Machine Learning Models for Field-Scale Soil Moisture Mapping via UAS-Based GNSS-R

Md. Ebtidaul Karim

doi:10.1109/jstars.2026.3717862

Machine Learning Models for Field-Scale Soil Moisture Mapping via UAS-Based GNSS-R

2026-07-29

0

OA

AI

M

Md. Ebtidaul Karim

M

Mohammad Abdus Shahid Rafi

DOI：10.1109/jstars.2026.3717862

Original

Original request for help

Save

Abstract

En 中文

Accurate mapping of field-scale soil moisture (SM) is a critical component of precision agriculture. This study proposes a machine learning (ML)-based framework that leverages multimodal observations collected by an unmanned aircraft system (UAS) to generate high-resolution SM estimates over 210 m × 110 m (2.31 ha) corn and cotton fields at Mississippi State University’s research farm. The proposed ML approach uses in situ SM measurements acquired from SM probes as ground-truth labels. The dataset spans over three growing seasons (2021–2023) and integrates global navigation satellite system reflectometry (GNSS-R) observables, multispectral vegetation indices, light detection and ranging-derived canopy structure, and temporal features, such as day of year, to characterize dynamic soil–vegetation interactions. Three ML models, namely, random forest, extreme gradient boosting (XGBoost), and light gradient boosting machine, were evaluated under challenging cross-validation strategies to assess spatial, temporal, and cross-crop generalization. In addition, multiple feature-selection techniques were applied to identify compact and physically meaningful predictor subsets. Results show that XGBoost achieves the strongest overall performance (e.g., root-mean-squared error = 0.0324 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{m}^{3}\text{m}^{-3}$</tex-math></inline-formula> under probewise validation), with realistic SM maps that align with normalized difference vegetation index and canopy height maps. A modalitywise ablation study and comparison with a classical linear regression baseline were also conducted to evaluate the contribution of individual sensing modalities and the effectiveness of nonlinear multimodal learning. A voting-based aggregation of selected features across all models and validation schemes highlights a small set of consistently informative variables related to temporal variability, UAS orientation, satellite geometry, and surface reflectivity behavior. The findings provide a detailed understanding of the opportunities and limitations of UAS-based GNSS-R SM prediction and point to promising directions for improving model generalization in future systems.

Keywords:

Feature selection

Global Navigation Satellite System reflectometry (GNSS-R)

machine learning (ML)

precision agriculture (PA)

soil moisture (SM)

unmanned aircraft system (UAS)

Machine Learning Models for Field-Scale Soil Moisture Mapping via UAS-Based GNSS-R

Abstract

Journal

Organization