## 2 Data and Methods
### Simulation Experiment
#### 2.2.2 Feature Generation and Data Augmentation  
We selected the resampled, weighted, and aggregated SR and the LST values from the Landsat bands, indices calculated from these bands, and the set of meteorological parameters taken from the reanalysis AgERA5 as predictors of a regression model to predict daily NEE, which is a product from Fluxnet stations (see Section 2.1.1).  
In this study, a set of additional features was created to increase the flexibility of the regression model and expand the interactions between the base predictive features and the target variable. First, all possible ratios were created between 6 Landsat bands and LST. Thus, we have five ratios for blue band, four ratios for green band, three ratios for red band, two ratios for NIR, and one ratio for SWIR1 (e.g., blue/green, blue/red, blue/NIR, 15 features in total). When we computed vice versa, we obtained five ratios for SWIR2, four ratios for SWIR1, three ratios for NIR, two ratios for red, and one ratio for green (e.g., green/blue, red/blue, NIR/blue, 15 features in total). Another set of features is a list of ratios between bands and LST (blue/LST, green/LST etc. 12 features in total). As a result, 42 features were added to the predictive dataset. Second, from meteorological variables such as solar radiation, 2 m air temperature, and precipitation flux, additional features were also created. For solar radiation, cumulative and mean radiation over five different periods were calculated: four days, one week, two weeks, three weeks, and four weeks (ten features as a result), which may show delayed phenological effects on sub-seasonal and seasonal scales. The same set of parameters was prepared for 2 m air temperature. For precipitation flux, cumulative precipitation for the last one, two, three, four, and five days was calculated (five features). Thus, 25 more features were added to the base predictive features. In total, 87 features were prepared for further training.  
Another issue in the dataset collection process is the availability of clear-sky observations from the Landsat constellation. After merging the dates with available satellite  
Figure 3: Left panels are maps of calculated indices for the same date; right panels are maps of daily weight functions for different dates.  
images and good quality NEE observations, fewer training points remain, mainly due to the images' cloud cover and other artifacts. We decided to apply training sample augmentation techniques to solve this problem.  
The procedure for increasing the training sample is based on the assumption that the surface reflectance on the day of satellite measurements and the neighboring days will be similar given similar weather conditions. Weather variables, i.e., precipitation, air temperature, short-wave, and incoming long-wave radiation, were determined using data from local meteorological measurements at each eddy-covariance station. From these four parameters, a weather profile was generated for the whole time series of daily NEE estimations, which was then compared with a similar profile on the day of satellite observations. If the \(R^{2}\) value was greater than 0.9, it was assumed that the day had similar weather conditions, and images of the original day could be applied to such a day. As a result, we used the same Landsat scenes for remote sensing parameters at augmented days from the original observation date, while applying the weighted footprint function for that specific day. The time considered in this study was a week before the date of Landsat observation. As a result, the size of the training sample was increased by about a factor of 3.