## 3 Materials and Methods
### Data Processing and Analysis  
The analysis of drought and heat effects on vegetation in Germany was based on the assessment of deviations of MODIS 16-day EVI measurements from their long-term (19-year, 2000-2018) averages. Data processing was conducted on the Google Earth Engine platform ([[https://eartheengine.google.com/](https://eartheengine.google.com/)]([https://eartheengine.google.com/](https://eartheengine.google.com/))) [34], from which the MODIS data was also provided. The MODIS dataset was mosaicked and resampled from sinusoidal to the WGS 84 reference coordinate system by the nearest neighbor method during processing. To avoid the analysis of MODIS observations of low quality, the MODIS \"pixel reliability\" layer was considered, and all pixels that did not show best quality (class 0) were excluded from further processing. In order to restrict the analysis of drought and heat effects to vegetated areas, all EVI values below zero were excluded from further processing, and additionally all areas were masked where the CORINE land cover/use dataset of the year 2012 indicated non-vegetated land cover classes \"urban\" and \"water.\" Further--to avoid confusions of drought impacts with land cover/use change related effects--all areas that showed land use/cover changes in the CLC change products for 2000-2006 and 2006-2012 were masked. For each 16-day composite, the 18-year median EVI was calculated based on the MODIS time series (see Figure 2). Subsequently, the deviation of each individual composite of the multi-annual time series from the respective long-term composite median of the same 16-days period was calculated. These 16-day deviations were then averaged to monthly and seasonal (i.e., spring: March-May; summer: June-August; autumn: September-November) intervals in order to minimize outlier effects. Each monthly anomaly was built with the two 16-day anomalies, for which most days lay within the month of interest. This led to one 16-day anomaly being included in two monthly means (April and May), as the 16-day composite of the 23 rd of April consists of eight days in April and eight days in May (see Table 1). The MODIS data shows data gaps during winter due to higher cloud coverage and poorer lightning conditions and the deviations are probably highly affected by snow cover. Therefore, only the spring to autumn periods (March-November) of every year were used in this study. The pixel-based seasonal and monthly deviations were aggregated to counties (\"Landkreise\", NUTS-3) by averaging. In addition, deviations from the long-term median, which exceed two standard deviations (positive and negative), were analyzed. The number of MODIS pixels in Germany, for which the EVI value exceeds the pixel-based median plus and minus two standard deviations, respectively, were counted for each monthly composite. These pixel counts were calculated as percentages of the total amount of valid pixels to enable an investigation of the area in abnormal condition (positive and negative) within each month between 2000 and 2018. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Monthly Anomaly** & **Start Date of Composite 1** & **Start Date of Composite 2** \\ \hline Jan & 1 January & 17 January \\ Feb & 2 February & 18 February \\ Mar & 6 March & 22 March \\ Apr & 7 April & 23 April \\ May & 23 April & 9 May \\ Jun & 25 May & 10 June \\ Jul & 26 June & 12 July \\ Aug & 28 July & 13 August \\ Sep & 29 August & 14 September \\ Oct & 30 September & 16 October \\ Nov & 1 November & 17 November \\ Dec & 3 December & 19 December \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Indication of the respective two 16-day composites used to create each monthly anomaly. For leap years, the start dates are one day earlier from March onward. The 23 rd of April is used for the April composite and for the May composite. Figure 2: Schematic flowchart.