College of Resources and Environmental Sciences, Gansu Agricultural University, Lanzhou 730070, China.
School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China.
Sensors (Basel). 2021 Mar 7;21(5):1869. doi: 10.3390/s21051869.
Leaf area index (LAI) is a key biophysical variable to characterize vegetation canopy. Accurate and quantitative LAI estimation is significant for monitoring vegetation growth status. ZhuHai-1 (ZH-1), which is a commercial remote sensing micro-nano satellite, provides a possibility for quantitative detection of vegetation with high spatial and spectral resolution. However, the band characteristics of ZH-1 are closely related to the accuracy of vegetation monitoring. In this study, a simulation dataset containing 32 bands of ZH-1 was generated by using the PROSAIL model, which was used to analyze the performance of 32 bands for LAI estimation by using the hybrid inversion method. Meanwhile, the effect of different band combinations on LAI estimation was discussed based on sensitivity analysis and the correlation between bands. Then, the optimal band combination from ZH-1 hyperspectral satellite data for LAI estimation was obtained. LAI estimation was performed based on the selected optimal band combination of ZH-1 satellite images in Xiantao city, Hubei province, and compared with the Sentinel-2 normalized difference vegetation index (NDVI) values and LAI product. The results demonstrated that the obtained LAI map based on the optimal band combination of ZH-1 was generally consistent with the overall distribution of Sentinel-2 NDVI and the LAI product, but had a moderate correlation with Sentinel-2 LAI (R = 0.60), which may not favorably indicate the validity of indirect validation. However, the method of this study on the analysis of hyperspectral data bands has application potential to provide a reference for selecting appropriate bands of hyperspectral satellite data to estimate LAI and improve the application of hyperspectral data such as ZH-1 in vegetation monitoring.
叶面积指数（LAI）是描述植被冠层的关键生物物理变量。准确、定量的 LAI 估计对于监测植被生长状况具有重要意义。ZhuHai-1（ZH-1）是一颗商业遥感微纳卫星，为具有高空间和光谱分辨率的植被定量检测提供了可能性。然而，ZH-1 的波段特征与植被监测的准确性密切相关。在这项研究中，通过使用 PROSAIL 模型生成了一个包含 32 个波段的 ZH-1 仿真数据集，该模型用于通过混合反演方法分析 32 个波段对 LAI 估计的性能。同时，基于敏感性分析和波段间相关性，讨论了不同波段组合对 LAI 估计的影响。然后，从 ZH-1 高光谱卫星数据中获得了用于 LAI 估计的最佳波段组合。在湖北省仙桃市，基于所选的 ZH-1 卫星图像最佳波段组合进行 LAI 估计，并与 Sentinel-2 归一化差异植被指数（NDVI）值和 LAI 产品进行比较。结果表明，基于 ZH-1 最佳波段组合获得的 LAI 图与 Sentinel-2 NDVI 和 LAI 产品的整体分布基本一致，但与 Sentinel-2 LAI 的相关性适中（R = 0.60），这可能不利于间接验证的有效性。然而，本研究中对高光谱数据波段的分析方法具有应用潜力，可为选择高光谱卫星数据的合适波段来估计 LAI 提供参考，并提高 ZH-1 等高光谱数据在植被监测中的应用。
