Abstract An analyzing program SMAS based on digital image processing technique is developed for quantifying soil microstructure. By using SMAS, a series of geometrical and morphological indexes of soil particles/pores in microscale can be quantitatively determined. Three examples of using SMAS to quantify the microstructure features are shown. The analyzing results indicate that the developed program can effectively identify the morphology of soil particle and pore and accurately extract the soil microstructure indexes. A classification criterion for particle shape category is proposed based on the obtained values of morphology ratio and roundness. Moreover, effects of magnification and observation area of SEM images on the quantitative analysis results are discussed. It is important to select an appropriate magnification and observation area can cover as much structural information as possible while with high imaging quality. A recommendation approach is to stitch several images with relatively high magnification to one large image for quantification. Moreover, performing multiple scans on different zones of interest and then making comparative analysis is also an effective way to reduce quantification errors in microstructure observation. The findings of this investigation would be valuable for improving the reliability of quantitative characterization of soil microstructure on the basis of SEM images.

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图像处理技术对土壤微观结构特征的量化与表征

摘要 开发了一种基于数字图像处理技术的土壤微结构量化分析程序SMAS。利用SMAS，可以定量测定微观尺度土壤颗粒/孔隙的一系列几何和形态指标。显示了使用 SMAS 量化微观结构特征的三个示例。分析结果表明，所开发的程序能够有效识别土壤颗粒和孔隙的形态，准确提取土壤微结构指标。根据获得的形貌比值和圆度值，提出了颗粒形状类别的分类标准。此外，还讨论了 SEM 图像的放大倍数和观察区域对定量分析结果的影响。选择合适的放大倍数很重要，观察区域可以覆盖尽可能多的结构信息，同时具有较高的成像质量。一种推荐方法是将几张放大率相对较高的图像拼接成一张大图像进行量化。此外，对不同的感兴趣区域进行多次扫描，然后进行比较分析也是减少微观结构观察中量化误差的有效方法。这项调查的结果对于提高基于 SEM 图像的土壤微观结构定量表征的可靠性具有重要意义。此外，对不同的感兴趣区域进行多次扫描，然后进行比较分析也是减少微观结构观察中量化误差的有效方法。这项调查的结果对于提高基于 SEM 图像的土壤微观结构定量表征的可靠性具有重要意义。此外，对不同的感兴趣区域进行多次扫描，然后进行比较分析也是减少微观结构观察中量化误差的有效方法。这项调查的结果对于提高基于 SEM 图像的土壤微观结构定量表征的可靠性具有重要意义。