Soil moisture monitoring in a temperate peatland using multi-sensor remote sensing and linear mixed effects
Remote Sensing , Volume 10 - Issue 6
The purpose of this research was to use empirical models to monitor temporal dynamics of soil moisture in a peatland using remotely sensed imagery, and to determine the predictive accuracy of the approach on dates outside the time series through statistically independent validation. A time series of seven Moderate Resolution Imaging Spectroradiometer (MODIS) and Synthetic Aperture Radar (SAR) images were collected along with concurrent field measurements of soil moisture over one growing season, and soil moisture retrieval was tested using Linear Mixed Effects models (LMEs). A single-date airborne Light Detection and Ranging (LiDAR) survey was incorporated into the analysis, along with temporally varying environmental covariates (Drought Code, Time Since Last Rain, Day of Year). LMEs allowed repeated measures to be accounted for at individual sampling sites, as well as soil moisture differences associated with peatland classes. Covariates provided a large amount of explanatory power in models; however, SAR imagery contributed to only a moderate improvement in soil moisture predictions (marginal R2 = 0.07; conditional R2 = 0.7, independently validated R2 = 0.36). The use of LMEs allows for a more accurate characterization of soil moisture as a function of specific measurement sites, peatland classes and measurement dates on model strength and predictive power. For intensively monitored peatlands, SAR data is best analyzed in conjunction with peatland Class (e.g., derived from an ecosystem classification map) to estimate the spatial distribution of surface soil moisture, provided there is a ground-based monitoring network with a sufficiently fine spatial and temporal resolution to fit the LME models.
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Millard, K, Thompson, D.K. (Dan K.), Parisien, M.-A. (Marc-André), & Richardson, M. (2018). Soil moisture monitoring in a temperate peatland using multi-sensor remote sensing and linear mixed effects. Remote Sensing, 10(6). doi:10.3390/rs10060903