Quantitative analysis of soil nitrogen and carbon at a farm scale using visible and near infrared spectroscopy coupled with wavelength reduction

Reducing large spectral datasets to parsimonious representations of wavelengths is of value for efficient storage and easing analysis, in addition to the potential to use a simpler and cheaper spectrophotometer. This study evaluated the potential of calibrating visible and near infrared (vis‐NIR) spectra to total nitrogen (N), total carbon (C), organic C and inorganic C in soil on a 15‐ha farm, with the aim of comparing several wavelength reduction algorithms and rates in terms of model prediction accuracy. We explored the uninformative variables elimination (UVE), UVE coupled with successive projections algorithm (SPA) and two uniform‐interval wavelength reduction approaches (UWR‐I and UWR‐II) with successive wavelength reduction rates (WRRs) of 2, 5, 10, 20, 50, 100, 200, 500 and 1000. The standard normal variate (SNV)‐transformed absorbance spectra of soil samples recorded from 400 to 2499 nm at 1‐nm intervals were used. The calibration sets were subjected to a partial least squares regression (PLSR) with leave‐one‐out cross‐validation. Prediction results showed that UVE can reduce wavelength variables significantly while retaining good model prediction accuracy. The UVE‐SPA produced only three or four wavelengths, with which PLSR models achieved competitive prediction performance, compared with those based on all 2100 wavelengths, with coefficient of determination (R²) of 0.91, 0.89, 0.91 and 0.53 and residual prediction deviation (RPD) of 3.53, 2.95, 3.27 and 1.53 for soil total N, total C, organic C and inorganic C, respectively. The UWR tests showed that PLSR models responded insensitively to various WRRs from 2 to 100. The models calibrated for the 100‐nm interval spectra (21 remaining wavelengths) performed almost as well as those for the 1‐nm interval spectra. Although these findings might be valid only at the farm scale, it is recommended that the proposed wavelength reduction algorithms for more soil types and soils originated from larger areas should be examined.     

煤矿区土壤有机碳含量的高光谱预测模型

可见—近红外光谱已被证明是一种快速、及时、有效的土壤有机碳含量预测工具。利用Field Spec4对济宁鲍店矿区的104个土壤样品进行光谱测量,采用Savitzky-Golay卷积平滑(SG)、多元散射校正(MSC)及数学变换等多种方式组合对光谱预处理,并运用偏最小二乘回归分析建立土壤有机碳含量预测模型,进而探讨煤矿区土壤有机碳含量的高精度预测方法。结果表明:(1)不同的光谱预处理方法对建模结果影响差异较大,建模结果以SG加MSC预处理再结合光谱反射率的一阶微分变换最优,建模R~2=0.86,RMSE=2.0g/kg,验证R~2=0.78,RMSE=1.81g/kg,RPD=2.69。(2)倒数和倒数的对数与土壤有机碳含量的相关性曲线接近重合,与反射率曲线成反比,但是建模效果远低于反射率;光谱反射率的一阶微分能明显提高500~600nm波段相关性。(3)光谱反射率随土壤有机碳的含量减少而增大,当有机碳含量较低时,其波谱的近红外波段反射率响应能力也随之降低,反射率直接建模难度加大。     

Prediction of soil organic matter using a spatially constrained local partial least squares regression and the Chinese vis–NIR spectral library

We need to determine the best use of soil vis–NIR spectral libraries that are being developed at regional, national and global scales to predict soil properties from new spectral readings. To reduce the complexity of a calibration dataset derived from the Chinese vis–NIR soil spectral library (CSSL), we tested a local regression method that combined geographical sub‐setting with a local partial least squares regression (local‐PLSR) that uses a limited number of similar vis–NIR spectra (k‐nearest neighbours). The central idea of the local regression, and of other local statistical approaches, is to derive a local prediction model by identifying samples in the calibration dataset that are similar, in spectral variable space, to the samples used for prediction. Here, to derive our local regressions we used Euclidean distance in spectral space between the calibration dataset and prediction samples, and we also used soil geographical zoning to account for similarities in soil‐forming conditions. We tested this approach with the CSSL, which comprised 2732 soil samples collected from 20 provinces in the People's Republic of China to predict soil organic matter (SOM). Results showed that the prediction accuracy of our spatially constrained local‐PLSR method (R² = 0.74, RPIQ = 2.6) was better than that from local‐PLSR (R² = 0.69, RPIQ = 2.3) and PLSR alone (R² = 0.50, RPIQ = 1.5). The coupling of a local‐PLSR regression with soil geographical zoning can improve the accuracy of local SOM predictions using large, complex soil spectral libraries. The approach might be embedded into vis–NIR sensors for laboratory analysis or field estimation.     

