一种融合多源数据的全国OCO-2 SIF降尺度方法

A National OCO-2 SIF Downscaling Method Integrating Multi-source Data

    摘要
  • 摘要: 日光诱导叶绿素荧光(solar-induced chlorophyll fluorescence,SIF)值对表征农作物长势状况、植被胁迫早期诊断等具有重要意义,目前主要是利用遥感技术反演得到大范围SIF值,但由于受低空间分辨率和稀疏采样等限制,难以满足省级以下空间尺度应用需求。针对上述问题,提出一种基于随机森林(random forest,RF)算法的SIF降尺度模型构建方法。首先将多源遥感数据与轨道碳观测2号(orbiting carbon observatory-2,OCO-2) SIF数据进行融合,建立同一尺度下的非线性关系,反演500 m×500 m空间分辨率的SIF数据,通过实验得出预测模型的决定系数、平均绝对误差和均方根误差分别达到0.72、0.24 mW·m-2·nm-1·sr-1和0.33 mW·m-2·nm-1·sr-1,表明了该方法的有效性；然后基于模型降尺度后的SIF数据与增强型植被指数、归一化差异植被指数、新OCO-2 SIF数据集进行空间相关性分析,并利用中国二氧化碳观测卫星SIF和地面SIF站点数据进行验证,结果均表现出较好的相关性。将降尺度后的SIF数据在中国河南省内进行时序性分析,结果显示降尺度产品能够较好地捕捉植被变化信息。通过RF算法融合多源数据能够反演出500 m×500 m空间分辨率的SIF数据。

     

    Abstract:
    Objective The value of solar-induced chlorophyll fluorescence (SIF) is of great significance for characterizing the growth status of crops and early diagnosis of vegetation stress. At present, a large range of SIF values are retrieved by remote sensing technology. However, due to the limitations of low spatial resolution and sparse sampling, it is difficult to meet the application of spatial scales below the provincial level. To solve the above problems, this paper proposes a SIF downscaling model construction method based on random forest (RF) algorithm.
    Methods First, multi-source remote sensing data are fused with orbiting carbon observation-2 (OCO-2) SIF, and the nonlinear relationship with characteristic variables is established under the same scale of SIF. Based on the assumption that the spatial scale relationship is constant, 500 m×500 m spatial resolution SIF data are inversely performed by RF algorithm.
    Results The coefficients of determination, mean absolute error and root mean squared error of the prediction model reached 0.72, 0.24 mW·m-2·nm-1·sr-1 and 0.33 mW·m-2·nm-1·sr-1, respectively, indicating the effectiveness of this method. Then, based on the SIF data after the model downscaling, the spatial correlation analysis is carried out with the enhanced vegetation index, normalized difference vegetation index, and the new OCO-2 SIF data, and verified with the SIF of the Chinese carbon dioxide observation satellite mission and the ground SIF station data. The results show good correlation. In addition, the time series analysis of the downscaled SIF in Henan Province, shows that the downscaled products can better capture vegetation change information.
    Conclusions The RF algorithm can perform SIF products with 500 m×500 m spatial resolution well by integrating multi-source data.

     

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