Modelling soil moisture in a high‐latitude landscape using LiDAR and soil data

• Published in: Earth Surface Processes and Landforms
• Main Authors: Kemppinen, Julia, Niittynen, Pekka, Riihimäki, Henri, Luoto, Miska
• Other Authors: Koneen Säätiö, Academy of Finland, Helsingin Yliopisto
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2017
• Subjects: Tundra
• Online Access: http://dx.doi.org/10.1002/esp.4301; https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Fesp.4301; https://onlinelibrary.wiley.com/doi/pdf/10.1002/esp.4301

Abstract Soil moisture has a pronounced effect on earth surface processes. Global soil moisture is strongly driven by climate, whereas at finer scales, the role of non‐climatic drivers becomes more important. We provide insights into the significance of soil and land surface properties in landscape‐scale soil moisture variation by utilizing high‐resolution light detection and ranging (LiDAR) data and extensive field investigations. The data consist of 1200 study plots located in a high‐latitude landscape of mountain tundra in north‐western Finland. We measured the plots three times during growing season 2016 with a hand‐held time‐domain reflectometry sensor. To model soil moisture and its temporal variation, we used four statistical modelling methods: generalized linear models, generalized additive models, boosted regression trees, and random forests. The model fit of the soil moisture models were R 2 = 0.60 and root mean square error (RMSE) 8.04 VWC% on average, while the temporal variation models showed a lower fit of R 2 = 0.25 and RMSE 13.11 CV%. The predictive performances for the former were R 2 = 0.47 and RMSE 9.34 VWC%, and for the latter R 2 = 0.01 and RMSE 15.29 CV%. Results were similar across the modelling methods, demonstrating a consistent pattern. Soil moisture and its temporal variation showed strong heterogeneity over short distances; therefore, soil moisture modelling benefits from high‐resolution predictors, such as LiDAR based variables. In the soil moisture models, the strongest predictor was SAGA (System for Automated Geoscientific Analyses) wetness index (SWI), based on a 1 m 2 digital terrain model derived from LiDAR data, which outperformed soil predictors. Thus, our study supports the use of LiDAR based SWI in explaining fine‐scale soil moisture variation. In the temporal variation models, the strongest predictor was the field‐quantified organic layer depth variable. Our results show that spatial soil moisture predictions can be based on soil and land surface properties, yet the ...