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Copernicus Global Land Operations – Lot 2 Date Issued: 15.11.2018 Issue: I1.07

Copernicus Global Land Operations

“Cryosphere and Water” ”CGLOPS-2”

Framework Service Contract N° 199496 (JRC)

QUALITY ASSESSMENT REPORT

LAKE WATER QUALITY

300M AND 1KM PRODUCTS

VERSION 1.2.0

Issue I1.07

Organization name of lead contractor for this deliverable: Brockmann Consult GmbH

Book Captain: Kerstin Stelzer, BC

Contributing Authors: Dagmar Müller, BC

Stefan Simis, PML


Document-No. CGLOPS2_QAR_LWQ300_1km_V1.2.0_I1.07 © C-GLOPS Lot2 consortium

Issue: I1.07 Date: 15.11.2018 Page: 2 of 52

Dissemination Level PU Public X

PP Restricted to other programme participants (including the Commission Services)

RE Restricted to a group specified by the consortium (including the Commission Services)

CO Confidential, only for members of the consortium (including the Commission Services)




Document Release Sheet

Book captain: Kerstin Stelzer (BC) Sign Date

Approval: Sign Date

Endorsement: Sign Date

Distribution: Public




Change Record

Issue/Rev Date Page(s) Description of Change Release

I1.00 28.04.2017 37 First Version for QAR Lake Water

Demonstration products Version 1.1.0

I1.02 30.06.2017 37 Integration of RIDs from review cycle

I1.03 04.11.2017 37 Update to 1km products and NRT production

I1.04 05.12.2017 50 Consistency check MERIS with NRT OLCI

production

I1.05 18.06.2018 48 Update considering reviewer’s comments

I1.07 15.11.2018 51 Consolidation of versions




TABLE OF CONTENTS

1 Background of the document ............................................................................................. 11

1.1 Executive Summary ............................................................................................................... 11

1.2 Scope and Objectives............................................................................................................. 11

1.3 Content of the document....................................................................................................... 11

1.4 Related documents ............................................................................................................... 12

1.4.1 Applicable documents ................................................................................................................................ 12

1.4.2 Input ............................................................................................................................................................ 12

1.4.3 Output ......................................................................................................................................................... 12

1.4.4 External documents (if any) ........................................................................................................................ 12

2 Review of Users Requirements ........................................................................................... 14

3 Quality Assessment Method .............................................................................................. 16

3.1 Overall procedure ................................................................................................................. 16

3.2 Satellite Reference Products .................................................................................................. 16

3.3 In situ Reference Products ..................................................................................................... 17

4 Results .............................................................................................................................. 18

4.1 Visual Inspection - Consistency of Time Series and Maps ........................................................ 18

4.1.1 Historical data ............................................................................................................................................. 18

4.1.1 Performance of mapping per optical water type ........................................................................................ 29

4.1.2 Comparison of archived (MERIS) and NRT (OLCI) data ............................................................................... 31

4.2 Comparison with in-situ data – Lake Water Reflectance ......................................................... 39

4.3 Comparison with in-situ data – Turbidity ................................................................................ 40

4.3.1 Time series at sampling stations ................................................................................................................. 40

4.3.2 Match-up analysis ....................................................................................................................................... 44

4.4 Comparison with in-situ data – Trophic State Index ................................................................ 46

5 Conclusions ....................................................................................................................... 48

5.1 summary ............................................................................................................................... 48

5.2 Limitations and known issues ................................................................................................ 49

6 Recommendations ............................................................................................................. 51

7 References ........................................................................................................................ 52




List of Figures

Figure 1: Maps showing turbidity of Lake Vättern in 2005, the first decade of each month is presented

.............................................................................................................................................. 19

Figure 2: Time series in lake Vättern showing the consistency of parameters (turbidity, Rw443,

Rw560, Rw665 and trophic state). Trophic state classes range between 0 and 100 with

oligotrophic between 0 and 30; meso between 40-50, eutrophic 60-80 and hypertrophic 80-100.

.............................................................................................................................................. 20

Figure 3: Maps showing turbidity of Lake Kyoga in 2005, the first decade of each month is presented.

.............................................................................................................................................. 21

Figure 4: Time series in lake Kyoga showing the consistency of parameters (turbidity, Rw443,

Rew560, Rw665 and trophic state). Trophic state classes range between 0 and 100 with

oligotrophic between 0 and 30; meso between 40-50, eutrophic 60-80 and hypertrophic 80-100

.............................................................................................................................................. 22

Figure 5: Maps showing turbidity of Lake Müritz in 2009, the first decade of each month is presented.

.............................................................................................................................................. 23

Figure 6: Time series in Lake Müritz showing the consistency of parameters (turbidity, Rw443,

Rew560, Rw665 and trophic state). Trophic state classes range from 0 and 100, separated in

10 categories corresponding to CHL concentration following Carlson et al. 1977 .................. 24

Figure 7: LSR 443 nm (left) and TUR (right) for Lake Müritz in 2006, 6 example decades showing

the loss of information coverage during the processing step from reflectance to water constituent

concentrations. ...................................................................................................................... 25

Figure 8: Maps showing turbidity of Lake Kasumigaura in 2010, the first decade of each month is

presented ............................................................................................................................... 26

Figure 9: Time series in Lake Kasumigaura showing the consistency of parameters (turbidity, Rw443,

Rew560, Rw665 and trophic state Trophic state legend: 0: oligotrophic, 1: mesotrophic, 2:

eutrophic, 3: hypertrophic. ...................................................................................................... 28

Figure 8: Lake Huron OWT (left) and Turbidity (right) for OLCI acquisition 26.08.2017. The line marks

where the transect shown in Figure 9 .................................................................................... 29

Figure 9: Blended Turbidity and OWT classes along a transect in Lake Huron (position of transect in

Figure 8 ................................................................................................................................. 29

Figure 10: Lake Turkana OWT (left) and Turbidity (right) for OLCI acquisition 19.09.2017. The line

marks where the transect shown in Figure 11 ........................................................................ 30

Figure 11: Blended Turbidity and OWT classes along a transect in Lake Turkana (position of transect

in Figure 10 ............................................................................................................................ 30




