ESA-JAXA Pre-Launch EarthCARE
Science and Validation Workshop
13 – 17 November 2023 | ESA-ESRIN, Frascati (Rome), Italy

Spaceborne lidars for Ocean Color studies: the results of the COLOR projectfrom the perspective of the EarthCARE mission
D. Dionisi1, S. Bucci2, C. Cesarini1, S. Colella1, D. D’Alimonte3, L. Di Ciolo2, P. Di Girolamo4, M. Di Paolantonio1, N.
Franco4, G. Gostinicchi2, T. Kajiyama3, G. L. Liberti1, E. Organelli1, R. Santoleri1
1 - Consiglio Nazionale delle Ricerche, Istituto di Scienze Marine, Rome, Italy
2 - Serco Italia S.p.A., Frascati, Italy
3 - AEQUORA, Lisbon, Portugal
4 - Università degli Studi della Basilicata, Potenza, ItalyIntroduction❖ Scientific context
First investigations on
CALIOP subsurface bins
(Shi and Wang, 2008)

2008

CALIOP Integrated subsurface
backscatter vs chlorophyll-aconcentration (Lu et al., 2014)

2010
2008

Ocean particulate
backscatter from Caliop

Cloud Aerosol Lidar for Global Scale
Observations of the Ocean-Land-Atmosphere System –CALIGOLA – ASI three-Year Activity Plan (2021-2023)

Demonstrated opportunity
for future OC applications(IOCS, 2019)

2012
2010

Annual cycle of polar
phytoplankton biomass

Assessment of the oceanic
surface layer's optical properties
through CALIOP (2020-2022)

Going beyond standard ocean color
observation: Lidar and Polarimetry(Jamet et al., 2019)

2014
2012

2016
2014
2016

2018
2016
2018

New lidar era insatellite oceanography(Hostetler et al., 2018)

Ocean
subsurface
observations throughICEsat-2 (Lu et.,2019)CALIOP δT used as an oceanic
variable for the ocean
particulate optical properties(Dionisi et al., 2020)

Behrenfeld et al., 2013
Behrenfeld et al., 20172020
20182020

EarthCARE

2022
2020
2022

2024
2022

Vertical distribution ofphytoplankton opticalproperties
throughCALIOP and ICEsat-2
measurements (Lu etal., 2021)

GUANLAN mission forspace oceanography(S. Wu, Aeolus cal/valmeeting, 2020)

AEOLUS OC prototype
product (COLOR project)

Satellite lidars can complement passive ocean color data
ESA-JAXA Pre-Launch EarthCARE Science and Validation Workshop | 13 – 17 November 2023 | ESA-ESRIN, Frascati (Rome), Italy

Spaceborne lidars for Ocean Color studies

Introduction❖ Scientific context
The information content in the water leaving reflectances in the ∼350 to 400 nm interval is crucial to distinguish dissolved organic
material from chlorophyll in the upper ocean. The downward transport of organic material is a key process in Earth's carbon cycle andboth affects, and is affected by, climate change. Chromophoric Dissolved Organic Matter (CDOM) is the most relevant contributor to thelight absorption at 355 nm.

Lack of the UV information of past and current Ocean Color sensors

AEOLUS gives the opportunity to investigate the signal
backscattered by the ocean sub-surface:❑ First orbiting HSRL lidar❑ Ocean subsurface information content in the UV❑ The line-of-sight (LOS) points 35º from nadir

ESA-JAXA Pre-Launch EarthCARE Science and Validation Workshop | 13 – 17 November 2023 | ESA-ESRIN, Frascati (Rome), Italy

Spaceborne lidars for Ocean Color studies

Outline
COLOR (CDOM-proxy retrieval from aeOLus ObseRvations) was a feasibility study approved by ESA within the Aeolus+
Innovation program (ESA AO/1-9544/20/I/NS). March 2021 - April 2023. (ESA project responsible M.H.Rio)
Objective: COLOR proposes to evaluate and document the feasibility of deriving an in-water AEOLUS product at 355 nmConsortium

