Estimation of the Primary Productivity in Pearl River Estuary Using MODIS Data

Ye, Haibin; Chen, Chuqun; Sun, Zhan-Li; Tang, Shilin; Song, Xingyu; Yang, Chengduan; Tian, Liqiao; Liu, Fenfen

doi:10.1007/s12237-014-9830-5

Cited by 39 publications

(23 citation statements)

References 44 publications

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“…Some scholars (Deng et al, 2017;Jacox, Edwards, Kahru, Rudnick, & Kudela, 2015;Li et al, 2016;Tripathy, Ishizaka, Siswanto, Shibata, & Mino, 2012;Ye et al, 2015;Yin, Zhang, Shi, Liu, & Feng, 2012) have used vertically generalized production model (VGPM) to estimate primary productivity. The calculation of primary productivity was also based on the VGPM (Tripathy et al, 2012).…”

Section: Primary Productivitymentioning

confidence: 99%

Characteristics of phytoplankton community and water net primary productivity response to the nutrient status of the Poyang Lake and Gan River, China

et al. 2019

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Phytoplankton community characteristics and associative primary productivity, which reflect the ecological and nutritional status of water, are sensitive to environmental change. Particularly, nutrient inputs have a significant effect on phytoplankton growth and development as well as the biogeochemical cycle of watersheds. We investigated the phytoplankton community and nutrient status downstream from the Gan River into Poyang Lake. On a catchment scale, the phytoplankton communities and associative primary productivity from these two water bodies differed. The Poyang Lake phytoplankton community is composed of seven phyla of algae, whereas the Gan River is composed of six, the dominant phyla of phytoplankton being Bacillariophyta and Cryptophyta in Poyang Lake and Gan River, respectively. Phytoplankton abundance and primary productivity were significantly higher in Poyang Lake. Lower nitrogen (N) and phosphorous (P) concentrations and higher carbon (C) and silicon (Si) concentrations significantly affected the phytoplankton communities and primary productivity distribution in the entire Poyang Lake Basin. In this regard, we found a significant correlation between the phytoplankton community of the Gan River and P and N, whereas N and Si primarily regulated the phytoplankton community structure of Poyang Lake. Moreover, we found a significant correlation between DTN, Si/P, DIC/P, and primary productivity in the Gan River, whereas NH4+ jointly controlled primary productivity in Poyang Lake. Furthermore, the Margalef Richness Index (0.84–2.41) can be indicative of the environmental water quality status; and we determined that the most area of Poyang Lake and Gan River both were P restriction and moderately polluted, whereas the entrance of the Gan River into Poyang Lake was seriously polluted, which confirms that the Gan River is a main and crucial source of Poyang Lake pollution. Nanjishan Nature Reserve was already slightly eutrophic due to the high nutrient content and a large number of algae in the south‐east of Poyang Lake, but the Gan River had good river fluidity, so some areas of the Gan River were oligotrophic.

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Section: Primary Productivitymentioning

confidence: 99%

Characteristics of phytoplankton community and water net primary productivity response to the nutrient status of the Poyang Lake and Gan River, China

et al. 2019

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“…The repeated coverage by remote sensing enables the detection of the temporal and spatial variation, which has proven beneficial for rapidly estimating lake biogeochemical parameters [16]. Additionally, satellite remote sensing is more practical and economical than the other monitoring methods and also can be easily integrated into geographic information [17][18][19]. However, remote sensing data cannot directly provide information on primary production without support of models [20].…”

Section: Introductionmentioning

confidence: 99%

Temporal and Spatial Dynamics of Phytoplankton Primary Production in Lake Taihu Derived from MODIS Data

Deng

Zhang

et al. 2017

Remote Sensing

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Abstract:We investigated the long-term variations in primary production in Lake Taihu using Moderate Resolution Imaging Spectroradiometer (MODIS) data, based on the Vertically Generalized Production Model (VGPM). We firstly test the applicability of VGPM in Lake Taihu by comparing the results between the model-derived and the in situ results, and the results showed that a strong significant correlation (R 2 = 0.753, p < 0.001, n = 63). Then, VGPM was used to map temporal-spatial distributions of primary production in Lake Taihu. The annual mean daily primary production of Lake Taihu from 2003 to 2013 was 1094.06 ± 720.74 mg·C·m −2 ·d −1 . Long-term primary production maps estimated from the MODIS data demonstrated marked temporal and spatial variations. Spatially, the primary production in bays, especially in Zhushan Bay and Meiliang Bay, was consistently higher than that in the open area of Lake Taihu, which was caused by chlorophyll-a concentrations resulting from high nutrient concentrations. Temporally, the seasonal variation of primary production from 2003 to 2013 was: summer > autumn > spring > winter, with significantly higher primary production found in summer and autumn than in winter (p < 0.005, t-test), primarily caused by seasonal variations in water temperature. On a monthly scale, the primary production exerts a clear character of bimodality, increasing from January to May, decreasing in June or July, and finally reaching its highest value during August or September. Wind is another important factor that could affect the spatial variations of primary production in the large, eutrophic and shallow Lake Taihu.

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“…The fluorescence maximum near 685 nm has been used to estimate Chla [38,39]. The fluorescence line height (FLH) algorithm measures the height of the fluorescence peak at 685 nm from a linear baseline drawn between two points on either side of the peak [40]. It is important to consider that the FLH algorithm is only suitable for Chla concentrations generally not exceeding 30 mg m -3 as the backscattering peak near 700 nm overwhelms the fluorescence peak in high-biomass water.…”

Section: Chlorophyll-a (Chla)mentioning

confidence: 99%

Optical Satellite Remote Sensing of the Coastal Zone Environment — An Overview

Teodoro¹

2016

Environmental Applications of Remote Sensing

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Optical remote-sensing data are a powerful source of information for monitoring the coastal environment. Due to the high complexity of coastal environments, where different natural and anthropogenic phenomenon interact, the selection of the most appropriate sensor s is related to the applications required, and the different types of resolutions available spatial, spectral, radiometric, and temporal need to be considered. The development of specific techniques and tools based on the processing of optical satellite images makes possible the production of information useful for coastal environment management, without any destructive impacts. This chapter will highlight different subjects related to coastal environments shoreline change detection, ocean color, water quality, river plumes, coral reef, alga bloom, bathymetry, wetland mapping, and coastal hazards/vulnerability. The main objective of this chapter is not an exhaustive description of the image processing methods/algorithms employed in coastal environmental studies, but focus in the range of applications available. Several limitations were identified. The major challenge still is to have remote-sensing techniques adopted as a routine tool in assessment of change in the coastal zone. Continuing research is required into the techniques employed for assessing change in the coastal environment.

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Estimation of the Primary Productivity in Pearl River Estuary Using MODIS Data

Cited by 39 publications

References 44 publications

Characteristics of phytoplankton community and water net primary productivity response to the nutrient status of the Poyang Lake and Gan River, China

Characteristics of phytoplankton community and water net primary productivity response to the nutrient status of the Poyang Lake and Gan River, China

Temporal and Spatial Dynamics of Phytoplankton Primary Production in Lake Taihu Derived from MODIS Data

Optical Satellite Remote Sensing of the Coastal Zone Environment — An Overview

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