1.1 本试验方法描述了使用漫反射光谱法对从地面样品中获得的土壤进行取样和测试的程序，该方法使用手持式便携式光谱仪测量可见光和近红外（vis-NR）以及中红外（MIR）范围内的光谱。该传感器可以测量水分含量、PH值、有机物、阳离子交换容量（CEC）以及百万分之一（PPM）或百分比的宏观和微观元素营养素，包括但不限于氮、磷、钾、锌、铁、硼、硫、钙、镁和锰。 1.2 有两种方法可用于执行测试。 1.2.1 方法A- 在样品经过烘干和筛分后，在实验室对样品进行分析。 1.2.2 方法B- 在均匀化之后，在现场对潮湿的样品进行分析。在使用方法A和B对多反射率站点数据进行后处理后，可以测量水分含量，并对水分含量的光谱特征进行归一化。 1.3 这种方法的局限性在于，元素分析的单独测试结果与土壤科学家使用的传统湿化学实验室分析的精确参考值不同。 湿化学测试或土壤科学图书馆的测试结果可用于校准由许多单独测试组成的特定场地模型。有机物的光谱数据已被证明与传统方法（如测试方法）一样准确 1974年2月 . 1.4 对于土壤养分分析，样品没有像标准实施规程中概述的典型定性光谱分析那样精细研磨 第1522页 .在测试过程中，根据指南使用程序定期检查光谱仪 第1866页 性能测试。 1.5 水分含量是农业应用中的首选术语。对于本标准，可根据试验方法测量重量含水率 216年2月 当干燥样品并用于校准现场模型时，但光谱分析的总体结果更具定性，本标准中使用了术语“水分含量”。 1.6 单位- 以国际单位制或英寸磅单位[括号内给出]表示的数值应单独视为标准。 波长仅以纳米、纳米为单位。每个系统中规定的值可能不是精确的等效值；因此，每个系统应独立于其他系统使用。将两个系统的值组合在一起可能会导致不符合标准。 1.7 所有观测值和计算值应符合实践中制定的有效数字和四舍五入指南 D6026型 。本标准中用于规定如何收集、记录或计算数据的程序被视为行业标准。 此外，它们代表了通常应保留的有效数字。所使用的程序不考虑材料变化、获取数据的目的、特殊目的研究或用户目标的任何考虑因素；并且通常的做法是增加或减少报告数据的有效数字以与这些考虑相称。考虑工程设计分析方法中使用的有效数字超出了本标准的范围。 1.7.1 光谱数据由电气数据采集系统采集，因此数字数据通过记录并进入数据库，而无需对数字数据进行舍入。 1.8 本标准并非旨在解决与其使用相关的所有安全问题（如有）。本标准的使用者有责任在使用前制定适当的安全、健康和环境实践，并确定监管限制的适用性。 1.9 本国际标准是根据世界贸易组织技术性贸易壁垒委员会发布的《关于制定国际标准、指南和建议的原则的决定》中确立的国际公认的标准化原则制定的。 ====意义和用途====== 5.1 全世界都在使用农业用土壤的光谱分析来获取土壤养分的快速数据。用于农业管理，包括施肥和其他改良剂，如pH调节、有机补充剂等。正在使用卫星、空中和地面采样方法。该测试方法适用于地面、地面现场应用，其中从地面采集样本，通常在根部区域。 与土壤科学家过去用于土壤养分评估的老式繁琐的采样和湿化学测试方法相比，使用这些快速遥感技术可以获得更详细、更经济的数据。 5.2 本试验方法描述了使用漫反射光谱法对田间土壤进行取样和试验的程序，该方法使用手持式便携式光谱仪，使用干燥过筛或湿样品测量可见光和近红外（vis-NR）光谱。全世界都在努力收集土壤的光谱数据库。 此处规定的程序遵循联合国粮食及农业组织（粮农组织）土壤的近红外和红外光谱底漆中概述的程序 ( 1. ) 3. IEEE等其他组织正在积极制定额外的指导文件，这些文件将被纳入该测试方法的未来修订中。 5.2.1 本标准描述了程序（第 12 )对于使用高光谱传感器数据测量水分含量（以百分比计）、pH、有机物（OM）（以百分比表示），以10 cmol c/kg测量的阳离子交换容量（CEC）每公斤土壤可以容纳10 cmol的Na+阳离子（每个阳离子1个电荷单位），但只有5 cmol的Ca2+（每个阳离子2个电荷单位，以及土壤中的微观和宏观营养素，单位为百万分之几（PPM）或百分比，包括但不限于氮、磷、钾、硼、锌、铁、硫、钙、镁和锰。 5.2.2 研究表明，OM含量的Vis-NIR数据与其他测试（如测试方法中的燃尽测试）一样准确 1974年2月 ( 2. ) 使用方法B分析天然水分样品可以提供更快的测试和更好的OM估计，这是对水分的归一化 ( 3. ) 湿采样允许在现场快速扫描更多的样本，因此可以对现场进行更多的样本和更详细的覆盖。 5.3 该标准不涉及在中红外范围（MIR）内测量的传感器，这些传感器更昂贵，并且可用的光谱数据更少。 对经过精细研磨的干燥样品进行MIR光谱分析 ( 4. ) MIR建模需要根据公认的实验室程序和物理财产进行高水平校准。 5.4 光谱数据可能不同于通常基于湿化学方法的旧参考测试，如土壤调查手册中概述的孔隙流体提取 ( 5. ) 这些旧方法需要大量的劳动力成本和较长的周转时间。然而，土壤科学家正在积累大量的光谱库数据库，这些数据库已经用基线化学数据进行了检查和校准。 土壤调查手册 ( 5. ) 也有早期的（2014年）干燥样品的可见-近红外测试方法程序。 5.5 测量的精度由通过化学处理校准的基线测量的校准精度确定。在关键/新项目中，采样计划可能包括用于湿化学测试的样本，以帮助校准现场模型。在一个站点收集的大量数据被组合到一个特定站点的数据库中，该数据库经过复杂的模型训练以优化数据集。 本标准不会提供建模和粮农组织文件的详细指导 ( 1. ) 提供了当前数据集建模过程的良好概述。数据集建模需要对纹理、含水量和地质进行调整，通常与许多来源的其他适当光谱库相关联 ( 6. ) . 5.5.1 地平线和土壤分类顺序作为辅助变量，提高了模型的预测精度。区域、地方和过去的站点特定数据，以及分类历史数据库库可以用来帮助校准站点模型。 注1: 本标准产生的结果的质量取决于执行该标准的人员的能力以及所用设备和设施的适用性。符合实践标准的机构 第3740页 通常被认为能够进行合格且客观的测试/取样/检查等。本标准的使用者应注意遵守实践 第3740页 这本身并不能保证可靠的结果。可靠的结果取决于许多因素；实践 第3740页 提供了一种评估其中一些因素的方法。

1.1 This test method describes procedures for sampling and testing of soils obtained from ground-based samples using diffuse reflectance spectrometry using handheld portable spectrometers measuring spectra in visible and near infrared (vis-NR) and mid-infrared (MIR) range. The sensor can measure moisture content, PH, organic matter, Cation Exchange Capacity (CEC) as well as macro and micro elemental nutrients in parts per million (PPM) or percentage, including but not limited to nitrogen, phosphorous, potassium, zinc, iron, boron, sulfur, calcium, magnesium, and manganese. 1.2 There are two methods that can be used to perform the test. 1.2.1 Method A— The analysis is performed in the laboratory on the sample after the sample has been oven dried and sieved. 1.2.2 Method B— The analysis is performed in the field on a moist sample after homogenization. After post-processing of multiple reflectance site data using methods A and B, the moisture content can be measured, and the spectral signature is normalized for moisture content. 