Figure 14: Turbidity in Lake Superior in 4 different years (fist decade in October) for 2005, 2008,

2010 (all MERIS) and 2017 (OLCI) ........................................................................................ 32

Figure 15: In-situ stations at Lake Superior. MERIS L3 turbidity_mean ......................................... 33

Figure 16: Seasonal cycle in MERIS L3 and OLCI data at station GLWD00000002 EPA_GLNPO-

SU03...................................................................................................................................... 33

Figure 18: Turbidity in Lake Huron in 4 different years (fist decade in October) for 2005, 2008, 2010

(all MERIS) and 2017 (OLCI) ................................................................................................. 34

Figure 19: In-situ stations in Lake Huron as positions for time series extractions in MERIS L3 and

OLCI L2 products. Example of a MERIS turbidity_mean product (background). .................... 35

Figure 20: Time series at station 21MICH_WQX-090250. ............................................................. 36

Figure 21: Time series at station EPA_GLNPO-HU32. ................................................................. 36

Figure 22: Turbidity in Lake Turkana in 4 different years (fist decade in October) for 2005, 2008,

2010 (all MERIS) and 2017 .................................................................................................... 37

Figure 23: Time series in the North of Lake Turkana (turbid part of the lake) ................................ 38

Figure 24: Time series in the South of Lake Turkana. ................................................................... 39

Figure 25: POLYMER v3.5 validated against in situ reflectance data contained in LIMNADES

(source: GloboLakes). Here, results for a 3x3 pixel window with a 3-day difference between

satellite and in situ observation, are shown. ........................................................................... 40

Figure 26: Turbidity in Lake Huron (10D average 20080811-20080820). Red arrows indicate the

position of the stations shown in the time series plot (Figure 27). ........................................... 41

Figure 27: Time series of turbidity_mean (blue +) and in-situ turbidity (green). Lake Huron, Saginaw

Bay (above) and central lake (below). In-situ data: US Data bases STORET

(http://www3.epa.gov/storet/) and WQP (http://waterqualitydata.us /portal/). Note the different

scales of the y-axes. .............................................................................................................. 41

Figure 28: Turbidity in Lake Apopka in June 2005 (left) and June 2008 (right). The arrow indicates

the station that is shown in the time series Figure 29. ............................................................ 42

Figure 29: Time series of turbidity_mean (blue +) and in-situ turbidity (green) in Lake Apopka. In-situ

data: US Data bases STORET (http://www3.epa.gov/storet/) and WQP

(http://waterqualitydata.us/portal/) .......................................................................................... 42

Figure 30: Turbidity map of Lake Superior for decade 20080721-20080730. The arrows indicate the

position of the stations shown in the time series plots in Figure 31 (red arrow) and Figure 32

(green arrow). ........................................................................................................................ 43

Figure 31: Time series of turbidity_mean (blue +) and in situ turbidity (green) in Lake Superior at the

position of the red arrow in Figure 30. In situ data: US Data bases STORET

(http://www3.epa.gov/storet/) and WQP (http://waterqualitydata.us/portal/) ............................ 43




Figure 32: Time series of turbidity_mean (blue +) and in-situ turbidity (green) in Lake Superior at the

position of the green arrow in Figure 30. In situ data: US Data bases STORET



position of the orange arrow in Figure 30. In situ data: US Data bases STORET


Figure 34: Match-up analysis of turbidity in-situ data and 10-days-turbidity_mean. Time difference

between in situ and satellite product is coded in the shape of the points (circle: same day,

triangle: +/- 1day, square: +/- 2 days, pentagon: +/- 3 days). The number of observations

contributing to the mean turbidity ranges from 1 to 6 and is shown by the colour (black, red,

green, blue, grey, magenta). .................................................................................................. 45

Figure 35: Performance of chlorophyll-a retrieval across all optical water types (clusters 1-13) and

associated algorithms following tuning of each algorithm, for each OWT, against the LIMNADES

database (results courtesy University of Stirling, GloboLakes project, Neil et al. submitted.). 46

Figure 36 Satellite matchup analysis for chloropyll-a retrieval in the Calimnos-MERIS processing

chain. Linear regression analysis provides R2=0.62, slope=0.82, intercept=1.16, n=350. To

obtain sufficient matchup points, a matchup window of ±7 days is used. Temporal variation in

this timeframe can be significant, contributing to scatter in the observed relationship. The result

should be interpreted as worst-case. ..................................................................................... 47




List of Tables




List of Acronyms

ATBD Algorithm Theoretical Basis Document

BC Brockmann Consult

CCI Climate Change Initiative

C-GLOPS Copernicus Global Land Operations

FTP File Transfer Protocol

GLWD Global Lakes and Wetlands Database

GPT graph processing tool

LSR Lake Surface Reflectances

NERC National Environment Research Council (UK)

obs Observation

OC Ocean Colour

OWT Optical Water Type

PML Plymouth Marine Laboratory

PUM Product User Manual

QAA Quasi-Analytical Approach

QAR Quality Assessment Report

rep representative

Rw Water leaving reflectances

TS Trophic State

TSM Total Suspended Matter

TUR Turbidity

WGS84 World Geodetic System 1984




1 BACKGROUND OF THE DOCUMENT

1.1 EXECUTIVE SUMMARY

The Copernicus Global Land Service – Lake Water provides an optical characterization of nominally

1000 inland water bodies that belong to the world’s largest (according to the Global Lakes and

Wetlands Database, GLWD) or are otherwise of specific environmental monitoring interest. The

products contain four (sets) of parameters: lake water surface temperature, lake water reflectance

(all wavebands that are available after atmospheric correction), turbidity (derived from suspended

solids concentration estimates) and a trophic state index (derived from phytoplankton biomass by

proxy of chlorophyll-a). Production and delivery of the parameters are over 10-day intervals on a set

grid (starting the 1st, 11th and 21st day of each month) and mapped to a common global grid at either

nominally 300m (~0.0026°) or 1000m (~0.01°) resolution. The algorithms used to derive the input for

the optical lake water products are implemented in the Calimnos processing chain and were tuned

and validated against 13 predefined optical water types in the NERC (UK) GloboLakes project. This

Quality Assessment Report (QAR) describes the validation performed in precursor activities

(Globolakes, Diversity-II) as well as the quality control performed during and after the processing of

Lake Water products. The validation of OLCI is not yet performed due to lack of in-situ data and

period of processed data. A comprehensive assessment is will have been performed for the next

review cycle.

