Optimization Of Remote Sensing Inversion Algorithm For Chlorophyll-a Concentration In Daihai Lake

Chlorophyll-a concentration（Chl-a）is one of the important parameters for studying phytoplankton dynamics,carbon cycle and eutrophication assessment in the global ocean,including coastal and inland waters.Ocean colour remote sensing is a unique technology to obtain chlorophyll-a information on a large spatial and temporal scale.For clear seawater characterized by simple optical properties,an algorithm for chlorophyll-a inversion using the ratio of spectral remote sensing reflectance(R_rs)in blue and green bands has been proposed.However,turbid coastal or inland waters are still facing challenges.Due to the mixed optical signals of chlorophyll a,suspended sediment（SPM）and coloured dissolved organic matter（CDOM）,the optical characteristics are complex and highly variable.For these waters,it is usually difficult to use an algorithm to adapt to the optical complexity and achieve accurate chlorophyll a inversion.Therefore,it is necessary to constructed an chlorophyll a concentration inversion model suitable for complex water quality.In this study,Daihai Lake was taken as the research area to carry out field sampling and indoor experiments,and the spectral data of the water in the research area were obtained,and the spectral characteristics of the measured stations were analyzed,and the bands sensitive to chlorophyll a were selected.Using the measured spectral data and chlorophyll a data,the algorithm model suitable for chlorophyll a in Daihai Lake was calibrated on the basis of empirical algorithm,and the accuracy of chlorophyll a in Daihai Lake was verified by quality control model,and compared with other algorithms.The sensitivity of the algorithm was discussed,and the temporal and spatial distribution characteristics of chlorophyll a concentration in Daihai Lake were obtained by using the selected algorithm model and Sentinel-2 multispectral sensor（MSI）data.The main conclusions of the study are as follows:（1）Based on the measured data in the field,the empirical algorithm is calibrated,and the 2-band algorithm suitable for extremely turbid water bodies and the 3-band improved algorithm designed for moderately turbid water bodies are selected.Through the analysis of scatter plot,correlation and error,it is concluded that there are some errors in the inversion accuracy of 2-band algorithm model and 3-band algorithm model,and further algorithm optimization is needed.（2）Optimize the algorithm of chlorophyll a inversion algorithm,that is,test the quality control model,and verify the accuracy of chlorophyll a in Daihai.It is found that after applying the quality control model,the quality control model of 2-band algorithm can pass through 101 points,and the inversion accuracy is improved,and the determination coefficient R² can reach 0.83;Although the 3-band algorithm model is not effective,it can only pass through 64 data points,but R² can also reach 0.82;The model inversion precision of integration algorithm is the best,R² can reach0.87,and it can be applied to remote sensing inversion through 104 data points.（3）The atmospheric,topographic and cirrus data of Sentinel-2 MSI Level-1C atmospheric top reflectivity products were corrected by C2RCC（water color of regional coastal waters of Class II water bodies）,and the L2A surface reflectivity products were obtained.On the basis of L2A data,the inversion of chlorophyll a concentration fusion algorithm based on quality control is carried out,and the applicable area of Daihai inversion algorithm and the temporal and spatial distribution map of chlorophyll a concentration are obtained.2-band algorithm is suitable for most areas of Daihai,3-band is less applicable than 2-band,and the integration algorithm is more applicable.The spatial distribution of chlorophyll a concentration in Daihai Lake is low in the center of the lake and high in the lakeside area.The seasonal distribution of chlorophyll a concentration changes significantly,with a low concentration in spring and an increase in autumn.