Ocean Colour Climate Change Initiative

AI in Ocean ColourCarsten Brockmann (BC), Thomas Jackson (PML)

Material by M. Paperin, J. Wevers, K. Stelzer, D. Müller & Roland Doerffer (BC, HZG)

Slide 2

Ocean Colour Problem

• Radiative transfer – highly non linear process

▪ Not uniquely reversible

• Additional problems

▪ (S)IOPs highly variable

– space, time

▪ parametrisation of

radiative transfer equation

– inherent optical properties

of atmosphere and water

▪ Clouds

Slide 3

Cloud Screening using Machine Learning

• Idea:

▪ our eye and brain is the best cloud detector

▪ → train a machine to mimic a human‘s eye/brain for cloud detection

▪ Eumetsat IAVISA Study, 2008

• Implementation

▪ Collection of manually labelled pixels = training dataset

– No algorithm or any other machine involved in the process of identification

and labelling of a pixel

▪ Training of a neural network

– Classical fully connected multi-layer perceptron

– Feedforward – backpropagation training

– (SNNS toolkit, German award for educational software 1991)

Slide 4

Training Dataset

Slide 5

Training Dataset

• A priori definition of classes and frequency

distribution

• Hierarchy of classes

MERIS: 110 000 pixels

VIIRS: 60 000 pixels

OLCI: 44 100 pixels

53000

30654

12395

17509

750

22306

11522

5422

4042

1320

4987

2751

1265

971

0 10000 20000 30000 40000 50000 60000

Total number of pixels

Cloudy

Totally Cloudy

Semi-transparent clouds

Other turbid atmosphere

Clear

Clear sky land

Clear sky water

Clear sky snow/ice

Other clear cases

Other

Floating ice

Glint

Cloud shadow

distribution of surface types (PB-V)

PB-V: 53 000 pixels

Slide 6

NN Performance

opaque cloud

clear Land

semi-transparent cloud

spatially mixed cloudclear water

clear snow/ice

Slide 7

Validation

1 = Opaque

Slide 8

Validation

1 = Opaque

2 = Semi-transparent cloud

3 = Thick semi-transparent cloud

4 = Average density

semi-transparent cloud

5 = Thin semi-transparent cloud

Slide 9

Validation

1 = Opaque

2 = Semi-transparent cloud

3 = Thick semi-transparent cloud

4 = Average density

semi-transparent cloud

Slide 10

Example OLCI, 2016428

Slide 11

Inversion of the radiative transferCoupled ocean – atmosphere system

• Idea:

▪ Radiative transfer physics are well understood

▪ Formulation of „forward“ problem possible

▪ Numerical RT models well advanced and validated

→ Calculate a comprehensive database of spectra for representative waterand atmosphere conditions

→ Inversion by machine learning

• Implementation:

▪ Decomposition of problem into 2 parts (otherwise the manifold of thesolution space would be too large): ocean and atmosphere

▪ Set of neural nets for the inversions

▪ Starting with SNNS in mid-1990‘s for MERIS

– MLP with ffbp training

▪ Switching to Tensorflow/KERAS in 2018

– Experimenting with different architectures

– Same quality can be achieved with much less training samples

– Speed of the training significantly improved

NEURAL NETWORK BASED PROCESSING

water bio-opticalmodel

atmosp. parametrisation

aerosol

SIOPs

RT atm.

RT ocean

RT simulations: MERIS, OLCI,

MODIS, VIIRS, SeaWiFS,

S2 MSI, L8 OLI, RE

NNs training

FeedforwardBackpropagation MLP

aaNN

IOP

fwd

kd

rw

unc

SNAP C2RCC S2 Processor

SNAP C2RCC S3 Processor

ProcessorOLCI GS

SNAP C2RCC S2 Processor

TRAINING DATASETS:ATMOSPHERE MODEL

Solar zenith angle: 0-75 deg

Surface pressure: 800 – 1040 hPa

Max. rho_toa at 865 nm limited to 0.8

AOD Angstrom coeff.

AOD

frequency

frequency

Angstr.

TRAINING DATASET: BIO-OPTICAL MODEL

ranges derived from in-situ measurements

frequency

frequency

frequency

frequency

frequency

ad agapig

bp bw

TRAINING DATASET: BIO-OPTICAL MODEL

btot ad

apig

ag

Co-variances derived from in-situ measurements

NEURAL NETWORK BASED PROCESSING

water bio-opticalmodel

atmosp. parametrisation

aerosol

SIOPs

RT atm.

RT ocean

RT simulations: MERIS, OLCI,

MODIS, VIIRS, SeaWiFS,

S2 MSI, L8 OLI, RE

NNs training

FeedforwardBackpropagation MLP

aaNN

IOP

fwd

kd

rw

unc

SNAP C2RCC S2 Processor

SNAP C2RCC S3 Processor

ProcessorOLCI GS

SNAP C2RCC S2 Processor

VERIFICATON (SIMULATED DATA)ATMOSPHERE

water leaving reflectance,400 nm water leaving reflectance, 560 nm

„truth“

Re

trie

va

l (N

N)

Re

trie

va

l (N

N)

„truth“

VERIFICATON (SIMULATED DATA, WATER)

apig

Only water part(NN validation) Atmospere + Water

Adding extreme water cases(masking effect)

„truth“

retr

ieved

by

NN

apig

apig

„truth“

retr

ieved

by

NN

„truth“

retr

ieved

by

NN

VALIDATION COMPARISON AGAINST IN-SITU

Comparison OLCI S3A rho_w_nn with

AAOT rhon_w_is

NEURAL NETS FOR CONSISTENCY CHECKS

water → forward net fed

with retrived IOPs

atmosphere →

autoassociatove neural net

TO

A r

efle

cta

nce

wavelength wavelength

wa

ter

lea

vin

gre

fle

cta

nce

UNCERTAINTIES

RT Database

IOPs

NNIOP

rho_w

IOPs, estimated

∆(IOPs)

traininguncer-tainty

net

NNuncer-tainty

IOPs, estimated

∆(IOPs)

UNCERTAINTIESa

pig

longitude

apig Uncert. of apig

CH

L c

onc.

longitude

S3B OLCI 20190104

rho_toarho_w

Chlorophyll and TSM

Adg and z90max

TSM wit al3ex model

Slide 27

Conclusion

• The construction of the population (training sample, validation sample) ismost critical for the quality of the retrieval quality

▪ Cloud screening: representing all different types of clear sky and cloudyconditions

▪ Covering the range of optical properties of the water body and the atmosphere

▪ Reflecting the inner structure (dependencies, co-variances) of the IOP space

▪ Containing sufficient samples of everything which shall be retrieved

– Constructing the training data set such that it represents the frequency distribution of conditions as they appear in reality is a wrong approach; It would cause rare cases being poorly retrieved.

• The choice among different AI methods (deep learning, RF, conv.NNs, …) has a minor effect.

▪ All tested methods so far deliver excellent performance of inverting the validationdataset.

▪ However, a 99% accuracy on the validation dataset (which is from the same population as the training dataset) is irrelevant if the population is not properlyrepresenting nature.

Slide 28

Future use – Water Type Classification

Objective: Increased automation of processing up to end of water class set

generation allows more time for scientific interpretation and rapid

updates/application to new data sources.

Slide 29

Last Slide

• RT inversion in a coupled ocean-atmosphere system is a highly

non-linear, underdetermined problem

▪ „Ocean Colour retrieval seems impossible“ (Roland Doerffer)

• Articifial Intelligence is a method to address this problem

▪ „Let the data tell us the solution“ (Helmut Schiller)