Estimation of local scale snow depth and snow water equivalent over a winter period in northern Finland with an object-based ensemble machine learning approach

Seasonal snow occurs in high latitude and altitude regions of the globe, and throughout the winter and post-winter period can rapidly alter the makeup of these regions. Commonly studied snow features include snow depth, snow water equivalent (SWE), and snow density. These features can be measured on the ground while also capable of being remotely sensed with airborne and spaceborne instruments. Individually both approaches can be utilized to assess these snow features. However, field-based techniques tend to be limited to smaller spatial scales while remotely sensed methods tend to excel at larger spatial scales. At local scales (10 km²) a hybrid technique can be employed to better estimate such snow features. This can be realized by utilizing machine learning modeling to upscale field measurements with high-resolution remote sensing imagery. We performed this over a 10 km² area in Sodankylä, Finland by combining repeat field snow depth and SWE data with 2-meter resolution WorldView-2 (WV-2) and Light Detection and Ranging (LiDAR) data over a winter period between the middle of December 2022 to the end of April 2023. Snow depth field measurements were upscaled to a local spatial scale with an object-based machine learning approach before harnessing the estimated snow depth products to permit an enhanced estimation of SWE to the same local scale from more limited field measurements. Snow density was then determined from the predicted snow depth and SWE. A weighted ensemble approach of multiple machine learning models proved to be most effective compared to the chosen base models of Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Network (ANN), and Multiple Linear Regression (MLR). The fluctuating outputs from these features over the winter period were found to strongly connect to dry and wet peatbogs and with forests containing carbon and mineral surface soils.