巢湖水体POC浓度遥感反演算法与验证

基于2013年巢湖实测数据,根据颗粒性有机碳(Particulate Organic Carbon,POC)浓度与叶绿素a之间的高度相关性,采用Gons和Simis算法估算浮游植物色素吸收a_(ph)(665),继而实现对POC浓度间接反演。结果表明,Gons和Simis算法可用于蓝藻水华未覆盖内陆水体POC浓度的估算;Gons算法(RMSE_(rel)=21.90%)相对于Simis算法(RMSE_(rel)=23.81%)可以更好地反演POC的浓度。Gons和Simis算法在巢湖POC反演中取得了较好的结果,可以结合MERIS卫星用于巢湖水体POC估算。也可以为内陆湖泊水体碳循环研究提供技术和数据支撑,具有重要的科学研究意义。

A novel algorithm to estimate POC concentrations in Chaohu Lake, China

Carbon sequestration, transformation and emission in inland lakes play an important role in the global carbon cycling. POC (Particulate Organic Carbon) is a key element in the biological physical and chemical processes of all aquatic ecosystems. Based on in situ data from Chaohu Lake, a novel algorithm was developed to estimate POC concentrations in eutrophic lakes. This two-step algorithm examined changes in absorption aph(665) and associated to POC concentrations though a measured relationship between aph(665) POC determined in extensive measurements in this large eutrophic lake. Two different aph(665) algorithms were explored. While both provided good estimates of POC concentrations, Gons algorithm (RMSErel=21.90%) was superior to that of Simis (RMSErel =23.81%). This approach was developed for medium-spectral resolution satellite senors, such as MERIS or Sentinel 3-OLCI. The application of this approach will provide new insights to our understanding of the aquatic carbon cycle in inland waters.