Institute of Marine Sciences(ISMAR) - CNR

University ofBasilicata

ESA-JAXA Pre-Launch EarthCARE Science and Validation Workshop | 13 – 17 November 2023 | ESA-ESRIN, Frascati (Rome), Italy

AequoraSerco Italia SpA

Spaceborne lidars for Ocean Color studies

Methods❖ COLOR general approach
Data Pool

setData#1
Subset #1

OptimizedRTM

Comparison
Interpretation
OutputNumerical
Exp #1

Answers
Inversion Algorithm Design

ESA-JAXA Pre-Launch EarthCARE Science and Validation Workshop | 13 – 17 November 2023 | ESA-ESRIN, Frascati (Rome), Italy

StatisticalAnalyses

OBSERVATIONSMODELInput
Numerical
Exp #1

Lidar Equations
AEOLUS Measurement Characteristics
Assumptions/Questions

OutputAnalyses
Subset #1

Spaceborne lidars for Ocean Color studies

Methods❖ LiOC in-water forward model
Lidar simulation code for Ocean Color applications (LiOC) developed fully accounting for
ALADIN characteristics
LiOC components:•𝑃𝑛 = 𝑃𝑛𝑠 + 𝑃𝑛𝑏 + 𝑃𝑛𝑤

At the surface: negligible contribution of the reflected signal forwind speed up to the formation of white caps (~8 ms-1)•At the bottom: negligible contribution of bottom effects inoligotrophic conditions for depth ≤ 80 m.

•In the water columns: MC ray tracing is driven by seawater Inherent
Optical Properties.
𝑏tot = 𝑏𝑤 + 𝑏𝑝 (Chl−a)𝑎𝑡𝑜𝑡 = 𝑎𝑤 + 𝜟𝒂𝑐tot = 𝑎tot + 𝑏tot
𝛽෨𝑝 𝛹, Chl−a

LiOC

Ray-tracing scheme

Pn

D’Alimonte et al., 2023 (submitted to Optic Express)
ESA-JAXA Pre-Launch EarthCARE Science and Validation Workshop | 13 – 17 November 2023 | ESA-ESRIN, Frascati (Rome), Italy

Spaceborne lidars for Ocean Color studies

Methods❖ AEOLUS in-water algorithm

Ancillary data:

Ingest input
data

AEOLUSL1B data

N

Bathymetry,
Atmospheric
Variables,
Chl-A(Aerosols)

Data screening
Dummies,
Bathymetry
Wind,Clouds, aerosol

Y
Data Integration
Range correction

Bw estimation

Simulation LUT’s.

atot(355) estimation

Write Output

Chl-a ancillary:ESA-0C-CCI multisensory V6 data
Spatial resolution: 4 km
Temporal resolution: daily
Link: https://climate.esa.int/en/projects/ocean-colour/data/

ESA-JAXA Pre-Launch EarthCARE Science and Validation Workshop | 13 – 17 November 2023 | ESA-ESRIN, Frascati (Rome), Italy

AEOLUS:
Level1B, IB11; Apr2020 – Mar2021
Spatial resolution: 3-7 km
Temporal resolution: 0.5-1 sec
Pn = Bw*ΔΩ𝑟Spaceborne lidars for Ocean Color studies

Results❖ Algorithm Assessment
Reference datasetsa) BGC-Argo (global scale; >40000 radiometric profiles at 4bands available since 2012, https://maps.biogeochemicalargo.com/bgcargo/)b) ESA Ocean Colour Climate Change Initiative (ESA-OC-CCI;
esa-oceancolour-cci.org)
AEOLUS L1B Dataset, Baseline 11: April 2020 - March 2021.
Region\Season
NASTG
SASTG
NWMED
SEMEDNASPG
BLSEA
SOIND

DJF
193
212
8340124

MAM
146
130
440049JJA
127
140
44144
3223SON
252
79
1764
11019AEOLUS

BGC-ARGO

ALL718
561
73282
43755

ESA-JAXA Pre-Launch EarthCARE Science and Validation Workshop | 13 – 17 November 2023 | ESA-ESRIN, Frascati (Rome), Italy