1.3 The limitation of this method is that the results of an individual test for elemental analysis would not be the same as exacting reference values from traditional wet chemical lab analysis used by soil scientists. Results of wet chemistry tests or tests from soil science libraries may be used to calibrate a specific site model comprised of many individual tests. Spectral data for organics has shown to be as accurate as conventional methods such as Test Methods D2974 . 1.4 For soil nutrient analysis the sample is not finely ground as in typical qualitative spectral analysis as outlined in standard Practice E1252 . The spectrometer is checked periodically during testing using procedures in accordance with Guide E1866 performance testing. 1.5 Moisture content is a preferred term in agricultural applications. For this standard, gravimetric water content may be measured in accordance with Test Methods D2216 when drying samples and used to calibrate the site model, but the overall results of spectral analysis are more qualitative, and the term Moisture Content is used in this standard. 1.6 Units— The values stated in either SI units or inch-pound units [given in brackets] are to be regarded separately as standard. Wavelengths are stated only in nanometers, nm. The values stated in each system may not be exact equivalents; therefore, each system shall be used independently of the other. Combining values from the two systems may result in nonconformance with the standard. 1.7 All observed and calculated values shall conform to the guidelines for significant digits and rounding established in Practice D6026 . The procedures used to specify how data is collected, recorded or calculated in this standard are regarded as the industry standard. In addition, they are representative of the significant digits that generally should be retained. The procedures used do not consider material variation, purpose for obtaining the data, special purpose studies, or any considerations for the user’s objectives; and it is common practice to increase or reduce significant digits of reported data to be commensurate with these considerations. It is beyond the scope of this standard to consider significant digits used in analysis methods for engineering design. 1.7.1 Spectral data is acquired by electrical data acquisition systems and therefore numeric data is carried through recording and into databases without rounding of numeric data. 1.8 This standard does not purport to address all of the safety concerns, if any, associated with its use. It is the responsibility of the user of this standard to establish appropriate safety, health, and environmental practices and determine the applicability of regulatory limitations prior to use. 1.9 This international standard was developed in accordance with internationally recognized principles on standardization established in the Decision on Principles for the Development of International Standards, Guides and Recommendations issued by the World Trade Organization Technical Barriers to Trade (TBT) Committee. ====== Significance And Use ====== 5.1 Spectral analysis of soils for agricultural use is being used worldwide to obtain rapid data on soil nutrients. for the purpose of agricultural management including fertilizer application and other amendments such as pH adjustment, organic supplements, etc. Satellite, aerial, and ground-based sampling methods are being used. This test method applies to ground-based, terrestrial field applications where samples are taken from the ground, generally in the root zone. Use of these rapid remote sensing techniques allow for more detailed and economic data acquisition than older cumbersome sampling and wet chemistry testing methods used in the past by soil scientists for soil nutrient evaluations. 