WO2020181594A1 - 一种定量测定土壤颗粒态有机质空间结构的方法 - Google Patents

一种定量测定土壤颗粒态有机质空间结构的方法 Download PDF

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WO2020181594A1
WO2020181594A1 PCT/CN2019/080778 CN2019080778W WO2020181594A1 WO 2020181594 A1 WO2020181594 A1 WO 2020181594A1 CN 2019080778 W CN2019080778 W CN 2019080778W WO 2020181594 A1 WO2020181594 A1 WO 2020181594A1
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organic matter
soil
image
spatial structure
particulate organic
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PCT/CN2019/080778
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French (fr)
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刘兴华
骆永明
章海波
谢寅雨
朱荣生
王诚
王怀中
呼红梅
黄保华
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山东省农业科学院畜牧兽医研究所
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Publication of WO2020181594A1 publication Critical patent/WO2020181594A1/zh

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N1/00—Sampling; Preparing specimens for investigation
    • G01N1/28—Preparing specimens for investigation including physical details of (bio-)chemical methods covered elsewhere, e.g. G01N33/50, C12Q
    • G01N1/34—Purifying; Cleaning
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
    • G01N15/10—Investigating individual particles
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N23/00—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00
    • G01N23/02—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00 by transmitting the radiation through the material
    • G01N23/04—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00 by transmitting the radiation through the material and forming images of the material
    • G01N23/046—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00 by transmitting the radiation through the material and forming images of the material using tomography, e.g. computed tomography [CT]

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  • the invention relates to a method for quantitatively determining the spatial structure of soil particulate organic matter, and belongs to the technical field of soil research.
  • Soil particulate organic matter has complex physical structure characteristics, and there is currently no unified and effective method at home and abroad to intuitively and quantitatively determine the spatial structure of soil particulate organic matter.
  • Scholars at home and abroad use non-destructive microanalysis techniques, such as electron microscopy, scanning electron microscopy-energy spectrum analysis technology (SEM-EDX) and other means to study the surface morphology and chemical composition of soil particulate organic matter.
  • SEM-EDX scanning electron microscopy-energy spectrum analysis technology
  • the soil granular organic matter of different sources and degrees of humification shows obvious structural differences under the electron microscope, such as flocculent structure, layered structure and inter-flocculent structure.
  • the use of electron microscopy, scanning electron microscopy and other methods can only visually distinguish the differences in soil organic matter structure, while the quantitative analysis of organic matter spatial structure and the determination of morphological and structural parameters cannot be achieved.
  • the synchrotron radiation-based micro-computed tomography (macro-CT) technology can capture the detailed features of the soil structure through the conversion of light and electrical signals and perform quantitative determinations.
  • This method has the advantages of fast speed, strong imaging contrast and high resolution.
  • scholars at home and abroad mostly use this technology to study soil pore structure, distribution and preferential flow relationship, and the fractal characteristics of soil aggregates.
  • the method is continuously optimized and upgraded in the application process, it is gradually applied to the analysis of the microstructure characteristics of soil aggregates, such as the analysis of changes in the fractal structure of pores in soil aggregates. Flavel R J et al.
  • micro-CT technology has the advantages of faster and more accurate pore structure research (Flavel R J, Guppy C N, Tighe M, et al. Non-destructive quantification of cereal roots in soil using high-resolution X-ray tomography [J]. Journal of Experimental Botany:2012:421).
  • the present invention provides a method for quantitatively determining the spatial structure of soil particulate organic matter.
  • This method first extracts the particulate organic matter in the soil by wet sieving classification and density extraction, and then scans the particulate organic matter with micro-CT technology.
  • the scanned image is processed by removing artifacts-calculating threshold-image segmentation-three-dimensional reconstruction Analyze the process of structure and other processes to quantify the parameters of the soil's mechanical morphology, organic matter quantity, volume ratio and volume distribution, and pore size distribution.
