Carbon dynamics in inland and
coastal ecosystems
Dragon 3 ID – 10561
Project Team
Ronghua Ma
Hongtao Duan
Yuchao Zhang
Juhua Luo
Lin Chen Steven Loiselle
Alessandro Donati
Claudio Rossi
Project Young Scientists (2014-2015)
• Ph.D. studies with Dragon support Kun Xue - Vertical algal biomass algorithm development
Jing Li – Temporal dynamics of algal biomass analysis
• Master students Cosimo Montefrancesco – Drivers of algal dynamics Zhigang Cao – Underwater light conditions
Zuochen Li – CDOM and POC blooms
Young Scientists Training • Algorithm development • Analysis and modelling tool development • Communication training • Dissemination activities (ASLO-Granada, Dragon 3, NIGLAS
conferences) • Seminars/short courses on (in Nanjing, in Siena)
– CDOM, POC, Phytoplankton dynamics, – Bloom algorithm development – Carbon modelling – Community science
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atmospheric carbonProject Goal: To develop new methodologies to
study and monitor carbon dynamics in aquatic ecosystems
• Particulate organic carbon
• Dissolved organic carbon
• Carbon cycling
• Radiative transfer
Carbon dynamics in inland and coastal ecosystems
Project schedule
Bio-optical properties
• Algorithms for CDOM, POC and Chla-a dynamics in Case II waters
• June 2012 – August 2014
Radiative transfer
• Optical conditions in optically complex waters • October 2012 – January 2015
Aquatic carbon
dynamics
• Carbon models exploring spatial and temporal dynamics • March 2014 – June 2016
So far…. Monitoring aquatic carbon dynamics by remote sensing algorithms development temporal and spatial analysis radiative transfer drivers analysis (ongoing) carbon sources and sinks (ongoing) carbon models (ongoing)
Main Results
Dragon Publications 2012 – 2015 (1 of 4) • Jiang, G., R. Ma, S.A. Loiselle and H. Duan (2012) Optical approaches to examining
the dynamics of dissolved organic carbon in optically complex inland waters. Environmental Research Letters 7(3), 034014.
• Duan, H., R. Ma, and C. Hu (2012) Evaluation of remote sensing algorithms for cyanobacterial pigment retrievals during spring bloom formation in several lakes of East China. Remote Sensing of Environment 126, 126-135.
• Jiang, G., R. Ma, H. Duan, S. A. Loiselle, J. Xu, D. Liu (2013) Remote Determination of Chromophoric Dissolved Organic Matter in Lakes, China, International Journal of Digital Earth DOI:10.1080/17538947.2013.805261
• Qi, L., R. Ma, W. Hu, S.A. Loiselle (2013) Assimilation of MODIS Chlorophyll-a Data Into a Coupled Hydrodynamic-Biological Model of Taihu Lake Selected Topics in Applied Earth IEEE Journal of Observations and Remote Sensing, DOI 10.1109/JSTARS.2013.2280815
• Duan H., Feng L., Ma R., Zhang Y., S.A. Loiselle (2014) Variability of Particulate Organic Carbon in inland waters observed from MODIS Aqua imagery Environ. Res. Lett. 9 084011
• Duan, H., R. Ma, Y. Zhang, S. A. Loiselle (2014) Are algal blooms occurring later in Lake Taihu? Climate local effects outcompete mitigation prevention J. Plankton Res. 0(0): 1–6. doi:10.1093/plankt/fbt132
• Duan H, R. Ma, S.A. Loiselle, Q. Shen, H. Yin, Y. Zhang (2014) Optical characterization of black water blooms in eutrophic waters. Science of The Total Environment 2014; 482–483: 174-183.
• Zhang M, R. Ma, J. Li, B. Zhang, H. Duan (2014) A Validation Study of an Improved SWIR Iterative Atmospheric Correction Algorithm for MODIS- Aqua Measurements in Lake Taihu, China. Geoscience and Remote Sensing, IEEE Transactions 1-10.
• Zhang Y., R. Ma, H. Duan, S. A. Loiselle, J. Xu, M. Ma (2014) A Novel Algorithm to Estimate Algal Bloom Coverage to Subpixel Resolution in Lake Taihu Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of 7.7 3060-3068
Dragon Publications 2012 – 2015 (2 of 4)
Dragon Publications 2012 – 2015 (3 of 4) • Zhang Y., R. Ma, H. Duan, S. A. Loiselle, J. Xu (2014) A Spectral Decomposition
Algorithm for Estimating Chlorophyll-a concentrations in Lake Taihu, China. Remote Sensing, 6(6), 5090-5106.