Spaceborne lidars for Ocean Color studies

Results❖ Match-up Assessment

Median+interquartile range comparison▪ ESA-OC-CCI @ 412 nm scaled @ 355 nm▪ Dataset: April 2020 - March 2021
▪ only AEOLUS data with ΔBw/Bw ≤ 1

Match-up criteria
1) Temporal matching: ±24 hrs2) Spatial matching: 5x5 pixels (20x20 km) around the AEOLUS measurement.
3) Number of valid pixels: ≥4 out of 25

AEOLUS𝛥𝑎 355 = 𝑎tot 355 − 𝑎w (355) − (0.052 ∙ 𝐶ℎ𝑙−𝑎0.635 )𝛥𝑎 355 = 𝑎𝐶𝐷𝑂𝑀 355 + 𝑎𝑁𝐴𝑃 355
Dionisi et al., to be submitted

𝑎𝐶𝐷𝑂𝑀 355 ≫ 𝑎𝑁𝐴𝑃 355

ESA-OC-CCI𝒂𝑪𝑫𝑶𝑴 𝟑𝟓𝟓 ≈ 𝛥𝑎 355
𝑎𝑑𝑔 412 = 𝑎𝐶𝐷𝑂𝑀 412 + 𝑎𝑁𝐴𝑃 412
𝑎𝐶𝐷𝑂𝑀 412 ≫ 𝑎𝑁𝐴𝑃 412
𝒂𝑪𝑫𝑶𝑴 𝟑𝟓𝟓 ≈ 𝑎𝑑𝑔 412 ∙ exp −0.014 355 − 412

ESA-JAXA Pre-Launch EarthCARE Science and Validation Workshop | 13 – 17 November 2023 | ESA-ESRIN, Frascati (Rome), Italy

Spaceborne lidars for Ocean Color studies

Conclusions✅ Development of a status of the art modeling tool (LIOC)✅ Demonstrated sensitivity of the AEOLUS measurements to marine optical properties✅ AEOLUS OC prototype product in a spectral region (355 nm) not covered by operational OC products✅ Overall demonstrated agreement between reference measurements and the proposed OC product❌ Atmospheric disturbances (cloud and aerosol contamination) not properly accounted for❌ Low SNR in the cloud free ground bin signal❌ Limited spatial and temporal coverage❌ HSRL not exploited (single channel (Mie) retrieval)❌ In-water profiling capability not available

ESA-JAXA Pre-Launch EarthCARE Science and Validation Workshop | 13 – 17 November 2023 | ESA-ESRIN, Frascati (Rome), Italy

Spaceborne lidars for Ocean Color studies

Roadmap
Applicability of the COLOR results to EarthCARE mission
Main differences between AEOLUS and ATLID for OC application:
ALADINATLID❑

Vertical resolution:

500 m

100 m

Better separation of the atmospheric signal in theground bin return (e.g. optimization of cloud andaerosols identification)❑Laser emitting angle:

35°

3°

Stronger surface return and signal
propagation❑

Acquisition channels:❑Instruments on platform:❑Local solar time (LST)sampling:-Co-polar Rayleigh A-Co-polar Rayleigh B-Co-polar Mie-

dawn/dusk-Co-polar Rayleigh-Co-polar Mie-Total cross polar

Optical characterization of the ocean
particles through polarization capability-Cloud Profiling Radar
Improvement of scene characterization-Multi-Spectral Imager(e.g. cloud detection)-Broad-Band Radiometer
day/night

ESA-JAXA Pre-Launch EarthCARE Science and Validation Workshop | 13 – 17 November 2023 | ESA-ESRIN, Frascati (Rome), Italy

Large variability of the background signal. Marine
diurnal cycle. OC information during nighttime.
LST passage closer to OC satellite missions

Spaceborne lidars for Ocean Color studies

Roadmap

Applicability of the COLOR results to EarthCARE mission
Actions to exploit COLOR's heritage for OC studies using EarthCARE➢ Data analysis procedure➢LiOC radiative transfer tool
➢Improved characterization of atmospheric (aerosols and clouds) disturbances➢Exploitation of High Spectral Resolution Lidar (HSRL) capabilities➢Exploitation of Polarization capabilities➢Possibility to implement Synergetic Inversion algorithm including, as input,
active and passive measurements (OC missions).

