Ocean Colour Climate Change Initiative5... · →train a machine to mimic a human‘seye/brain for...

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)

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

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)

Training Dataset

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

NN Performance

opaque cloud

clear Land

semi-transparent cloud

spatially mixed cloudclear water

clear snow/ice

Validation

1 = Opaque

Validation

1 = Opaque

2 = Semi-transparent cloud

3 = Thick semi-transparent cloud

4 = Average density


5 = Thin semi-transparent cloud

Validation

1 = Opaque

2 = Semi-transparent cloud

3 = Thick semi-transparent cloud

4 = Average density


Example OLCI, 2016428

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


ProcessorOLCI GS


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



ProcessorOLCI GS


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

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.

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.

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)

Ocean Colour Climate Change Initiative5... · →train a machine to mimic a human‘seye/brain for...

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