Tyler A - UEI Day 1 - Kochi Jan18

Exploiting EO capability to monitor the water quality
status and change in freshwater environments
Andrew Tyler & Vagelis Spyrakos
Expert Toolkit
Improving freshwater monitoring frameworks and data for research and management

Overview
• The Development of the Science
• Overview of the State of the Art: GloboLakes
• Example data from Lake Vembanad
• Outcomes from the IUKWC workshop Stirling:
Enhancing Freshwater Monitoring through Earth Observation
• Opportunities and Considerations
IUKWC-Kochi January 2018: Improving freshwater monitoring frameworks and data for research and management

Inspired by opportunity and need…
Lake Balaton, Hungary Landsat 7
Need to monitor for management, protection and resilience
Recognition of the spatial and temporal heterogeneity
Tendency for reactive monitoring
Scale of the problem
Algorithm stability
Challenges of optically complex waters
Growing capacity and capability of satellite platforms
Tyler et al., 2006, IJRS

Airborne Hyperspectral: PC retrieval
Hunter, et al. (2008). RSE
Hunter, et al. (2008). Limnol. Ocean
Hunter, et al. (2009). Envi. Sci. Tech.
NERC ARSF:
AISA Eagle and Hawk

MERIS in inland waters
Envisat: MERIS

Elterwater
Lat: 54.4273 / Long: -3.0230
World View-2: Water optical type classification
Turbid NIR-red ratio
R754/R659
Clear green-blue ratio
R546/R478
In-water algorithm
Mapped
Level-2
Chla
Hunter and Tyler (2012) EA Report
Inner basin
48.4 mg Chla m-3
Middle basin
13.1 mg Chla m-3
Outer basin
1.00 Chla mg m-3
Loughrigg Tarn
STIR: 27.8 mg m-3
EA: 27.2 mg m-3

Inner basin
48.4 mg Chla m-3
Middle basin
13.1 mg Chla m-3
Outer basin
1.00 Chla mg m-3
Loughrigg Tarn
STIR: 27.8 mg m-3
EA: 27.2 mg m-3
IS R(608) > 0.04
YES:
Chla ~ R(754)/R(659)
NO:
Chla ~ R(546)/R(478)
Hunter and Tyler et al. (2012) EA Report Report
With Citizen Science Based Validation

EO Challenges – Global Scale
EO key challenges:
 Diversity in retrieval algorithms & validation approaches
 Inland water remote sensing community appears fragmented
Search Keys: remote sensing, water quality,
lakes (2015)
Filter: use of in-situ data for
development/validation
Number of lakes per publication:

Our approach
Lake ecology & modelling
Global lakes observatory
for 1000 study lakes
Environmental statistics
EO lake water
quality
EO + modelled
catchment
drivers
EO lake water
temperature
Change over time
(within / between lakes)
Attributing drivers
of environmental
change
Time-series data &
web visualization

A Global Partnership: LIMNADES
www.limnades.org
o data from almost 1500 inland systems
o radiometric data ~4000 stations >250 lakes
o at least 40 peer-reviewed papers
Parameter Units Range Median Lakes
Chla mg m-3 0.03-13296.70 12.34 208
PC mg m-3 0 – 24677 28.79 60
TSM mg L-1 0.09-2533.30 10.54 81

OWT classification
• ~4,000 Rrs spectra
• K-means clustering
• Optimum number of
clusters determined
statistically
Spyrakos et al.,2017, Limnol. Ocean.

OWT classification
Lake Balaton, HungaryTONLÉ SAP Lake, Cambodia
Spyrakos et al., submitted ….

Algorithm validation
Step 1: Validation
of original
algorithms
Step 2: OWT
cluster-wise
algorithm tuning
Step 3: Algorithm
selection per
cluster or cluster
family
LIMNADES in situ
MERIS matchups
Optical Water Types

In situ algorithm validation
Y=0.8313x + 0.2197
R2=0.7969
MAPE=92.96
Dynamic algorithm selection per cluster
Neil et al., in review, RSE

POLYMER C2R Lakes
GloboLakes OWT
optical
water types
[LIMNADES
2016]
Reflectance algorithms
algorithm
mapping
algorithm
blending
chlorophyll-a, suspended
matter, phycocyanin
passes
database
S-3
[SAFE]
MERIS
[FSG/SAFE]
ESA
catalogue
Discover
Download
Ingest
Subset
Idepix
MPH
Masking
RGB
Quality flags, surface
properties, pixel statistics
1-d
aggregate
1-w
aggregate
1-m
aggregate
GloboLakes, v1.04
‘level 1B’ data
‘level 2’ processing
‘level 3’
mapping & aggregation
Architecture and performance

Operationalisation

Operationalisation
https://globolakes.eofrom.space

Operationalisation
TUNGABHADRA RESERVOIR
https://globolakes.eofrom.space

Operationalisation: ESA Ocean Colour CCI Portal
Monthly time series over box in northern Tanganyika
Data at 4-km but 1-km daily
also demonstrated
Monthly time series over box in northern Tanganyika
https://www.oceancolour.org/portal/

Vembanad Lake: Kerala
Setinel 2 B Christmas Day 2017
C2RCC neural network for Sentinel 2 and 3
Brockmann, C & Doerffer, R. (2016).. Proc. Living
Planet Symposium, ESA SP-470.