• Jiang, G., R. Ma, H. Duan, S.A Loiselle (2015) Remote sensing of particulate organic carbon dynamics in a eutrophic lake (Taihu Lake, China), accepted for publication in Science of The Total Environment
• Duan, H., S. A. Loiselle, L. Zhu, L. Feng, Y. Zhang, R. Ma (2015) Distribution and incidence of algal blooms in Lake Taihu. Aquatic Sciences, 1-8 (10.1007/s00027-014-0367-2)
• Villa, P., H. Duan, S. A. Loiselle (2015). Using Remote Sensing to Assess the Impact of Human Activities on Water Quality: Case Study of Lake Taihu, China. In Advances in Watershed Science and Assessment (pp. 85-110). Springer International Publishing.
Dragon Publications 2012 – 2015 (4 of 4)
• Duan H., X. Xu, R. Ma, L. Feng, S. A. Loiselle, M. Zhang, C. Hu (in review) Algal bloom dynamics in Lake Taihu: links to global and local drivers
• Loiselle S. A., H.T. Duan, Z.G. Cao (2015) Characteristics of underwater light. In: Field Photochemical processes taking place in surface waters, role of natural organic matter in photochemical reactions and to recently developed tools, analytical techniques (in review) Royal Society of Chemistry
• Zhang Y., R.Ma, M. Zhang, H. Duan, S.A. Loiselle, Ji. Xua (2015) Fourteen year record (2000-2013) of the spatial and temporal dynamics of cyanobacterial blooms in Lake Chaohu observed from time-series MODIS images (in review)
• Xue K., Y. Zhang, H. Duan, R. Ma, S.A. Loiselle (2015) A novel remote sensing approach to estimate vertical distribution of phytoplankton in a eutrophic lake (in review)
Issues and Challenges
• Complex atmospheric conditions reduce temporal resolution
• Complex catchment and hydrological conditions reduce identification of drivers (direct and indirect impacts)
• Many optical conditions are short term (“blooms”)
• Hyperspectral data needed for optically complex waters
• Field work is ongoing (e.g. small lake survey), but time intensive and costly
• Experimenting with community data gathering
EO data planning – 2015 and 2016
HY-1 CZI - new and archived (data quality limitations) HJ-1 CCD - new and archived (under study) MERIS – archived (being used) MODIS – archived (being used) SMOS L2 – new and archived (under study) Sentinel 2 (fingers crossed) Additional data GOCI Geostationary Ocean Color Imager HICO (Hyperspectral Imager for the Coastal Ocean)
Project Planning – 2015 and 2016
• Laboratory / in situ comparison and model development (POC & DOC sources)
• POC /Chla profile studies
• Driver analysis, carbon modelling
• Land use effects on aquatic carbon characteristics and dynamics
• Junior scientist exchange
Bio-optical
properties
• 2012 –2013
Radiative transfer
• 2012 –2014
Aquatic carbon
dynamics
• 2013 –2015
2014 – 2015 preliminary result presentations
Variability of Particulate Organic Carbon in eutrophic lakes presented by Dr. Yuchao Zhang
Vertical distribution of algal biomass presented by Kun Xue
Algal inventory estimation approaches presented by Jing Li
A novel algorithm to estimate POC concentrations in eutrophic lakes Dr. Yuchao Zhang
Carbon cycle in inland lakes
Inland lakes are:
• recipients of terrestrial carbon.
• reserves of stored carbon.
• emitters of greenhouse gases.
POC in inland lakes
Particulate Organic Carbon
POC
Organic Carbon in
inland lakes
Dissolved Organic Carbon
DOC
Greenhouse gas
CO2
CO2 CH4
Dissolved Organic Carbon (DOC)
Through the 0.45μm filter
POC Sources and Sinks Sources:
biological production during photosynthesis
transformation from DOC
upwelling of organic sediment
Sinks:
transformation to DOC
export out of the surface waters
biological removal mechanisms
Remote sensing of water color
According to the optical properties of
water
Apparent optical properties
Inherent optical properties
Biological optical properties of the
water body
Case-I water(Ocean)
Case-II water(Inland lakes/Coastal zone)
Research Challenges
Water color remote sensing satellites such as CZCS, MODIS,
MERIS, SeaWiFS and GOCI with algorithms to estimate the
distribution of POC in case-I waters
1
Algorithms for case-I water do not apply for case-II waters,
where major POC transformations occur. 2
Research objectives
Developed a new biological optical models to estimate POC concentration
Identify relationships between POC and inherent(or apparent)optical properties
Analyze the relationship between POC concentrations and particulate characteristics chlorophyll, suspended solids concentration, etc.
