Shivam Rawat

I am a PhD student in Radar Remote Sensing at SLU, Umeå, with a research focus on how radar observations can be used to better understand water dynamics in boreal forests. My work integrates continuous ground-based radar measurements from the BorealScat-2 tower with Sentinel-1 SAR time series, meteorological observations, and field measurements such as dendrometer and sap flow data. The overarching goal is to link variations in radar backscatter to ecohydrological processes, including evapotranspiration, canopy wetness, stem water storage changes, and the influence of weather drivers (e.g., temperature, vapor pressure deficit, precipitation, and wind).

I hold a Bachelor’s degree in Electronics & Communication Engineering and a Master’s degree in Computer Science & Engineering, which together provide a strong foundation in signal processing, programming, and machine learning. During my Master’s thesis, I worked with optical remote sensing, applying Sentinel-2 imagery and machine learning classifiers for post-flood impact assessment. I also previously worked as an Associate Software Engineer at Accenture, which strengthened my software development skills and my approach to building robust, reproducible data-processing workflows.

My doctoral research focuses on integrating multi-source observations to interpret radar signatures in terms of forest water exchange processes in boreal ecosystems.

Key themes include:

• Linking BorealScat-2 radar backscatter dynamics (time series and vertical profiles) with tree water status and meteorological drivers.
• Comparing tower radar observations with Sentinel-1 SAR to assess consistency across scales and improve interpretation of satellite backscatter over forests.
• Multi-sensor data fusion using meteorological variables and in-situ measurements (dendrometer and sap flow) to study evapotranspiration-related processes.
• Developing data-driven models (regression and machine learning) to predict or fill gaps in Sentinel-1 backscatter using dense tower-radar and meteorological observations.
• Building reproducible processing pipelines for SAR/radar time series analysis, quality control, and statistical evaluation.