Gao Hailiang

Method study and uncertainty analysis of calibration coefficients validation based on the Inner Mongolia test site

Calibration coefficients validation is the foundation for ascertaining the sensor performance and carrying out the quantitative application. Based on the analysis of the differences between the calibration and validation, two calibration coefficients validation methods were introduced in this paper. Taking the HJ-1A satellite CCD1 camera as an example, the uncertainties of calibration coefficients validation were analyzed. The calibration coefficients validation errors were simulated based on the measured data at an Inner Mongolia test site. The result showed that in the large view angle, the ground directional reflectance variation and the atmospheric path variation were the main error sources in calibration coefficients validation. The ground directional reflectance correction and atmospheric observation angle normalization should be carried out to improve the validation accuracy of calibration coefficients.

Inversion and validation of leaf area index based on CBERDS02B image data in GuangXi province of China

CBERDS02B satellite has been successfully launched in September 2007, the target of this paper is to get the vegetation index from visible red-band, near-infrared band and the blue-band surface reflectance data of CBERDS02B satellite, through the empirical model of the relations between the vegetation index and LAI, and combined with the classification data to integrate the appropriate model, in order to get the regional leaf area index image in Binyang County of Nanning City in Guangxi Province of China. To make the operation more rapid and feasible, I decided to use an empirical model to obtain LAI, This method is simple and easy to calculate, more realizable, and suitable for remote sensing application. In this paper I use part of the measured data to validate a wide range of VI-LAI models. In order to identify the advantages and disadvantages of the various models, different plants use different types of vegetation model, I finally choose four VIs, such as SR, NDVI, SAVI, EVI, then combine these with the classification data to get the best mixed model so as to attain the leaf area index image of the research region. Then I use the other part of the measured data to get the validation of the mixed model. Ultimately I improve the overall accuracy of the model, and gain more accurate LAI images in the region.

Synthetic Modeling of 3D Canopys Radiation Transfer in the VNIR and TIR Domains

In this paper, a synthetic strategy has been employed to model 3D canopys radiation transfer in the whole optical spectral domains. 3D plant architecture model (the Clumped Architecture Model of Plants: CLAMP) (1) is used to generate the realistic vegetation scene. In the visible and NIR region, the canopy BRDF was decomposed into three parts: single scattering contribution from leaves, single scattering contribution from the soil, and multiple scattering part of the canopy. The single scattering contributions come from illuminated leaves and soil components which are computed by the reverse ray-tracing procedure (2) with their corresponding reflectance. The multiple scattering contribution is approximated by the four-stream theory. As a result, the modeling of VNIR region is more efficient and fairly accurately describes the anisotropically scattering features of vegetation. In the TIR region, the directional brightness temperature of canopy is calculated as the linear combination of four components (illuminated leaves, illuminated ground, shadowed leaves, and shadowed ground) brightness temperature multiplied by its fractional cover computed by the reverse ray-tracing procedure. Initial modeling results show typical features of vegetations anisotropic scattering and directional temperature distributions, for example, hot spot, bowl shape and reach a good agreement with theoretical results in those three domains. This strategy shows potential of exploring the impact of canopy structure on the radiometric response measured by remote sensors.