## 2 Materials and Methods
### Remote Sensing-Based Surface Reflectance Data  
Surface reflectance (SR) is the most fundamental remotely sensed surface parameter, serving as the primary input for a wide range of higher-level land products that depend on solar reflectance characteristics such as vegetation indices, leaf area index, land cover classification, land cover change, and surface albedo [38]. SR is a measure of the fraction of incoming solar radiation that is reflected from the Earth's surface. It is strongly dependent on the optical properties and structures of the surface. For example, vegetation, soil, water, and snow exhibit distinct reflectance characteristics; snow-covered surfaces typically show much higher reflectance, particularly in the visible spectrum, compared to non-snow-covered land [39], while near-infrared reflectance is specifically sensitive to the vegetation type [40]. Especially in urban areas, SR exhibits significant spatial heterogeneity based on urban geometry surface component variation such as the design, structure, and material of buildings and surfaces in cities (e.g., building shapes, materials, road layouts) [41]. SR is sensitive to changes in land surface caused by both natural and anthropogenic factors [42], and can therefore serve as a proxy for the nature and extent of reflection experienced by downwelling solar radiation at a given location.  
As illustrated in Figure 1, surface-reflected radiation forms a significant component of the the total radiative flux at a given point. There could be a radius of surrounding reflectivity that could be affecting and contributing to our SSI measurements, particularly in densely built urban landscapes. Therefore, we augmented the model's input features with SR data. The data were sourced from the Harmonized Sentinel-2 MSI: MultiSpectral Instrument, Level-2A (SR) product [43]. The MultiSpectral Instrument (MSI) onboard the European Space Agency's Sentinel-2 satellite constellation is a wide-swath, high-resolution imaging sensor designed for Earth observation across multiple spectral bands. It provides orthorectified, atmospherically corrected SR in 13 spectral bands spanning wavelengths from 442.3 nm to 2202.4 nm, with spatial resolutions of 10 m, 20 m, and 60 m depending on the band. The revisit interval is 5 days. The data are available to query on the Google Earth Engine (GEE) platform. This particular SR product was chosen for several reasons. Firstly, the relatively higher spatial resolution (\(\sim\)\(\mathcal{O}(10\,\mathrm{m})\)) and spectral resolution ensure more accurate and detailed sampling. Secondly, the revisit interval of 5 days is comparatively much smaller than the temporal scale of the inter-day variability in SR [44]. This is especially true in urban settings where surface characteristics remain relatively unchanged over such short intervals, and seasonal trends dominate over daily fluctuations. Finally, its availability on GEE allows spatial and temporal data retrieval to be handled on the cloud,removing the burden of computationally expensive spatial interpolation and large data downloads on the host device. This significantly reduces processing and storage overhead on resource-constrained sensor modules deployed in the field, while still enabling near-real-time integration with local sensor measurements for on-device predictive modeling with edge computing.  
We chose SR bands B1, B2, B3, B4, B5, B6, B7, B8, and B8A, which fall within the spectral range of interest for our SSI estimation as inputs to the model.  
The Table 1 below lists all the input features used as predictors to the SSI estimation model.