Remote Sensing Model And Inversion For Water Quality Of Kun-cheng Lake Based On Measured Spectrum

Water quality monitoring is the main basis of water quality appraisal and water pollution prevention. With the increasingly serious water pollution problems, water quality monitoring becomes the major issues that the socio-economic sustainable development must address. At present, the conventional water quality monitoring method is that on-site sampling, laboratory analysis, and then assesses water quality based on concrete analysis data, but this approach is difficult to obtain wide range of distribution and variation of water quality parameters, and disable to satisfy the requirement of timely and large scale monitoring assessment.Water quality monitoring by remote sensing not only can reflect the distribution and variation of water quality in space and time, also can found the sources of pollution and pollutant migration characteristics, which the conventional methods can not reveal, moreover it has advantages as large area monitoring, quick results, low-cost and continuous dynamic monitoring. Based on the measured spectrum and remote sensing technology, this article constructs models and inversion of chl-a and SS in the study area. It gets the Thesis results in the following areas:1. The measured spectral data obtained in study area are pretreated such as raw data correction, data average, spectral data transformation, noise removing, spectral normalization, first derivative smoothing preprocessing. The results can satisfy the model construction.2. By using handheld GPS to select ground control points in the study area, geometric correction method was used to correct ETM remote sensing images. By using the brightness difference in some bands between water and other surface features in the ETM + images, the study try using the single-band method and multi-band method in order to extract the water body respectively from the remote sensing images. By contrast analysis, multi-band method of Normalized Difference Water Index can rule out the influence of river and other small water bodies, the full water body can be extracted in the study area. Atmospheric correction of remote sensing images is a key step in quantitative remote sensing. FLAASH method is used to correct ETM remote sensing images in the study area. The calibration results of the method can meet the needs of model construction.3. After preprocessing the spectral data and remote sensing data, the paper analyzes their correlation by PEARSON way, and define the sensitive wavelength of chlorophyll-a. Its results show that the average processing spectral values for some largest positive correlation can improve the inversion accuracy. The paper identifies the first derivative spectrum AVE (691+692-609) linear model to estimate the chlorophyll-a. Constructing the inversion models based on the semi-empirical method. It uses ETM remote sensing data to invert chlorophyll-a concentration.4. For the inversion of the concentration of suspended matter, this paper extracts sensitive bands corresponding suspensions by taking the normalized and first-order differential treatment. For the normalized spectrum, that the maximum spectral value of the positive correlation divided by smallest negative spectral value can improve the correlation. For the first order derivative spectra, that the maximum spectral value of the positive correlation subtract the minimum spectral values of negative correlation can improve relevance. Both the normalized spectrum and the first order derivative spectrum can estimate the effect better. The latter is more accurate. The paper constructs the SS inversion model with the determined sensitivity bands reflectance value and measured concentration data and inverses the concentration of the suspended solids by using ETM data in the study area. The verification results show that the concentration of chlorophyll-a and suspended solids by the model inversion in the study area are basically similar to the actual monitoring results.The SS inversion model established for remote sensing applications is better, but to some extent, its accuracy is affected by the water content of other water quality parameters. Remote sensing inversion model for chlorophyll-a has high accuracy to the medium content and has better applicability, but to lower chlorophyll-a content and the spraying water, its accuracy is low.