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ASD | Monitoring the particulate phosphorus concentration of

Date: 2022-04-25
浏览次数: 5

In recent decades, massively increasing nutrient discharge from soil erosion, agricultural activity, and anthropogenic sewage has resulted in the significant deterioration and eutrophication of freshwater ecosystems, becoming a pressing environmental problem in many countries. Eutrophication results in undesirable ecological consequences. Phosphorus is an essential bio-limiting nutrient responsible for eutrophication and water quality deterioration, thereinto particulate phosphorus (PP) serves as an important potential source of phosphorus for the growth of phytoplankton and bacteria, thereby playing an indispensable role in the primary production process and energy flow of aquatic ecosystems. Therefore, monitoring the concentration of PP (CPP) is very important for understanding aquatic primary production and its potential contribution to lake eutrophication. Many efforts have been made to monitor CPP in various settings, however, such studies have mainly been based on field measurements and laboratory analyses, which are labor-intensive and time consuming, making it difficult to determine more precisely the spatiotemporal variations in CPP. By comparison, the ability of satellite remote sensing to achieve large-scale coverage and repeat visitation has led to its widespread employment in monitoring various water quality parameter. However, few studies have assessed this in eutrophic inland lakes, owing to a lack of suitable bio-optical algorithms allowing the use of remote sensing data.

Based on this, to fill these knowledge gaps, in this article, a group of Chinese scientists collected in-situ water quality data (including water clarity (SDD, m), concentrations of total phosphorus (CTP), particulate phosphorus (CPP), chlorophyll-a (CChla), inorganic suspended matter (CISM), total suspended matter (CTSM), and the absorption coefficients of colored dissolved organic matter (CDOM) (aCDOM(λ)), phytoplankton pigment (aph(λ)), and non-algal particles (anap(λ))), spectra data (using an ASD FieldSpec Pro instrument ) and auxiliary data (including precipitation (Pre., in mm), wind speed (WS, in m/s), air temperature (Tem, in °C), and sunshine duration (SS, in h)) from some large lakes of Yangtze Plain (Fig.1) during 2016 and 2020. What's more, they calculated the processed monthly and annual normalized difference vegetation index (NDVI) and nighttime light (NTL) values within the watershed of each lake and downloaded a total of 8773 cloud-free Sentinel-3A OLCI Level-1 images, captured in 23 examined lakes with a water area >50 km2 from the European Space Agency (ESA) data hub. Finally, they developed a novel semi-analytical algorithm of CPP to estimate CPP by using three NIR bands (709 nm, 754 nm, and 779 nm) based on Sentinel-3A OLCI data. The objectives of this study were to: (1) develop and validate a general CPP estimation model; (2) reveal the spatiotemporal distribution patterns of CPP in these lakes using Ocean and Land Color Instrument (OLCI) data; (3) explore the potential factors that manipulate the CPP to improve future water quality management.

ASD | Monitoring the particulate phosphorus concentration of  

Fig.1 Location of the examined lakes on the Yangtze Plain; The locations of the meteorological stations are annotated by red stars.

 

[Results]

The independent validations of the proposed algorithm showed a satisfying performance with the mean absolute percentage error and root mean square error less than 27% and 27 μg/L, respectively. The Ocean and Land Color Instrument observations revealed a remarkable spatiotemporal heterogeneity of CPP in 23 lakes on the Yangtze Plain (YP) from 2016 to 2020, with the lowest value in December (62.91 ± 34.59 μg/L) and the highest CPP in August (114.9 ± 51.69 μg/L). Among the 23 examined lakes, the highest mean CPP was found in Lake Poyang (124.58 ± 44.71 μg/L), while the lowest value was found in Lake Qiandao (33.51 ± 4.71 μg/L). Additionally, 13 lakes demonstrated significant decreasing or increasing trends (P < 0.05) of annual mean CPP during the observation period. The driving factor analysis revealed that four natural factors (wind speed, air temperature, precipitation, and sunshine duration) and two anthropogenic factors (the normalized difference vegetation index and nighttime light) combined explained more than 91% of the variation in CPP, while the impacts of these factors on CPP showed considerable differences among lakes.

ASD | Monitoring the particulate phosphorus concentration of

Fig.2 Model validation of the developed algorithm using the independent dataset (a). Validation of the developed algorithm between field-measured particulate phosphorus concentration (CPP) and concurrent OLCI-derived CPP (b).

ASD | Monitoring the particulate phosphorus concentration of

Fig.3 Pearson's correlation coefficient between the monthly particulate phosphorus concentration (CPP) and driving factors in 23 examined lakes.

ASD | Monitoring the particulate phosphorus concentration of

Fig.4 The relative contribution (in percentages) rates of the driving factors to the dynamics of the annual particulate phosphorus concentration (CPP).


[Conclusions] 

The accurate remote estimation of CPP in inland waters is beneficial for the ecological restoration of aquatic ecosystems. In this study, the authors initially proved the feasibility of estimating particulate phosphorus in inland waters based on remote sensing, and constructed a novel and general semi-analytic algorithm for CPP estimation in 23 lakes on the YP. The independent validated results (MAPE < 27%, RMSE < 27 μg/L) showed that the proposed model, based on the MUMM-corrected OLCI images, could be used to estimate CPP with satisfactory performance. Next, the spatiotemporal distribution patterns of CPP in the YP lakes were obtained from OLCI images, and the relationships between CPP and potential driving factors were also analyzed. The driving forces leading to changes varied significantly between the different lakes. Anthropogenic activities played significant roles in small and shallow lakes, while climatic factors had a more pronounced effect on large and deep waters. To the best of their knowledge, this study is the first effort to quantitatively estimate the CPP dynamics of watershed-scale lakes on the Yangtze Plain, providing the first baseline datasets of spatiotemporal distribution patterns of CPP in 23 large lakes in the Yangtze River Plain. The findings could serve as important references for the formulation of nutrient reduction and pollution control strategies in inland waters.


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