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, Riihimaki, Henri, Luoto, Miska
• Other Authors: Department of Geosciences and Geography
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2020
• Subjects: soil wetness; tundra; LiDAR; SAGA wetness index (SWI); spatial modelling; GENERALIZED LINEAR-MODELS; TOPOGRAPHIC WETNESS INDEX; REMOTE-SENSING DATA; CLIMATE-CHANGE; SPECIES DISTRIBUTION; BOREAL FOREST; SIERRA-NEVADA; ACTIVE-LAYER; VEGETATION; TEMPERATURE; 1171 Geosciences; 1172 Environmental sciences; 114 Physical sciences; Tundra
• Online Access: http://hdl.handle.net/10138/313172

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 1m(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 temporal models ...