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.