Date of Award
2023-08-01
Degree Name
Master of Science
Department
Geology
Advisor(s)
Hernan Moreno
Abstract
This study presents machine learning-based approaches for understanding and predicting plot-scale soil moisture's spatial variability using hydrometeorological and biogeophysical data from in-situ, multi-sensor and remote sensing sources. The high-resolution input features include numerical and categorical data such as land surface temperature, ground albedo, 1-day, 1-week and 2-week antecedent precipitation, soil type, land cover type, distance to nearest vegetation individual, terrain slope and elevation, and normalized differenced vegetation index (NDVI). Soil moisture measurements are collected within a 3 km x 3 km desert grid with Campbell Scientific Hydrosense II-12 soil moisture sensors and validated with a gravimetric method of measuring volumetric soil moisture samples. Eight field campaigns were conducted within the Jornada Experimental Range (JER), New Mexico, that allowed the collection of multiple measurements within the sampling grid during Summer 2022. Three non-parametric machine learning (ML) models Random Forest (RF), Gradient Boosting (GB), and Support Vector (SV) regressors, were used to identify the most important drivers for the prediction of soil moisture across the study grid. The results demonstrate predictive accuracies with a RMSE of 1.347% in the most optimal model. The analysis highlights the significance of antecedent precipitation, topographic features, and soil type as crucial predictors, even in the absence of direct soil moisture measurements or physics-based hydrologic model outputs. RF generated soil moisture values for a hyper-resolution grid (i.e. 100 m resolution) that effectively captured the true spatial variability of observed data and other geostatistical interpolations. This further emphasizes the robustness and applicability of machine learning in understanding and predicting soil moisture dynamics. The findings underscore the importance of specific environmental variables and highlight the potential of non-parametric machine learning models for accurate soil moisture estimation, even in data-scarce or model-limited scenarios.
Language
en
Provenance
Recieved from ProQuest
Copyright Date
2023-08
File Size
92 p.
File Format
application/pdf
Rights Holder
Stephanie Nicole Marquez
Recommended Citation
Marquez, Stephanie Nicole, "Quantifying The Contribution Of Atmospheric And Land Surface Characteristics To The Prediction Of Sub-Pixel Scale Surface Soil Moisture In The Jornada Experimental Range Through Interpretable Machine Learning" (2023). Open Access Theses & Dissertations. 3923.
https://scholarworks.utep.edu/open_etd/3923