Seasonal variation in photosynthetic rates and satellite-based GPP estimation over mangrove forest

Lele, Nikhil; Kripa, M. K.; Panda, Mrutyunjaya; Das, Sauren; Nivas, A. Hari; Divakaran, N.; Naik-Gaonkar, Sankrita; Sawant, Apoorva; Pattnaik, Ajit K.; Samal, R. N.; Thangaradjou, T.; Saravanakumar, A.; Rodrigues, B.F.; Murthy, T. V. R.

doi:10.1007/s10661-021-08846-0

“…Temporal estimation of the GPP value of mangrove forests can be known using remote sensing technology. In this case remote sensing provides repeated and systematic satellite imagery data in a temporal scale that allows characterizing the vegetation structure including leaf area index (LAI) and photosynthetic active radiation absorption (APAR) [9,10]. Remote sensing technology is highly effective in monitoring GPP at regional and local scales by linking the absorbed photosynthetic active radiation fraction (fAPAR) driven by the vegetation index [11,12].…”

Section: Introductionmentioning

confidence: 99%

Annual characteristics of gross primary productivity (GPP) in mangrove forest during 2016-2020 as revealed by Sentinel-2 remote sensing imagery

Romadhoni

¹

,

As-syakur

²

,

Hidayah

³

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

View full text Add to dashboard Cite

Carbon dioxide (CO2) is the most responsible gas in the phenomenon of global warming on earth because of its greatest concentration and longevity in the long atmosphere. Meanwhile mangrove is one of the blue carbon parameters that can take CO2 for photosynthesis and store it into biomass and sediment, so the existence of Mangrove plays a key role in the balance of the global carboncycle. Gross Primary Productivity (GPP) is one of the key variables in conducting a carboncycle study because the GPP values constitute the total value of carbon fixation by terrestrial ecosystem through vegetation photosynthesis. The aim of this study is to recognize the annual characteristics of GPP values in the mangrove ecosystem using remote sensing satellite Sentinel-2 during the period of 2016 to 2020 in TAHURA Ngurah Rai, Bali, Indonesia. The vegetation photosynthesis model (VPM) model is used to calculate annual GPP by using remote sensing indices of Enhanced Vegetation Index (EVI) and Land Surface Water Index (LSWI). Both remote sensing indices are supported by temperature and solar radiation data to determine photosynthesis fraction. The results of the current study indicated that the annual GPP values in the mangrove forest of TAHURA Ngurah Rai have decreased during the observation year. The total value of GPP in 2016 is reached to 28790 tC m−2 year−1, while the total amount of GPP value in 2020 decrease to 26223 tC m−2 year−1. The decline in GPP values may be due to changes of land cover and mangrove mortality that occurred around Benoa port in 2018.

show abstract

“…For the first time, the effect of salinity on the mangrove LUE was investigated. Lele, et al 25 proposed a vegetation photosynthesis model which can be well applied to relatively small-scale mangrove forest by incorporating in-situ LUE and high-resolution environmental scalars.…”

Section: Introductionmentioning

confidence: 99%

Improving Remote Sensing-based Estimation of Mangrove Forest Gross Primary Production by Quantifying Environmental Stressors: Sea Surface Temperature, Salinity, and Photosynthetic Active Radiation