基于局部加权回归的土壤全氮含量可见-近红外光谱反演

全氮是土壤肥力的重要指标,对作物产量具有决定性作用,采用土壤可见-近红外(Vis-NIR)光谱预测技术及时获取土壤全氮含量信息具有重要意义。采用来自5省的450个土壤样本来验证局部加权回归方法(LWR)结合Vis-NIR光谱技术预测大面积土壤全氮含量的适用性。结果表明,LWR模型的预测效果优于偏最小二乘回归(PLSR)、人工神经网络(ANN)和支持向量机(SVM),选取主成分数为5,相似样本为40时,模型验证的决定系数(RP2)为0.83,均方根误差(RMSEP)为0.25 g kg-1,测定值标准偏差与标准预测误差的比值(RPD)达到2.41。LWR从建模集中选取与验证样本相似的土样作为局部建模样本,降低了差别大的样本对模型的干扰,从而提高了模型的预测能力。因此,LWR建模方法通过大范围、大样本土壤光谱数据进行大尺度区域的全氮等土壤属性预测时能够发挥更好的作用。     

Visible and Near-Infrared Reflectance Spectroscopy for Assessment of Soil Properties in the Caucasus Mountains,Azerbaijan

ABSTRACT

This study aimed to predict soil properties using visible–near infrared (VIS-NIR) spectroscopy combined with partial least square regression (PLSR) modeling. Special emphasis was given to evaluating effect of pre-processing methods on prediction accuracy and important wavelengths. A total of 114 samples were collected and involved in chemical and spectral analyzes. PLSR model of each soil property was calibrated for all pre-processing methods using all samples, and leave-one-out cross-validation was used to make comparisons between them. Then, PLSR model of each best pre-processing method was calibrated using a 75% of all samples and correspondingly validated with the remaining a 25%. Model accuracy was evaluated based on coef?cient of determination (R²), root mean-squared errors (RMSE), and residual prediction deviations (RPD). The high correlation coefficients were found between the tested soil properties and reflectance spectra. The pre-processing methods considerably improved prediction accuracy and filtering methods outperformed linearization methods, and the latter outperformed normalization methods. The performance of cross-validation, calibration and independent validation was similar. An excellent prediction (RPD>2.5) model was obtained for soil organic carbon (SOC) and calcium-carbonate (CaCO₃), good quantitative (2.0< RPD<2.5) prediction for sand, silt, and clay, fair prediction (1.4< RPD<1.8) for pH, and poor prediction (1.0< RPD<1.4) for hygroscopic water content (WC). Important wavelengths varied depending on soil property, but some wavelengths were common. This study can be a precursor to building a pioneering soil spectral database, calibrating satellite data, and hyperspectral image mapping of soils as well as digital soil mapping, environmental, and erosion modeling in the Caucasus Mountains.     

绿肥还田在稻作生态系统的效应分析及研究展望

绿肥种植利用是中国传统农业技术的精华,也是绿色生态循环农业发展的关键性举措,为中国粮食稳定和绿色增效发挥着十分重要的作用.水稻栽种之前的绿肥还田耕作模式就是将绿肥植物体直接耕翻于稻田中作为肥料或是将绿肥植物体沤堆成肥再施用于稻田土壤,这不仅能够培肥稻田土壤、增强土壤供肥能力和减少稻季化学肥料施用,而且能够减少稻田周年化...     

Near-Infrared-Spectroscopy for Determination of Carbon and Nitrogen in Indian Soils

The present study aims to evaluate the potential of near-infrared reflectance (NIR) spectroscopy to determine the carbon and nitrogen content in soils and also to assess the effectiveness of NIR spectroscopy to predict carbon and nitrogen content in freshly collected soil samples. Soil samples (n = 179) were collected from different locations in India. Soil carbon and nitrogen contents were successfully predicted (R² = 0.90 for carbon and R² = 0.85 for nitrogen) by NIR spectroscopy. The root mean square error (RMSE) and ratio performance deviation (RPD) for the validation of predicted equations for carbon and nitrogen were 0.83 and 2.83 and 0.01 and 6.98, respectively. The efficacy of NIR spectroscopy on the prediction of carbon and nitrogen content in Indian soils is highly reliable. Water content in soil samples could affect the NIR absorbance spectra and in turn affect the quantification of carbon and nitrogen.     

Predicting Total Organic Carbon Content of Soils from Walkley and Black Analysis

Globally, there is problem of computing soil carbon stock because the Walkley–Black method gives only an approximation of soil organic carbon content. Until now, no universal relationship between Walkley–Black carbon (WBC) and total soil organic carbon (TOC) has been developed that could be applicable in all kinds of soil. In the present study, relationships between WBC and TOC were established from samples collected from central and northern India. TOC was measured by dry combustion technique and WBC was determined by wet digestion methods. A relationship between WBC and TOC was developed by taking into account silt + clay content (SICL) of soil and mean annual rainfall (MAR) of the region (adj. R² = 0.99, n = 100). The present study gives an easy approach to measure TOC by easily available data sets (WBC, SICL, and MAR). Using this relationship, computation of soil carbon stock that was done earlier with WBC values could be revisited and improved.     

