## 2 Materials and Methods
### Seasonal NDVI Timeseries-Based Classification of Patch-Types at US-LA3 from HLS  
We used HLS data containing two main surface reflectance products: (1) Settnial-2A and Sentinel-2B and (2) Landsat ([[https://lpdaac.usgs.gov/products/hlsl30v002/](https://lpdaac.usgs.gov/products/hlsl30v002/)]([https://lpdaac.usgs.gov/products/hlsl30v002/](https://lpdaac.usgs.gov/products/hlsl30v002/)) (accessed 1 August 2023) and [[https://lpdaac.usgs.gov/products/hlss30v002/](https://lpdaac.usgs.gov/products/hlss30v002/)]([https://lpdaac.usgs.gov/products/hlss30v002/](https://lpdaac.usgs.gov/products/hlss30v002/)) (accessed on 1 August 2023) for Landsat and Sentinel datasets, respectively). Our site falls within tile 16 RBT.  
For each pixel, the Normalized Difference Vegetation Index was calculated using the HLS-retrieved red and NIR bands:  
\[\text{NDVI}\ =(\text{NIR}\ -\ \text{Red})/(\text{NIR}\ +\ \text{Red}) \tag{1}\]  
Using Equation (1), we calculated the NDVI index for each pixel of the HLS dataset and retrieved a timeseries for each year in the range of 2016-2023.  
We matched each pixel locations in the HLS images during the growing season surrounding August 2018 to the corresponding pixels in the upscaled WorldView image. We further identified the locations selected as \"pure pixels\". For each pure pixel, we extracted the seasonal timeseries of the NDVI during that growing season. We averaged these time-series per patch-type (keeping all the temporal points within the season and averaging over all pure pixel locations of the same patch-type within each time frame). This average timeseries was defined as the characteristic NDVI seasonal \"standard\" per patch (Figure 3). We then used the Dynamic Time Warping (DTW) approach [23] to calculate the similarity between the seasonal timeseries of each of the other HLS pixels (including locations that were not selected as \"pure\" during the growing season of 2018, and all pixel locations in the HLS images of other years) and these characteristic seasonal standards. We identified the dominant patch-type as the type with the characteristic timeseries that is the most similar to the one observed in each pixel location, each year. The area of interest for ELM simulations is the vegetation cover within the fetch of the Eddy Covariance tower, which was \(\sim\)250 m in our case. Thus, the area within the tower's observation footprint can be roughly defined as a 480 m \(\times\) 480 m square centered at the tower. This area was included in the patch-type classification used as an input for the ELM. This classification was conducted for each of the years of 2016-2023. Figure 3 shows the timeseries of HLS-classified patch-types for US-LA3.  
Figure 2: Result of patch-type classification using K-means approach: (**a**) WorldView-3 initial high-resolution classification (1.24 m grid) and (**b**) WorldView-3 upscaled classification to HLS resolution (30 m grid). Circular dots represent examples of “pure pixels” of uniform land cover used to derive the standard NDVI timeseries. The yellow square indicates a 480 \(\times\) 480 m\({}^{2}\) area centered at the flux tower, corresponding roughly with the area sampled by the flux tower observations.