Tian Guoliang

Inversion of chlorophyll-a concentration in turbid water of Lake Taihu based on optimized multi-spectral combination

Inversion of phytoplankton chlorophyll-a concentration of inland water body is hotspot and difficulty problem in water quality remote sensing.This paper provides a method to resolve this problem.Basing on the characteristic spectral analysis of chlorophyll-a,suspended matter,chromatic dissolved inorganic matter and pure water molecule in inland water body,the three-band model was spectrally tuned in accord with optical properties of Lake Taihu to optimize spectral bands combination for accurate chlorophyll-a concentration estimation.The remarkable linear relationship was established between analytically measured chlorophyll-a concentration and the three-band model.Depended on the favorable theory basis of the model developed,this method achieved good result with high determination coefficient 0.8358 and low root-mean-square error(3.816mg/m~3),which was proved to be an useful tool to retrieval chlorophyll-a concentration in very turbid,hyper-eutrophic inland waters.

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.

Research on remote sensing model for soil water content on rough surface

Roughness of soil surface not only performs a remarkable influence to the BRDF of ground, and it is variable as well. So it’s one of important factors that affect the precision of remote sensing for soil water content. In this paper, it is demonstrated that the relationship between soil reflected spectrum and the soil water content is about obey the Beer Law, and then the absorption coefficient curve of moisture in soil was measured. The conclusion also be reached that the essential cause of soil surface roughness effects the soil reflectance is the slant of sub slopes on clod surface which result in the increasing of the rate of multi-reflecting light. Through analyzing the physical process of soil surface reflecting under the inference of soil water, a geometrical BRDF model for soil water remote sensing on rough surface is put forward in this paper. The result of test data showed that the model has a satisfied precision.

Spatial data and model management based on XML

In this paper, we present an approach based on XML for managing spatial information, spatial analysis decision models and remote sensing models. It is easy to manage information using XML, in order to share information. The basic framework and management scheme are also presented.

The quantification of remote sensing

The paper firstly defined the remote sensing information quantification, analyzed the necessity of developing remote sensing quantification, figured out the application guidelines requirement, and pointed out the importance of quantification research. Then taking the remote sensing application research of CBERS-02 data quantification as the example, the paper described the whole quantification system of “remotely sensed digital signal-radiation information-field parameter inversion”. Finally the paper gave the prospect for the development trend of the quantitative remote sensing.

Estimating surface evapotranspiration using combined MODIS and CBERS-02 data

Spatial scale error is one of the most serious problems in the estimates of land surface heat fluxes of sensible and latent from satellite-borne data such as MODIS 1km resolution reflectance and emissive data. One of the feasible and economic ways to decrease the spatial scale error is to use high resolution land use class data together with the MODIS data. CBERS-02 data were used to produce land use class of Baiyangdian area, Hebei Province, China in the autumn of 2004. The area ratio of each class in MODIS pixel was calculated, and used to derive the heat fluxes of the mixed pixel. The results showed that the estimated heat fluxes of soil, sensible and latent have been changed remarkably after using the high resolution land class data. It could be concluded from the comparison between simulated and ground-measured fluxes as well as the theoretical analysis that high resolution land class data are useful to diminishing the scale error of heat fluxes estimated from low resolution satellite data.Spatial scale error is one of the most serious problems in the estimates of land surface heat fluxes of sensible and latent from satellite-borne data such as MODIS 1km resolution reflectance and emissive data. One of the feasible and economic ways to decrease the spatial scale error is to use high resolution land use class data together with the MODIS data. CBERS-02 data were used to produce land use class of Baiyangdian area, Hebei Province, China in the autumn of 2004. The area ratio of each class in MODIS pixel was calculated, and used to derive the heat fluxes of the mixed pixel. The results showed that the estimated heat fluxes of soil, sensible and latent have been changed remarkably after using the high resolution land class data. It could be concluded from the comparison between simulated and ground-measured fluxes as well as the theoretical analysis that high resolution land class data are useful to diminishing the scale error of heat fluxes estimated from low resolution satellite data.