Retrieval Of Inherent Optical Properties And Chromophonc Dissolved Organic Matter Based On Remote Sensing And In Situ Observations

Inherent optical properties(IOPs)are important physical parameters in the field of ocean color remote sensing,the study of which is the basis of ocean color remote sensing.IOPs can provide reliable quantitative information for radiative transfer of light in water,inversion of ocean color parameters and the monitoring of aquatic environment,etc.It is of great applicable significance in the development of bio-optical model in ocean color remote sensing,the calibration and validation of ocean color satellite sensors,and research on global climate change.Chromophoric dissolved organic matter(CDOM)is one of the important optically active constituents(OACs)widely found in natural water,and it is the main research object in ocean color remote sensing with phytoplankton and Non-algal particles(NAP).CDOM has become a significant indicator for the distribution,migration and transformation of organic matter in estuary or coastal waters,and it is closely related to the human activity and biogeochemical cycle.However,the present inversion algorithms for IOPs and CDOM are mostly developed for specific regions or a single type of water body,and have limited precision and low applicability.Moreover,there is a significant deficiency in the inversion algorithm of CDOM for ocean color satellites,and there are still great challenges in retrieving CDOM accurately.In addition,there are few systematic studies for algorithms of IOPs and CDOM inversion,especially in near-shore and inland waters with complex optical properties.All these reasons result in a low level of operational applications of IOPs and CDOM in ocean color satellites.Therefore,a retrieval of inherent optical properties and CDOM based on remote sensing and in situ observations were carried out in this thesis.The main conclusions are as follows:(1)The global in situ data set-the NASA Bio-Optical Marine Algorithm Data set(NOMAD)was used in this study,and several different surveys were conducted to synchronously measure the bio-optical in situ data,including the East China Sea(ECS),Lake Qiandaohu(QDH),Lake Taihu,and the Wuzhizhou island in South China Sea.The inherent optical properties of diverse waters were analyzed in detail,including the total absorption and backscattering,the absorption and backscattering of each OACs within water body,the absorption budget and the difference resulting from the optical properties of different water bodies,and the corresponding spectral model of absorption and scattering was established as well.In addition,this thesis analyzed the apparent optical properties(AOPs),and put forward the decision tree classification based on remote sensing reflectance to complete the classification of water bodies with different optical properties.(2)A novel semi-analytical algorithm(QAA-GRI)for IOPs inversion,which can be applied to Type-1 and Type-2 waters,was developed.It can be used to retrieve total absorption coefficient and backscattering coefficient of particulates.The QAA-GRI algorithm has been validated by the ECS and QDH in situ data,and shows that it performs reasonably well,has good applicability,and significantly improves the accuracy of IOPs inversion compared with the QAA and GSM algorithms.The QAA-GRI can be further used to the inversion of CDOM.(3)Based on the IOPs inversion,a novel algorithm(CDOMLH)for separating absorption coefficient of CDOM from total absorption coefficient is developed,which can be used to retrieve absorption coefficients of CDOM(aCDOM).The CDOMLH algorithm has been validated by the in ECS and QDH datasets,and the overall performance of which is improved to be about 30%and 25%compared with the CO-a443S and QAA-E algorithms.Moreover,CDOMLH is also performs well in Lake Taihu.These indicated that the algorithm has good applicability in diverse waters.(4)The QAA-GRI and CDOMLH algorithms were applied to the MERIS and OLCI satellite images,and were compared with the other inversion algorithms,respectively.The results demonstrate that QAA-GRI and CDOMLH were feasible for IOPs and CDOM inversions when applied to the OLCI and MERIS images.Moreover,the algorithms were applied to the time series MERIS images as well,and produced the average monthly,seasonal and annual a(443)and aCDOM(443)MERIS images.The spatial-temporal variation of a(443)and aCDOM(443)in the ECS was analyzed.A method based on absorption coefficient retrieved by QAA-GRI was proposed to differentiate the algal bloom,and analyzed the temporal-spatial distribution characteristics of algal bloom waters in the ECS as well.The main innovations of this paper are as follows:(1)A green-red index(GRI)was first proposed,and a novel model for absorption coefficient at reference band was established based on this index.The model is different from the empirical model and has obvious physical meaning.Based on this model,the QAA-GRI algorithm for IOPs inversion was then developed,which solves the problem of applicability of IOPs inversion in different regions and types of water bodies,and improves the accuracy significantly.(2)Through the physical mechanism of absorbing interaction within OACs,a novel and general algorithm for CDOM inversion(CDOMLH)was developed based on the spectral height index of aphc(?)-LH(443).The algorithm solves the problem that previous algorithms could not separated aNAP from aCDOM.This makes applicability more extensive in diverse waters and improves the performance of aCDOM inversion significantly.(3)Based on points(1)and(2),we first attempted to apply the two algorithms to OLCI images for IOPs and CDOM inversion,and produced a series of satellite products of IOPs and CDOM in the ECS.Moreover,a new method was propose to identify the algal bloom based on these products,which provides a demonstration for the operational application of IOPs.