Scientific Background & Water Quality Monitoring

Why Monitor Water Quality?

Water quality monitoring is essential for understanding the ecological and trophic status of aquatic environments. Accurate and consistent data captures critical changes in:

Phytoplankton Biomass - Indicates primary productivity and potential eutrophication
Total Suspended Matter (TSM) - Reflects particulate load, turbidity, and water clarity
Colored Dissolved Organic Matter (CDOM) - Represents dissolved organic compounds affecting light penetration
Water Transparency - Influences light availability for photosynthesis

These parameters are vital for:

✅ Environmental monitoring and ecosystem health assessment
✅ Detecting harmful algal blooms and eutrophication
✅ Coastal zone management and marine spatial planning
✅ Climate change impact assessment
✅ Water resource management

Sentinel-2 Satellite System

Mission Overview

Sentinel-2, operated by the European Space Agency (ESA), is specifically designed for high-resolution Earth observation of land and coastal areas. It is ideal for water quality monitoring due to its:

✅ High spatial resolution (10-60m depending on band)
✅ Multispectral capabilities (13 spectral bands)
✅ Frequent revisit time (5-day global coverage with constellation)
✅ Open data policy (free/open access)
✅ Extended archive since 2015

Satellite Constellation

The Sentinel-2 constellation consists of:

Satellite	Launch Date	Status	Notes
S2A	June 2015	Operational	Original Sentinel-2
S2B	March 2017	Operational	Improves revisit time to 5 days
S2C	September 2024	Operational	Replaced S2A (maintaining constellation)

Multispectral Instrument (MSI)

Each satellite carries the Multi-Spectral Instrument (MSI), which acquires Level-1C Top-of-Atmosphere (TOA) reflectance data in 13 spectral bands:

Spectral Bands for Water Quality Monitoring

Band	Wavelength (nm)	Resolution (m)	Application
B1	443 (Coastal)	60	Aerosol detection, water properties
B2	490 (Blue)	10	Water absorption, chlorophyll
B3	560 (Green)	10	Vegetation & water reflectance
B4	665 (Red)	10	Chlorophyll-a absorption
B5-B7	705-783 (NIR)	20	Vegetation indices, atmospheric correction
B8	842 (NIR)	10	Vegetation & water discrimination
B8A	865 (NIR)	20	Advanced vegetation indices
B9	940 (Water vapour)	60	Atmospheric correction
B10	1375 (Cirrus)	60	Cloud detection
B11-B12	1610-2190 (SWIR)	20	Vegetation & soil discrimination

Data Availability

This toolkit downloads Level-1C products with less than 5% cloud cover from the Copernicus Data Space Ecosystem, which provides:

✅ Open access to all Sentinel-2 data
✅ Data archive from 2015 to present
✅ Global coverage
✅ Regular processing and updates

C2RCC Processor Workflow

What is C2RCC?

C2RCC (Case-2 Regional Coast Colour) is a state-of-the-art atmospheric correction and bio-optical inversion algorithm specifically designed for optically complex waters such as:

🌊 Coastal zones
🌊 Estuaries
🌊 Inland lakes
🌊 Turbid/eutrophic waters

Unlike Case-1 waters (open ocean), these environments contain suspended and dissolved substances that significantly influence spectral reflectance.

Processing Architecture

The C2RCC algorithm is implemented in this toolkit through:

SNAP Integration: Uses ESA’s Sentinel Application Platform (SNAP)
Graph Processing Tool (gpt): Command-line processing engine
Neural Network Model: C2RCC-Nets for parameter inversion
XML Configuration: User-customizable processing parameters

Processing Steps

Level-1C TOA Reflectance
    ↓
[Atmospheric Correction]
    - Aerosol optical depth estimation
    - Rayleigh scattering correction
    - Atmospheric water vapor estimation
    - Ozone correction
    ↓
[Bio-optical Inversion]
    - Water-leaving reflectance calculation
    - Phytoplankton-specific absorption
    - CDOM absorption modeling
    - TSM backscatter estimation
    ↓
[Quality Flags & Masks]
    - Valid pixel identification
    - Cloud/shadow masking
    - Land masking
    ↓
Water Quality Parameters (NetCDF output)

