Xiao Qing

Correlation-based temperature and emissivity separation algorithm

Based on analyzing the relationship between the atmospheric downward radiance and surface emissivity, this paper proposes a correlation criterion to optimize surface temperature during the process of temperature and emissivity separation from thermal infrared hyperspectral data, and puts forward the correlation-based temperature and emissivity separation algorithm (CBTES). The algorithm uses the correlation between the atmospheric downward radiance and surface emissivity to optimize surface temperature, and obtains surface emissivity with this temperature. The accuracy of CBTES was evaluated by the simulated thermal infrared hyperspectral data. The simulated results show that the CBTES can achieve high accuracy of temperature and emissivity inversion. CBTES has been compared with the iterative spectrally smooth temperature/emissivity separation (ISSTES), and the comparison results show that they have relative accuracy. Besides, CBTES is insensitive to the instrumental random noise and the change of atmospheric downward radiance during the measurements. As regards the nonisothermal pixel, its radiometric temperature changes slowly with the wavenumber when its emissivity is defined as r-emissivity. The CBTES can be used to derive the equivalent temperature of nonisothermal pixel in a narrow spectral region when we assumed that the radiometric temperature is invariable in the narrow spectral region. The derived equivalent temperatures in multi-spectral regions in 714–1250 cm−1 can characterize the change trend of nonisothermal pixel’s radiometric temperature.

Biomass estimation and uncertainty analysis based on CBERS-02 CCD camera data and field measurement

The study site is located in Qianyanzhou experimental station and the surrounding area. Based on CBERS-02 satellite data and field measurement, we not only discussed the relationship between NDVI and biomass of two species of coniferous plantations, namely,Pinus massoniana Lamb andPinus elliottii Engelm, but also introduced the biomass models based on NDVI. The comparison between measured biomass in Qianyanzhou and biomass derived from CBERS-02 CCD data showed that it is feasible to estimate biomass based on NDVI. But its limitations cannot be ignored. This kind of model depends on the dominant vegetation species. There are some effect factors in estimating biomass based on NDVI. This paper analyzes these factors based on fine-resolution CBERS-02 CCD data and some conclusions are drawn: In Qianyanzhou, the area with good vegetation coverage, the nonlinearity of NDVI has little influence on scaling-up of NDVI. As a result of surface heterogeneity, scaling-up can cause NDVI within each pixel to change. Because scaling-up can cause pixel attribute to change, the applicability of biomass model is one of the sources of error in estimating biomass.

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.

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;