  • the technical scheme of the present invention is: a method for quantitatively determining the spatial structure of soil particulate organic matter, which is characterized by including the following steps:
  • the scanned projection image is processed by removing artifacts, calculating thresholds and image segmentation, dividing the image into three parts: pores, organic matter and soil minerals, and then performing three-dimensional reconstruction to restore the original appearance of soil granular organic matter;
  • the wet sieve classification of the step 1) is as follows: add soil into the centrifuge tube and slowly add water to infiltrate it to avoid the rapid increase in water pressure and cause the destruction of the soil structure; then the centrifuge tube is placed upside down on a 2mm sieve (2mm sieve lower layer is placed in sequence and matched 250 ⁇ m and 53 ⁇ m sieve) below the water surface until the soil sample completely sinks into the sieve; move the sieve up and down, and classify through the wet sieve to obtain large aggregates with a particle size of 250-2000 ⁇ m and micro-aggregates with a particle size of 53-250 ⁇ m;
  • the density extraction in step 1) is:
  • the lower reorganized organic matter is dispersed with 5g/L sodium hexametaphosphate and then wet sieved to obtain the granular organic matter bound inside the macroaggregates (microaggregates);
  • the artifact removal, threshold calculation and image segmentation in the step 3) are: preprocessing removes the artifacts and then performs slice segmentation, outputs it as a binary image, converts the binary image into an octal image and performs threshold segmentation; the threshold is selected
  • the global threshold method is selected by observing the gray value histogram. The pores have no absorption of X-rays, the gray value is the smallest, and the soil mineral absorption is the largest. The gray value is larger, and the organic matter is between the two. Build a histogram with different gray values. The histogram will have two peaks. The gray value of the middle trough of the two peaks can be selected as the threshold to divide the image into three parts: pores, organic matter, and soil minerals.
  • the organic matter components and soil minerals can be dyed to enhance the visual contrast between the two.
  • step 4) quantitatively calculates the spatial structure characteristics of the organic matter using image J software, and the size of the organic matter pores is expressed in the form of equivalent diameter.
  • the present invention first extracts the particulate organic matter in the soil through wet screening and density extraction methods and grouping them, thereby solving the problem of "soil particulate organic matter is randomly distributed in the soil, complicated in structure, and relatively difficult to quantitatively determine”. Provides the possibility for the quantitative determination of soil particulate organic matter;
  • the present invention uses the micro-CT technology for the first time to quantitatively determine the spatial structure characteristics of soil granular organic matter (morphological characteristics, organic matter quantity, volume ratio and volume distribution, pore size distribution and other parameters). This is a soil science, especially soil organic matter.
  • the in-depth study laid the foundation.
  • Figure 1 is a micro-CT scan image of soil particulate organic matter
  • Figure 2 is the reconstructed slice of soil granular organic matter micro-CT scan image converted into octal image
  • Figure 3 is an example diagram of threshold analysis to distinguish between organic matter and soil minerals
  • Figure 4 is a distribution diagram of soil organic matter particles after segmentation and dyeing, where gray is the organic matter component and green is the attached mineral component;
  • Figure 5 is a 3D reconstruction image of soil granular organic matter whose volume pixels are 500 ⁇ 500 ⁇ 500.
  • the method of density extraction extracts particulate organic matter
  • Use NaI solution with a density of 1.85g/cm 3 to classify the density of macroaggregates and microaggregates that is, take 5g agglomerates sample in a 100mL centrifuge tube with a solid-to-liquid ratio of 1:7, upside down for 1 min, and let stand for 30 min Afterwards, the upper layer of light organic matter is separated by filtration, and the lower layer sample repeats the above steps until the light organic matter is completely separated, and free particulate organic matter (fPOM) of macroaggregates (250-2000 ⁇ m) and microaggregates (53-250 ⁇ m) is obtained.
  • fPOM free particulate organic matter
  • the lower reorganized organic matter was dispersed with 5g/L sodium hexametaphosphate for 16 hours and then wet sieved to obtain the particulate organic matter (iPOM) bound inside the agglomerate.
  • the samples of each component are marked as: fPOM (250-2000 ⁇ m), iPOM (250-2000 ⁇ m) ), fPOM (53-250 ⁇ m), iPOM (53-250 ⁇ m).
  • the granular organic matter of the fPOM (250-2000 ⁇ m) component is taken as an example; the micro-CT experiment analysis is carried out.
  • the micro-CT scanning imaging experiment of soil granular organic matter is in Shanghai Light Source BL13WX
  • the radiography beam line station is completed.
