Development of a hybrid multisource soil moisture estimation framework (HMSMEF) using machine learning and remote sensing data

การพัฒนากรอบการทำงานแบบผสมผสานหลายแหล่งที่มาเพื่อการประมาณค่าความชื้นในดิน โดยใช้การเรียนรู้ของเครื่องและข้อมูลการสำรวจระยะไกล

Name: Navavit Ponganan

keyword: Soil moisture estimation

; Remote sensing

; Machine learning

; Data fusion

; Rice cultivation

Abstract: Accurate, high-resolution soil moisture data is essential for optimizing agricultural water use and supporting environmental monitoring, particularly in rice cultivation areas with complex terrain and spatiotemporal variability. This study aims to develop a Hybrid Multisource Soil Moisture Estimation Framework (HMSMEF) that integrates advanced machine learning techniques with diverse remote sensing and auxiliary datasets to downscale SMAP soil moisture data to 10 m resolution. The framework synthesizes optical, radar, land use, precipitation, and topographic data through permutation testing of nine synergy combinations and applies three regression models: Random Forest (RF), Gradient Boosting (GB), and Classification and Regression Trees (CART). The optimal configuration—Synergy 6, incorporating Sentinel-1 SAR, CHIRPS precipitation, and SRTM topography—achieved the best results using the CART model (R² = 0.927, RMSE = 0.005). Bias correction methods improved R² to 0.936, and a stacking ensemble of CART, RF, and GB further enhanced accuracy. Validation with two in-situ datasets showed strong performance: (1) 2018–2019 field data (R² = 0.91, RMSE = 5.35) for 10–40 cm depth, with a widely sensor distance of 73 m ; and (2) 2021–2023 green manure plots (R² = 0.67 untreated, R² = 0.60 treated), with dense sensor spacing (6.8 m), reflecting local variability. HMSMEF demonstrates a scalable and robust solution for high-resolution soil moisture estimation, with significant implications for precision agriculture, sustainable water management, and environmental modeling.

Thammasat University. Thammasat University Library

Address: BANGKOK

Email: preserv@tu.ac.th

Name: Teerayut Horanont

Role: advisor

Created: 2025

Modified: 2025-11-25

Issued: 2025-11-25

วิทยานิพนธ์/Thesis

application/pdf

DOI: 10.14457/TU.the.2024.545

eng

DegreeName: Doctor of Philosophy

Level: Doctoral Degree

Descipline: Engineering and Technology

Grantor: Thammasat University

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