Remote sensing of environment and disaster laboratory

Institute of Industrial Science, the University of Tokyo, Japan

Satellite-based modeling of gross primary production andnet ecosystem exchange in mangrove ecosystems
Zheng Yuhan*, and Wataru Takeuchi
Institute of Industrial Science, the University of Tokyo, Japan

Abstract: Mangrove ecosystems play an important role in the global carbon budget, however, the quantitative relationships between environmental drivers
and productivity in these forests remain poorly understood. In this study, we develop a satellite-based vegetation productivity model to estimate the grossprimary production (GPP) and net ecosystem exchange (NEE) in mangrove forests. The model considers sea surface temperature and salinity as environmental
scalars in the mangrove light use efficiency (LUE) model. Besides, the LUEmax and PAR scalar were determined by different temperatures. Sentinel-2 images
were used to map the fraction of absorbed photosynthetically active radiation (fAPAR) and validated with the data from two carbon flux towers in mangrove
forests of China. The LUE, GPP and NEE predicted by the model generally agreed with observed values. These results demonstrate the potential of thesatellite-driven productivity model for scaling-up GPP/NEE in mangrove forests, a key for exploring the carbon cycle in mangrove ecosystems at larger scales.

2 Methodology

1 Introduction

Fig. 1 Locations of two carbon flux
towers in China: (a) Zhangjiang flux
tower; (b) Zhanjiang flux tower.

Fig. 2 Pearson correlations among different
environmental variables and mangrove lightuse efficiency (LUE).

Objective:
Modeling
thegross primary
production (GPP) and net ecosystem
exchange (NEE) of mangrove
ecosystems by considering tidaleffects on the light use efficiency
model and using remote sensing data;
Originality:
1. The first study to estimate themaximum LUE of mangroves;
2. The first study to model the GPPand NEE in mangrove forests
based on remote sensing data.

Fig. 3 Overall flowchart of mangrove GPP and NEE modeling.

3 Results
3.1 LUE modeling

3.3 PAR

Fig. 8 fAPAR inZhangjiang obtained
from Sentinel 2images.

Fig. 9 Monthly average
PAR distributions in thepotential mangrove growing
area of China interpolated
from PAR station data.

3.4 ReThe responses of LUE toPAR (Fig.5) and NEE toPAR (Fig.6) vary withdifferent temperatures.
ThePARscalar
andLUEmax in this study weredeterminedbasedondifferent temperatures.

Fig. 11 Validation of modeled GPP with (a)measured LUE; (b) modeled LUE with is-situ data;(c) modeled LUE with satellite data.

Fig. 6 NEE-PAR relationships.

Table. 1 Validation of LUE by individual environmental variables
specified in this study.

Environmental scalar

Pearson’s rRMSE

SST

0.48

0.0185

Salinity

0.49

0.0203PAR0.850.0048Fig. 13 Time-series GPP comparisons among MODIS
products, in-situ measurement and established model inthis research.

Mangrove LUE and productivity are controlled by the climaticand environmental factors that regulate terrestrial forests, suchas temperature, solar irradiance, and vapor pressure deficit,
and by others like SST and salinity unique to coastal habitats.
PAR shows the highest impact on LUE while salinity has low
effect due to the low values in that region. (Fig.4, Table1)

4 Conclusions and future work

Fig. 10 Comparison of modeled respiration (Re) andmeasured Re.

3.5 GPP and NEE

Fig. 5 LUE-PAR relationships.

Fig. 4 Seasonal variations of (a) Temperature (Tair and SST), (b)PAR and VPD, (c) Salinity, Rainfall and surface water level, (d) GPP,
NEE and LUE.

3.2 fAPAR

Fig. 7 Validation of LUE estimated from terrestrial model and tidebased mangrove model with daily-scale and hourly-scale data.

Fig. 12 Validation of modeled GPP and NEE with in-situ
data from (a)&(d) Zhangjiang 2017; (b)&(e) Zhangjiang
2012; (c)&(f) Zhanjiang 2015.

• GPP modeled with in-situ data performs
better than satellite data;• GPP and NEE models have similar resultsin different years and regions;• GPP estimated from our model has moresimilar trend with the measured value,
while MODIS GPP products have larger
fluctuances.
• GPP modeled has higher accuracies
compared with MODIS GPP products
which improved the RMSE from 39.09 to19.05 g C/m2/8days in 2012, and from33.76 to 19.51 g C/m2/8days in 2015.

Uncertainties:
1. The satellite data available for VPD and salinity was not well validated with the insitu data.
2. The relatively low spatial and temporal resolution of the environmental data would
influence the accuracy of the model.
3. The determination of scalar parameters and fAPAR didn’t consider the different
species and ages of the mangrove.

• Tide-based LUE model considering the effects of SST, PAR, salinity and different responses of LUEmax and PAR to temperature performs better than the existing terrestrial LUE model inestimating the mangrove LUE;
• The GPP and NEE estimated from satellite-based model generally agrees with the in-situ values which indicated the feasibility and applicability of this model.
Future work: Mapping the GPP and NEE for the mangroves in the whole coastal zone of China and estimating the annual biocapacity and carbon footprint of mangrove ecosystems.
For references and details, contact: Zheng Yuhan, Bw-601, 6-1, Komaba 4-chome, Meguro, Tokyo 153-8505 JAPAN (URL:http://wtlab.iis.u-tokyo.ac.jp/E-mail: yuhan@g.ecc.u-tokyo.ac.jp)