Wen Jianguang

Seasonal suspended sediment estimating models in Lake Taihu using remote sensing data

Suspended sediment in Lake Taihu has its seasonal character according to the analysis of in situ data ac- quired by Taihu Monitoring Wuxi Station during 1996-2002,so seasonal models may better than a single model for es- timating suspended sediment in Lake Taihu.After analyzing the spectral characteristic of Lake Taihu,seasonal sus- pended sediment estimating models were built based on four Landsat TM/ETM images,respectively in spring,sum- mer,autumn and winter,as well as synchronous in situ data.Result shows that (B2+B3)/(B2/B3) is a good in- dex for estimating suspended sediment in Spring,Autumn and Winter (R~2＞0.52).The summer model is not sound due to the disturbance of high chlorophyll concentration,as alga boom in summer.The winter model has the best effect in estimating suspended sediment (R~2=0.81).The Winter model is lnSS=14.656×(B2 +B3)/(B2/B3)+ 1.661,in which lnSS is the natural logarithm of suspended sediment concentration,B2 and B3 are the reflectance in Band 2 and B3 of the Landsat TM/ETM images after 6S atmospheric correction and a 3×3 low-pass filtering.

Extraction of chlorophyll-a concentration based on spectral unmixing model using field hyperspectral data in Taihu Lake

Abstract : In China, one of the most common ecological problems of inland water bodies is represented by the eutrophication which diminishes water quality. And the chlorophyll-laden water becomes an obvious sign. Chlorophyll-a concentration measurement is usually used for assessing tropic status of lakes. The development of spectral resolution enables hyperspectral technology possible to monitor water quality successfully, which is based on developing relationships between radiance/reflectance in single band or band ratios and chlorophyll concentration. In this paper, a spectral unmixing model was established based on single-phase field hyperspectral data. Three data types were supported for this model: original data, normalization data and differential data. Selected end-member from known reflectance spectrum, we retrieved chlorophyll-a concentration. The result shows the spectral unmixing model based on differential data gives the best result. Validated this model and shows a good precision and stabilization. Finally, three-phase field hyperspectral datum were processed and chlorophyll-a concentration was extracted using the best model. The result shows that spectral unmixing model is a feasible model in the practical application of remote sensing water quality monitoring.

The evaluation of water eutrophication using spectrum reflectance at Taihu Lake

The water quality of Taihu Lake is declining due to eutrophication, and the chlorophyll-laden water becomes an obvious sign. As to reflectance spectra of water vary with concentrations of organic and inorganic sediments, in this paper field reflectance spectra have been applied for monitoring the water quality of Taihu Lake, China. As the key-monitoring index, the chlorophyll-a contents were evaluated by linear spectral unmixing using water and chlorophyll-a endmember spectra of known content the results were compared to laboratory analyses of in situ, water samples.

Simulation of atmospheric radiation transfer for high-resolution thermal infrared imaging

The consistent end-to-end simulation of them is an important task, sometimes the only way for the adaptation and optimisation of a sensor and its observation conditions, the choice and test of algorithms for data processing, error estimation and the evaluation of the capabilities of the whole sensor system. It is essential to accomplish simulation of atmospheric radiative transfer, if a complete imaging simulating system is to be expected. Based on given resolution and directional capabilities of the instrument, and combination with land surface temperature and emissivity data obtained from airborne imagery, TOA (top of atmosphere) radiance images have been simulated pixel by pixel coupling the atmospheric radiative transfer analytic model extended from MODTRAN4 and the atmospheric adjacency effect model derived from point spread function (for atmospheric directional and adjacency effect). In this way, all major scattering and emission contribution of atmosphere were considered. Through analysing results, it indicates that analytic model and adjacency effect model is more adequate for thermal infrared imaging simulation than others existing models.

The monitoring of water quality using remote sensing at Taihu Lake

Two types remote sensing data(TM and EO-1 HYPERION) was applied to derive the chlorophyll-a concentration by empirical regression and linear spectral unmixing technique. The result was compared supported by the field measurement data, the spectral unmixing method show better performance. Keywords-remote sensing; chlorophyll-a; linear spectral unmixing;