## 2 Materials and Methods
### STARFM Parameters and Input Text Creation  
STARFM can either use one or two base pairs. Based on [PERSON] and [PERSON]'s [32] satisfactory results, we opted for one base pair to be used. In addition, to coincide with [PERSON] and [PERSON] [32], the surface reflectance uncertainties were set to 0.002 for the Red and 0.005 for the NIR bands, and 40 for the spectral similarity test. However, we doubled the search distance to 1500 m to help compensate for the heterogeneous urban environment. As recommended by the software README file, the spatial flag was set to on. STARFM requires an input text file that specifies all of these input parameters and the names of the base pair images and the predicted image. Two custom Python scripts automated this, one for creating synthetic images for days which do not coincide with a base pair, and one for creating synthetic images for days which do coincide with a base pair. Three separate sets of input text files were created for each base pair selection method.  
The overall approach for these scripts first identified the base pairs for the Red bands, or the Landsat and MODIS scenes that were closest to each other in date. To do this, the scripts compared the middle date (8 th day) of the 16 day MODIS acquisition time to the Landsat acquisition date. If the difference between the Landsat and MODIS dates was between \(-4\) and \(3\), this signified that the MODIS scene had the middle acquisition day closest to the Landsat acquisition date and the two images should be a base pair. If the difference was outside of this range, the MODIS scene became a coarse resolution image that had a high resolution image predicted for it. For each predicted scene identified, these scripts created a text file with a unique name, a unique name for the predicted high resolution image was specified, and the name of the MODIS scene for the predicted day was written to the text file. Next, the names of the base pairs that had the Landsat acquisition date closest to the middle acquisition day of the predicted MODIS scene were added to the text file as base pairs. Last, the file had all constant input parameters written to it. The scripts then copied the file, changed the names from representing Red to NIR images, and the process was repeated for the next MODIS image (see Figure 4).  
A similar script created the input files for prediction days that coincide with base pair dates. The reason for this prediction is mainly to have a 30 m file with the same name as the rest of the predicted days, but also to account for and apply any underlying biases that may or may not have existed in the methodology in a uniform manner. The main differences are that during the initial pairing, the predicted day assumed the MODIS name as well as the base pair day. STARFM software version 1.1.2 was then run by looping through the input texts for both the informal selection criteria and the 95% threshold criteria. The 90% threshold criteria was processed with STARFM version 1.2.1. Version 1.2.1 improves computational efficiency by allowing predictions for multiple dates with one run when the pair image is the same. It can also run in parallel computing mode [30]. STARFM can fill small gaps or clouds in the prediction MODIS image using information from neighbor pixels if Landsat pixels are valid. These predictions are useful but may be less accurate. The version 1.2.1 constrains the predictions for the cloud pixels in the MODIS pair and prediction images. Predictions for clear pixels in Landsat and MODIS images are the same from both versions.