  • the sample scanning parameters are set as follows: photon energy is 18keV, resolution is 3.25 ⁇ m, the sample stage rotates at a constant speed from 0 to 180° in the horizontal direction, exposure time is 1.2s, a total of 1080 projection images are collected, and the CCD detector records scanning projections at various angles (The picture shown in Figure 1 is one of them). Then 1080 projections of each sample were used for reconstruction of CT images, and 1508 slices were obtained, and the resolution of each projection image was 2048 pixels ⁇ 2048 pixels.
  • the reconstruction of the internal structure of the sample uses a filtered back projection algorithm.
  • the image is divided into three parts: pores, organic matter and soil minerals.
  • Quantitative analysis mainly selects a typical area with a size of 500 ⁇ 500 ⁇ 500 pixels.
  • Quantitative analysis parameters the analysis of the size, volume, quantity and pore size of organic matter is completed by image J software. The pore size of organic matter is expressed in terms of equivalent diameter.
  • the specific operation process is as follows: preprocess the image to remove artifacts and then perform slice segmentation, output as a binary image (all black), convert the binary image to an octal image (as shown in Figure 2) and perform threshold segmentation, with the gray value range from 0 to 255, where 0 means black with the lowest brightness, and 255 means pure white with the highest brightness.
  • the selection of the threshold value adopts the global threshold value method to conduct experimental analysis on the image to be processed, and use the observation histogram to select.
  • the pores have no absorption of X-rays, the gray value is the smallest, the soil mineral absorption is the largest, the gray value is larger, and the organic matter is medium. Between the two, build a histogram according to the different gray values of each component.
  • the histogram will have 2 peaks.
  • the gray value of the middle trough of the 2 peaks can be selected as the threshold, which distinguishes the segmentation threshold of organic matter and minerals.
  • An example of analysis is shown in Figure 3. Appropriate smoothing can be performed before conversion to make the boundary contour clear and improve the signal-to-noise ratio.
  • the organic matter components and soil minerals can be dyed to enhance the visual contrast between the two ( Figure 4).
  • a 3D analysis tool was used to reconstruct 1508 slices to restore the original appearance of the soil granular organic matter.
  • the volume ratio and the quantity per unit volume were quantitatively analyzed, and the results are shown in Table 1. Among them, the volume ratio is the ratio of organic matter to the sampling volume (500 ⁇ 500 ⁇ 500).
  • the method of micro-CT technology to study soil particulate organic matter can learn from the description of soil pore morphology, and the morphology factor (or pore) of particulate organic matter is expressed as follows:
  • A is the actual surface area of the particulate organic matter.
  • the pore size of soil granular organic matter is expressed by equivalent diameter.
  • the granular organic pores are divided into four parts, which are ultramicro pores ( ⁇ 5 ⁇ m), micropores (5-30 ⁇ m), mesopores (30-80 ⁇ m) and macropores (>80 ⁇ m).
  • ultramicro pores ⁇ 5 ⁇ m
  • micropores 5-30 ⁇ m
  • mesopores (30-80 ⁇ m)
  • macropores >80 ⁇ m.
  • the porosity distribution characteristics of granular organic matter are shown in Table 3.
  • the porosity is the ratio of the pore volume to the sampling volume (500 ⁇ 500 ⁇ 500).
  • the above method can continue to quantitatively determine the spatial structure of iPOM (250-2000 ⁇ m), fPOM (53-250 ⁇ m) and iPOM (53-250 ⁇ m).
  • the invention provides an effective method for the quantitative determination of the spatial structure of soil granular organic matter (morphological characteristics, organic matter quantity, volume ratio and volume distribution, pore size distribution and other parameters), which lays a foundation for the in-depth study of soil science, especially soil organic matter
  • the basic method The basic method.