1.2 SCOPE AND OBJECTIVES

The document presents the results of the quality assessment of Lake Water products (Turbidity,

Trophic State, Lake Water Reflectances) of version 1.1.0. It shows single examples as well as

provides a wider overview by providing time series and match-up analyses of LSR and quality

parameters.

1.3 CONTENT OF THE DOCUMENT

This document is structured as follows:

• Chapter 2 recalls the users’ requirements, and the expected performance

• Chapter 3 describes the methodology for quality assessment, the metrics and the criteria of

evaluation

• Chapter 4 presents the results of the analysis

• Chapter 5 summarizes the main conclusions of the study

• Chapter 6 makes recommendations based upon the results




1.4 RELATED DOCUMENTS

1.4.1 Applicable documents

AD1: Annex I – Technical Specifications JRC/IPR/2015/H.5/0026/OC to Contract Notice 2015/S 151-

277962 of 7th August 2015

AD2: Appendix 1 – Copernicus Global land Component Product and Service Detailed Technical

requirements to Technical Annex to Contract Notice 2015/S 151-277962 of 7th August 2015

1.4.2 Input

Document ID Descriptor

CGLOPS2_SSD Service Specifications of the Global

Component of the Copernicus Land

Service.

CGLOPS2_SVP Service Validation Plan of the Global

Land Service

CGLOPS2_ATBD_LWQ300_1km_v1.2.0_I1.08 Algorithm Theoretical Basis Document of

the Lake Water Quality Products, 300m,

Demonstration product, historic and NRT

data

1.4.3 Output

Document ID Descriptor

CGLOPS2_PUM_LWQ300_1km_v1.2.0_I1.06 Product User Manuals summarizing all

information about the Lake Water Quality

Products, 300m, Demonstration products,

historic and NRT data

1.4.4 External documents (if any)

N/A




2 REVIEW OF USERS REQUIREMENTS

According to the applicable document [AD2], the user’s requirements relevant for Lake Water – Lake

Surface Reflectance, lake turbidity and trophic state are:

• Definition:

The Lake Water Products are composed by the Lake Surface Reflectance (LSR) and Lake Turbidity

(TUR) and an estimate of Trophic State (TS). The products shall be provided as 10days averages

of the respective parameters, except for LSR for which the observation that is statistically most

representative for the observed time period, is given. Reason for the latter is that an average of LSR

is not physically accurate and would not be useful to feed into alternative algorithms for the retrieval

of optical water quality parameters. Turbidity is a key indicator of water clarity, quantifying the

haziness of the water and acting as an indicator of underwater light availability. Trophic State refers

to the degree at which organic matter accumulates in the water body and is most commonly used in

relation to monitoring eutrophication.

• Geometric properties:

The baseline datasets pixel size shall be provided at resolutions of 100m and/or 300m and/or 1km.

The target baseline location accuracy shall be 1/3 of the at-nadir instantaneous field of view. Pixel

co-ordinates shall be given for centre of pixel.

• Geographical coverage:

Global window

The initial window definition is aligned to the global datasets produced during the GIO phase for the

most widely used output data:

• geographic projection: lat long,

• geodetical datum: WGS84

• pixel size: 1/112°

• accuracy: min 10 digits

• coordinate position: pixel centre

• global window coordinates: UL: 180W-75N, BR: 180E, 56S (40320 col, 14673 lines)

The following output specifications are further optimised with respect to the requirements:

• pixel size at 300m: 0.25/112°

• pixel size at 1km: 1/120°

• global window coordinates: UL: 180°W-90°N, BR: 180°E, 90°S

• global grid size at 300m: 161280 columns, 80640 lines

• global grid size at 1km: 43200 columns, 21600 lines




Continental windows:

Continental windows may be asked by the contracting authority to satisfy specific user needs.

Wherever applicable, continental windows shall be drawn from global output data.

• Accuracy requirements:

Lake Water Reflectance is an Essential Climate Variable (defined in 2016) with an associated

accuracy requirement of 30%. Currently only a relative accuracy requirement is known. Reflectance

at some wavebands (near-infrared, and depending on water type, also shorter wavebands) can be

near-zero and relative errors could thus be very large even when absolute errors are small. The

relative accuracy requirement is therefore for practical quality assessment purposes interpreted as

the spectrum average accuracy requirement.

• Temporal Definition

As a baseline the biophysical parameters are computed by and representative of decades, i.e. for

ten-day periods a decade is defined as follows: days 1 to 10, days 11 to 20 and days 21 to end of

month for each month of the year.




3 QUALITY ASSESSMENT METHOD

3.1 OVERALL PROCEDURE

Different validation methods were applied in order to assess the quality of the lake water products.

First, spatial and temporal patterns have been investigated by visual inspection of maps and time

series. Maps showing decades from different decades of one year or from the same decade over

different years are used to assess the comparability of spatial patterns in selected lakes. Time series

of selected positions in different lakes were extracted and plotted for several parameters in order to

assess the reliability of seasonal trends, outliers, unexpected patterns. Comparisons are performed

between different MERIS products as well as between archive processing (MERIS) and NRT

processing (OLCI). Here, the consistency of the NRT data of S3-OLCI is checked against the 10-

day averages of MERIS data by comparing the extraction of time series with the observed seasonal

cycles at the same position. This validation of product consistency is shown in section 4.1.

In a second step, the different parameters have been validated against in situ data. Here, the

assessment is performed by a) time series (from 10D averages) at selected measurement stations

and b) match-up analyses. For the match-up analysis, which is based on the 10-day averages, the

time difference between an in situ measurement and the averaged day of the 10-day product can be

up to 3 days. Such long intervals are nevertheless usually necessary to collect sufficient match-ups

because the in situ observation data archive for inland waters is scarce. The scatterplots differentiate

between samples that are collected the same day, +/- 2 days and +/- 3 days. The match-up analyses

of L2 products, which has been performed within GloboLakes allows a time difference of 1, 3, or 7

days depending on measurement parameter, to optimize between introducing errors due to

spatiotemporal dynamics and limited availability of in situ data.

