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The use of full range spectroradiometer data to assess properties of a heterogeneous soil set in a regional scale survey

机译:使用全光谱光谱仪数据评估区域规模调查中异质土壤组的特性

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The spectral assessment of soil properties is handicapped by the fact that spectral predictive mechanisms often vary from one population to another. In a landscape approach, heterogeneous conditions with a wide variety of combinations of spectrally active factors have to be considered. Heterogeneity, however, is one main reason for poor predictions from spectroscopic data, as an optimal calibration needs limited but sufficient set heterogeneity. For our study, the investigated plots were located in an area that covered about 600 km~2; geologic conditions and sampled soil types were highly variable. In total, 172 soil samples were taken from the top horizon of agricultural fields, afterwards analysed in the laboratory for total organic carbon (OC) and black carbon (BC) and additionally measured with a full range ASD FieldSpec-instrument. The heterogeneity of the sample set was reflected by both the analysed soil parameters and the measured soil spectra. As a consequence, one "global" calibration model (with PLSR) provided only moderate results for the studied soil variables. In the following we focused on two issues, which were i) to replace the global calibration by local calibration procedures, and ii) to study the effect of spectral variable selection for calibration success. For the CARS selection procedure ("competitive adaptive reweighted sampling"), the results demonstrated that more accurate estimates can be obtained using selected variables instead of the full spectrum.
机译:土壤特性的光谱评估因以下事实而受到阻碍:光谱预测机制通常在一个种群之间变化。在景观方法中,必须考虑具有多种光谱活性因子组合的非均质条件。但是,异质性是光谱数据预测不佳的主要原因之一,因为最佳校准需要有限但足够的集合异质性。对于我们的研究,被调查的地块位于一个覆盖约600 km〜2的区域。地质条件和取样的土壤类型变化很大。总共从农田的最高层采集了172个土壤样品,然后在实验室中分析了总有机碳(OC)和黑碳(BC),并另外使用了全系列ASD FieldSpec仪器进行了测量。样品集的异质性通过分析的土壤参数和测得的土壤光谱反映出来。结果,一个“全局”校准模型(使用PLSR)仅为所研究的土壤变量提供了中等结果。在下文中,我们重点关注两个问题,即:i)用局部校准程序代替全局校准,以及ii)研究光谱变量选择对​​校准成功的影响。对于CARS选择程序(“竞争性自适应重加权采样”),结果表明,使用选定的变量而不是整个频谱可以获得更准确的估计。

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