Scalable Sentinel-1 Processing Framework

Synthetic aperture radar (SAR) data is powerful for snowmelt tracking but painful to work with at scale — inconsistent acquisitions, heavy preprocessing, and no off-the-shelf tooling for comparing SAR time series against physically based snow models.

Overview

I built a scalable software framework that automates Sentinel-1 acquisition, quality control, and multitemporal time-series analysis, and integrates the results directly with physically based snow models — turning a manual, one-off analysis workflow into a repeatable pipeline.

Engineering Challenges

SAR backscatter is noisy and acquisition geometry varies scene to scene, so naive time-series comparisons are dominated by artifacts rather than signal. Automated quality control — flagging and excluding bad acquisitions before they contaminate a melt-progression estimate — mattered as much as the melt-detection algorithm itself.

Architecture

The framework automates acquisition and preprocessing, applies quality-control filters tuned for snowmelt detection, and runs multitemporal time-series analysis that gets compared directly against physically based snow model output — closing the loop between remote sensing observation and model validation.

Lessons Learned

This work became the foundation for a peer-reviewed evaluation of Sentinel-1 melt progression across a warm alpine snowpack (see Publications) — a reminder that building good infrastructure for a hard measurement problem is often the actual research contribution, not just a means to one.

Future Directions

Extending the quality-control approach to additional SAR platforms and integrating results directly into the operational snow prediction pipeline.