For most of the comparison exercises with in situ data, a window of 3 x 3 pixels (micropixel) around

the location of the measurement stations has been extracted from the satellite products and filtered

for invalid pixels before averaging. For filtering, the following flags are applied:

l1_flags.INVALID or ide_cloud_classif_flags.F_LAND or ide_cloud_classif_flags.F_CLOUD or

ide_cloud_classif_flags.F_CLOUD_BUFFER or ide_cloud_classif_flags.F_CLOUD_SHADOW or

ide_cloud_classif_flags.F_MIXED_PIXEL

The same valid pixel expression is applied for the generation of the Level3 products.

3.2 SATELLITE REFERENCE PRODUCTS

Analyses are performed on the one hand with L2 products processed with the Calimnos processing

chain (not a Copernicus Land user product) for the validation of atmospheric correction and

chlorophyll concentration underlying the Lake Water Quality products. On the other hand, the user

products L3, 10-day averages have been evaluated for the Lake Water Quality products.

Results for a number of example lakes are provided:




• Lake Vättern, Sweden

• Lake Kyoga, Uganda

• Lake Müritz, Germany

• Lake Kasumigaura, Japan

• Lake Huron, USA

• Lake Superior, USA

• Lake Apopka, USA

Match-up Analysis has been performed among all lakes with suitable in situ data (derived from

LIMNADES data base).

3.3 IN SITU REFERENCE PRODUCTS

Different data sources have been used for the quality assessment:

• US Data bases STORET (http://www3.epa.gov/storet/)

• Water Quality Portal (WQP) (http://waterqualitydata.us /portal/)

• LIMNADES (https://www.limnades.org/home.psp)

LIMNADES has been established within the GloboLakes project and is held at the University of

Stirling, UK. It is a centralised database of ground bio-optical measurements of worldwide lakes

through voluntary cooperation across the international scientific community and the results were

available in the GloboLakes project, from which some validation results are adopted here. Part of

the LIMNADES dataset is also available directly to CGLOPS. LIMNADES will provide a repository

for inherent and apparent optical property datasets and associated water constituent measurements;

and in situ water constituent measurements for satellite validation.




4 RESULTS

The results are presented starting with the consistency check of the time series for several lakes

either from the data archive of MERIS or the NRT S3-OLCI data. They shall provide an overview of

different lake types, globally distributed. This is followed by the comparison with in-situ data, started

with Lake Water Reflectances (match-up analysis) (4.2), the Turbidity (time series (4.3.1) and match-

up analysis (4.3.2)) and finally the Chlorophyll Concentration as input for the Trophic State Index

(4.4).

4.1 VISUAL INSPECTION - CONSISTENCY OF TIME SERIES AND MAPS

4.1.1 Historical data

4.1.1.1 GLWD00000095 - Lake Vättern

Lake Vättern is the second largest lake in Sweden and is

characterized by high transparency.

The depth, the relatively large water volume, and the

transparency make it a unique body of water. The ratio of

drainage area/lake area is only 2.3, which suggests a low areal

loading and hence an oligotrophic status (World Lake

Database). The chlorophyll concentration is around 1 mg m-3

and quite stable during the year (slight increase in June).

During winter months, remote sensing is less suitable due to

light conditions.




Figure 1: Maps showing turbidity of Lake Vättern in 2005, the first decade of each month is presented

The time series of the LWQ products show the low turbidity with slightly seasonal trends and an

oligotrophic status over the full year. They confirm the expected values of under 1 mg/m³ chlorophyll

concentration and suspended matter concentration (Noges et al. 2008).




Figure 2: Time series in lake Vättern showing the consistency of parameters (turbidity, Rw443, Rw560,

Rw665 and trophic state). Trophic state classes range between 0 and 100 with oligotrophic between 0

and 30; meso between 40-50, eutrophic 60-80 and hypertrophic 80-100.




4.1.1.2 GLBL00000013 - Lake Kyoga

Lake Kyoga is located in Uganda and occupies a

very shallow saucer-like depression. Depth does

not exceed 5.7 m and in the greater part is less

than 4 m. Large areas less than 3 m are covered

by a continuous presence of water lilies. The

shoreline is fringed with papyrus and other

swamps sometimes forming a belt of several

miles width between land and the open water.

The lake is divided into three environments: the

open water deeper than 3 m; the water less than

3 m deep which is covered completely with a

growth of water lilies; the swamps chiefly

papyrus, which fringe the shoreline (2). There

are numerous floating papyrus islands in the

lake. (World Lake Database).

Products are only derived from open water areas

which are not covered by floating vegetation.

Figure 3: Maps showing turbidity of Lake Kyoga in 2005, the first decade of each month is presented.




Figure 4: Time series in lake Kyoga showing the consistency of parameters (turbidity, Rw443, Rew560,

Rw665 and trophic state). Trophic state classes range between 0 and 100 with oligotrophic between 0

and 30; meso between 40-50, eutrophic 60-80 and hypertrophic 80-100




4.1.1.3 GLWD00001649 - Lake Müritz

Lake Müritz is the largest lake in Germany that is completely

located within Germany. It has been strongly influenced by

waste water, which is reduced since the 1970s. Improved

water quality is observed since 1990s, reflected in reduced

turbidity of the lake. The lake has large inter-annual changes

in pytho- and zooplankton succession. It is categorized as

mesotrophic.

(https://www.umweltbundesamt.de/themen/wasser/seen/zust

and#textpart-1)

Figure 5: Maps showing turbidity of Lake Müritz in 2009, the first decade of each month is presented.

The data from Lake Müritz show many data gaps. Partly this is due to clouds, but also because the

derived parameters (TUR, TSI) are not available for each valid reflectance spectrum (seeFigure 7).

This issue is caused by very low class memberships and is described in 5.2. The time series show

expected ranges of turbidity (relatively clear water in the centre of the lake with slight seasonal

patterns, a trophic state index varying over the season and seasonal patterns in the reflectances.




