement area was represented by one time series of reflectance values for each spectral band captured by the satellite in each six-week field-measurement period. Given that we used 13 spectral bands (including the derived ones), a total of 13 time series of basic reflectance values for each considered pixel and each six-week measurement period was obtained. Construction of one such time series for one pixel is symbolically shown in Figure 2.  
Figure 2 indicates that basic reflectance values themselves may not suffice--their differences may actually carry more useful information. Therefore, from each basic reflectance series, several derived ones were created. The first of them was the simple difference series, which contained the difference of two consecutive values in the original reflectance series. Since these differences can be negative, we also constructed an absolute difference series, which only contained positive values. Then, a normalized difference series was created, where the same difference was divided by the number of days separating the two consecutive satellite photos. Finally, we also constructed an absolute value version of the \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Name** & **Scale** & **Pixel Size** & **Wavelength** & **Description** \\ \hline AOT & 0.001 & 10 m & & Aerosol optical thickness \\ \hline B1 & 0.0001 & 10 m & 443.9 nm & Aerosols \\ \hline B2 & 0.0001 & 10 m & 496.6 nm & Blue \\ \hline B3 & 0.0001 & 10 m & 560.0 nm & Green \\ \hline B4 & 0.0001 & 10 m & 664.5 nm & Red \\ \hline B6 & 0.0001 & 20 m & 740.2 nm & Red Edge 2 \\ \hline B8 & 0.0001 & 10 m & 835.1 nm & NIR \\ \hline L1 B10 cir & 0.0010 & 60 m & 1373.5 nm & Cirrus \\ \hline B11 & 0.0001 & 20 m & 1613.7 nm & SWIR 1 \\ \hline NDVI (normalized difference vegetation index) & 0.0001 & 10 m & NDVI = (B8 \(-\) B4)/(B8 \(+\) B4) \\ \hline NDWI (normalized difference water index) & 0.0001 & 10 m & NDWI = (B3 \(-\) B8)/(B3 \(+\) B8) \\ \hline NDSI (normalized difference soil index) & 0.0001 & 20 m & \
\begin{tabular}{c} NDSI = \\ (B3 \(-\) B11)/(B3 \(+\) B11) \\ \end{tabular} \\ \hline Moisture index & 0.0001 & 20 m &
\begin{tabular}{c} moisture index = \\ (B8 \(-\) B11)/(B8 \(+\) B11) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of used satellite spectral bands including normalized difference indices. normalized difference series, termed absolute normalized difference. In this way, five time series for each spectral band and each pixel in the measurement area were created. Given that there were 13 spectral bands, this process led to 5 \(\times\) 13 = 65 time series per pixel. Considering the high number of pixels in each measurement area, we decided to aggregate these time series by six basic statistical functions: average, minimum, maximum, and three main quantiles (25%, 50%, and 75%). In order to get rid of individual pixels, we aggregated the values twice: first along the time axis for each pixel separately, and then spatially, for all pixels in a given measurement area. Since all possible combinations of the six basic statistical functions were permitted, this produced 6 \(\times\) 6 = 36 unique double aggregation combinations.