Configuration Parameters

Key settings for C2RCC processing are defined in 02_config/snap_graphs/c2rcc_param.xml:

Atmospheric Model:  Maritime/Coastal/Desert
Salinity:           35.0 (default for coastal seawater)
Temperature:        30.0°C (regional average)
CHL Factor:         21.0 (empirical scaling)
TSM Factor:         1.06 (empirical scaling)
Valid Pixel Expr:   B8 > 0 && B8 < 0.1 (NIR thresholds)

Bio-geophysical Parameter Extraction

1. Chlorophyll-a Concentration (Chl-a)

Definition: Proxy for phytoplankton biomass and primary productivity

Physical Basis:

Chlorophyll-a absorbs strongly in the blue (B2: 490 nm) and red (B4: 665 nm) regions
Reflectance minimum at 665 nm indicates high Chl-a concentration
C2RCC uses neural network inversion of spectral reflectance

Units: mg/m³

Typical Ranges:

Oligotrophic waters: 0.1 - 0.5 mg/m³
Mesotrophic waters: 0.5 - 2.0 mg/m³
Eutrophic waters: 2.0 - 20.0+ mg/m³

Interpretation:

🟢 Low values: Clear, nutrient-poor waters
🟡 Medium values: Productive coastal waters
🔴 High values: Potential eutrophication, harmful algal blooms

Quality Flag: Flagged as invalid if outside physical range or affected by atmospheric correction uncertainty

2. Total Suspended Matter (TSM)

Definition: Mass concentration of particulates suspended in water column

Physical Basis:

Particles scatter light across visible and near-infrared wavelengths
TSM concentration inversely related to water transparency
C2RCC estimates from red (B4) and NIR (B8) reflectance ratio

Units: g/m³

Typical Ranges:

Clear waters: 0.5 - 2.0 g/m³
Moderate turbidity: 2.0 - 5.0 g/m³
High turbidity (rivers/estuaries): 5.0 - 50.0+ g/m³

Sources:

✅ Plankton and phytoplankton
✅ Terrigenous sediment (river plumes, erosion)
✅ Anthropogenic particles (pollution)
✅ Resuspended bottom material

Interpretation:

🟢 Low TSM: Good water transparency, healthy ecosystem
🔴 High TSM: Turbid water, potential stress, sediment plume detection

3. Colored Dissolved Organic Matter (CDOM)

Definition: Dissolved organic carbon with ability to absorb light (chromophoric DOM)

Physical Basis:

Originates from terrestrial and aquatic sources
Absorbs strongly in blue wavelengths (B1: 443 nm)
Custom band-math expression applied to C2RCC water-leaving reflectance

CDOM Algorithm:

The CDOM absorption coefficient (a_CDOM at 443 nm) is calculated using:

CDOM = exp(0.544⋅log(rhown_B1) − 0.571⋅log(rhown_B2)
           − 2.181⋅log(rhown_B3) + 1.398⋅log(rhown_B4) − 1.406)

Where:

rhown_B1, B2, B3, B4 = water-leaving reflectance from C2RCC (Bands 1-4)
Coefficients derived from regional calibration dataset
Regional applicability: Australian coastal waters

Units: m⁻¹

Typical Ranges:

Clear offshore: 0.01 - 0.05 m⁻¹
Coastal waters: 0.05 - 0.5 m⁻¹
River plumes/estuaries: 0.5 - 4.0+ m⁻¹

Interpretation:

🟢 Low CDOM: Clear offshore waters, minimal organic matter
🟡 Moderate CDOM: Coastal waters with natural organic loading
🔴 High CDOM: River plume, terrestrial discharge, potential pollution

Light Penetration:

CDOM reduces underwater light availability (affects photosynthesis)
High CDOM + high TSM = severely reduced light penetration
Important for phytoplankton productivity assessment

Single vs Multi-Tile Processing

Why Tiles Matter?