Figure 6: Time series in Lake Müritz showing the consistency of parameters (turbidity, Rw443, Rew560,

Rw665 and trophic state). Trophic state classes range from 0 and 100, separated in 10 categories

corresponding to CHL concentration following Carlson et al. 1977




Figure 7: LSR 443 nm (left) and TUR (right) for Lake Müritz in 2006, 6 example decades showing the

loss of information coverage during the processing step from reflectance to water constituent

concentrations.

4.1.1.1 GLWD00001204 - Lake Kasumigaura

Lake Kasumigaura, located in Japan is with an

average depth of 4m shallow when compared to its

total area. The lake has a rather long water retention

time, roughly 200 days. Furthermore, due to typically

high water temperatures (from 3 to 31 degrees

Celsius), and factors such as the large water volume

to lake area ratio, the lake is particularly prone to

eutrophication.

(http://www.wepa-

db.net/policies/cases/kasumigaura/03-1.htm)

Chlorophyll-a concentration ranges between 20 and

200 mg m-3, with the majority of observations between

50 and 100 mg m-3.

National Institute for Environmental Studies (2016) Lake Kasumigaura

Database, National Institute for Environmental Studies, Japan. Accessed

via http://db.cger.nies.go.jp/gem/moni-

e/inter/GEMS/database/kasumi/index.html




Figure 8: Maps showing turbidity of Lake Kasumigaura in 2010, the first decade of each month is

presented

The time series of the lake show slightly seasonal patterns but also some scatter, especially in the

first half of the period. Data availability is increasing and significantly higher after 2006. The trophic

state index is indicating an eutrophic lake.




Figure 9: Time series in Lake Kasumigaura showing the consistency of parameters (turbidity, Rw443,

Rew560, Rw665 and trophic state Trophic state legend: 0: oligotrophic, 1: mesotrophic, 2: eutrophic,

3: hypertrophic.




4.1.1 Performance of mapping per optical water type

The optical water type itself is not a parameter within the final LWQ products delivered to the users.

However, some examples of their distribution and the influence they have on the blending of

algorithms shall be demonstrated here. Maps of the OWT in 4 lakes as well as spatial transects of

trophic status and turbidity parameters are provided shall show whether changes of classes

introduce spatial discontinuities into the parameters. Both – the image and the transect do not show

any transitions at class borders, however the algorithm for turbidity is the same for the three identified

OWT classes 3, 9 and 13.

Figure 10: Lake Huron OWT (left) and Turbidity (right) for OLCI acquisition 26.08.2017. The line marks

where the transect shown in Figure 11

Figure 11: Blended Turbidity and OWT classes along a transect in Lake Huron (position of transect in

Figure 10

Figure 12 and Figure 13 show the same setup for Lake Turkana, which is characterized by OWT

classes 2,4 and 9. The small peaks in the turbidity correspond to single pixels of OWT class 9 inside

OWT Turbidity




larger areas of OWT class 2. No transition is visible between the OWT classes 4 and 9 in the turbidity.

Different algorithms are applied for OWT dominant classes 2 and 4 than for OWT dominant class 9.

Figure 12: Lake Turkana OWT (left) and Turbidity (right) for OLCI acquisition 19.09.2017. The line marks

where the transect shown in Figure 13

Figure 13: Blended Turbidity and OWT classes along a transect in Lake Turkana (position of transect

in Figure 12

Turbidity OWT




4.1.2 Comparison of archived (MERIS) and NRT (OLCI) data

This section presents the results of the comparison of spatial and temporal structures in the data

sets. First, we compared the spatial patterns in maps of the first decade in October for 4 selected

years: 2005, 2008, 2010 for the archive production and 2017 for the NRT production.

As the NRT data from S3-OLCI and the MERIS archive are not overlapping in time, a direct

comparison of in-water products on a pixel-by-pixel basis is not possible.

The MERIS L3 10-days products cover the years 2003 to 2011 (full years). They can be averaged

into a seasonal cycle of inherent optical properties like water leaving reflectance, and of turbidity,

and a standard deviation of the seasonal cycle.

The OLCI L2 products, which consist of observations from the second half of 2017 and first half of

2018, should fall into a similar range as the MERIS products. Although, environmental changes in

the last 5 years could occur, which lead to significant differences between the time series baseline

and the recent observations.

Each color in the time series plots of the following chapters corresponds with a year (2003 blue to

2012 red crosses for MERIS), the averaged seasonal cycle is represented by the solid line, the 1.5

standard deviation of the time series is shown as a dashed line. OLCI products are represented by

black dots for 2017 and red dots for 2018.

4.1.2.1 GLWD00000002 – Lake Superior

Spatial distribution of turbidity in Lake Superior is shown in Figure 14. The maps are showing

comparable level and spatial distribution of turbidity.




Figure 14: Turbidity in Lake Superior in 4 different years (fist decade in October) for 2005, 2008, 2010

(all MERIS) and 2017 (OLCI)

The time series at one measurement station (EPA_GLNPO-SU03), at which in-situ measurements

are regularly taken (measurement stations in Figure 15), are shown in Figure 16. The OLCI water

leaving reflectances appear systematically lower in 2018 (red dots in Figure 16) and systematically

higher in 2017 (black dots), but there might be environmental changes in the lake between the

MERIS period and the seasonal trend in 2017/2018. Turbidity from MERIS and OLCI correspond

well, except one exceptional value in OLCI 2018. The trophic state index is one category higher in

the OLCI products than in the MERIS products.

Turbidity (FNU)




Figure 15: In-situ stations at Lake Superior. MERIS L3 turbidity_mean

Figure 16: Seasonal cycle in MERIS L3 and OLCI data at station GLWD00000002 EPA_GLNPO-SU03.




4.1.2.2 GLWD00000005 – Lake Huron

Spatial distribution of turbidity in Lake Huron is shown Figure 17. The maps are showing comparable

level and spatial distribution of turbidity. Lake Huron is a clear lake, except some bays, especially

the turbidity in the Saginaw Bay. The product from 2017 show bit less turbidity than the other years.

Figure 17: Turbidity in Lake Huron in 4 different years (fist decade in October) for 2005, 2008, 2010 (all

MERIS) and 2017 (OLCI)

2005/10/01 2008/10/01

2010/10/01 2017/10/01

Turbidity (FNU)




Figure 18: In-situ stations in Lake Huron as positions for time series extractions in MERIS L3 and

OLCI L2 products. Example of a MERIS turbidity_mean product (background).