ESA-JAXA Pre-Launch EarthCARE Science and Validation Workshop | 13 – 17 November 2023 | ESA-ESRIN, Frascati (Rome), Italy

Spaceborne lidars for Ocean Color studies

ESA-JAXA Pre-Launch EarthCARE Science and Validation Workshop | 13 – 17 November 2023 | ESA-ESRIN, Frascati (Rome), Italy

Spaceborne lidars for Ocean Color studies

Methods❖ AEOLUS in-water algorithm

Elastic Backscatter Lidar AEOLUS-adapted inversion algorithm

Rationale: use the information in bins 21 and 22 to account for the instrumental and atmospheric contribution in thebin 23 signal∗INPUTS: (Mie channel) Range Corrected Signals (𝑆𝑥𝑥) and geometry of bins 21, 22, 23 (𝑧𝑥𝑥 , ∆𝑧𝑥𝑥 )ANCILLARY: Atmospheric density profile, surface wind (𝑇𝑠2 ), sea temperature and salinity, aerosols scale height (zs) .
OUTPUT: sea water contribution (backscattering+extinction) to ground bin signal (𝐵𝑤 )𝐵𝑤𝑎𝑡 ≈∗ −𝑆 ∗ ) 𝐵
2 1(𝑆2321𝑚 𝑛2222∗𝑆21
𝑇𝑠2 𝑇𝐵𝐿𝑚𝑇𝐵𝐿𝑎≈∗ −𝑆 ∗ ) 𝐵
2(𝑆2321𝑚 𝑛222∗𝑆21
𝑇𝑠2 𝑇𝐵𝐿𝑚𝑒+2𝜌0 𝜎𝑒𝑥𝑡cos 𝜃−𝑧23−𝑧21−𝑧22𝑧𝑧𝑠𝑠𝑒∆𝑧21 + 𝑒∆𝑧22 + 𝑒 𝑧𝑠 ∆𝑧23

ASSUMPTIONS:▪ Contributions of sea surface backscattering and of sea bottom reflection are negligible▪ Sea surface trasmittance Ts = 0.97, independent from the direction of propagation▪ Refractive index of sea water n=1.356 independent from salinity and temperature▪ Difference between atmospheric backscattering contribution of bin 22 and 23 is negligible compared to the contribution of sea
water in bin 23▪ Atmospheric backscattering dominated by molecular contribution both for the optical thickness and for the shape of the phasefunction▪ Homogeneity of the aerosol type in the Marine Atmospheric Boundary Layer (MABL) and known vertical distribution
ESA-JAXA Pre-Launch EarthCARE Science and Validation Workshop | 13 – 17 November 2023 | ESA-ESRIN, Frascati (Rome), Italy

Spaceborne lidars for Ocean Color studies

Results
Estimation of associated uncertainty
Identified sources of uncertainties:➢ Data homogeneity➢ Radiometric noise➢ Cloud contamination➢ Assumptions in the analytical model➢ Aerosols➢ Bw→α conversion

𝛥𝐵𝑤𝑎𝑡 355 =𝜕𝐵𝑤
∗𝑑𝑆212

𝜕𝐵𝑤∗ 2∙ ∆𝑆21+∗𝑑𝑆222

𝜕𝐵𝑤∗ 2∙ ∆𝑆22+∗𝑑𝑆232∗ 2∙ ∆𝑆23

Associated uncertainty to the prototype product is based on the effect of radiometric noise recognized as major (and quantitatively documented) source ofuncertainty.
A robust quantitative and detailed validation-based estimation of uncertainties of the AEOLUS OC product is not possible due to the small dimension of thecomparison dataset.

