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TIAN Dingfang, YANG Siqi, XU Dawei, et al. FAPAR retrieval from GF-5 hyperspectral images based on unified BRDF model. [J]. National Remote Sensing Bulletin 27(3):711-723(2023)
植被光合有效辐射吸收比率（FAPAR）是描述植被光合作用能量交换过程的重要参数，广泛应用于植被长势监测、植被生产力估算、全球变化等研究领域。遥感是大范围获取FAPAR的唯一途径，与多光谱传感器相比，高光谱传感器能更加精确、细致地观测植被的光谱特征，有利于分析植被冠层反射、吸收特性，进而反演植被冠层FAPAR。本文首先在植被BRDF统一模型和FAPAR-P模型的基础上，构建了BRDF-FAPAR统一模型UBFM（Unified BRDF-FAPAR Model）；进而基于高分五号高光谱传感器特征模拟了不同情况下植被冠层反射率和相应的FAPAR；然后运用改进的最佳指数法选择FAPAR反演的特征波段组合；在此基础上，将特征波段反射率与FAPAR模拟结果作为神经网络的输入参数，构建针对高光谱数据的FAPAR神经网络反演算法。研究结果表明，改进的最佳指数法能有效地筛选出FAPAR估算的敏感波段；综合考虑波段信息量和实际影像数据噪声影响，本研究针对高分五号高光谱传感器选择8个波段作为FAPAR反演特征波段。基于UBFM模型构建的神经网络反演精度较高，模拟实验算法误差约为0.014。选择内蒙古呼伦贝尔市谢尔塔拉草原为主要研究区，采用高分五号高光谱影像数据反演了研究区的FAPAR，并利用同步地面实测数据开展验证，反演误差为0.048。该算法简化了传统机理方法的中间环节和繁琐的参数设置，有较好的可行性、稳定性和精度，为国产卫星高光谱传感器地表植被参数定量反演提供了新途径。
The Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) is a key parameter in characterizing the photosynthesis process of vegetation and widely used in many study areas, such as vegetation monitoring, NPP estimation, and global change. Remote sensing is the only way to obtain FAPAR at large scales. Compared with the multispectral instrument, the hyperspectral instrument has an advantage in analyzing the canopy reflectance and absorption on the basis of the high accuracy of the spectrum measurement, which is important in FAPAR retrieval. This study developed a new FAPAR retrieval algorithm for the Chinese GF-5 Visible-shortwave Infrared Advanced Hyperspectral Imager (AHSI) data on the basis of BRDF unified model and the neural network (NNT). The validation was performed in Hulun Buir Xeltala, which is a grassland and farming-pastoral area in Inner Mongolia. First, the simulated GF-5 AHSI reflectance-FAPAR datasets were generated by the BRDF unified model, and the characteristics of the data set were analyzed. Five groups of NNT input bands were selected based on the Optimal Index Factor (OIF) and a new factor OIFR, which was modified by the relevance of the band reflectance and the FAPAR. Different groups of bands were used to build the NNT, and the results were assessed by a test set in the simulation dataset. Finally, the best feature bands and the NNT were selected to generate the FAPAR map of the study area from the GF-5 AHSI image. Validation with in-situ observations was made. Overall, the new factor OIFR is more efficient than the origin factor OIF in band selection. As the amount of input bands increases, the NNT accuracy gradually increases, but the trend stops when the amount reaches a certain level. Considering both band information and instrument noise, 8 bands were selected as the feature bands of FAPAR retrieval with the FAPAR RMSE of NNT is 0.014. The FAPAR map of the study area was generated, and the comparison with in-situ FAPAR shows the applicability of the method with RMSE=0.048. The reflectance and absorption by the hyperspectral data when the NNT reduced the middle term and parameters of the traditional methods simultaneously can be analyzed, presenting a new approach to the surface parameters retrieval of domestic satellite hyperspectral instruments.
光合有效辐射吸收比率高光谱遥感特征波段神经网络高分五号
FAPARHyperspectral remote sensingfeature bandneural networkGF-5
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