土壤颜色由CIE向Munsell系统的定量转换

颜色是土壤重要形态特征之一，也为判断土壤理化特征、过程与功能的重要依据和指标。土壤颜色目前采用Munsell颜色系统表示，主要依靠将土壤样品与土壤Munsell比色卡比对而确定。该方法由于受个人视觉感官和判别环境等因素限制，导致土壤颜色判别结果存在主观性强，易出现偏差等问题。为此，寻求客观、定量、方便地表征土壤Munsell颜色的方法，对于今后有关土壤颜色应用的研究工作极为重要。该研究试图利用分光色度仪，结合Python计算机语言编程，采用邻近等明面插值等方法，提出了一套可操作的从CIE到Munsell颜色系统转换的方法，以实现土壤颜色从CIE到Munsell颜色系统的方便、快速、高精度转换，达到土壤颜色的客观、定量、标准化测定。由分光色度仪测得样品CIELAB颜色值，利用CIE和Munsell两大颜色系统的已有的转换模型和方法，由CIELAB值计算获得Munsell明度值V和色品坐标，以Python语言编制程序，实现样品的明度值和色品坐标的自动计算。进而采用邻近等明面数学插值法和色度变换图确定Munsell系统H和C值。以《中国标准土壤色卡》的419张色片和22个土壤与古土壤样品进行验证，结果表明利用本文方法色调（H）测定值与色片标准值相比较，仅有12张色片存在误差，验证准确率为97%。《中国标准土壤色卡》的明度值（V）、彩度值（C）测定值与标准值之间相关系数分别为0.987（p＜0.001）和0.976（p＜0.001），测定结果与标准值之间存在极显著相关关系。土壤与古土壤样品测定值与目视判别色调差异较小，主要差别是明度和彩度，这可能与目视判读受个人主观、光学环境等因素影响所致。基于过去已有的由CIE到Munsell颜色系统转换方法，该研究利用Python计算机语言编程编写了程序代码，实现CIELAB值到Munsell明度V值和色品坐标的转换过程的自动计算，并提出一种基于邻近等明面新的插值方法，转换步骤简明，易实现，为土壤颜色的快速、定量获取提供途径。

Quantitative Conversion of Soil Color from CIELAB to Munsell System

Color is one of the critical morphological features of soils, which can be used as a basic index or proxy to reveal numerous soil physical and chemical properties, processes and functions. Soil color is universally expressed by the Munsell color system, and detected by the visual sense through the match of soil samples with the soil standard charts. However, the errors and bias in soil color readily occur using the current method due to the constrain of the difference in individual vision and variation in optical environment. An objective, quantitative and rapid method to determine the soil Munsell color is urgent to be invented.Here, with the combination of color measurement using the colorimeter, automatic calculation and linear interpolation between the adjacent Value (V) isoplanes, a procedure for the conversion of CIE color to Munsell color was proposed to obtain the soil color conveniently, rapidly and precisely. This protocol includes: (1) CIELAB color values of the samples are measured by the colorimeter; and (2) the color coordinates and the Munsell V values are computed automatically with the program in Python language on a base of the knowledge of the conversion between CIE and Munsell color systems; and (3) the linear interpolation between the adjacent V isoplanes and color conversion chart are employed to assign the Munsell Hue (H) and Chroma (C). 419 color chips from Munsell Soil Color Chart of China and 22 soil and paleosol samples were used to validate the protocol. The Munsell H values of 12 chips using our procedure were different from those in Munsell Soil Color Chart of China, and the measured accuracy reached 97%. Meanwhile, the correlation coefficients in Munsell V and C between the measured values and standard values were 0.987 (p<0.001) and 0.976 (p<0.001), respectively, exhibiting a robust significance. For the soil and paleosol samples, the difference in Munsell H between the measured values using our protocol and judged by visual sense is least while a discrepancy in Munsell V and C occurs possibly because of the visual values affected by the visual sense and optical environment. Based on the previous literatures on transformation in soil color between CIE to Munsell color systems, we established a procedure, particularly in making a Python program in order to complete the automatic calculation from CIELAB to the color coordinates and the Munsell V values, and providing a new interpolation method to acquire the Munsell V and C values, to facilitate the rapid and applicable conversion of CIE color system to Munsell color system for soils.