AEOLUS SCIENCE CONFERENCE 2023, RHODES ISLAND, GREECEESA AO/1-9544/20/I/NS

Methods❖ General approach: ground bin characterization
The potential information on ocean subsurface optical properties is contained in the AEOLUS ground bin volume (Δrgrd)
0r1grd

rgrd
r2grd

r3grd

���

𝑆𝑋 𝑔𝑟𝑑 = 𝑀𝑋__∆�≈ 500 𝑚X= RayA,B
X= Mie∆𝑟𝑟𝑎𝑡𝑚 + 𝑎𝑡𝑚
𝑛2𝐵
𝑇𝑟𝑎𝑡𝑚 + 𝑆𝑏𝑘𝑑𝑔𝑟𝑑
𝐴2(1)

(2)

_𝑟𝑤𝑎𝑡
r4grd

𝐴𝐵𝑤𝑎𝑡 = න0′𝑟𝑤𝑎𝑡′𝛽𝑤𝑎𝑡 (𝜋, 𝑟𝑤𝑎𝑡) 𝑒𝑥𝑝 −2 න0′′′′′(3)𝐾𝐿𝐼𝐷 𝑟𝑤𝑎𝑡
𝑑𝑟𝑤𝑎𝑡
𝑑𝑟𝑤𝑎𝑡

This characterization is based on:a) Radiative transfer numerical modellingb) AEOLUS data analysis
ESA-JAXA Pre-Launch EarthCARE Science and Validation Workshop | 13 – 17 November 2023 | ESA-ESRIN, Frascati (Rome), Italy

Spaceborne lidars for Ocean Color studies

Methods❖ LiOC in-water forward model

Chl-aPn

a1

Pn

a2

Pn

c1

a1

p1

…

..

…

cN

aN

pN
Retrieved

a4

Pn

Ref. case
a5

Pn

a6

Pnatot

1.46E-13 0.0021 7.73E-14 0.0221 2.49E-14 0.1021 5.49E-15 0.5021 1.11E-15 2.5021 1.86E-16 15.0021 1.18E-13 0.0078

0.03

1.20E-13 0.0037 6.98E-14 0.0237 2.48E-14 0.1037 5.62E-15 0.5037 1.14E-15 2.5037 1.91E-16 15.0037 8.62E-14 0.015

0.1

9.10E-14 0.0078 6.17E-14 0.0278 2.47E-14 0.1078 5.87E-15 0.5078 1.19E-15 2.5078 2.00E-16 15.0078 5.61E-14 0.0322

0.3

6.84E-14 0.0168 5.16E-14 0.0368 2.40E-14 0.1168 6.10E-15 0.5168 1.27E-15 2.5168 2.14E-16 15.0168 3.60E-14 0.0658

14.82E-14 0.0408 3.98E-14 0.0608 2.21E-14 0.1408 6.71E-15 0.5408 1.42E-15 2.5408 2.41E-16 15.0408 2.23E-14 0.146

33.49E-14 0.0934 2.96E-14 0.1134 2.04E-14 0.1934 7.11E-15 0.5934 1.68E-15 2.5934 2.87E-16 15.0934 1.33E-14 0.3047

102.33E-14 0.2334 2.12E-14 0.2534 1.61E-14 0.3334 7.70E-15 0.7334 2.19E-15 2.7334 3.90E-16 15.2334 8.19E-15 0.6873

301.51E-14 0.5405 1.42E-14 0.5605 1.23E-14 0.6405 7.65E-15 1.0405 2.69E-15 3.0405 4.88E-16 15.5405 5.08E-15 1.4524

100

7.62E-15 1.3586 7.18E-15 1.3786 6.65E-15 1.4586 5.27E-15 1.8586 2.33E-15 3.8586 5.40E-16 16.3586 2.91E-15 3.3173

LUT (bullets) generated based on
MC simulations with Chl-a andabsorption as input