Sentinel-2 divides Earth into a global reference grid of ~101,300 tiles (100 km × 100 km each). Study areas can fall within:

Single Tile: Research area entirely within one S2 tile
Multiple Tiles: Research area spans 2 or more adjacent tiles (requiring mosaic)

Processing Implications

🟢 Single-Tile Scenarios

Typical Study Areas:
- Small lakes (<50 km²)
- Coastal bays and inlets
- Urban water bodies
- Localized monitoring zones

Workflow: C2RCC → CDOM Calculation → Plotting
Benefits:
  ✅ 30-50% faster processing (no mosaic)
  ✅ 30-50% less disk space required
  ✅ Direct output to final products
  ✅ Lower computational overhead

🟠 Multi-Tile Scenarios

Typical Study Areas:
- Large lakes (>100 km²)
- Entire estuaries
- Archipelagos
- Regional coastal zones
- Global tile-spanning areas

Workflow: C2RCC → Mosaic → CDOM Calculation → Plotting
Benefits:
  ✅ Seamless coverage across tile boundaries
  ✅ Single unified product per date
  ✅ Automatic tile stitching
  ✅ Continuous spatial analysis

Automatic Detection

This toolkit automatically detects which scenario applies by:

Analyzing C2RCC output filenames
Counting tiles per acquisition date
Creating mosaic if multiple tiles found
Adapting downstream processing accordingly

Result: No manual configuration needed - just focus on your science! 🚀

True-Color Visualization

Purpose

True-color RGB composites provide natural-looking visual assessments and support reporting/publication.

Band Selection

Red Channel:   Band 4 (665 nm) - Chlorophyll absorption
Green Channel: Band 3 (560 nm) - Vegetation/water reflectance
Blue Channel:  Band 2 (490 nm) - Water absorption

Output Format

Format: PNG (lossless)
Resolution: 300 DPI (publication-ready)
Location: 05_final_products/true_color/
Use: Visual validation, reporting, presentations

Interpreting True-Color Images

Color	Interpretation
🔵 Dark Blue	Deep, clear water (low TSM, low CDOM)
🟦 Light Blue	Shallow or turbid water (high TSM)
🟩 Green/Brown	High CDOM, river plume, or algal bloom
🟨 Yellow/Tan	Very high TSM, suspended sediment plume
⚪ White	Clouds, foam, or extreme turbidity

Quality Control & Data Validation

Cloud Cover Filtering

The toolkit automatically excludes scenes with:

❌ > 5% cloud cover (configurable)
❌ Persistent cloud shadows
❌ Cloud-affected coastal zones

Valid Pixel Filtering

C2RCC outputs quality flags for each pixel based on:

✅ Atmospheric correction confidence
✅ Water/land discrimination
✅ Parameter inversion convergence
✅ Physical plausibility ranges

Default Expression: B8 > 0 && B8 < 0.1 (NIR thresholds)

Physical Range Validation

Parameter	Min	Max	Default Flags
Chl-a	0.01	20.0 mg/m³	Low SSC alert
TSM	0.5	50.0 g/m³	High turbidity
CDOM	0.01	4.0 m⁻¹	River plume