Study area – Lake Chaohu
Chaohu
Materials and Methods
The field sampling Data processing
• Inherent optical characteristics
measurement absorption coefficient / backscattering coefficient
• Water quality parameters
measured Chla、SPM(SPIM、SPOM)
• Carbon component measurement
POC、DOC、C/N
• Surface/Underwater spectral
measurement and processing
• Water sampling
• Backscatter coefficient
measurement
• Others:Wind speed/direction,
transparency, water depth
POC vs. water parameters
POC ∝ aph(665)
POC ∝ aph(665)
Gons and Simis Models
Calibrate model parameters
Estimated aph(665)
p=2.232 γ=0.601
POC retrieval model
Accuracy assessment
Evaluation indicators
RMSE :root mean square of α
Absolute error α=Yi-Xi Relative error β=(Yi-Xi) /Xi
RRMSE: root mean square of β
MNB: arithmetic mean value of β
NRMS: standard deviation of β
Model validation
Model comparison
Model comparison
POC was highly related to the particulate absorption at 665 nm and
strongly correlated with chla.
Gons algorithm (RMSErel=21.90%) can provide a better result than Simis
(RMSErel =23.81%).
Gons and Simis algorithms both achieve good results and can be
combined with MERIS satellite for POC estimates in Chaohu Lake. This
study can provide technical and data support for inland lake water carbon
cycle research.
Conclusions
A novel remote sensing approach to estimate vertical distribution of phytoplankton
Kun Xue PhD student
Nanjing Institute of Geography and Limnology, CAS
Outline
• Background • Study region • Data • Results • Summary
Background
• Increasing occurrence and
intensity of algal blooms
• Monitored using remote
sensing technology
• Most models assumed to be vertical
homogeneous
• Blooms area change dramatically
• Vertical movement of algae
Vertical distribution of phytoplankton
Study region – Lake Chaohu
Methods
• Field measurements
– Chla, SPIM, DOC
– Rrs
– Wind speed
• MODIS satellite data
– Rrc data
Rrc(λ) = ρt(λ) − ρr(λ) = ρa(λ) + πt(λ)t0(λ)Rrs(λ)
Vertical characteristics of optically active substances
water surface value CV of vertical profile
N mean SD min max N mean(%) SD(%) min(%) max(%)
Chla 74 352.68 922.32 26.0 6988.29 74 67.13 67.79 3.59 238.94
SPIM 41 31.33 17.47 10.0 88.00 41 27.99 13.50 7.61 64.33
DOC 64 27.27 23.21 3.23 126.32 9 13.94 9.26 6.39 33.52
Vertical distribution type of Chla
Type N average
CV
vertical Chla
type fitted function R2
Type 1 27 19.53% uniform --
Type 2 9 29.25% Gaussian 0.85
Type 3 12 97.73% exponential 0.91
Type 4 16 163.60% power 0.86
1(z)f C=
22 0
1(z) exp[ ( ) ]22
h zf Cσσ π
= + −
3 1 2(z) exp( )f m m z= × ×
24 1(z) znf n= ×
Vertical distribution type of Chla
Rrs response to different algae vertical types
Relationship of Chla vertical type and wind speed
(550) (675)NDBI(550) (675)
−=
+rs rs
Rrsrs rs
R RR R
(748) (675)NDVI(748) (675)
−=
+rs rs
rs rs
R RR R
(700) (675)CSI(700) (675)
−=
+rs rs
rs rs
R RR R
Chla vertical type decision tree
NDBI using MODIS
Chla vertical distribution type
Conclusions
1. Analysis of the vertical profiles of algal biomass,
2. Integrated remote sensing reflectance data and wind speed
to identify the vertical distribution type,
3. Map vertical distribution using satellite reflectance data.
50
Satellite-based algal inventory estimation approaches
Jing Li
PhD student Nanjing Institute of
Geography and Limnology, CAS
Outline
51
Background 1
Approach 2
Results 3
Long term trends 4
52
Cyanobacterial blooms frequently occur in lakes – some are toxic Question: How to assess them remotely?
53
Previous Approach: Surface information Variation in
Days( /day) One Day( /hour)
(Sun et al, 2015)
54
Study area: Lake Chaohu
7/7/2015 55
Algal inventory algorithm
56
Surface Chl-a
(Zha
ng e
t al,
2015
)
Results
57
58
Results: water column validation before calibration after calibration
Monthly variation of algal inventory
59
Highest: October (63.88t) Lowest: April (53.11t)
60
Spatial and temporal algal inventory patterns
61
Spatial and temporal algal inventory patterns
62
Highest:2007 (61.50t) Lowest: 2004 (40.34t)
Annual variation in algal inventory
63
Spatial and temporal algal inventory patterns
64
Spatial and temporal algal inventory patterns
65
Conclusions
A new algorithm was developed and tested for algal inventory under non-blooming conditions
The remote-sensing estimates of algal inventories in both the point’s water column and Lake Chaohu were consistent with the in-situ data
Long-term (2003-2013) algal inventory distributions were derived for Lake Chaohu for the first time
Results led to basic understanding of evaluating bloom conditions and also eutrophic status in future.