传感器光谱指标对植被光谱模拟精度的影响

高光谱卫星数据模拟是卫星遥感数据模拟的重点研究方向，基于星载多光谱数据和地物光谱先验知识是一种快速模拟高光谱数据的方法，但数据模拟精度受传感器光谱指标的限制。文章针对EO-1/ALI的可见光/近红外波长范围进行实验，研究了波段数量、半波宽度和波长位置等光谱指标与植被光谱模拟精度的关系，分析了两者之间的变化规律。研究表明，光谱指标决定了植被光谱特征提取，是影响光谱模拟精度的直接原因。文章总结了适于光谱重构模型的光谱参数范围，实验结果有利于提高植被光谱模拟精度。该结论可用于多光谱传感器的性能评价及其分光滤色结构的改进。

Evaluation of Sensor Spectral Parameters for the Simulation Accuracy of the Vegetation Spectrum

Hyperspectral imaging (HSI) has become one of the most promising and emerging techniques in remote sensing. Due to hundreds of co-registered bands used in HSI system, hyperspectral imagery may provide more spectral information than multi-band images. Unfortunately, original hyperspectral images are more expensive and difficult to achieve than multi-band ones. However, an abundance of spectral information has to be acquired by part of special research for the purpose of ground monitoring, which original HSI systems can easily provide. Then a solution, called hyperspectral satellite data simulation, is proposed for studies in satellite data simulation. It is also one of the most important studies to simulate satellite remote sensing data. In the method, the model with low computational complexity can simulate hyperspectral data quickly, which is based on the priori spectral knowledge of the ground objects. But the accuracy of the simulation data depends on spectral parameters of the sensor. In the present paper, the authors experiment with EO-1/ALI bands in VIS/NIR wavelengths. Then the relationship between the spectral parameters, including the number of bands, bandwidth and the peak wavelength, and the simulation accuracy of the vegetation spectrum are analyzed from their variation principles. According to the results, spectral parameters can determine the effective spectral feature of the vegetation, and impact simulation model directly. Optimal parameters are also summarized for spectral reconstruction in the paper. The experiment results are beneficial to enhancing spectral simulation precision. The conclusions can help evaluate the performance of multispectral sensors and perfect spectroscope and filter design.