TY - GEN
T1 - Multi-Source Remote Sensing of Soil Moisture Profiles - A Case Study Over Monticello, Utah
AU - Du, Jinyang
AU - Kimball, John S.
AU - Jarchow, Christopher J.
AU - Steckley, Deborah
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - The U.S. Department of Energy Office of Legacy Management (DOE LM) is investigating options for future management of selected uranium mill tailings disposal cell covers as vegetated, evapotranspiration (ET) covers. ET limits drainage of water through the cell cover profile, while soil structure and drying by plants can increase radon diffusion; therefore, soil water content is a key performance parameter. This study used theoretical simulations to analyze the sensitivity of multi-frequency radar backscatter to soil moisture (SM) at different depths of an in-service DOE LM disposal cell. A machine-learning approach was then developed using Google Earth Engine to integrate multi-source observations and estimate SM across six soil layers from depths of 0-2 m. The model predictors included backscatter observations from satellite Synthetic Aperture Radar, vegetation and temperature products from optical-infrared sensors, and accumulated rainfall data from Daymet. The model was trained using in-situ SM measurements from 2019 and validated using data from 2014-2018 and 2020-2021. The approach produced accurate SM estimates for the six soil layers (R-values from 0.75 to 0.94; RMSE from 0.003 to 0.017 cm3/cm3; bias ~0.00 cm3/cm3). Additionally, the approach captured seasonal SM variability and spatial heterogeneity at 30-m resolution. The machine-learning based multi-source data fusion approach may characterize soil moisture dynamics at DOE LM disposal sites better than in situ measurements alone.
AB - The U.S. Department of Energy Office of Legacy Management (DOE LM) is investigating options for future management of selected uranium mill tailings disposal cell covers as vegetated, evapotranspiration (ET) covers. ET limits drainage of water through the cell cover profile, while soil structure and drying by plants can increase radon diffusion; therefore, soil water content is a key performance parameter. This study used theoretical simulations to analyze the sensitivity of multi-frequency radar backscatter to soil moisture (SM) at different depths of an in-service DOE LM disposal cell. A machine-learning approach was then developed using Google Earth Engine to integrate multi-source observations and estimate SM across six soil layers from depths of 0-2 m. The model predictors included backscatter observations from satellite Synthetic Aperture Radar, vegetation and temperature products from optical-infrared sensors, and accumulated rainfall data from Daymet. The model was trained using in-situ SM measurements from 2019 and validated using data from 2014-2018 and 2020-2021. The approach produced accurate SM estimates for the six soil layers (R-values from 0.75 to 0.94; RMSE from 0.003 to 0.017 cm3/cm3; bias ~0.00 cm3/cm3). Additionally, the approach captured seasonal SM variability and spatial heterogeneity at 30-m resolution. The machine-learning based multi-source data fusion approach may characterize soil moisture dynamics at DOE LM disposal sites better than in situ measurements alone.
KW - GEE
KW - SAR
KW - contaminant
KW - machine learning
KW - soil moisture
UR - http://www.scopus.com/inward/record.url?scp=85181569616&partnerID=8YFLogxK
U2 - 10.1109/IGARSS52108.2023.10282070
DO - 10.1109/IGARSS52108.2023.10282070
M3 - Conference contribution
AN - SCOPUS:85181569616
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 3178
EP - 3181
BT - IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023
Y2 - 16 July 2023 through 21 July 2023
ER -