Remote Sensing & Earth Observation for Hydrology

Welcome to the second module of the training program. This module shifts focus from desktop GIS systems to the spaceborne sensors orbiting the Earth. We will cover the principles of Remote Sensing (RS) and Earth Observation (EO), exploring how multi-spectral and radar satellite missions capture environmental characteristics.

We will focus on how to locate, download, preprocess, and analyze satellite observations to extract crucial hydrological variables such as snow extent, surface water boundaries, soil moisture indices, and digital terrain parameters.

Learning Objectives

By the end of today's sessions, you will be able to:

Explain the principles of electromagnetic reflection and absorption as they relate to water, soil, and vegetation.
Contrast the features, applications, and boundaries of optical satellite sensors (e.g., Sentinel-2, Landsat) vs. Synthetic Aperture Radar (SAR) sensors (e.g., Sentinel-1).
Evaluate spatial, spectral, temporal, and radiometric resolutions to choose the optimal satellite dataset for specific catchment studies.
Understand multi-spectral band composites (True Color and False Color) to visually isolate hydrological features.
Explain raster preprocessing workflows, including coordinate projection, clipping, cell resampling, and grid alignment.
Explain how to extract surface water boundaries using index equations like the Normalized Difference Water Index (NDWI).

Day 2 Learning Roadmap

flowchart TD
    subgraph Row1 [" "]
        direction LR
        A["1. Remote Sensing<br/>Principles<br/>(EMR & Sensors)"] --> B["2. Satellites & Resolutions<br/>(Sentinel/Landsat/SAR)"]
    end
    subgraph Row2 [" "]
        direction RL
        C["3. Bands & Composites<br/>(RGB/FCC/NDWI/NDVI)"] --> D["4. Preprocessing & Lab<br/>(Data Downloads & Metadata)"]
    end
    B --> C

    style A fill:#f9f9f9,stroke:#333
    style D fill:#d2f8d2,stroke:#2b8a2b,stroke-width:2px
    style Row1 fill:none,stroke:none,stroke-width:0px
    style Row2 fill:none,stroke:none,stroke-width:0px

Topics and Schedule

Topic 1: Introduction to Remote Sensing Covers physical principles, the electromagnetic spectrum, active vs. passive sensors, and optical vs. SAR properties.
Topic 2: Earth Observation Missions and Sensors Familiarization with Sentinel-1/2, Landsat, MODIS, SRTM DEMs, and CHIRPS precipitation grids.
Topic 3: Understanding Satellite Data Characteristics An in-depth breakdown of the four resolutions (spatial, spectral, temporal, radiometric) and their trade-offs.
Topic 4: Satellite Bands and Composites Details band math, True Color, False Color (NIR-Red-Green), and spectral absorption properties of water and soil.
Topic 5: Accessing Open Satellite Data A practical guide to data hubs: CDSE (Copernicus), USGS EarthExplorer, and NASA Earthdata database.
Topic 6: Satellite Data Preparation Workflows Preprocessing workflows: downloading, clipping AOIs, grid alignment, resampling, and reprojecting.
Topic 7: DEMs and Terrain Data Deep-dive into DTM vs. DSM, terrain derivations (slope, aspect, hillshade), ALOS/Copernicus, and mountain shadow limits.
Topic 8: Remote Sensing Applications in Hydrology Calculates NDWI, NDVI, NDSI, and SAR backscatter threshold masks for floods, snow cover, and erosion.
Topic 9: Practical Lab & Assignment Hands-on exercises: registering account credentials, searching public portals, online band visualizations, querying STAC APIs, and downloading raw bands and DEM grids, concluding with an independent satellite basin dataset acquisition and metadata catalog compiling assignment.

Expected Outputs for Today

At the end of Day 2, you will submit a zipped folder containing:

Downloaded raw Sentinel-2 Green (Band 3) and NIR (Band 8) datasets.
Downloaded raw Digital Elevation Model (DEM) tiles covering the target basin.
A completed Basin Metadata Registry spreadsheet (CSV or Excel) detailing coordinates, acquisition dates, cloud cover, sensor platform, and file sizes.
A screenshot of a structured, local workspace directory tree containing the downloaded raw datasets.