High-precision canopy chlorophyll content (CCC) inversion for marsh vegetation is of great significance for marsh protection and restoration. However, it is difficult to collect the CCC measured data for marsh vegetation that matches the pixel scale of remote sensing image. This article proposes a new method based on unmanned aerial vehicle (UAV) multispectral images to obtain multiscale marsh vegetation CCC sample data. A random forest (RF) regression algorithm was used to evaluate the application performance of GF-1 wide field view (WFV), Landsat-8 Operational Land Imager (OLI), and Sentinel-2 multispectral instrument (MSI) satellite remote sensing data in marsh vegetation CCC inversion. In addition, parameter optimization of the RF regression model was used to construct an optimization algorithm suitable for marsh vegetation, and the importance of input variables was quantitatively evaluated. The results showed that the UAV multispectral images assisted in the acquisition of marsh vegetation CCC sample data, as the method expanded the number of CCC samples while quantifying the CCC sample data collection accuracy [ R ² ≥ 0.86, root mean square error (RMSE) ≤ 6.98 SPAD], which improved the CCC inversion accuracy compared with traditional sampling methods. Extracting pure vegetation pixels through binary classification reduces the uncertainty of the UAV-scale CCC inversion results. Parameter optimization of the RF regression model further improves the CCC inversion accuracy at GF-1 WFV, Landsat-8 OLI, and Sentinel-2 MSI scales. Among the three satellite remote sensing data, Sentinel-2 MSI achieved the highest CCC inversion accuracy for marsh vegetation ( R ² = 0.79, RMSE = 10.96 SPAD) due to the inclusion of red-edge bands that are more sensitive to vegetation properties. Red-edge Chlorophyll Index (Cl _red-edge ) and Green Chlorophyll Index (Cl _green ) have the highest influence on the CCC inversion accuracy among input variables.

中文翻译：

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基于多尺度遥感数据的沼泽植被冠层叶绿素含量估算的有效方法


沼泽植被高精度冠层叶绿素含量（CCC）反演对于沼泽保护和恢复具有重要意义。然而，很难收集到与遥感影像像素尺度相匹配的沼泽植被CCC实测数据。本文提出了一种基于无人机多光谱图像获取多尺度沼泽植被CCC样本数据的新方法。采用随机森林（RF）回归算法评估GF-1宽视场（WFV）、Landsat-8业务陆地成像仪（OLI）和Sentinel-2多光谱仪器（MSI）卫星遥感数据在沼泽植被 CCC 反演。此外，利用RF回归模型的参数优化构建了适合沼泽植被的优化算法，并定量评估了输入变量的重要性。结果表明，无人机多光谱图像辅助获取沼泽植被CCC样本数据，该方法扩大了CCC样本数量，同时量化了CCC样本数据采集精度[R ² ≥ 0.86，均方根误差（RMSE）≤ 6.98 SPAD]，较传统采样方法提高了CCC反演精度。通过二值分类提取纯植被像素，降低了无人机尺度CCC反演结果的不确定性。 RF回归模型的参数优化进一步提高了GF-1 WFV、Landsat-8 OLI和Sentinel-2 MSI尺度的CCC反演精度。在三颗卫星遥感数据中，Sentinel-2 MSI取得了沼泽植被最高的CCC反演精度（R ² = 0.79，RMSE = 10。96 SPAD），因为包含对植被特性更敏感的红边带。输入变量中，红边叶绿素指数（Cl _red-edge ）和绿色叶绿素指数（Cl _green ）对 CCC 反演精度影响最大。