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REMOTE SENSING OF FOLIAR CHEMISTRY OF INUNDATED RICE WITH IMAGING SPECTROMETRY

机译:成像光谱法对淹没水稻叶化学的遥感研究。

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The applicability of imaging spectrometry to the measurement of foliar chemistry of herbaceous wetland vegetation was assessed for cultivated rice. Multiple samples were obtained from five California rice fields for analyses of nitrogen and lignin concentrations, and Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) images were obtained in August 1992. Multiple linear regression (MLR) was used to develop calibration equations for nitrogen and lignin based on AVIRIS reflectance (R) spectra transformed as the first difference of absorbance [log(1/R)]. Transformed AVIRIS absorbances were not as well correlated to lignin (r(2) = 0.44) as to nitrogen (r(2) = 0.74); the relative standard error of calibration (SEC) for nitrogen was 11%. To further test the predictive ability of this technique with respect to nitrogen concentration, four new calibration equations were derived based on each possible set of three fields. With each new subset of the data, MLR selected different wavelengths for the nitrogen calibration equation. Coefficients of multiple determination (r(2)) ranged from 0.69 to 0.85, with relative standard SECs ranging from 8.4% to 11.4%. Each of these equations was then used to predict the nitrogen concentration of samples in the omitted field, resulting in relative standard errors of prediction (SEP) ranging up to 37%. Results from this study suggest that calibration equations derived from multiple linear regression of AVIRIS spectra can be used to predict the nitrogen concentration of rice in the field. It was not possible to derive a general equation for the detection of nitrogen concentration with AVIRIS because the calibration equations developed with MLR were based on a different set of wavelengths for each subset of the data. [References: 37]
机译:评估了成像光谱法在耕作水稻中对草本湿地植被叶面化学测量的适用性。从加利福尼亚的五个稻田中获得了多个样品,用于分析氮和木质素的浓度,并于1992年8月获得了机载可见/红外成像光谱仪(AVIRIS)图像。使用了多元线性回归(MLR)来开发氮和木质素的校准方程根据AVIRIS反射率(R)光谱转换为吸光度的第一个差异[log(1 / R)]。转化的AVIRIS吸光度与木质素(r(2)= 0.44)和氮(r(2)= 0.74)的相关性不高。氮气的相对标准校准误差(SEC)为11%。为了进一步测试该技术相对于氮浓度的预测能力,根据三个场的每个可能集合推导了四个新的校准方程式。对于数据的每个新子集,MLR为氮校准方程式选择了不同的波长。多重测定系数(r(2))的范围为0.69至0.85,相对标准SEC的范围为8.4%至11.4%。然后,这些方程式中的每一个都用于预测遗漏字段中样品的氮浓度,从而导致相对标准的预测误差(SEP)高达37%。这项研究的结果表明,从AVIRIS光谱的多元线性回归中得出的校准方程可用于预测稻田中的氮含量。由于用MLR开发的校准方程是基于数据的每个子集的不同波长集,因此不可能用AVIRIS得出检测氮浓度的通用方程。 [参考:37]

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