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Soil moisture estimation: a comparative analysis of remote sensing methods using ground penetrating radar

Lima Ferro De Paoli, Yan
(2024)

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LimaFerroDePaoli_10562300_2024.pdf
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Details

Supervisors
Lambot, Sébastien ; Matthieu Taymans
Faculty
Faculté des bioingénieurs
Degree label
Master [120] en sciences agronomiques et industries du vivant, à finalité spécialisée: Geo-Information Science and Earth Observation for Environmental Modelling and Management
Abstract
Accurately estimating soil moisture is essential for effective water resource management, crop production, and environmental monitoring, especially in the context of climate change and increasing drought conditions. This study evaluates the qualitative performance of two approaches to access soil moisture information, using Synthetic Aperture Radar (SAR) data and optical/thermal data sources. Specifically, a SAR-based change detection and the Temperature-Vegetation Dryness Index (TVDI) are selected and benchmarked against Ground Penetrating Radar (GPR)-based estimations, which are known for their high spatial resolution. The research is conducted on the Grand Villers field, part of the DuraTechFarm project in Wallonia, Belgium, with GPR measurements taken between August and September 2023. Additionally, a two-year comparison (2022-2023) between the SAR-based and TVDI methods is performed. The findings reveal a strong correlation (75.86%) between recent precipitation events (within the last two days) and high values estimated by the SAR-based approach. For the TVDI, 92.86% of high values correspond to the absence of rainfall within the last two days. When comparing the SAR-based with the GPR-based estimation, a good alignment between them is observed, with a Spearman correlation of 0.78 and a p-value of 0.02. In terms of spatial distribution, both TVDI and GPR-based results indicate similar patterns, showing a concentration of moisture in the center of the field on September 11th. This study highlights the challenges of validating satellite-based soil moisture estimations and underscores the need for more high-resolution, time-continuous measurements to bridge the gap between remote sensing data and ground truth.