The Study Of Water Quality Monitoring In Dianchi Lake Based On HJ-1A/1B Remote Sensing Data

In recently years，water quality pollution and eutrophication of lake is thevery important issue which seriously hindering the development of economy andimpacting on the living and health of people. It is necessary to monitor quicklyand accurately the water quality of lake. But the conventional method takes muchtime and energy, which only realize the water quality of monitoring sections, andit is difficult to acquire the spatial distribution and variation trend of broad waterbody and cannot satisfy the monitoring need of real time and large scale. With thegrowth of remote sensing retrieval, new chance and choice are provided for thewater quality monitoring. In the help of remote sensing data, using the empiricalmethod, semiempirical method and physical analytical method, we establish somemodels for estimating the water quality parameters. With spatial and temporalinformation, remote sensing technique is suitable to obtain such informationquickly and frequently, and it’ll be widely applied in water quality monitoring oflakes in future because of its advantage of fastness, economy and long-termdynamic monitoring.Dianchi Lake is the typical and largest lake which was polluted seriously inthe Yunnan-Guizhou plateau. Water bloom lasted long time from April toNovember every year. The management of Dianchi Lake had been listed one of thekey control project three rivers, three lakes. This research attempts to explorethe possibility of simulating and predicting water quality of Dianchi Lake usingHJ-1A/1B data. HJ-1A/1B is significant in water quality monitoring because of high spatialresolution, high spectrum resolution, short revisit period and broad observationrange. So information of chlorophyII-a, algae and secchi disk depth in the watercan be reflected from images of different HJ-1A/1B bands. This research wasaimed to apply HJ-1A/1B data to monitor the spatial distribution of water qualit yparameters and analyze the response of eruptible algae in the HJ-1A/1B images.Based on the study above and the spectral characteristic of secchi disk depth,total nitrogen, total phosphorus and ammonia nitrogen, the paper analyzed thecorrelation between water quality parameters and band radiance using the Pearsonmethod. And then, the most suitable bands and band combinations were chose. Somultivariable linear regression model, BP and RBF network were conducted toestablish the relationship of water quality parameters and the radiance ofHJ-1A/1B bands. And the paper assessed the precision of three models and theadaptability applying the HJ-1A/1B for water quality monitoring. Resultsindicated that artificial neural network model exhibited more satisfactoryperformance than multivariable linear regression on the basis of HJ-1A/1B data.