Opt Express. 2022 Jan 31;30(3):4028-4045. doi: 10.1364/OE.447399.
Secchi disk depth (SDD) has long been considered as a reliable proxy for lake clarity, and an important indicator of the aquatic ecosystems. Meteorological and anthropogenic factors can affect SDD, but the mechanism of these effects and the potential control of climate change are poorly understood. Preliminary research at Lake Khanka (international shallow lake on the China-Russia border) had led to the hypothesis that climatic factors, through their impact on suspended particulate matter (SPM) concentration, are key drivers of SDD variability. To verify the hypothesis, Landsat and MODIS images were used to examine temporal trend in these parameters. For that analysis, the novel SPM index (SPMI) was developed, through incorporation of SPM concentration effect on spectral radiance, and was satisfactorily applied to both Landsat (R= 0.70, p < 0.001) and MODIS (R= 0.78, p < 0.001) images to obtain remote estimates of SPM concentration. Further, the SPMI algorithm was successfully applied to the shallow lakes Hulun, Chao and Hongze, demonstrating its portability. Through analysis of the temporal trend (1984-2019) in SDD and SPM, this study demonstrated that variation in SPM concentration was the dominant driver (explaining 63% of the variation as opposed to 2% due to solar radiation) of SDD in Lake Khanka, thus supporting the study hypothesis. Furthermore, we speculated that variation in wind speed, probably impacted by difference in temperature between lake surface and surrounding landscapes (greater difference between 1984-2009 than after 2010), may have caused varying degree of sediment resuspension, ultimately controlling SPM and SDD variation in Lake Khanka.
透明度盘深度(SDD)长期以来一直被认为是湖泊清澈度的可靠指标,也是水生态系统的重要指标。气象和人为因素会影响 SDD,但这些影响的机制以及气候变化的潜在控制因素还了解甚少。对中俄边境的大堪湖(国际浅水湖)的初步研究提出了一个假设,即气候因素通过对悬浮颗粒物(SPM)浓度的影响,是 SDD 变化的关键驱动因素。为了验证这一假设,使用 Landsat 和 MODIS 图像来检查这些参数的时间趋势。为此分析,开发了一种新的 SPM 指数(SPMI),通过将 SPM 浓度对光谱辐射的影响纳入其中,并成功应用于 Landsat(R=0.70,p<0.001)和 MODIS(R=0.78,p<0.001)图像,以获得 SPM 浓度的遥感估算。此外,SPMI 算法成功应用于浅水湖泊呼伦湖、巢湖和洪泽湖,证明了其可移植性。通过对 SDD 和 SPM 的时间趋势(1984-2019 年)进行分析,本研究表明 SPM 浓度的变化是 SDD 的主要驱动因素(解释了 63%的变化,而太阳辐射仅占 2%),从而支持了研究假设。此外,我们推测风速的变化,可能受湖表面和周围景观之间的温差(1984-2009 年期间的温差大于 2010 年之后)的影响,可能导致不同程度的泥沙再悬浮,最终控制大堪湖的 SPM 和 SDD 变化。