Declined trends of chlorophyll a in the South China Sea over 2005–2019 from remote sensing reconstruction

Wang, Tianhao; Sun, Yu; Su, Hua; Lu, Wenfang

doi:10.1007/s13131-022-2097-y

Cited by 4 publications

(1 citation statement)

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“…Although multiple factors including monsoons, river runoff, ocean circulation, eddy, upwelling, stratification, mixing, and fronts interact to drive the spatial and temporal patterns of Chl-a [36][37][38][39][40][41], the wind and temperature have been suggested to more strongly link with the spatial and temporal variability of Chl-a in the SCS [41][42][43][44]. In addition, the spatial distribution of Chl-a in the SCS has a similar spatial consistency with the topography, presenting the characteristics of high near-shore concentration and low offshore concentration.…”

Section: Determination Of Explanatory Variablesmentioning

confidence: 99%

Improvement in Spatiotemporal Chl-a Data in the South China Sea Using the Random-Forest-Based Geo-Imputation Method and Ocean Dynamics Data

Li,

Shao,

Zhang

et al. 2023

JMSE

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The accurate estimation of the spatial and temporal distribution of chlorophyll-a (Chl-a) concentrations in the South China Sea (SCS) is crucial for understanding marine ecosystem dynamics and water quality assessment. However, the challenge of missing values in satellite-derived Chl-a data has hindered obtaining complete spatiotemporal information. Traditional methods for deriving Chl-a are based on the modeling of measured sensor data and in situ measurements. Spatiotemporal imputation of Chl-a is difficult due to the inaccessibility of the measured Chl-a. In this study, we introduce an innovative approach that incorporates an ocean dynamics dataset and utilizes the random forest algorithm for predicting the Chl-a concentration in the SCS. The method combines the spatiotemporal feature pattern of Chl-a and the main influencing factors, and it introduces ocean dynamics data, which has a high correlation with the spatiotemporal distribution of Chl-a, as the input data through feature engineering. Also, we compared Random Forest (RF) with other Machine Learning (ML) methods. The results show that (1) ocean dynamics datasets can provide important data support for Chl-a imputation by capturing the impact of dynamical processes on ecological roles in the South China Sea. (2) The RF method is the superior imputation method for the reconstruction of Chl-a in the South China Sea, with better model performance and smaller errors. This study provides valuable insight for researchers and practitioners in choosing suitable machine learning methods for the imputation of the Chl-a concentration in the SCS, facilitating a better understanding of the region’s marine ecosystems and supporting effective environmental management.

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