For the consistency of seasonal development of the different parameters, the time series at selected

stations have been compared. The positions of in-situ stations in Lake Huron fall into two categories

(Figure 18). They are either stations in Saginaw Bay (station names begin with 21MICH_WQX), a

very shallow region with higher turbidity and trophic state, or in the central part of the lake

(EPA_GLNPO_...) with low turbidity and trophic state.

At station 21MICH_WQX-090250 (Saginaw Bay) the time series of OLCI products agrees quite well

with the MERIS observations (Figure 19). Most OLCI reflectances are at the lower range of the

seasonal cycle. Also the trophic state agrees well, changing between 50 and 60 during the season

in both sensors. Turbidity is in 2018 in the lower range of the MERIS values, while in 2018 OLCI is

covering the full range that MERIS covered and is thus in good agreement.

At the position of EPA_GLNPO-HU32 (central, clear lake) there is a clear development between

years leading to different offsets in the reflectances at 490 to 665nm (Figure 20). The OLCI data is

following the shape of the seasonal cycle nicely, though reflectance in the blue are overestimated

(which is a known OLCI issue). Turbidity and TSI agree well between MERIS and OLCI.




Figure 19: Time series at station 21MICH_WQX-090250.

Figure 20: Time series at station EPA_GLNPO-HU32.




Tu

rbid

ity (

FN

U)

4.1.2.3 GLWD00000022 – Lake Turkana

Lake Turkana is characterized by very turbid waters in the North and decreasing turbidity towards

the southern parts of the lake. Suspended material is carried into the lake by the Omo river; the

concentrations follow a clear seasonal trend. The comparison of spatial patterns between the

different years (always month October) are shown in Figure 21. The three MERIS products and the

one OLCI product (lower right) all show this trend.

Figure 21: Turbidity in Lake Turkana in 4 different years (fist decade in October) for 2005, 2008, 2010

(all MERIS) and 2017

The time series is shown for 2 different positions: one in the high turbid area in the North and one in

the clearer part in the South. The northern part of Lake Turkana, which is very much influenced by

high sediment loads from river inflow, shows very similar seasonal patterns in turbidity and all

spectral bands except 665nm. in the time series between MERIS and OLCI, while the southern part

2005/10/01 2008/10/01

2010/10/01 2017/10/01




has a much stronger seasonal appearance in the OLCI data. It is assumed that this is not due to

sensor differences, but to changes in the ecosystem. 2017 seems to be a year where the sediment

plume has stronger influence on the southern part of the lake than the years 2002 - 2012. The trophic

state shows a good agreement between MERIS and OLCI in the southern part of the lake.

Figure 22: Time series in the North of Lake Turkana (turbid part of the lake)




Figure 23: Time series in the South of Lake Turkana.

4.2 COMPARISON WITH IN-SITU DATA – LAKE WATER REFLECTANCE

POLYMER v3.5 was validated against in situ reflectance data contained in LIMNADES within the

GloboLakes project. POLYMER gave the best performance (unbiased error) in each waveband,

although a consistent underestimation is apparent (Figure 24). This systematic error is cancelled out

in the whole-chain validation of TSM and chlorophyll-a retrieval. A correction is not attempted for the

LSR because there are not enough in situ validation available to inspect the performance per Optical

Water Type. Improvement of the POLYMER atmospheric correction over inland waters is subject to

an agreed evolution of the CGLOPS Lake Water service in 2017-2018. It should be noted that

underestimation of reflectance has a minimal effect on water constituent retrieval algorithms that

operate primarily on the shape of the reflectance spectrum. This is the case for turbid water

chlorophyll-a algorithms and some TSM algorithms.




Figure 24: POLYMER v3.5 validated against in situ reflectance data contained in LIMNADES (source:

GloboLakes). Here, results for a 3x3 pixel window with a 3-day difference between satellite and in situ

observation, are shown.

4.3 COMPARISON WITH IN-SITU DATA – TURBIDITY

4.3.1 Time series at sampling stations

Time series comparison between in situ data and EO derived parameters show the behaviour of

both measurement techniques over time. The focus is on the consistency of the time series on the

one hand and on the comparability of the data sets on the other hand. The order of magnitude and

seasonal patterns are investigated. A small selection of lakes is presented here: Lake Huron, Lake

Apopka and Lake Superior. The selection comprises US Lakes, due to the availability of in-situ

measurements, which were extracted from the US Data bases STORET

(http://www3.epa.gov/storet/) and WQP (http://waterqualitydata.us /portal/).

Lake Huron is characterized by two water types. While most of the lake is dominated by clear water,

Saginaw Bay is characterized by more turbid waters. This can be clearly seen in Figure 25. For

demonstration, two time series are shown for a station in the clear lake water and for one station in

the Saginaw Bay. They are marked with arrows in Figure 25. The respective time series are shown

in section 4.3.1, while the upper plot shows the time series in the Saginaw Bay with turbidity between




Turb

idity (

FN

U)

2 and 10 FNU and in the main lake (below) with turbidity below 0.5. FNU. In situ data (green points)

and CGLOPS Turbidity product (blue crosses) correspond in their magnitude.

Figure 25: Turbidity in Lake Huron (10D average 20080811-20080820). Red arrows indicate the position

of the stations shown in the time series plot (Figure 26).

Figure 26: Time series of turbidity_mean (blue +) and in-situ turbidity (green). Lake Huron, Saginaw

Bay (above) and central lake (below). In-situ data: US Data bases STORET

(http://www3.epa.gov/storet/) and WQP (http://waterqualitydata.us /portal/). Note the different scales of

the y-axes.

http://www3.epa.gov/storet/




The turbidity in Lake Apopka is varying over the years quite significantly. This can be observed in the time series shown in Figure 28 in both data sets - in-situ data (green cycles) as well as in the CGOPS Turbidity products (blue crosses). Overall, the in-situ data show higher turbidity values.

Figure 27: Turbidity in Lake Apopka in June 2005 (left) and June 2008 (right). The arrow indicates the

station that is shown in the time series Figure 28.

