Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China.
School of Environment and Planning, Liaocheng University, Liaocheng, 252000, China.
Environ Sci Pollut Res Int. 2019 Oct;26(29):30098-30111. doi: 10.1007/s11356-019-06122-w. Epub 2019 Aug 16.
The comprehensive analysis of the relationships between the attenuation of photosynthetic active radiation (K(PAR)) and light absorption is an imperative requirement to retrieve K(PAR) from remote sensing data for aquatic environments. The spatial distributions of the K(PAR) and light absorption of optically active components (a) were routinely estimated in China lakes and reservoirs. Spatial K(PAR) was relatively dependent on the inorganic particles (average relative contribution of 57.95%). The a could explain 70-87% of K(PAR) variations. A linear model is used to predict K(PAR), as a function of light absorption coefficient of phytoplankton (a), colored dissolved organic matter (a), and inorganic particles (a): K(PAR) = 0.41 + 0.57 × a + 0.96 × a + 0.57 × a (R = 0.87, n = 741, p < 0.001). In the lakes with low TSM concentration and non-eutrophic lakes with high TSM, a was the most powerful predicting factor on K(PAR). In eutrophic lakes with high TSM, a had the most significant impact on K(PAR). This study allowed K(PAR) to be predicted from a values in the inland waters.
对光吸收与光合有效辐射（K（PAR））衰减之间关系的综合分析，是从遥感数据中反演水生环境 K（PAR）的必要要求。中国的湖泊和水库中通常会估算 K（PAR）和光吸收活性成分（a）的空间分布。空间 K（PAR）相对依赖于无机颗粒（平均相对贡献为 57.95%）。a 可以解释 70-87%的 K（PAR）变化。线性模型用于预测 K（PAR），其为 a（浮游植物的光吸收系数）、有色溶解有机物（a）和无机颗粒（a）的函数：K（PAR）= 0.41 + 0.57 × a + 0.96 × a + 0.57 × a（R = 0.87，n = 741，p < 0.001）。在 TSM 浓度低的湖泊和富营养化低的高 TSM 湖泊中，a 是 K（PAR）的最强预测因子。在高 TSM 的富营养化湖泊中，a 对 K（PAR）的影响最大。本研究允许根据内陆水域的 a 值来预测 K（PAR）。