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Abstract

一种定量测定土壤颗粒态有机质空间结构的方法。该方法首先通过湿筛分级和密度提取的方法提取土壤中的颗粒态有机质，然后采用显微CT技术对颗粒态有机质进行图像扫描，扫描后的图像经过去除伪影—计算阈值—图像分割—三维重构后，对土壤有机制形态特征、有机质数量、体积比及体积分布、孔隙大小分布等进行定量测定。

Description

一种定量测定土壤颗粒态有机质空间结构的方法 技术领域
本发明涉及一种定量测定土壤颗粒态有机质空间结构的方法，属于土壤研究技术领域。
背景技术
土壤颗粒态有机质具有复杂的物理结构特征，国内外目前尚无统一有效的方法直观定量测定土壤颗粒态有机质的空间结构。国内外学者利用非破坏性的微观分析技术，比如电子显微镜、扫描电镜-能谱分析技术(SEM-EDX)等手段研究土壤颗粒态有机质的表面形态结构与化学组成。通过这些方法可以发现不同来源、腐殖化程度的土壤颗粒态有机质在电子显微镜下呈现出明显的结构差异，如絮状结构、层状结构及层、絮状相间结构。但是利用电子显微镜、扫描电镜等手段只能从视觉上定性分辨土壤有机质结构的差异性，而有机质空间结构的定量分析、形态结构参数的确定等方面的研究无法实现。
因此，引入一种新的技术手段实现土壤颗粒态有机质结构定量测定是非常有必要的。基于同步辐射的显微计算机断层扫描成像(macro-CT)技术可以通过光、电信号转换的方式捕获土壤结构的细节特征并进行定量测定，该方法具有快速、成像对比度强、分辨率高的优点。目前，国内外学者多用此技术进行土壤孔隙结构、分布及其优先流关系以及土壤团聚体的分形特征等方面的研究。随着该方法在应用过程中不断被优化、升级，逐渐被应用于土壤团聚体的微结构特征分析，比如分析土壤团聚体内部孔隙分形结构的变化等。Flavel R J等运用显微CT技术和标准方法研究对比了不同磷肥处理的土壤剖面中的谷类根系的分布情况，结果显示用显微CT技术在孔隙结构研究方面具有更快速、准确的优点(Flavel R J,Guppy C N,Tighe M,et al.Non-destructive quantification of cereal roots in soil using high-resolution X-ray tomography[J].Journal of Experimental Botany:2012:421)。
但是土壤颗粒态有机质在土壤中分布随机，结构复杂，定量测定相对困难，需要对土壤颗粒态有机质进行一系列的预处理和实验方法的优化、改进，因此，目前尚没有人把该方法应用在土壤颗粒态有机质空间形态结构的测定上，基于前人研究的基础，发明人首次采用提出采用显微CT技术对土壤颗粒态有机质的空间结构特征进行定量测定。
发明内容
针对上述问题，本发明提供了一种定量测定土壤颗粒态有机质空间结构的方法。该方法首先通过湿筛分级和密度提取的方法提取土壤中的颗粒态有机质，然后显微CT技术对颗粒态有机质进行图像扫描，扫描后的图像经过去除伪影—计算阈值—图像分割—三维重构等过程分析，对土壤有机制形态特征、有机质数量、体积比及体积分布、孔隙大小分布等参数进 行定量。
本发明的技术方案是：一种定量测定土壤颗粒态有机质空间结构的方法，其特征是，包括以下步骤：
1)通过湿筛分级得到大团聚体(250-2000μm)和微团聚体(53-250μm)，然后通过密度提取的方法，分别得到大团聚体的游离颗粒态有机质、微团聚体的游离颗粒态有机质、大团聚体内部结合的颗粒态有机质和微团聚体内部结合的颗粒态有机质；
2)任选一组上面的颗粒态有机质，采用显微CT对其从0到180°匀速旋转进行图像扫描，共采集960幅以上扫描投影图像，并记录各角度的扫描投影图像；
3)对扫描投影图像经去除伪影、计算阈值和图像分割处理，将图像分为孔隙、有机质和土壤矿物质三部分，然后进行三维重构，恢复土壤颗粒态有机质原貌；