Figure 28: Time series of turbidity_mean (blue +) and in-situ turbidity (green) in Lake Apopka. In-situ

data: US Data bases STORET (http://www3.epa.gov/storet/) and WQP

(http://waterqualitydata.us/portal/)

Lake Superior is the second largest lake in the world next to the Caspian Sea. According to the World

Lakes Database, Lake Superior water is still oligotrophic and transparency at the center of the lake

is generally around 9m. Higher turbidity is shown in the map (Figure 29) only in the Black Bay in

Canada, unfortunately, no in situ measurements are available from that region for verification. But

according to the World Lakes Database, “the lower transparency in Black Bay and Batchawana Bay

was attributed to the natural re-suspension of bottom sediments by wave action and the low

transparency in Thunder Bay and Nipigon Bay was to urban and industrial sources of suspended

solids.” (http://wldb.ilec.or.jp/data/databook_html/nam/nam-04.html)


http://waterqualitydata.us/portal/




Figure 29: Turbidity map of Lake Superior for decade 20080721-20080730. The arrows indicate the

position of the stations shown in the time series plots in Figure 30 (red arrow) and Figure 31 (green

arrow).

The low turbidity mainly all over the lake can be seen in different time series plots, of which three

examples are shown in the following figures. Only close to the coast, the turbidity goes up to 2

(station GPC5_WQX-LS_PT_9, green arrow) for the other stations it stays below 1, mainly below

0.5.


position of the red arrow in Figure 29. In situ data: US Data bases STORET

(http://www3.epa.gov/storet/) and WQP (http://waterqualitydata.us/portal/)






Figure 31: Time series of turbidity_mean (blue +) and in-situ turbidity (green) in Lake Superior at the

position of the green arrow in Figure 29. In situ data: US Data bases STORET



position of the orange arrow in Figure 29. In situ data: US Data bases STORET


4.3.2 Match-up analysis

The 10-days-mean extractions in time at the in situ stations are temporally filtered, so that turbidity

values with a time difference of up to 3 days are compared.

While the observation time of the in-situ measurement can be used in a straightforward fashion, the

time of a 10-days-mean value needs some refinement. The lake product holds the information of the

day of the first and last satellite observation after the starting day of the observation period. If the

number of observations is one, the first and last observation is the same and the corresponding date

is well-defined. With two observations, the date can be defined as the time in the middle between








first and last observation. If both observations are temporally close, the change in the product can

be assumed to be linear and the ‘mean’ time will correspond well with the mean value of the product.

The approach is dismissed to simply use the middle date of the aggregation period. If the

observations are taken at the beginning and the end of the 10-day period, the average day will be

the middle of the period. But if both observations are taken more to the beginning or the end of the

period, the average day will reflect this better than the approximation by using the middle of the

period.

The time difference between an in-situ measurement and the averaged day of the 10-day product

can be up to 3 days. The collection of all data pairs at all stations in Lake Huron and Lake Apopka

respectively are shown in Figure 33. The shape of points marks this time difference with circle: same

day, triangle: +/- 1day, square: +/- 2 days, pentagon: +/- 3 days. The colour of the points refers to

the number of satellite observations, which are used in calculating the mean turbidity. There is no

apparent dependence on time difference or number of observations in the match-up data for Lake

Apopka. The 10-day turbidity mean value is systematically lower in this lake.

Figure 33: Match-up analysis of turbidity in-situ data and 10-days-turbidity_mean. Time difference

between in situ and satellite product is coded in the shape of the points (circle: same day, triangle:

+/- 1day, square: +/- 2 days, pentagon: +/- 3 days). The number of observations contributing to the

mean turbidity ranges from 1 to 6 and is shown by the colour (black, red, green, blue, grey,

magenta).

In Saginaw Bay as part of Lake Huron, the relationship between in-situ and 10-day-mean turbidity is

not as clear as for the much smaller Lake Apopka. For the latter, the results suggest that the retrieval




of turbidity is presently underestimated, even though the observations are consistent over time and

space.

4.4 COMPARISON WITH IN-SITU DATA – TROPHIC STATE INDEX

The chlorophyll concentration is the input parameter for the trophic state product and can be

validated against in situ data. Figure 34 shows the results of the validation for chlorophyll algorithms

performed in the Globolakes project using in situ concentration and reflectance data. This is shown

for reference as the best-attainable result when using satellite imagery. The scatter plots show

the chlorophyll concentration separated by the different water types (colour) and corresponding

chlorophyll algorithms (shape). The data set of satellite matchups and in situ observations is notably

smaller and necessitates validation over a ±7 day matchup window. These results, shown in Figure

35 and which do not cover all water types, should be interpreted as worst-case. These show a slight

systematic underestimation, and a coefficient of determination R2=0.62 over the observed range (0-

70 mg m-3).

Figure 34: Performance of chlorophyll-a retrieval across all optical water types (clusters 1-13) and

associated algorithms following tuning of each algorithm, for each OWT, against the LIMNADES

database (results courtesy University of Stirling, GloboLakes project, Neil et al. submitted.).




Figure 35 Satellite matchup analysis for chloropyll-a retrieval in the Calimnos-MERIS processing chain.

Linear regression analysis provides R2=0.62, slope=0.82, intercept=1.16, n=350. To obtain sufficient

matchup points, a matchup window of ±7 days is used. Temporal variation in this timeframe can be

significant, contributing to scatter in the observed relationship. The result should be interpreted as

worst-case.




5 CONCLUSIONS

5.1 SUMMARY

The assessment of sample lakes shows that the products are consistent in time and mainly also in

space. Seasonal patterns are as expected. The turbidity is sometimes showing spatial patterns that

are caused by a) the merging of algorithms and b) the temporal averaging. The comparison between

the products and in-situ data show same magnitude, but only a few analyses could be performed

here. In future and in a scope of a second level validation these investigations will be intensified.

The following list provides an overview on the performance of the products with respect to the

requirements.

Requirement LWQ product specification

Spatial resolution: 100m and/or 300m and/or

1km.

300m products are included in the current

version of the quality assessment

location accuracy shall be 1/3 of the at-nadir

instantaneous field of view.

