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首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >Estimation of Multi-Species Leaf Area Index Based on Chinese GF-1 Satellite Data Using Look-Up Table and Gaussian Process Regression Methods
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Estimation of Multi-Species Leaf Area Index Based on Chinese GF-1 Satellite Data Using Look-Up Table and Gaussian Process Regression Methods

机译：使用查找表和高斯过程回归方法基于中国GF-1卫星数据的多物种叶面积指数估计

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摘要

Leaf area index (LAI) is an important biophysical parameter, which can be effectively applied in the estimation of vegetation growth status. At present, amounts of studies just focused on the LAI estimation of a single plant type, while plant types are usually mixed rather than single distribution. In this study, the suitability of GF-1 data for multi-species LAI estimation was evaluated by using Gaussian process regression (GPR), and a look-up table (LUT) combined with a PROSAIL radiative transfer model. Then, the performance of the LUT and GPR for multi-species LAI estimation was analyzed in term of 15 different band combinations and 10 published vegetation indices (VIs). Lastly, the effect of the different band combinations and published VIs on the accuracy of LAI estimation was discussed. The results indicated that GF-1 data exhibited a good potential for multi-species LAI retrieval. Then, GPR exhibited better performance than that of LUT for multi-species LAI estimation. What is more, modified soil adjusted vegetation index (MSAVI) was selected based on the GPR algorithm for multi-species LAI estimation with a lower root mean squared error (RMSE = 0.6448 m /m ) compared to other band combinations and VIs. Then, this study can provide guidance for multi-species LAI estimation.
机译：叶面积指数（LAI）是重要的生物物理参数，可以有效地用于估算植被生长状况。目前，大量研究仅集中在单一植物类型的LAI估计上，而植物类型通常是混合的而不是单一分布。在这项研究中，通过使用高斯过程回归（GPR）和查找表（LUT）结合PROSAIL辐射传递模型，评估了GF-1数据对多物种LAI估计的适用性。然后，根据15种不同的波段组合和10种公布的植被指数（VI），分析了LUT和GPR在多物种LAI估计中的性能。最后，讨论了不同频段组合和已发布的VI对LAI估计准确性的影响。结果表明，GF-1数据显示了多物种LAI检索的良好潜力。然后，在多物种LAI估计中，GPR表现出比LUT更好的性能。此外，基于GPR算法选择改良土壤调整植被指数（MSAVI）进行多物种LAI估计，与其他波段组合和VI相比，其均方根误差更低（RMSE = 0.6448 m / m）。然后，该研究可以为多物种LAI估计提供指导。

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