Zheng

¹

,

Takeuchi

²

2021

Preprint

0

View full text Add to dashboard Cite

Mangrove ecosystems play an important role in global carbon budget, however, the quantitative relationships between environmental drivers and productivity in these forests remain poorly understood. This study presented a remote sensing (RS)-based productivity model to estimate the light use efficiency (LUE) and gross primary production (GPP) of mangrove forests in China. Firstly, LUE model considered the effects of tidal inundation and therefore involved sea surface temperature (SST) and salinity as environmental scalars. Secondly, the downscaling effect of photosynthetic active radiation (PAR) on the mangrove LUE was quantified according to different PAR values. Thirdly, the maximum LUE varied with temperature and was therefore determined based on the response of daytime net ecosystem exchange and PAR at different temperatures. Lastly, GPP was estimated by combining the LUE model with the fraction of absorbed photosynthetically active radiation from Sentinel-2 images. The results showed that the LUE model developed for mangrove forests has higher overall accuracy (RMSE = 0.0051, R2 = 0.64) than the terrestrial model (RMSE = 0.0220, R2 = 0.24). The main environmental stressor for the photosynthesis of mangrove forests in China was PAR. The estimated GPP was, in general, in agreement with the in-situ measurement from the two carbon flux towers. Compared to the MODIS GPP product, the derived GPP had higher accuracy, with RMSE improving from 39.09 to 19.05 g C/m2/8 days in 2012, and from 33.76 to 19.51 g C/m2/8 days in 2015. The spatiotemporal distributions of the mangrove GPP revealed that GPP was most strongly controlled by environmental conditions, especially temperature and PAR, as well as the distribution of mangroves. These results demonstrate the potential of the RS-based productivity model for scaling up GPP in mangrove forests, a key to explore the carbon cycle of mangrove ecosystems at national and global scales.

show abstract

“…For the first time, the effect of salinity on the mangrove LUE was investigated. Lele et al 26 proposed a vegetation photosynthesis model which can be well applied to relatively small-scale mangrove forest by incorporating in-situ LUE and high-resolution environmental scalars. However, the LUE models developed in their studies focused on the mangrove forests at a local scale, determining environmental parameters based on in-situ measurements.…”

mentioning

confidence: 99%

Estimating mangrove forest gross primary production by quantifying environmental stressors in the coastal area

Zheng

¹

,

Takeuchi

²

2022

Sci Rep

View full text Add to dashboard Cite

Mangrove ecosystems play an important role in global carbon budget, however, the quantitative relationships between environmental drivers and productivity in these forests remain poorly understood. This study presented a remote sensing (RS)-based productivity model to estimate the light use efficiency (LUE) and gross primary production (GPP) of mangrove forests in China. Firstly, LUE model considered the effects of tidal inundation and therefore involved sea surface temperature (SST) and salinity as environmental scalars. Secondly, the downscaling effect of photosynthetic active radiation (PAR) on the mangrove LUE was quantified according to different PAR values. Thirdly, the maximum LUE varied with temperature and was therefore determined based on the response of daytime net ecosystem exchange and PAR at different temperatures. Lastly, GPP was estimated by combining the LUE model with the fraction of absorbed photosynthetically active radiation from Sentinel-2 images. The results showed that the LUE model developed for mangrove forests has higher overall accuracy (RMSE = 0.0051, R2 = 0.64) than the terrestrial model (RMSE = 0.0220, R2 = 0.24). The main environmental stressor for the photosynthesis of mangrove forests in China was PAR. The estimated GPP was, in general, in agreement with the in-situ measurement from the two carbon flux towers. Compared to the MODIS GPP product, the derived GPP had higher accuracy, with RMSE improving from 39.09 to 19.05 g C/m2/8 days in 2012, and from 33.76 to 19.51 g C/m2/8 days in 2015. The spatiotemporal distributions of the mangrove GPP revealed that GPP was most strongly controlled by environmental conditions, especially temperature and PAR, as well as the distribution of mangroves. These results demonstrate the potential of the RS-based productivity model for scaling up GPP in mangrove forests, a key to explore the carbon cycle of mangrove ecosystems at national and global scales.

show abstract

Seasonal variation in photosynthetic rates and satellite-based GPP estimation over mangrove forest

Cited by 12 publications

References 45 publications

Annual characteristics of gross primary productivity (GPP) in mangrove forest during 2016-2020 as revealed by Sentinel-2 remote sensing imagery

Annual characteristics of gross primary productivity (GPP) in mangrove forest during 2016-2020 as revealed by Sentinel-2 remote sensing imagery

Improving Remote Sensing-based Estimation of Mangrove Forest Gross Primary Production by Quantifying Environmental Stressors: Sea Surface Temperature, Salinity, and Photosynthetic Active Radiation

Estimating mangrove forest gross primary production by quantifying environmental stressors in the coastal area

Contact Info

Product

Resources

About