Vembanad Lake: Kerala
Setinel 3 B Christmas Day 2017
C2RCC neural network for Sentinel 2 and 3

Existing Projects
EU HORIZON 2020- SC5 2016-2017 (2018-2022)
Multiscale Observation Networks for Optical monitoring of Coastal waters
Lakes and Estuaries
Existing Projects: Bridging the gap

Hyperspectral Drones
• Shallow waters, indented coastlines,
narrow channels make access difficult
• Develop a hyperspectral sensor mounted
on a quad-copter, to study intermediate
scales between satellites and in situ
observations
• Components are available in the market.
Other Platforms
In-Situ – WISP-3

Near-real time monitoring of
reservoir storage using satellite
observations in India
Vimal Mishra and Amardeep Tiwari
Indian Institute of Technology (IIT) Gandhinar
Near real time reservoir storage

Near real time reservoir storage
Figure 1.Changes in groundwater
storage from observation well and
GRACE data during 2002-2013.
Monthly trends in groundwater
anomaly are from GRACE (in
cm/year) and in-situ well observations
from the CGWB for 2002-2013.
Stippling in A-D indicates statistically
significant changes at 5% level. (E-H)
Wells that experienced significant
declines and increases in groundwater
levels (cm/year) during 2002–2013..
Asoka et al. 2017, Nature Geoscience

Reservoir storage area
Reservoirs 
Dantiw
ada
Hirakund Rengali Supa Tungabhadra
Date
15-Oct-
15
23-Oct-15 30-Sep-15 24-Sep-15 19-Oct-15
LANDSAT Area (km2) 38.5029 710.1774 301.959 84.4560 298.4139
MODIS Area (km2) 32.6875 625.3750 262.9375 94.9375 290.6250
Percentage Error (%) 15.10 11.94 12.92 12.41 2.61

Reservoir storage height
• Reservoir surface area were
estimated using K-means
classification of EVI images
followed by image
enhancement.
• Reservoir water surface
elevation estimated from
NASA ICESat/GLAS datasets.
• Water surface area and water
surface elevation are
combined to calculate area
elevation relationship for a
reservoir
• Using depth-area relationship,
reservoir storage was
estimated in near-real time

Reservoir storage with rainfall

Drought Monitoring System
Linking reservoir information to drought
monitoring system
https://sites.google.com/a/iitgn.ac.in/high_resolution_south_asia_drought_monitor/home
South Asia Drought Monitoring
Ending on 18/01/2018

Terrestrial Water Storage (TWS)
Helen Bonsor, Alan
MacDonald, Murray Lark, Kay
Smith,
Laurent Longuevergne
Application of GRACE data to understand
groundwater resources – examples from Africa and
SE Asia

Total water storage from Grace
(CSR release 04)
Rainfall data from NASA TRMM
Soil Moisture from NOAH land
model of GLDAS
Statistical approach – empirical
temporal variograms, linear mixed models
Data Sets Used

Global/Continental TWS Models

SummaryIUKWC EO Workshop Summary I
List of variables that EO could monitor in relation to SDG6

SummaryIUKWC EO Workshop Summary II
• Problems of Water Quality and Quantity are multidimensional
• Need for data on water quality and quantity from EO
• EO could make a real difference by delivering freely available
data to promote data democracy.
• Need to grow capacity and capability to exploit EO capability
• two way exchange of knowledge and skills, PhDs
• promoting the development of validation sites
• EO used to understand water use and:
• promote water use efficiency
• resolve tensions and conflicts in water use
• improve surface water quality to reduce pressures on
groundwater
• Input to models to derive conventional parameters of
water quality such as nutrients and pathogens.
• Citizen science based observatories to promote data validation
and environmental stewardship

SummaryIUKWC EO Workshop Summary III
• Integrate across systems to:
• Cross-calibrate across platforms to provide consistent
product for freshwater monitoring (Satellite, UAV-
Drone, In-situ and Citizen – smartphone)
• Couple satellites with in-situ capability to deliver higher
frequency data to fill data gaps and characterise the
changing phenology
• Link terrestrial/catchment processes to water,
groundwater to surface water, headwaters to coastal
waters to understand the water continuum
• Deliver a complete understanding of the pressures on
water resources and the consequences of policy and
management decisions
• Promote interdisciplinarity
• Promote interaction between Universities, Research
Institutions, Business and Industry, NGOs, Governance
and Society for develop sustainable futures

Thank you!
Andrew Tyler
Professor of Environmental Monitoring
Associate Dean for Research
Faculty of Natural Sciences
Biological and Environmental Sciences
University of Stirling
t +44 1786 467838
e a.n.tyler@stir.ac.uk
w www.stir.ac.uk
w www.globolakes.ac.uk
w www.limnades.org
follow @globolakes
Acknowledgements
Natural Environment Research Council UK
ESA Diversity II (Brockmann Consult)
Over 30 data contributors around the world
H2020 funding: EOMORES, CoastObs,
Monocle, DANUBIUS-RI

Tyler A - UEI Day 1 - Kochi Jan18

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