Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China.
University of Chinese Academy of Sciences, Beijing 100049, China.
Sensors (Basel). 2017 Jul 8;17(7):1593. doi: 10.3390/s17071593.
Leaf area index (LAI) is an important vegetation parameter that characterizes leaf density and canopy structure, and plays an important role in global change study, land surface process simulation and agriculture monitoring. The wide field view (WFV) sensor on board the Chinese GF-1 satellite can acquire multi-spectral data with decametric spatial resolution, high temporal resolution and wide coverage, which are valuable data sources for dynamic monitoring of LAI. Therefore, an automatic LAI estimation algorithm for GF-1 WFV data was developed based on the radiative transfer model and LAI estimation accuracy of the developed algorithm was assessed in an agriculture region with maize as the dominated crop type. The radiative transfer model was firstly used to simulate the physical relationship between canopy reflectance and LAI under different soil and vegetation conditions, and then the training sample dataset was formed. Then, neural networks (NNs) were used to develop the LAI estimation algorithm using the training sample dataset. Green, red and near-infrared band reflectances of GF-1 WFV data were used as the input variables of the NNs, as well as the corresponding LAI was the output variable. The validation results using field LAI measurements in the agriculture region indicated that the LAI estimation algorithm could achieve satisfactory results (such as R² = 0.818, RMSE = 0.50). In addition, the developed LAI estimation algorithm had potential to operationally generate LAI datasets using GF-1 WFV land surface reflectance data, which could provide high spatial and temporal resolution LAI data for agriculture, ecosystem and environmental management researches.
叶面积指数（LAI）是描述叶片密度和冠层结构的重要植被参数，在全球变化研究、陆面过程模拟和农业监测中发挥着重要作用。中国 GF-1 卫星搭载的宽视场（WFV）传感器可以获取具有分米级空间分辨率、高时间分辨率和宽覆盖范围的多光谱数据，是动态监测 LAI 的宝贵数据源。因此，本文基于辐射传输模型，开发了一种用于 GF-1 WFV 数据的自动 LAI 估算算法，并在以玉米为主要作物类型的农业区评估了所开发算法的 LAI 估算精度。首先，该辐射传输模型用于模拟不同土壤和植被条件下冠层反射率与 LAI 的物理关系，并形成训练样本数据集。然后，使用训练样本数据集通过神经网络（NNs）来开发 LAI 估算算法。GF-1 WFV 数据的绿光、红光和近红外波段反射率被用作 NNs 的输入变量，相应的 LAI 是输出变量。在农业区使用实地 LAI 测量值进行的验证结果表明，LAI 估算算法可以取得令人满意的结果（如 R²=0.818，RMSE=0.50）。此外，所开发的 LAI 估算算法有可能使用 GF-1 WFV 陆地表面反射率数据来操作生成 LAI 数据集，这可为农业、生态系统和环境管理研究提供高时空分辨率的 LAI 数据。