Evaluation of a rapid microwave digestion method for determination of total organic carbon in soil

Precise measurement of soil organic carbon (SOC) is essential for constructing regional inventories, developing best agricultural management practices, and modeling purposes. Currently, the automated dry combustion method is considered standard, but the method is both costly and time-consuming. There is a need for a simple, easy to use and cost-effective method of organic C determination in soil. A simple method of total organic carbon (TOC) determination in soil that involved wet digestion of K₂Cr₂O₇-H₂SO₄-soil mixture in a commercial microwave oven followed by spectrophotomteric measurement of Cr (III) was evaluated. The method was compared with automated dry combustion and two other wet digestion methods. The method showed close agreement with dry combustion method (R² = 0.90; root mean square error = 0.70) and the TOC measured with the two methods did not differ for a range of soils drawn from lowland and upland land-uses and varying in pH (6.2–9.3), TOC (2.8–14 g kg^?1), and calcium carbonate content (0–6.7%). The recovery of the added organic C by the microwave method was 98.6 ± 4.2%. The results suggested that microwave-spectrophotometric method could be easily adopted in routine soil analysis as it is not only precise, rapid, and cost-effective but also produced small volume of reagent waste.     

可见-近红外与中红外光谱预测土壤养分的比较研究

对土壤养分的快速和准确测定有助于适时指导施肥。为进一步研究可见-近红外（350～2500 nm）与中红外光谱（4000～650 cm-1）对土壤养分的预测能力,以贵州省500个土样为例,对光谱进行Savitzky-Golay（SG）平滑去噪处理,再用标准正态化（SNV）方法进行基线校正,然后分别应用偏最小二乘回归（PLSR）和支持向量机（SVM）两种方法进行建模,探讨了可见-近红外和中红外光谱对土壤全氮（TN）、全磷（TP）、全钾（TK）和碱解氮（AN）、有效磷（AP）、速效钾（AK）共六种土壤养分的预测效果。结果表明：（1）无论基于可见-近红外光谱还是中红外光谱,PLSR模型的预测精度整体均优于SVM模型。（2）中红外光谱对TN、TK和AN的预测精度均显著高于可见-近红外光谱,可见-近红外和中红外光谱均可以可靠地预测TN和TK（性能与四分位间隔距离的比率（RPIQ）大于2.10）,中红外光谱可相对较可靠地预测AN（RPIQ=1.87）;但两类光谱对TP、AP和AK的预测效果均较差（RPIQ<1.34）。（3）当变量投影重要性得分（VIP）大于1.5时,PLSR模型在中红外光谱区域预测TN和TK的重要波段多于可见-近红外光谱区域,TN的重要波段主要集中于可见-近红外光谱区域的1910和2207 nm附近,中红外光谱区域的1 120、1 000、960、910、770和668 cm-1附近;TK的重要波段主要集中于可见-近红外光谱区域的540、2176、2225和2268 nm附近,中红外光谱区域的1 040、960、910、776、720和668 cm-1附近。因此,中红外光谱技术结合PLSR模型对土壤养分预测效果较好,可快速准确预测土壤TN和TK,可为指导适时施肥提供技术支撑。     

The use of visible and near‐infrared spectroscopy for the analysis of soil water repellency

This study investigated the potential of visible/near‐infrared reflectance spectroscopy (Vis‐NIRS) to predict soil water repellency (SWR). The top 40 mm of soils (n = 288) across 48 sites under pastoral land‐use in the North Island of New Zealand, which represented 10 soil orders and covered five classes of drought proneness, were analysed by standard laboratory methods and Vis‐NIRS. Soil WR was measured by using the molarity of ethanol droplet (MED) and the water drop penetration time (WDPT) tests. Soil organic carbon content (%C) was also measured to examine a possible relationship with SWR. A partial least squares regression (PLSR) model was developed by using Vis‐NIRS spectral data and the reference laboratory data. In addition, we explored the power of discrimination based on WDPT classes using partial least squares discriminant analysis (PLS‐DA). The PLSR of the processed spectra produced moderately accurate prediction for MED (R²_val = 0.61, RPD_val = 1.60, RMSE_val = 0.59) and good prediction for %C (R²_val = 0.82, RPD_val = 2.30, RMSE_val = 2.72). When the data from the 10 soil orders were considered separately and based on soil order rather than being grouped, the prediction of MED was further improved except for the Allophanic, Brown, Organic and Ultic soil orders. The PLS‐DA was successful in classifying 60% of soil samples into the correct WDPT classes. Our results indicate clearly that Vis‐NIRS has the potential to predict SWR. Further improvement in the prediction accuracy of SWR is envisaged by increasing the understanding of the relationship between Vis‐NIRS and the SWR of all New Zealand soil orders as a function of their physical properties and chemical constituents such as hydrophobic compounds.     