Aeolus

𝑎𝑡𝑜𝑡

Pn

0.01

3. The 𝑎𝑡𝑜𝑡 value retrieved by interpolation

Chl−aa30.003 1.75E-13 0.0014 8.19E-14 0.0214 2.49E-14 0.1014 5.43E-15 0.5014 1.10E-15 2.5014 1.83E-16 15.0014 1.54E-13 0.0041

1. LUT computed through forward MC simulations todetermine Pn as a function of IOPs.

Chl-a ancillaryPn0.001 1.92E-13 0.0012 8.48E-14 0.0212 2.49E-14 0.1012 5.39E-15 0.5012 1.09E-15 2.5012 1.82E-16 15.0012 1.83E-13 0.0025

Overview of the backward scheme for 𝒂𝒕𝒐𝒕 retrieval

2. The LUT entered through the observed Chl−aand Pn values

𝜟𝒂 log-uniformly varied between 0 and 15 𝒎−𝟏

𝑷𝑵 = 𝐁𝒘𝒂𝒕 ∙ 𝚫𝛀𝒓

LUT dimension entered basedon the Chl-a retrieved withindependent satellite image

ESA-JAXA Pre-Launch EarthCARE Science and Validation Workshop | 13 – 17 November 2023 | ESA-ESRIN, Frascati (Rome), Italy

Example of retrieved(interpolated) absorption
value for given Chl-a and Pnobservations

LUT dimension enteredbased on the Pn retrieved
with Aeolus

Spaceborne lidars for Ocean Color studies

Methods❖ AEOLUS in-water algorithm
High Spectral Resolution Lidar approach
Objective: coupling the signals coming from Brillouin and Mie spectra (Rayleigh+Miechannels)

HSRL in water𝑤
𝑆𝑀𝑖𝑒 = 𝑀𝑀𝑖𝑒𝑤
𝑆𝑅𝑎𝑦 = 𝑀𝑅𝑎𝑦𝑝𝑚𝛽𝑤𝑎𝑡 = 𝛽𝑤𝑎𝑡➢➢𝐴∆𝑟𝑟𝑎𝑡𝑚 + 𝑎𝑡𝑚𝑛𝑝2𝐴2∆𝑟𝑟𝑎𝑡𝑚 + 𝑎𝑡𝑚
𝑛𝑚𝐶4𝑤 𝛽𝑤𝑎𝑡
+ 𝐶3𝑤 𝛽𝑤𝑎𝑡 𝑒𝑥𝑝 −2 න𝑟𝑤𝑎𝑡

0𝑝𝑚𝐶1𝑤 𝛽𝑤𝑎𝑡
+ 𝐶2𝑤 𝛽𝑤𝑎𝑡 𝑒𝑥𝑝 −2 න′′
𝐾𝐿𝐼𝐷 𝑟𝑤𝑎𝑡
𝑑𝑟𝑤𝑎𝑡
𝑇𝐴2 𝑟𝑎𝑡𝑚

𝑟𝑤𝑎𝑡

0′′
𝐾𝐿𝐼𝐷 𝑟𝑤𝑎𝑡
𝑑𝑟𝑤𝑎𝑡
𝑇𝐴2 𝑟𝑎𝑡𝑚𝑤𝑤
𝑆𝑅 𝑀𝑀𝑖𝑒𝐶4𝑤 − 𝑀𝑅𝑎𝑦𝐶1𝑤𝑤𝑤𝑤
𝑀𝑅𝑎𝑦 𝐶2 − 𝑆𝑅 𝑀𝑀𝑖𝑒 𝐶3𝑤

Calibration and cross talk coefficients estimated for atmosphericapplication cannot be used for the Brillouin scattering in waterHigh variability of the Rayleigh ground bin signal

ESA-JAXA Pre-Launch EarthCARE Science and Validation Workshop | 13 – 17 November 2023 | ESA-ESRIN, Frascati (Rome), Italy

FWHM

1,78 GHz

FSR

10,95 GHz

Spacing between A and B

5,5 GHz

Filter A Peak Transmission

81 %Filter B Peak Transmission

67 %Spaceborne lidars for Ocean Color studies