Soil Moisture Modelling with Limited ISMN Data | Remote Sensing #Sciencefather #Researcherawards



Introduction

Soil moisture is a key component of Earth’s critical zone and influences diverse processes in hydrology, agriculture, and ecosystem stability. In regions where long-term ground-based monitoring is not feasible, numerical models supported by short-term in situ observations can provide a reliable alternative for estimating soil moisture dynamics. Remote sensing precipitation datasets further support this modelling approach, especially in data-scarce environments. The present research focuses on integrating limited in situ observations with numerical modelling and satellite precipitation products to better characterize the long-term soil moisture behaviour in the Northern Territory, Australia.

Importance of Soil Moisture in Environmental and Hydrological Research

Soil moisture plays a fundamental role in governing infiltration, runoff, evapotranspiration, and vegetation health, making it a central variable for environmental modelling. In semi-arid and tropical savannah climates, such as the Northern Territory, the spatial and temporal variability of soil moisture is particularly significant. Limited monitoring networks often restrict comprehensive understanding; therefore, the use of short-term measurements combined with numerical modelling enhances the capacity to evaluate ecosystem responses, agricultural sustainability, and water balance under changing climatic conditions.

Development and Calibration of the HYDRUS-1D Numerical Model

The HYDRUS-1D model, based on solving Richards’ equation for variably saturated flow, serves as the backbone of this research. The study utilized van Genuchten soil hydraulic parameters, which were optimized using measured soil moisture to achieve realistic simulations. Given that initial estimates from catalogues differ for the same soil texture class, a detailed uncertainty analysis was incorporated to reduce calibration bias. The calibrated model demonstrated strong performance during the 2012–2013 period, achieving low RMSE and MAE values and a reliable correlation with observed measurements.

Independent Validation of Soil Moisture Simulation Performance

After calibration, the HYDRUS-1D model was subjected to rigorous validation across three different time periods from 2013 to 2016. The model exhibited stable predictive performance, with RMSE values ranging between 0.035 and 0.054 m³/m³ and strong correlation coefficients. These results confirm the model’s ability to capture soil moisture dynamics across seasonal variability and wet–dry cycles. The consistency of MAE and R values across independent validation windows reflects the robustness and adaptability of the modelling framework.

Evaluation of Remote Sensing Precipitation Products for Soil Moisture Estimation

Publicly available remote sensing precipitation datasets such as CHRS-PERSIANN, CHRS-CCS, and CHRS-PDIR-Now were assessed for their applicability in driving soil moisture simulations in data-scarce regions. Although all products tended to underestimate soil moisture, CHRS-CCS showed superior performance with lower average errors and higher correlation with observed values. These findings underline the importance of selecting appropriate satellite rainfall products when modelling hydrological processes in tropical savannah ecosystems.

Integration of Modelling and Remote Sensing for Long-Term Soil Moisture Assessment

The study highlights the effectiveness of integrating numerical modelling with remote sensing inputs to overcome data scarcity in remote regions. By coupling HYDRUS-1D simulations with optimized soil parameters and satellite-derived rainfall inputs, the research provides a comprehensive method for understanding soil water balance over extended periods. This integrated approach supports better decision-making for natural resource management, agricultural planning, and climate impact assessments in the Northern Territory, Australia.

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#Sciencefather, #Reseacherawards, #SoilMoistureResearch, #HydrologicalModelling, #HYDRUS1D, #RemoteSensingData, #PERSIANN, #CCS, #PDIRNow, #RichardsEquation, #VanGenuchtenModel, #SoilHydraulics, #TropicalSavannah, #AustraliaResearch, #EnvironmentalMonitoring, #WaterBalance, #ClimateImpactStudies, #NumericalSimulation, #SoilDataScarcity, #PrecipitationEstimation, #HydroInformatics, #EarthCriticalZone,

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