Soil Moisture Estimation from Sentinel 1 Microwave Remote Sensing Data Using Optimisation Methods

Описание: Description:
This project integrates Remote Sensing, Environmental Science, and Applied Mathematics to estimate soil moisture using Sentinel-1 Synthetic Aperture Radar (SAR) data. Soil moisture is a critical environmental variable for agriculture, hydrology, and climate monitoring, and this project demonstrates how satellite radar signals can be transformed into meaningful ground information.

📘 You’ll Learn How To:
✅ Link Sentinel-1 SAR backscatter (VV, VH) to soil moisture
✅ Solve inverse problems using Least Squares and regularization
✅ Apply Tikhonov regularization for stable soil moisture retrieval
✅ Select optimal parameters using L-curve and GCV methods
✅ Visualize soil pixels, feature matrices, and retrieved moisture values
✅ Interpret results for agricultural and environmental applications

🎓 Perfect For Students Of:
BS Geoinformatics / Remote Sensing – satellite data processing & geospatial analysis
BS Agriculture 🌱 – soil monitoring, irrigation planning & crop health
BS Hydrology / Water Resources 💧 – drought, flood & water balance studies
BS Climate & Environmental Studies 🌍 – soil moisture as a climate indicator

🧠 Technologies Used:
Data Source: Sentinel-1 SAR (dual-polarization VV, VH)
Programming: Python
Libraries: NumPy, SciPy, Matplotlib
Techniques:
• Radar backscatter–soil moisture modeling
• Least Squares estimation
• Tikhonov regularization
• L-curve & Generalized Cross Validation (GCV)
• Pixel-level visualization and analysis

💾 Deliverables Include:
✔ Python source code for soil moisture estimation
✔ Detailed documentation with theory & worked examples
✔ Figures of feature matrices, soil pixels & retrieval results
✔ Comparison tables of estimation methods
✔ Optional interactive Jupyter Notebook

🚀 Why It’s a Great Capstone:
This project connects satellite remote sensing, mathematical modeling, and environmental science to solve a real-world problem in agriculture and climate monitoring. It is portfolio-ready, easy to explain in viva/defense, and highly relevant for research-oriented students.

🔗 Get the Full Project Here:
👉 www.capstoneprojectshub.com

Hashtags:
#SoilMoisture #Sentinel1 #SARData #RemoteSensing #Geoinformatics #AgricultureTechnology #Hydrology #EnvironmentalScience #InverseProblems #Regularization #Tikhonov #LCurve #GCV #PythonProjects #GeospatialAnalysis #SatelliteData #ClimateStudies #CapstoneProjects #CapstoneProjectsHub #SoilMonitoring #EarthObservation