Fulfilled according to Bicheron et al. 2005

(AMORGOS processing step in the Calimnos

processing chain)

Coverage: global windows Currently the products are stored in a global

window, however a split into continental subsets

might be necessary due to performance issues

for dissemination

Lake Water Reflectance with an associated

accuracy requirement of 30%.

According to the validation performed within

Globolakes, the accuracy of the LSR differs per

waveband, with the detrended normalised root-

mean-square-error (dNRMSE%) ranging 15-

109%. Best performance is seen in the red and

NIR bands with errors of 25%, 19%, 17%, and

15% in bands 665, 709, 754, and 779 nm

respectively. These scores are decisive for

retrieval of the chlorophyll-a and TSM estimates

in a wide range of inland water types. The

average dNRMSE% across the spectrum is

46.6%, owing to larger errors in the blue

wavebands.

Decades: days 1 to 10, days 11 to 20 and days

21 to end of month for each month of the year

fulfilled




5.2 LIMITATIONS AND KNOWN ISSUES

This version is the demonstration version of Lake Water Quality products. In addition to the

reprocessing of MERIS, also OLCI products are now integrated in the processing. Known issues

have been highlighted in the ATBD and are repeated here for completeness of the assessment.

The OLCI products are currently assessed by comparing the available time series with the seasonal

cycle calculated from the historical data. A validation of OLCI is not yet feasible due to lack of in-situ

data and the period of processed data and will likely take several years to complete.

We first note that some of the spectral bands serve their primary purpose in atmospheric correction

and may have been assigned negative values. All results from atmospheric correction are

nevertheless included to support the widest possible use of the data. There is a systematic

underestimation of Rrs data in the current product version. We aim to include the mineral absorption

model in the next release to improve the reflectance bands.

The most crucial assumption in version 1.2.0 of the Calimnos processing chain is that optical water

types which have been defined from a large set of in situ data from optically complex waters (lakes,

reservoirs, lagoons, estuaries, and coastal areas) can be assigned successfully to each satellite

observation (pixel) containing open water. In practise, atmospheric correction may have systematic

errors for some water types, leading to low membership scores for these water types. This, in turn,

implies that suboptimal reflectance algorithms for chlorophyll-a and suspended matter may be

selected. Solutions for this issue are being explored.

The simultaneous classification of turbid and clear water types is challenging because this method

relies on a set of covariance matrices which can become invalid when there is no significant

difference in the amplitude between consecutive wavebands, such as can be the case with clear

water pixels at longer wavebands. This is a known issue that results in gaps in the output data over

clear water types. At present the issues is solved by filling such pixels with results from clear-water

algorithms (e.g. a tuned OC2 algorithm for chlorophyll-a). Gap filling is based on the assumption that

the reflectance band ratio Rw(412)/Rw(560) is greater than 1 for clear water types. Nevertheless,

different number of valid pixels can occur in the different products (LSR, TUR and TSI). Different

groups of algorithms map to different groups of OWTs for the two variables. In both cases, negative

values are removed. It is possible that there are more invalid or negative results that are masked out

for turbidity, compared to TSI.

If the pixel identification and thus the flagging of erroneous pixels is not working properly, i.e. at cloud

borders, thin clouds or lake ice coverage, the water leaving reflectance and subsequent turbidity and

trophic state retrieval may fail. Flagging is always subject to improvement - in the current version

some subsequent filter steps are applied in order to remove suspect results from the images.

The spatial blending of different algorithms and/or the averaging of different, very inhomogeneous

days under partial cloud cover can lead to visually inconsistent maps, showing patchy patterns where

coverage on different days of observation was incomplete, and if optical conditions in the lake

changed over this period. This is considered normal behaviour for the decadal product. The




upcoming blending algorithm will consider the optical water type membership as weighting factor to

avoid jumps within one acquisition date.

The radiometric accuracy of OLCI on Sentinel 3A is known to show a positive bias in the order of 2-

3% in the visible spectrum. Because a final correction is not yet available and the effects of an

arbitrary correction on the performance of the atmospheric correction is not yet investigated, no

correction is yet attempted for the demonstration product. It is expected that further alignment will

be achieved in a future version.

For the time being, no uncertainty information is provided with the products. This information is

currently very difficult to derive with the end-to-end validation and small number of in-situ per optical

water type. The issue is foreseen to be covered in future R&D related projects.




6 RECOMMENDATIONS

Manual inspection of all products for more than 1000 water bodies is impossible and in most cases

requires local knowledge. The validation of the products is, and always will be, based on a small

sample of well-studied areas. Users of these products are therefore advised to inspect the results

for their area of interest before generating derivative products. This inspection could include, for

example, histograms to identify outliers. Users are also advised to take into account the number of

observations underlying the results. Where observations are sparse, having a small number of

satellite passes to cover a large water body can lead to visual inconsistencies that do not reflect the

state of the water body at any particular time – this is merely the nature of creating aggregate

products.

Expert users are encouraged to take part in the calibration and validation of these products that is

increasingly taking place at the global scale. The spatiotemporal coverage and quality of the global

lake water products can only be improved if the algorithms underlying these products can be

accurately adjusted to waters of each optical type (and in some cases, new water types may need

to be defined). We point interested users to the LIMNADES initiative (www.limnades.org), from

where many of the presented validation results are derived.




7 REFERENCES

Bicheron, P., Amberg, V., Bourg, L., Petit, D., Huc, M., Miras, B., Arino, O. (2011). Geolocation

Assessment of MERIS GlobCover Orthorectified Products. IEEE Transactions on Geoscience

and Remote Sensing, 49(8), 2972–2982.

ILEC/UNEP: World Lake Database. - http://wldb.ilec.or.jp/

Noges, T, Eckmann, R., Kangur, K., Noges, P. Reinart, A., Roll, Gl, Simola, H., Viljanen, M. (2008):

Euorpean Large Lakes - Ecosystems changes and their ecological and socioeconomic

impacts.- Report from Hydrobiologia, volume 599

Neil, C., Spyrakos, E., Hunter, PD, Tyler, AN (exp. 2018): Evaluation of algorithms for chlorophyll

retrieval in optically-complex inland waters: establishing a framework for a global approach

based on optical water types. Submitted to Remote Sensing of Environment

http://wldb.ilec.or.jp/

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