The instrument simulation of space-borne remote sensing systems is an important work for the adaptation and optimization of fundamental instrument parameters for a sensor and its observation conditions. The multispectral imaging simulator has been developed with the framework of GF-4 mission, which is a geostationary satellite in the national high-resolution earth observation system of China. The presented simulator consists of two processing modules producing GF-4 like digital number data in VNIR and MIR bands. The first processing module converts at sensor radiance to photons considering the spectral response, optical transmission, noise, modulation transfer function (MTF), et al. The second part of the simulation is electronic data processing module including an analogue-to-digital converter. The verification of the simulation is performed by comparing the real output of radiometric calibration in laboratory with simulated DN. Analysis of the final simulation data has shown the accurate and reliable performance of the established simulator enabling the system to support technical decision-making processes required for the development of the next generation geostationary satellite.
The observed radiance at-sensor level generally records the radiation changes caused by multiple factors during the imaging process for Earth observation satellites. Most methods can not retrieve the precise reflectance because of the neglecting of the mutual effects between the atmosphere and the topography. In this study, based on a physical component model of at-surface level radiation, demonstrates that the earth-atmosphere coupling effect is playing a key role in a practical reflectance retrieval method over rugged terrains. The comparison of retrieving reflectance on sunlit areas and shady areas shows how the accuracy is affected after applying earth-atmosphere coupling correction.
For the satellite remote sensing data, it is necessary to evaluate the adjacency effect due to atmospheric scattering. Accurate modeling of the adjacency effect requires capabilities dealing with rugged areas and multiple scattering. In this paper, estimation of the adjacency effect is done by calculating the contribution of photons after the multiple scattering process through a many layered atmosphere. For the requirement of fast calculation in remote sensing simulation system, we adopt the approximate ISAACS 2-stream and flux adding method to model the adjacency effect. We evaluate the multiple scattering model by simulating the at-sensor radiance observed over synthetic rugged scenes under varying atmospheric conditions. Radiance comparisons with a single scattering model show good agreement in the clear atmosphere. Relative radiance differences are found to be about 11% in the dust atmosphere, increasing to 15% in the steep areas. Being coupled with the simulation model for remote sensing, it can be used in generation of simulated datasets and validation of the data processing algorithms.
For an earth observation system, an increase in the off-nadir viewing angle leads to an increase in line of sight scattered radiance. Meanwhile, steep terrain over mountainous area induces changes in irradiance at ground level and then affects the top of atmosphere (TOA) signal. In this paper, a methodology is presented to simulate and analyze the adjacency effects. In the second section of the paper, the radiative transfer equations are built separately for the nadir viewing geometry and the off-nadir viewing geometry over mountainous area. In order to model the adjacency effects, the radiance items related to the adjacency effects are estimated. During the procedure, the molecular/aerosol scattering phase functions, different viewing geometry, ground heterogeneity and topography are taken into account. The performance of this methodology is validated through simulating a set of space-borne data over mountainous areas for nadir viewing angles and off-nadir viewing angles. The results indicate that the contributions of adjacency effect at TOA are significantly different for the varying viewing geometry conditions. The proposed method proved to be useful in understanding the mechanisms of adjacency effects and it will be applied to atmospheric correction of remotely sensed data.
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