基于无人机-卫星影像尺度转换的谷子关键生育期叶绿素相对含量反演方法

Inversion Method of Chlorophyll Relative Content in Key Growth Stages of Millet Based on Unmanned Aerial Vehicle-Satellite Image Scale Conversion

摘要:

叶绿素相对含量（SPAD）是评估谷子生长势态与氮素营养状况的关键指标。为实现谷子关键生育期（拔节期、抽穗期、灌浆期、成熟期）叶绿素相对含量的高精度、广覆盖动态监测，本研究融合田块尺度的无人机与卫星多源遥感数据，提出一种基于改进灰狼优化算法（IGWO）优化长短期记忆神经网络（LSTM）的叶绿素相对含量反演模型。同步获取各关键生育期的无人机遥感数据、卫星遥感数据及地面点状SPAD实测值，通过地面实测样点的经纬度坐标，将点状SPAD值与对应位置的无人机影像像元、卫星影像像元进行空间匹配关联，构建影像-实测对应数据集；采用点扩散函数法（Point spread function, PSF）进行无人机影像的尺度上推，结合均值-方差法对卫星数据进行初步修正，再利用支持向量回归（Support vector regression, SVR）建立无人机-卫星多源遥感数据协同校正模型；基于校正后的高精度卫星数据，通过皮尔逊相关性分析与XG-Boost特征重要性排序，筛选对叶绿素相对含量敏感的光谱特征参数；引入非线性收敛因子提升灰狼优化算法的超参数寻优能力，最终构建IGWO-LSTM 叶绿素相对含量反演模型。结果表明，相较于其他重采样方法，点扩散函数法在升尺度过程中信息损失最小，处理后影像像元的平均值与标准差分别为0.103和0.056；经直方图匹配法校正后的卫星遥感数据有效保持原始光谱形状，光谱角映射值（Spectral angle mapper, SAM）低至0.062°；SVR算法在关键生育期的B/G/R/NIR 波段校正模型精度均最高，四波段决定系数分别为0.920、0.961、0.963、0.900；IGWO-LSTM 模型在关键生育期叶绿素相对含量反演中的决定系数（R2）达0.985，均方根误差（RMSE）为0.111，显著优于偏最小二乘回归（PLSR）、BP神经网络（BPNN）及随机森林回归（RF）等传统模型。本研究实现了谷子关键生育期叶绿素相对含量的精准动态反演，对作物生长智能监测与氮肥精准施用具有重要意义。

Abstract:

SPAD is a key indicator for evaluating the growth potential and nitrogen nutrition status of millet. To achieve high-precision and wide-coverage dynamic monitoring of the relative chlorophyll content of millet during key growth stages (jointing stage, heading stage, filling stage, and maturity stage), taking the field scale as the research area and integrated multi-source remote sensing data from unmanned aerial vehicles (UAV) and satellites, an SPAD inversion model for optimizing long short-term memory neural networks (LSTM) based on the improved grey wolf optimization algorithm (IGWO) was proposed. During the research process, UAV remote sensing data, satellite remote sensing data and ground point-like SPAD measured values of each key growth period were obtained simultaneously. Through the longitude and latitude coordinates of the ground measured sample points, the point-like SPAD values were spatially matched and associated with the UAV image pixels and satellite image pixels at the corresponding positions to construct an image-measured corresponding data set. The point spread function (PSF) method was adopted for scale upward inference of UAV images. The mean-variance method was combined to preliminarily correct the satellite data. Then the support vector regression (SVR) was used to establish a collaborative correction model for multi-source remote sensing data of UAV and satellite. Based on the corrected high-precision satellite data, the spectral characteristic parameters sensitive to SPAD were screened through Pearson correlation analysis and XG-Boost feature importance ranking. The nonlinear convergence factor was introduced to enhance the hyperparameter optimization ability of the grey wolf optimization algorithm, and finally the IGWO-LSTM SPAD inversion model was constructed. The results showed that compared with other resampling methods, the point spread function method had the least information loss during the scale-up process. The average value and standard deviation of the processed image pixels were 0.103 and 0.056, respectively. The satellite remote sensing data corrected by the histogram matching method effectively retained the original spectral shape, and the spectral angle mapping value (SAM) was as low as 0.062°. The SVR algorithm had the highest model accuracy in the B/G/R/NIR bands during the critical growth period, and the determination coefficients of the four bands were 0.920, 0.961, 0.963 and 0.900, respectively. The coefficient of determination (R2) of the IGWO-LSTM model in the inversion of SPAD during the critical growth period reached 0.985, and the root mean square error (RMSE) was 0.111, which was significantly superior to that of traditional models such as partial least squares regression (PLSR), BP neural network (BPNN), and random forest regression (RF). The research achieved the precise dynamic inversion of SPAD during the key growth period of millet, which was of great significance for the intelligent monitoring of crop growth and the precise application of nitrogen fertilizer.