Key Laboratory of Virtual Geographic Environment of Education Ministry, Nanjing Normal University, Nanjing 210023, China.
Key Laboratory of Virtual Geographic Environment of Education Ministry, Nanjing Normal University, Nanjing 210023, China; Jiangsu Center for Collaboration Invocation in Geographical Information Resource Development and Application, Nanjing 210023, China.
Sci Total Environ. 2021 May 20;770:145249. doi: 10.1016/j.scitotenv.2021.145249. Epub 2021 Jan 20.
Data on the concentration of particulate organic carbon (POC) and its endmembers provide a basis for the characterisation of lake biogeochemical cycles. Here, a novel remote sensing strategy (the SC algorithm) was developed to determine total POC concentrations, as well as terrestrial and endogenous POC concentrations in lakes. This strategy provides a successful example for the combination of isotope tracer and remote sensing technology. First, we obtained the terrestrial and endogenous POC concentration at the sampling point based on isotope tracing technology. Afterwards, we established a relationship between the phytoplankton absorption coefficient and the endogenous POC concentration (C), and applied a semi-analytical algorithm to invert the C value. Finally, the POC source ratio model and C value were combined to obtain the POC concentration (C) and terrestrial POC (C). The results of synchronisation verification based on ocean and land colour instrument (OLCI) images show that the SC algorithm has high C, C, and C inversion accuracy, with MAPE values of 26.07%, 30.43%, and 42.28%, respectively. In fact, the SC algorithm not only improved the accuracy of lake POC mapping, but also fills the gap of optical retrieval of POC endmember concentrations. Additionally, data from the OLCI images indicated that the studied lakes were dominated by external POC. However, because of the greater contribution of algal blooms to POC, this dominant advantage weakens in summer, although the terrestrial organic carbon carried by rainfall runoff also affects lake POC composition. Different POC sources have different ecological roles in lakes, and the superior POC end-element estimation capability of the SC algorithm can not only be used as a supplement to traditional tracing methods, but also provides accurate spatial data for lake management.
有关颗粒有机碳（POC）及其端元浓度的数据为描述湖泊生物地球化学循环特征提供了基础。在此，提出了一种新的遥感策略（SC 算法），用于确定湖泊总 POC 浓度以及陆地和内源性 POC 浓度。该策略为同位素示踪与遥感技术的结合提供了成功范例。首先，我们基于同位素示踪技术获得了采样点的陆地和内源性 POC 浓度。然后，我们建立了浮游植物吸收系数与内源性 POC 浓度（C）之间的关系，并应用半分析算法反演 C 值。最后，将 POC 源比模型与 C 值相结合，得到 POC 浓度（C）和陆地 POC（C）。基于海洋陆地成像仪（OLCI）图像的同步验证结果表明，SC 算法具有较高的 C、C 和 C 反演精度，其 MAPE 值分别为 26.07%、30.43%和 42.28%。实际上，SC 算法不仅提高了湖泊 POC 制图的精度，而且填补了 POC 端元浓度光学反演的空白。此外，OLCI 图像数据表明，所研究的湖泊主要以外源 POC 为主。然而，由于藻类大量繁殖对 POC 的贡献更大，这种主导优势在夏季会减弱，尽管雨水径流带来的陆地有机碳也会影响湖泊 POC 组成。不同的 POC 来源在湖泊中具有不同的生态作用，SC 算法卓越的 POC 端元估计能力不仅可以作为传统示踪方法的补充，还可以为湖泊管理提供准确的空间数据。
