Tracking the spatiotemporal dynamics of particulate phosphorus concentration (CPP) and understanding its regulating factors is essential to improve our understanding of its impact on inland water eutrophication. 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. Herein, a novel semi-analytical algorithm of CPP was developed to estimate CPP in lakes on the Yangtze Plain, China. 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 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. This study offered a novel and scalable algorithm for the study of the spatiotemporal variation of CPP in inland waters and provided new insights into the regulating factors in water eutrophication.
Keywords: Driving factors; Large-scale monitor; Particulate phosphorus; Remote sensing; Semi-analytical algorithm.
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