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Study On Quantitative Estimation Of Particu Lar Organic Carbon In Inland Water Using Remote Sensing

Posted on:2016-04-20Degree:MasterType:Thesis
Country:ChinaCandidate:J ZhangFull Text:PDF
GTID:2180330464965186Subject:Remote sensing technology and applications
Abstract/Summary:PDF Full Text Request
As the main biological resource element, carbon is one of the most critical factors in ecology which determines the sustainable use of biological resources in water. The behavior of carbon in the oceans and lakes as well as biological pump process guided by carbon can effect the global and regional climate change trends to some extent. Although particulate organic carbon (POC) accounts for a small part of the total carbon, it plays an important role in carbon sequestration and the downward transportation of related elements because of its Sedimentation. Particulate organic carbon also has the function of biological pump. Currently, ocean waters particulate organic carbon (POC) inversion model is more mature, but that is relatively small in inland waters. Therefore, the remote sensing inversion of particulate organic carbon in inland water is great significance for monitoring of POC spatial and temporal changes in inland water macroscopically.In this paper, we use field measured data (88 measured data) of the Taihu lake and Dongting lake to build the remote sensing retrieval model of particulate organic carbon in inland lakes, including empirical models and semi-analytical model. We also analyzed the temporal changes of POC concentration on the Taihu lake region by using MODIS satellite image data in 2013. The main contents and conclusions can be summarized in four aspects:(1) Construction of simple experience inversion model. This paper at first demonstrated that the remote sensing inversion algorithm for the oceans is not suitable for inland lakes using in situ data collected in Taihu lake and Dongting Lake. As a result, three empiric estimating models were developed for the inland eutrophic turbid lakes, and the band ratio of 825nm and 550nm can be successfully used to estimate POC with the MAPE of 33% and RMSE of 0.73mg/L.(2) Construction of classified inversion model. In order to further improve the retrieval accuracy and the model’s applicability, the water optical classification method was firstly adopted before estimating POC concentration. An optical index, NDT (NDT=0.4*Rrs (705)+0.68Rrs (655)-Rrs (675)) was used to identify the water optical characteristics. The water can be divided into two types, i.e., the water dominated by pigment particles and non-pigment particles. Then the POC inversion models for different water types were built, and the validation results showed that retrieval accuracy of this method was improved significantly. For the water with dominated pigment particles, MAPE of the estimation model is 25% and RMSE is 0.71 mg/L, and for the water with dominated non-pigment particles, MAPE of the model is 28%, RMSE is 0.63mg/L.(3) Construction of semi-analytical model. As the empiric estimating models always determined by the data set, a general model with wide applicability for Chinese inland lakes was needed to develop, because of the spatial-temporal optical variation in inland lakes. So the semi-analytical estimating model for POC inversion was studied based on the analysis of the absorption properties of water constituents. And a good response relation of non-pigment particles absorption coefficient and POC was found. Finally, based on bio-optical theory a three bands model using Rrs510、Rrs670 and Rrs750 was developed for estimation POC concentration. Compared to the empiric model, the semi-analytical model has higher accuracy and wider versatility with MAPE of 22%, and the RMSE of 0.63mg/L.(4) Model example shows. The parameters of the estimating models were re-calibrated for the MODIS data. Using MODIS data (2013) analysis of the POC seasonal changes and monthly change in Taihu Lake region.The results showed that there are obvious seasonal variation and monthly variation of POC concentration in Taihu lake. For seasonal variation, the concentration of POC constantly declined from spring to summer and then increased slightly from summer to winner. For monthly change, POC concentration in three bays(meiliang bay,Gong bay,zhushan bay) and eastlake changed relatively small, and relatively stable throughout the year. POC concentration in Central area, northwest and southwest of the lake changed significantly, continued decreasing in January to July and then increase from July to December, showing a significant V-shaped.
Keywords/Search Tags:Inland lakes, Quantitative retrieval using remote sensing, Empirical algorithm, Semi-analytical algorithm, Particulate organic carbon
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