Processing Workflow Summary

┌─────────────────────────────────────────────────────────────┐
│         Level-1C TOA Reflectance (12-bit DN)               │
└──────────────────────┬──────────────────────────────────────┘
                       │
                       ↓
        ┌──────────────────────────────┐
        │  Step 1: Resample & Subset    │ (10m resolution)
        └──────────────────────────────┘
                       │
                       ↓
        ┌──────────────────────────────┐
        │  Step 2: Reproject to WGS84   │ (EPSG:4326)
        └──────────────────────────────┘
                       │
                       ↓
        ┌──────────────────────────────┐
        │  Step 3: True Color Composite │ (RGB PNG)
        └──────────────────────────────┘
                       │
                       ↓
        ┌──────────────────────────────────────┐
        │  Step 4: C2RCC Atmospheric Correction │
        │          (Per-tile processing)       │
        └──────────────────────────────────────┘
                       │
                ┌──────┴──────┐
                │             │
         Single-Tile    Multi-Tile
              │             │
              │             ↓
              │    ┌──────────────────────┐
              │    │  Step 5: Mosaic      │
              │    │  (Tile Stitching)    │
              │    └──────────────────────┘
              │             │
              └──────┬──────┘
                     ↓
        ┌──────────────────────────────────────┐
        │  Step 6: CDOM Calculation            │
        │  (Adaptive source selection)         │
        └──────────────────────────────────────┘
                     │
                     ↓
        ┌──────────────────────────────────────┐
        │  Step 7: Water Quality Plots         │
        │  (CHL, TSM, CDOM visualization)     │
        └──────────────────────────────────────┘
                     │
                     ↓
        ┌──────────────────────────────────────┐
        │  Publication-Ready Products          │
        │  05_final_products/                  │
        └──────────────────────────────────────┘

References & Further Reading

Key Publications

C2RCC Algorithm: Brockmann et al. (2016) - Remote Sensing of Environment
Sentinel-2 Mission: Drusch et al. (2012) - Remote Sensing of Environment
Water Colour Remote Sensing: Gege et al. (2020) - Optical Remote Sensing of Inland Waters
Coastal Water Quality: Moore et al. (2009) - Remote Sensing of Environment

Data Sources

🛰️ Sentinel-2 Data: https://dataspace.copernicus.eu/
📚 ESA Documentation: https://sentinel.esa.int/web/sentinel/missions/sentinel-2
🔧 SNAP Software: https://step.esa.int/main/download/snap-download/

Glossary

Term	Definition
TOA	Top-of-Atmosphere reflectance (L1C product level)
C2RCC	Case-2 Regional Coast Colour atmospheric correction algorithm
MSI	Multispectral Instrument aboard Sentinel-2 satellites
CDOM	Colored Dissolved Organic Matter (chromophoric DOM)
TSM	Total Suspended Matter (particulate concentration)
Chl-a	Chlorophyll-a pigment concentration (phytoplankton proxy)
Eutrophication	Excessive nutrient enrichment and algal growth
Neural Network Inversion	ML-based retrieval of geophysical parameters from reflectance
Mosaic	Seamless combination of adjacent satellite tiles
Quality Flag	Metadata indicating pixel validity and confidence

Last Updated: 2026-03-29 Version: 1.0 Contact: mdrony.golder@uwa.edu.au

Scientific Background & Water Quality Monitoring

Why Monitor Water Quality?

Sentinel-2 Satellite System

Mission Overview

Satellite Constellation

Multispectral Instrument (MSI)

Spectral Bands for Water Quality Monitoring

Data Availability

C2RCC Processor Workflow

What is C2RCC?

Processing Architecture

Processing Steps

Configuration Parameters

Bio-geophysical Parameter Extraction

1. Chlorophyll-a Concentration (Chl-a)

2. Total Suspended Matter (TSM)

3. Colored Dissolved Organic Matter (CDOM)

Single vs Multi-Tile Processing

Why Tiles Matter?

Processing Implications

🟢 Single-Tile Scenarios

🟠 Multi-Tile Scenarios

Automatic Detection

True-Color Visualization

Purpose

Band Selection

Output Format

Interpreting True-Color Images

Quality Control & Data Validation

Cloud Cover Filtering

Valid Pixel Filtering

Physical Range Validation

Processing Workflow Summary

References & Further Reading

Key Publications

Data Sources

Related Tools

Glossary