基于地面光谱水稻重金属胁迫监测光谱特征尺度识别

为探究地面高光谱遥感监测不同光谱尺度对水稻重金属胁迫区分度，以不同污染水平地面ASD高光谱数据为基础，通过光谱敏感特征优选确定450～900 nm为水稻重金属胁迫敏感波段，利用DB5小波变换产生的多尺度小波特征系数模拟不同光谱分辨率，结合小波参数的信息熵特征和分形维数特征，对水稻重金属胁迫特征光谱尺度进行识别，通过构建胁迫相关的叶绿素光谱指数 MCARI/OSAVI、NDSI_R、Depth验证所识别的特征尺度的可靠性和精准性。结果表明：①小波分解各尺度细节系数计算出的信息熵在分解的5～7尺度附近不同胁迫水平有明显的特征转折点；②随着分解尺度的增加，不同胁迫水平的分维数差异值变小，第5尺度是水稻受不同重金属胁迫8层尺度分解和重构下光谱曲线尺度的最明显的转折点，在尺度5下，光谱曲线的峰谷细节得到更好的反映; ③研究水稻重金属污染光谱特征尺度既保留光谱信息的主要特征，又最大程度的减少了光谱数据量，不仅提高了水稻重金属污染监测的效率而且为环境监测提供了新的手段。

Deriving the Spectral Characteristic Scale for Heavy Metal Stress Monitoring in Rice Based on Ground Spectral Data#br#

In order to explore the sensitivity of hyperspectral remote sensing monitoring to heavy metal stress in rice at different spectral scales based on ASD hyperspectral data under different pollution levels, 450~900 nm of ASD spectral band was selected as the sensitive band of heavy metal stress, and the multi-scale wavelet coefficients generated by DB5 wavelet transform were used to simulate different spectral resolutions. Furthermore, the wavelet fractal dimension and wavelet detail coefficient entropy were determined to identify the turning point of rice spectral characteristic scale，the reliability and accuracy of the identified characteristic scales were verified by constructing stress-related chlorophyll spectral indices MCARI/OSAVI, NDSI_R and Depth. The results indicated that: ① The wavelet information entropy calculated by the detail coefficients of each scale of wavelet decomposition had obvious characteristic turning points of distinguishing stress levels near the 5～7 scale of decomposition. ②The statistical analysis of wavelet fractal dimension indicated that the scale 5 was the most obvious turning point of the spectral curve scale of rice under different scales of heavy metal stress and reconstruction. At scale 5, the peak and valley details of the spectral curve were better reflected. ③ The study of the spectral characteristics of heavy metal pollution in rice not only retained the main characteristic information of spectral information, but also minimized the amount of spectral data, which improved the efficiency of monitoring heavy metal pollution in rice,and provided a new means for environmental monitoring.