5.2 This test method describes procedures for sampling and testing of field soils using diffuse reflectance spectrometry using handheld portable spectrometers measuring spectra in visible and near infrared (vis-NR) using dried sieved or wet samples. There is a worldwide effort to collect spectral databases of soils. The procedures specified here follow procedures as outlined in the United Nations Food and Agricultural Organization (FAO) primer on Vis-NIR and MIR spectroscopy of soils ( 1 ) 3 . Other organizations such as IEEE are actively working on additional guidance documents that will be incorporated in future revisions of this test method. 5.2.1 This standard describes the procedures (Section 12 ) for using hyperspectral sensor data to measure moisture content as a percentage, pH, Organic Matter (OM) as a percentage, Cation Exchange Capacity (CEC) measured in 10 cmol c /kg could hold 10 cmol of Na + cations (with 1 unit of charge per cation) per kilogram of soil, but only 5 cmol Ca 2+ (2 units of charge per cation), as well as micro and macro nutrients in soils measured in PPM (parts per million)or a percentage, including, but not limited to nitrogen, phosphorous, potassium, boron, zinc, iron, sulfur, calcium, magnesium, and manganese. 5.2.2 Research has shown that the Vis-NIR data for OM content is as accurate as other tests such as the burn off test in Test Methods D2974 ( 2 ) . Analysis of natural moisture samples using method B can provide faster testing and better estimates of OM are normalization for moisture ( 3 ) . Wet sampling allows for many more samples to be rapidly scanned in the field and therefore more samples and more detailed coverage of the site. 5.3 This standard does not address sensors that measure in the mid infrared range, MIR, are more expensive and there is less spectral data available. MIR spectral analysis is performed on dried samples that are finely grinded ( 4 ) . MIR modeling requires a high level of calibration against recognized laboratory procedures and physical properties. 5.4 Spectral data can differ from older reference tests typically based on wet chemistry methods such as pore fluid extractions such as those outlined in soil survey manuals ( 5 ) . These old methods require extensive labor costs and long turnaround times. However, soil scientists are accumulating large databases of spectral libraries which have been checked and calibrated with baseline chemical data. The soil survey manual ( 5 ) also has early (2014) procedures for Vis-NIR testing methods on dry specimens. 5.5 The accuracy of the measurement is determined by the accuracy of the calibration of the baseline measurements that are calibrated by chemical processing. On critical/new projects the sampling plan may include samples for wet chemistry testing to help calibrate the site model. The large amount of data that is collected at a site is combined into a site-specific database which is subject to complex model training to optimize the dataset. This standard will not provide detailed guidance on modeling and the FAO document ( 1 ) provides a good overview of the current procedures for dataset modeling. Dataset modeling requires adjustments for texture, water content, and geology and generally is linked to other appropriate spectral libraries available from many sources ( 6 ) . 5.5.1 Horizon and Soil taxonomic order as auxiliary variables improve prediction accuracy of models. Regional, local, and past site-specific data, and taxonomic historic data base libraries may be used to help calibrate a site model. Note 1: The quality of the result produced by this standard is dependent on the competence of the personnel performing it, and the suitability of the equipment and facilities used. Agencies that meet the criteria of Practice D3740 are generally considered capable of competent and objective testing/sampling/inspection/etc. Users of this standard are cautioned that compliance with Practice D3740 does not in itself assure reliable results. Reliable results depend on many factors; Practice D3740 provides a means of evaluating some of those factors.