Microwave Remote Sensing in Hydrology

Week 1: Fundamentals of Electromagnetic Waves, Introduction to microwave remote sensing, Overview of non-imaging and imaging microwave sensors, principles, physical fundamentals, Installation of python using Anaconda Environment and basic commandsWeek 2: Scattering of Microwaves, Fundamentals of Synthetic Aperture Radar (SAR), Basics of Image formation, Basics of SAR Image processing using pythonWeek 3: Radar equation, Image defects - Geometric distortions, Introduction to Sentinel Application Platform (SNAP)Week 4: Speckle, Doppler Shift in SAR Imagery, Multilooking, Spatial Convolution, Introduction to plotting and image statistics in pythonWeek 5: Introduction to Texture, GLCM, Introduction to Image statistics in PythonWeek 6: Radar remote sensing, Speckle filtering using pythonWeek 7: Image classification, geometrical basis, Supervised Classification, SAR Image Classification using SNAPWeek 8: Unsupervised classification, Accuracy Assessment, Fuzzy Classification, Handling Active microwave data in PythonWeek 9: Active microwave remote sensing: Principles, Application of active microwave remote sensing in hydrology, Doppler weather radar data visualizationWeek 10: Radar Altimetry, concepts and applications in hydrology, Measuring soil moisture using active microwave remote sensing, Fundamentals of Passive microwave remote sensing and data handling using pythonWeek 11: Applications of passive microwave remote sensing in hydrology, Handling Precipitation data in pythonWeek 12: Radar Interferometry, using phase as a relative distance measure, Digital Elevation Models, Hydrological Models – An Introduction