4)定量计算土壤颗粒态有机质空间结构特征，包括形态特征、有机质数量、体积比及体积分布、孔隙大小分布等参数中的一种或者一种以上。
所述步骤1)的湿筛分级为：将土壤加入离心管内缓慢加水浸润，避免快速加水压力剧增造成土壤结构的破坏；然后将离心管倒置在2mm筛(2mm筛下层依次放置配套的250μm和53μm筛)内的水面以下，直至土样完全沉入到筛中；上下移动筛子，通过湿筛分级得到粒径为250-2000μm大团聚体和53-250μm的微团聚体；
所述步骤1)的密度提取为：
将密度为1.85g/cm 3的NaI溶液和大团聚体(或者微团聚体)加入离心管中，上下颠倒，静置，过滤分离上层轻组有机质，下层样品重复上述步骤，直至完全分离轻组有机质，得到大团聚体(微团聚体)的游离颗粒态有机质；
下层重组有机质用5g/L的六偏磷酸钠分散后湿筛，得到大团聚体(微团聚体)内部结合的颗粒态有机质；
分级提取得到的这些组分均用超纯水反复洗去盐分离子，50℃烘干。
所述步骤3)的去除伪影、计算阈值和图像分割为：预处理将图片去伪影后进行切片分割，输出为二进制图像，将二进制图片转化为八进制图片后进行阈值分割；阈值的选择采用全局阈值方法，采用观察灰度值直方图来选择，孔隙对X射线没有吸收，灰度值最小，土壤矿物质吸收最大，灰度值较大，有机质介于二者之间，根据各组分灰度值不同建立直方图，直方图会有2个波峰，选择2个波峰的中间波谷的灰度值即可作为阈值，将图像分为孔隙、有机质和土壤矿物质三部分。
进一步的，对图像进行分割后还可以对有机质组分和土壤矿物质进行染色处理，以增强二者的视觉对比。
进一步的，所述步骤4)定量计算有机质空间结构特征采用image J软件完成，有机质孔隙大小采用等效直径的方式来表示。
本发明的有益效果是：
1、本发明首先通过湿筛分级和密度提取的方法提取土壤中的颗粒态有机质并进行分组，从而解决了“土壤颗粒态有机质在土壤中分布随机，结构复杂，定量测定相对困难”的问题，为土壤颗粒态有机质的定量测定提供了可能；
2、本发明首次采用显微CT技术对土壤颗粒态有机质的空间结构特征进行定量(形态特征、有机质数量、体积比及体积分布、孔隙大小分布等参数)测定，这为土壤学尤其是土壤有机质的深入研究奠定了基础。
附图说明
图1是土壤颗粒态有机质显微CT扫描图片；
图2是土壤颗粒态有机质显微CT扫描图片重构后的切片转化为八进制图片；
图3是区分有机质和土壤矿物质的阈值分析示例图；
图4是分割染色后的土壤有机质颗粒分布图，其中灰色为有机质组分，绿色为附着的矿物组分；
图5是选取体积像素为500×500×500的土壤颗粒态有机质的3D重构图。
具体实施方式
本技术发明方案不局限于以下所列举的具体实施方式，具体实施方式鉴定方法按照以下步骤进行。
实施例1
1、湿筛分级提取土壤中的团聚体
采集黄河三角洲滨海湿地芦苇生长区的土壤样品，准确称取20g土样于100mL离心管中，缓慢加水浸润，水土比约为2:1，此过程防止风干土中快速加水造成团聚体孔隙内气压不稳，团聚体崩裂，破坏其稳定性。然后将离心管倒置在2mm筛(2mm筛下层依次放置配套的250μm和53μm筛)内的水面以下，直至土样完全沉入到筛中。上下移动筛子，3cm/次，25次/分钟。通过湿筛分级得到粒径为250-2000μm大团聚体和53-250μm的微团聚体样品，再进一步分离颗粒态有机质；
2、密度提取的方法提取颗粒态有机质
用密度为1.85g/cm 3的NaI溶液对大团聚体和微团聚体进行密度分级，即取5g团聚体样品于100mL离心管中，固液比为1:7，上下颠倒1min，静置30min后，过滤分离上层轻组有机质，下层样品重复上述步骤，直至完全分离轻组有机质，得到大团聚体(250-2000μm) 和微团聚体(53-250μm)的游离颗粒态有机质(fPOM)。
下层重组有机质用5g/L的六偏磷酸钠分散16h后湿筛，得到团聚体内部结合的颗粒态有机质(iPOM)，各组分样品标记为：fPOM(250-2000μm)、iPOM(250-2000μm)、fPOM(53-250μm)、iPOM(53-250μm)。分离得到的这些组分均用超纯水反复洗去盐分离子，50℃烘干。
