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Sentinel 2 Pre-processing Requirements for coastal and inland waters K A I SØ R E NSE N NIVA CA R ST E N B R O CK MA NN
Brockmann Consult GmbH
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Ecological and chemical classification of water bodies in Norway
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Water quality - products potentially possible from EO data
• Monitoring of surface phytoplankton
• Monitoring particle load, Turbidity/Total susp. material
• Monitoring turbidity/water transparency (Secchi Disc Depth)
• Coloured Dissolved Organic material
• Surface Water Temperature
• Ratio between green and blue-green algae
• Detection of Harmful Algal Blooms
• Mapping of shallow waters sea bed, coastline and shore
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The in situ component are important for validation and verification
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Example of input to marine classification! Chlorophyll-a for June-Sept. 2011 in Trondheimsfjorden
From month mean to year or seasonal values.
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Water reflectance spectra of main optical components in water
Brockman, DIVERSITY II
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Examples from some Norwegian Lakes
• Souther Norway
• MERIS 300m resolution
Yearly Median Rhow spectra
0
0.001
0.002
0.003
0.004
0.005
0.006
0.007
0.008
0.009
400 500 600 700 800 900
Lambda
Rh
ow
FEM1
FREVAR2
ISE1
LYS
RØD1
VAN1
VAN2
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Example form a trend of phytoplanton in a Norwegian Lake
Need to have a robustnes in the algorithm. Around +/- 1-2 microgram/l Chl-a
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Advantage of S2: spatial resolution
0.75
1.00
1.15
1.40
1.62
1.85
2.05
2.25
2.50
L8, Lake Vanern & Vattern, Sweden
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Water Quality in small bays and Fjords Full 30m resolution
0.75
1.00
1.15
1.40
1.62
1.85
2.05
2.25
2.50
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Example Lake Alakol
0.5
80
40
Saltwater lake in Kasachstan 2650km², 354m altitude
Landsat 8 14.07.2014
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Alakol, full resolution
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What do we need for good water quality retrieval (instrument point of view)? • The right radiometry
• VIS-NIR bands resolving absorption and scattering of the water column
• NIR-SWIR bands for atmospheric correction
• Good SNR at typical water spectrum
• Precisely calibrated
• Frequent observations
• Water is a „moving target“: advection & plankton dynamics
• Cloud coverage
• Ideally multiple images per day
• Spatial resolution
• 100m or better for coastal area
• 30m or better for inland waters
• Good geolocation
• 1 pixel @ resultion < 100m
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Spectral Comparison
400 900 1400 1900 2400
MERIS OLCI S2 L8
from ESA Data User Element Project „Diversity 2“
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Spectral Comparison
400 500 600 700 800 900 1000 1100
MERIS OLCI S2 L8
from ESA Data User Element Project „Diversity 2“
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What do we need for a good water quality retrieval (algorithm requirements)?
Example: ESA DUE DIVERSITY II Processing Chain
geometric processing
pixel classification
atmospheric correction
in-water processing
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What do we need for a good water quality retrieval (algorithm requirements)? • Geometric processing
• accuracy 1 pixel is good enough
• subsetting of large data volumes
• Pixel Classification
• Clouds, cloud shadow, snow-ice, mixed water/non-water, floating vegetation, …
• Atmospheric correction
• ancillary meteorological data: ozone, surface pressure, water vapour
• aerosol retrieval
• atmospheric path radiance retrieval including reflection at the water surface (sky radiance, Fresnel reflection, white caps, sun glint, …)
• In-water processing
• concentrations of chlorophyll concentration, suspended sediments, CDOM etc
• Higher level processing
• temporal and spatial aggregation, phenological parameters, …
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Cloud Screening – Pixel Classification
MERIS, 20.07.2010
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Cloud Shadow
Cloud Shadows sharper, more distinct compared to medium resolution sensors - not addressed in DIVERSITY II MERIS
- algorithm under development - applicable to MERIS, OLCI, S2, L8
- mandatory for S2 / L8
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• Prerequisite: Cloud screening
• Step 1: Potential cloud shadow area identification
– Cloud top height (CTH) and cloud base (CBH) estimation
– Intersection of line-of-sight with cloud
– Gap filling and shadow buffering
– Segmentation of potential cloud shadow areas (CloudShadowID)
• Step 2: Cloud shadow identification
– For each segmented cloud shadow area perform k-means cluster analysis
– Sort clusters into either clear and cloud shadow
Cloud Shadow MERIS/OLCI
CBH
CTH
Mountain Shadows on Fjords
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Cloud Shadow – Example - MERIS
MERIS-FSG 24. September 2011 16:31:44
• RGB
• Cloud detection IdePix
• Potential cloud shadow
• Cloud shadow & potential cloud shadow
• Cloud shadow
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Atmospheric Correction (AC)
• „Ocean“ type AC – Condition:
• Surface signal contributes only few % to TOA signal • Low contrast of surface signal
– Requirement: 3-5% accuracy of surface reflectance – Approaches:
• analytical or numerical inversion of radiative transfer equation • assuming a water spectrum & retrieval of aerosol in the NIR
• “Land” type AC – Condition:
• Surface signal dominates TOA signal • High contrast of surface signal • Large spatial and temporal variability of surface signal as well as atmospheric
conditions
– Requirement: less demanding than ocean – Approaches:
• aerosol from VIS or as external input • explicit calculation of atmospheric path radiance
• For coastal & inland waters with S2 we will need both approaches!
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„Ocean“ Type AC Example Landsat 8
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Landsat 8 – MODIS comparison
19.04.2014
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Landsat processed with NN MODIS L2 processing – TSM = 400.75 * PI * Rrs_678 /(1-PI*Rrs_678/0.1774)
TSM comparison
Structures captured, more definition with Landsat
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Data Access and Availability
• Service Requirements
• bi-weekly/monthly cloudfree product in order to catch events and resolve critical periods
• blooming
• ice melting
• phenological cycle
• Data Access
• NRT for operational purposes (fish farms)
• 1 day latency for other
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Service Requirements Stage 1: preprocessing, storage, access • Spatial subsetting
• probably supported by data hub
• System corrections
• geometric and radiometric correction if neccessary
• Pixel classification
• cloud screening and other pixel classification annotation
• Projection / mapping
• if required projection to common grid (possibly better done at Level 2)
• Meta data, cataloguing and storage
• Hosted processing for time series processing and full mission reprocessing
• Long term stewartship
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GLaSS Core System
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Service Requirements Stage 2: Value Adding
• Fast access to preprocessed data
• data push / pull
• search by features
• QC on input data
• Combination of all available source data (S1+2+3, L8, …)
• intercalibration if required
• vicarious calibration
• Sensor merging
• Atmoshperic correction
• Water type classification
• in-water processing: chl-a, TSM, YS
• filtering
• statistical analysis
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Requirements / unresolved issues • S2 calibration accuracy
• intercalication - CEOS WGCV?
• Atmospheric correction for inland waters
• boreal area is specific
• reflection from forests (adjacency effect)
• mountain shadows and reflection
• In-water properties
• characterisation of specific inherent optical properties
• database of aparent optical properties (surface reflectances)
• Product & serviec development
• In-situ network
• coastal waters …. excellent NIVA experience from ENVISAT, needs continued
• dedicated ship cruises with dedicated equippment, internationally intercalibrated
• needs to be complemented with inland water stations