2022 journal article

Characterizing soil water content variability across spatial scales from optimized high-resolution distributed temperature sensing technique


co-author countries: United States of America 🇺🇸
author keywords: Distributed Temperature Sensing; Soil thermal properties; Soil moisture; Single-Probe Heat-Pulse; Statistical learning; Remote sensing
Source: Web Of Science
Added: February 13, 2023

Fiber-optic Distributed Temperature Sensing, when combined with the Single-probe Heat-pulse technique can measure soil moisture (θ) across spatial scales. The key limitation of this system is in obtaining the relationship between soil thermal conductivity (λ) and θ for a specific field. Using the Department of Energy Atmospheric Radiation Measurement (ARM) site, this study tested a new methodology to account for the spatial variability in the λ-θ relationship using a Gaussian processes model. The resulting accurate θ measurements (RMSE = 0.03 m3m−3) were used to characterize the spatial variability of θ across scales and to develop an empirical equation that can correct for the changes in the θ spatial variability observed at different spatial resolutions. In addition, the number of required samples to accurately characterize θ and its variability over scales ranging from 5 m and 350 m were estimated. These findings provide key information to scale soil moisture from centimeters to hundreds of meters for process understanding.