3、用显微CT技术对颗粒态有机质进行图像扫描
任选一组上面的颗粒态有机质，本研究以fPOM(250-2000μm)组分的颗粒态有机质为例；进行显微CT实验分析，土壤颗粒态有机质的显微CT扫描成像实验在上海光源BL13WX射线成像光束线站完成。
将样品放置在0.6mL的塑料离心管中，固定在样品台上。样品台距离探测器约为15cm。样品扫描参数设置为：光子能量为18keV，分辨率3.25μm，样品台水平方向从0到180°匀速旋转，曝光时间1.2s，共采集1080幅投影图像，CCD探测器记录各角度的扫描投影图(如图1所示的图是其中一幅)。然后将每个样品的1080个投影用于CT图像的重构，得到1508张切片，每个投影图的分辨率为2048像素×2048像素。样品内部结构的重建采用滤波反投影算法。
4、对扫描图像进行分析
显微CT扫描过程中可能会存在环状伪影，伪影在切片分割前需要去除，去除伪影和切片分割采用免费软件PITRE完成(http://webint.ts.infn.it/en/research/exp/beats2/pitre.html)。图像分割和三维重构渲染采用免费软件image J 1.50(the National Institute of Health，USA；http://rsb.info.nih.gov/ij/)以及插件BoneJ和3D模块完成。图像分割时首先通过灰度值直方图中的双峰，以双峰的中间的谷点作为分割阈值，确定分割阈值后，将图像分为孔隙、有机质和土壤矿物质三部分。定量分析主要选取大小为500×500×500像素的典型区域进行。定量分析参数：有机质大小、体积、数量以及孔隙大小等分析采用image J软件完成。有机质孔隙大小采用等效直径的方式来表示。
具体操作过程为：预处理将图片去伪影后进行切片分割，输出为二进制图像(全黑)，将二进制图片转化为八进制图片(如图2)后进行阈值分割，灰度值范围为0～255，其中，0表示黑色，亮度最低，255表示亮度最高的纯白色。阈值的选择采用全局阈值方法，对需处理的图像进行实验分析，采用观察直方图来选择，孔隙对X射线没有吸收，灰度值最小，土壤矿物质吸收最大，灰度值较大，有机质介于二者之间，根据各组分灰度值不同建立直方图，直方图会有2个波峰，选择2个波峰的中间波谷的灰度值可作为阈值，其中区分有机质和矿物质的分割阈值分析示例如图3所示。转换前可适当进行平滑处理让边界轮廓清晰提高信噪 比。对图像进行分割后还可以对有机质组分和土壤矿物质进行染色处理，以增强二者的视觉对比(图4)。最后选用3D分析工具将1508张切片重构，恢复土壤颗粒态有机质原貌，鉴于数据庞大，处理过程中可以选则提取500×500×500像素的体积(图5)进行分析，并对颗粒态有机质的体积比、单位体积的数量进行定量分析，结果如表1所示。其中，体积比是有机质与取样体积(500×500×500)的比值。
表1土壤颗粒态有机质体积比、数量和形态特征
Figure PCTCN2019080778-appb-000001
5、土壤颗粒态有机质定量分析
显微CT技术研究土壤颗粒态有机质的方法可以借鉴土壤孔隙形貌的描述方式，将颗粒态有机质形貌因子(或孔隙)表示如下：
F＝As/A
其中As是与颗粒态有机质体积相等的球体的表面积；A是颗粒态有机质的实际表面积。当F＝1时，表示颗粒态有机质是完美的球体，当F值减小，颗粒态有机质则呈现拉长型或无规则型。
颗粒态有机质的形态特征分析结果如表1所示。
进一步对颗粒态有机质的体积分布进行统计，结果如表2所示。
表2土壤颗粒态有机质体积分布特征
Figure PCTCN2019080778-appb-000002
土壤颗粒态有机质孔隙大小均使用等效直径来表示。根据等效直径的大小，将颗粒态有机质孔隙分成四部分，分别为超微孔隙(<5μm)、微孔隙(5-30μm)、中孔隙(30-80μm)和大孔隙(>80μm)。考虑到本研究扫描图片的分辨率为3.25μm，故将颗粒态有机质中超微孔隙和微孔隙划分为一个区间，即3.25-30μm。颗粒态有机质的孔隙度分布特征如表3所示。其中孔隙度是孔隙体积与取样体积(500×500×500)的比值。
