Chlorophyll A Monitoring In The Surrounding Waters Of Dalian Based On Multi-source Remote Sensing Data

Chlorophyll-a is the main photosynthetic pigment in phytoplankton.The content of chlorophylla in phytoplankton is stable,and it is an important indicator to reflect the primary productivity of the ocean,and evaluate the eutrophication of water bodies.Therefore,monitoring the spatiotemporal distribution of chlorophyll-a can provide basic data for marine ecological environment evaluation and marine biological resource estimation.In addition,the global analysis of chlorophyll-a concentration data is of great significance to the study of large-scale climate change.Satellite remote sensing technology provides a global perspective for marine chlorophylla observation,which can realize large-scale,high-efficiency and low-cost observation,and is more and more widely used in the field of marine ecological environment monitoring.However,limited by the weather conditions,the accuracy of sensor and the satellite revisit cycle,satellite remote sensing is difficult to meet the requirements of both spatial and temporal scales.Remote sensing data fusion improves the utilization efficiency of remote sensing data by using data with different advantages to generate images with high temporal resolution and high spatial resolution.In this paper,enhanced spatial and temporal adaptive reflectance fusion model(ESTARFM)was used to fuse the remote sensing data of chlorophyll-a in the coastal waters of the North Yellow Sea to obtain chlorophyll-a images with high spatial resolution.According to the data characteristics of chlorophyll-a,the ESTARFM model is modified and a new fusion algorithm is proposed.The main research contents and results of this paper are as follows:(1)Using the ESTARFM model,the GOCI chlorophyll-a image with 500 m spatial resolution and the Landsat chlorophyll-a image with 30 m spatial resolution were fused to obtain chlorophylla data with high spatial and temporal resolution.This improves the temporal and spatial accuracy of monitoring chlorophyll-a concentration in the coast of the North Yellow Sea.(2)According to the characteristics of chlorophyll-a,the ESTARFM model was modified,a new model(ESTARFM?p)more suitable for chlorophyll-a data fusion was proposed,and the results of the two models were compared and analyzed.The results show that the new model performs better in terms of root mean square error(RMSE)and correlation coefficient(CC),and maintains a good spatial consistency with the actual observed Landsat images.(3)The results of the two models are compared in terms of the amount of basic data and the time interval from the target date.The results show that with the increase of the time interval,the RMSE of the ESTARFM model increases by 48%,and the CC decreases significantly.The new model algorithm expands the basic data selection for chlorophyll-a data fusion.In addition,this study also tested fusion using only a single base image.The results show that the results of the new model using a single piece of basic data fluctuate greatly,but in the short time interval(within100 days),a reasonable fusion result can be obtained.