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利用红边参数反演作物参数是定量遥感研究的一个热点,红边参数中红边位置与作物生化组分强相关,为监测作物胁迫提供了一个非常敏感的指标。准确估测植被叶绿素含量,对于研究森林健康和胁迫、森林生产力的估计,碳循环的研究有着重要的意义。介绍几种红边位置算法,并对这些算法及其应用进行了比较,通过选取红边位置的不同敏感波段来估测植被叶片叶绿素含量。经室内光谱获取叶片的光谱数据,采用一阶光谱导数法、平滑处理后一阶光谱导数法、线性四点内插法、五次多项式拟合法四种算法处理光谱数据,获得红边位置变量,并与叶绿素含量进行拟合,构建估测木荷叶片叶绿素含量的回归模型。结果表明:各种算法获取的红边位置变量所构建的回归模型估测叶绿素含量是可行的;五次多项式拟合法估算精度是最高的,其获取红边位置计算相对复杂;线性四点内插法估算精度次之,但计算较简便。
The red edge parameter inversion of crop parameters is a hot spot in quantitative remote sensing research. The position of red edge in red edge parameter is strongly correlated with crop biochemical components, which provides a very sensitive indicator for monitoring crop stress. Accurate estimation of vegetation chlorophyll content is of great significance for the study of forest health and stress, estimation of forest productivity, and carbon cycle. Several red-edge location algorithms are introduced, and compared with these algorithms and their applications, the chlorophyll content of vegetation is estimated by choosing different sensitive bands of red-edge locations. The spectral data of leaf were obtained by indoor spectroscopy. The spectral data of the first-order spectral derivative method, the first-order spectral derivative method after smoothing treatment, the linear four-point interpolation method and the fifth-order polynomial fitting method were used to obtain the red- And fitted with the chlorophyll content to construct a regression model to estimate the chlorophyll content of Schima superba leaves. The results show that it is feasible to estimate the chlorophyll content by the regression model constructed by the red-edge position variables obtained by various algorithms. The precision of quintic polynomial fitting method is the highest, and the calculation of red edge position is relatively complicated. Linear four-point interpolation The accuracy of the method is second, but the calculation is simpler.