表3土壤颗粒态有机质孔隙大小分布
Figure PCTCN2019080778-appb-000003
采用上述方法可以继续定量测定iPOM(250-2000μm)、fPOM(53-250μm)、iPOM(53-250μm)的空间结构。
本发明为土壤颗粒态有机质的空间结构(形态特征、有机质数量、体积比及体积分布、孔隙大小分布等参数)的定量测定提供了有效的方法，这为土壤学尤其是土壤有机质的深入研究奠定了基础方法。

Claims (8)

  1. 一种定量测定土壤颗粒态有机质空间结构的方法，其特征是，包括以下步骤：
    1)通过湿筛分级得到250-2000μm的大团聚体和53-250μm的微团聚体，然后通过密度提取的方法，分别得到大团聚体的游离颗粒态有机质、微团聚体的游离颗粒态有机质、大团聚体内部结合的颗粒态有机质和微团聚体内部结合的颗粒态有机质；
    2)任选一组上面的颗粒态有机质，采用显微CT对其从0到180°匀速旋转进行图像扫描，共采集960幅以上扫描投影图像，并记录各角度的扫描投影图像；
    3)对扫描投影图像经去除伪影、计算阈值和图像分割处理，将图像分为孔隙、有机质和土壤矿物质三部分，然后进行三维重构，恢复土壤颗粒态有机质原貌；
    4)定量计算土壤颗粒态有机质空间结构特征。
  2. 如权利要求1所述的一种定量测定土壤颗粒态有机质空间结构的方法，其特征是，所述步骤4)土壤颗粒态有机质空间结构特征包括形态特征、有机质数量、体积比及体积分布、孔隙大小分布中的一种或者一种以上。
  3. 如权利要求2所述的一种定量测定土壤颗粒态有机质空间结构的方法，其特征是，所述步骤4)定量计算有机质空间结构特征采用image J软件完成，有机质孔隙大小采用等效直径的方式来表示。
  4. 如权利要求1所述的一种定量测定土壤颗粒态有机质空间结构的方法，其特征是，所述步骤1)的湿筛分级为：将土壤加入离心管内缓慢加水浸润；然后将离心管倒置在2mm筛内的水面以下，直至土样完全沉入到筛中；所述2mm筛下层依次放置配套的250μm和53μm筛；上下移动筛子，通过湿筛分离得到粒径为250-2 000μm大团聚体和53-250μm的微团聚体。
  5. 如权利要求1所述的一种定量测定土壤颗粒态有机质空间结构的方法，其特征是，
    所述步骤1)的密度提取为：
    将密度为1.85g/cm 3的NaI溶液和大团聚体或者微团聚体加入离心管中，上下颠倒，静置，过滤分离上层轻组有机质，下层样品重复上述步骤，直至完全分离轻组有机质，得到大团聚体或者微团聚体的游离颗粒态有机质；
    下层重组有机质用5g/L的六偏磷酸钠分散后湿筛，得到大团聚体或者微团聚体内部结合的颗粒态有机质。
  6. 如权利要求5所述的一种定量测定土壤颗粒态有机质空间结构的方法，其特征是，所述分离得到的组分均用超纯水反复洗去盐分离子，50℃烘干。
  7. 如权利要求1所述的一种定量测定土壤颗粒态有机质空间结构的方法，其特征是，
    所述步骤3)的去除伪影、计算阈值和图像分割为：预处理将扫描投影图像去伪影后进 行切片分割，输出为二进制图像，将二进制图片转化为八进制图片后进行阈值分割；阈值的选择采用全局阈值方法，采用观察灰度值直方图来选择，孔隙对X射线没有吸收，灰度值最小，土壤矿物质吸收最大，灰度值较大，有机质介于二者之间，根据各组分灰度值不同建立直方图，直方图会有2个波峰，选择2个波峰的中间波谷的灰度值即可作为阈值，将图像分为孔隙、有机质和土壤矿物质三部分。
  8. 如权利要求1-7中任意一项所述的一种定量测定土壤颗粒态有机质空间结构的方法，其特征是，所述步骤3)对图像进行分割后对有机质组分和土壤矿物质进行染色处理，以增强二者的视觉对比。
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