## 6 Discussion and Future Directions  
In the current study, we have built and validated our framework for SSI estimation based on data collected within the Dallas-Fort Worth climate region, which is humid subtropical with hot summers and generally mild winters [61] and is predominantly urban. This naturally excludes any extensive rural landscapes or heavily snow/ice-covered terrains where phenomena such as snow-cloud confusion [62] become relevant, and where snow cover, in combination with canopy structure, influences reflectance properties in distinct ways [39]. In future work, we aim to expand the data collection across diverse climatic and land use zones to assess the broader applicability of our framework. While the present work showcases the effectiveness of our proposed framework at SSI estimation using the data collected so far from November 2024 to May 2025, since SSI exhibits seasonal variation, the data collection is ongoing, and we intend to incorporate additional data to extend the model's applicability across the full year. While we have attempted to curate our low-cost sensor ensemble to include sensors with long-term operational stability, over time, sensors, especially low-cost ones, can experience drift where their measurements slowly become less accurate due to environmental exposure or internal hardware degradation. This can compromise the readings obtained from these, and consequentially the SSI inferred from these readings. Therefore, once the sensor modules are deployed, it is imperative that periodic checks are carried out to ensurereliable performance with ongoing routine calibration and validation. It is also worth noting that the proposed framework for SSI estimation is bound by the capabilities and limitations of the reference instrument used for calibration. While we have utilized an NIST-calibrated Konica Minolta CL-500A conforming to both DIN and JIS standards in order to enable research-grade SSI measurements, it is limited by its range of wavelength measurement, i.e., 360 nm to 780 nm. While deep learning architectures such as convolutional neural networks are well suited for modeling complex spatial or sequential patterns such as those found in spectral data, they were not explored in the current study. A primary focus was on achieving deployment efficiency on resource-constrained platforms, where tree-based models like RFRs provide a favorable balance between performance and computational cost. They perform well with smaller datasets, whereas neural networks typically require larger amounts of data to achieve similar accuracy [63]. Many CNN architectures require specialized hardware like GPUs, which can be impractical to facilitate on low-cost devices meant for deployment at scale. Tree-based algorithms have also been widely and successfully applied in the literature for estimating environmental parameters from low-cost sensor data and in various remote sensing tasks [64; 65]. While not explored within the scope of the present study since the predictive performance of our approach using RFR was found to be sufficiently strong, a quantitative comparison between RFR and deep learning approaches would be a valuable direction for future research. The Sentinel-2 SR product provides reflectance values every five days, captured at a single time during the day. Although SR is a directional quantity influenced by solar illumination geometry, and thus varies diurnally with changes in solar zenith angle, the use of once-daily Sentinel-2 SR data proves effective for predicting SSI at various times of the day, as is apparent from the performance metrics of the model. This is because SR serves as a robust proxy for surface characteristics relevant to irradiance. As detailed in Section 2.2, SR encodes intrinsic land surface properties, such as land cover type, vegetation density, and built environment features, all of which strongly modulate local solar reflectance and, consequently, SSI.