Present paper is an initial attempt to study chemical and hyperspectral characteristics of topsoil of different soil taxonomic units in El-Fayoum depression. Archival data, field survey, soil profiles sampling and analyses, hyperspectral data assessment and spectral indices applications were integrated with statistical investigations to achieve the aim of the present study. Soil map of El-Fayoum shows the following main sub-great groups; Typic Torrifluvents, Typic Torrerts, Typic Quartizpsamments, Typic Calciorthids, Calcic Gypsiorthids and Typic Saliorthids. Typic Torriorthents recorded the highest levels of EC (260.5 dS/m), ESP (73.6), CEC (107.5 meq/ 100 g soil) and K+ (91.6 meg/L). However, the highest levels of Na (53.69 meq/L), Ca (28.61 meq/L) and Cl (86.01 meq/L) were associated with Typic Torripsamments. Typic Haplocalcids reported the maximum levels of SO4 (35.9 meq/L). The analyses of Tukey’s and one way ANOVA showed a great efficiency of SWIR2 to discriminate topsoil of all investigated taxonomic units of soil. On the other hand, topsoil of Typic Torriorthents, Typic Torripsamments, Typic Haplocalcids and Typic Quartizipsamments can be identified in all spectral bands (UV, VIS and IR) except the green band which showed limitation to define Typic Torripsamments and Typic Haplocalcids. The power of investigated hyperspectral bands to discriminate topsoil of various soil taxonomic units can be ordered as follows; SWIR2 > SWIR1 > NIR > Blue > Red > UV > Green. Based on linear regression analyses, innovative model for assessing gravel % was generated using SAVI retrieved from hyperspectral data, giving a promising accuracy (70 %). Further, a novel hyperspectral library for the topsoil of the investigated six sub-great groups of soil was developed for further applications of soil taxonomy on basis of hyperspectral data. Present findings are useful for encouraging the wide utilization of in-situ hyperspectral data sets for studying soil different variables.

本论文是研究 El-Fayoum 洼地不同土壤分类单元表层土壤化学和高光谱特征的初步尝试。档案数据、实地调查、土壤剖面采样和分析、高光谱数据评估和光谱指数应用与统计调查相结合，以实现本研究的目的。El-Fayoum 的土壤图显示了以下主要的亚大组；典型的 Torrifluvents、典型的 Torrrerts、典型的 Quartizpsamments、典型的 Calciorthids、Calcic Gypsiorthids和Typic Saliothids。典型的Torriorthents记录了最高水平的 EC (260.5 dS/m)、ESP (73.6)、CEC (107.5 meq/100 g 土壤) 和 K+ (91.6 meg/L)。然而，最高水平的 Na (53.69 meq/L)、Ca (28.61 meq/L) 和 Cl (86.01 meq/L) 与典型 Torripsamments相关。典型的Haplocalcids报告了 SO4 的最高水平 (35.9 meq/L)。Tukey 和单向 ANOVA 的分析显示 SWIR2 在区分所有调查的土壤分类单位的表土方面具有很高的效率。另一方面，典型的Torriorthents、Typic Torripsamments、Typic Haplocalcids和Typic Quartizipsamments的表土可以在所有光谱波段（UV、VIS 和 IR）中被识别，除了对定义Typic Torripsamments和典型 Torripsamments 有限制的绿色波段。典型的Haplocalcids。调查的高光谱波段区分各种土壤分类单元的表土的能力可以如下排序；SWIR2 > SWIR1 > NIR > 蓝色 > 红色 > UV > 绿色。基于线性回归分析，使用从高光谱数据中检索到的 SAVI 生成了用于评估砾石 % 的创新模型，具有良好的准确性 (70%)。此外，在高光谱数据的基础上，为研究的六个亚大类土壤的表层土开发了一个新的高光谱库，以便进一步应用土壤分类学。目前的研究结果有助于鼓励广泛利用原位高光谱数据集来研究土壤不同变量。