Evaluating a low‐cost portable NIR spectrometer for the prediction of soil organic and total carbon using different calibration models

This study aims to assess the performance of a low‐cost, micro‐electromechanical system‐based, near infrared spectrometer for soil organic carbon (OC) and total carbon (TC) estimation. TC was measured on 151 soil profiles up to the depth of 1 m in NSW, Australia, and from which a subset of 24 soil profiles were measured for OC. Two commercial spectrometers including the AgriSpec^TM (ASD) and NeoSpectra^TM (Neospectra) with spectral wavelength ranges of 350–2,500 and 1,300–2,500 nm, respectively, were used to scan the soil samples, according to the standard contact probe protocol. Savitzky–Golay smoothing filter and standard normal variate (SNV) transformation were performed on the spectral data for noise reduction and baseline correction. Three calibration models, including Cubist tree model, partial least squares regression (PLSR) and support vector machine (SVM), were assessed for the prediction of soil OC and TC using spectral data. A 10‐fold cross‐validation analysis was performed for evaluation of the models and devices accuracies. Results showed that Cubist model predicts OC and TC more accurately than PLSR and SVM. For OC prediction, Cubist showed R² = 0.89 (RMSE = 0.12%) and R² = 0.78 (RMSE = 0.16%) using ASD and NeoSpectra, respectively. For TC prediction, Cubist produced R² = 0.75 (RMSE = 0.45%) and R² = 0.70 (RMSE = 0.50%) using ASD and NeoSpectra, respectively. ASD performed better than NeoSpectra. However, the low‐cost NeoSpectra predictions were comparable to the ASD. These finding can be helpful for more efficient future spectroscopic prediction of soil OC and TC with less costly devices.     

Calibration of visible and near infrared spectroscopy for soil analysis at the field scale on three European farms

The development of accurate calibration models for selected soil properties is a crucial prerequisite for successful implementation of visible and near infrared (Vis‐NIR) spectroscopy for soil analysis. This paper compares the performance of calibration models developed for individual farms with that of general models valid for three farms in three European countries. Fresh soil samples collected from farms in the Czech Republic, Germany and Denmark were scanned with a fibre‐type Vis‐NIR spectrophotometer. After dividing spectra into calibration (70%) and validation (30%) sets, spectra in the calibration set were subjected to partial least squares regression (PLSR) with leave‐one‐out cross‐validation to establish calibration models of soil properties. Except for the Czech Republic farm, individual farm models provided successful calibration for total carbon (TC), total nitrogen (TN) and organic carbon (OC), with coefficients of determination (R²) of 0.85–0.93 and 0.74–0.96 and residual prediction deviations (RPD) of 2.61–3.96 and 2.00–4.95 for the cross‐validation and independent validation respectively. General calibration models gave improved prediction accuracies compared with models of farms in the Czech Republic and Germany, which was attributed to larger ranges in the variation of soil properties in general models compared with those in individual farm models. The results revealed that larger standard deviations (SDs) and wider variation ranges have resulted in larger R² and RPD, but also larger root mean square errors of prediction (RMSEP). Therefore, a compromise solution, which also results in small RMSEP values, should be found when selecting soil samples for Vis‐NIR calibration to cover a wide variation range.     

Prediction of soil organic and inorganic carbon contents at a national scale (France) using mid‐infrared reflectance spectroscopy (MIRS)

This work aimed to evaluate the potential of mid‐infrared reflectance spectroscopy (MIRS) to predict soil organic and inorganic carbon contents with a 2086‐sample set representative of French topsoils (0–30 cm). Ground air‐dried samples collected regularly using a 16 × 16‐km grid were analysed for total (dry combustion) and inorganic (calcimeter) carbon; organic carbon was calculated by difference. Calibrations of MIR spectra with partial least square regressions were developed with 10–80% of the set and five random selections of samples. Comparisons between samples with contrasting organic or inorganic carbon content and regression coefficients of calibration equations both showed that organic carbon was firstly associated with a wide spectral region around 2500–3500 cm^?1 (which was a reflection of its complex nature), and inorganic carbon with narrow spectral bands, especially around 2520 cm^?1. Optimal calibrations for both organic and inorganic carbon were achieved by using 20% of the total set: predictions were not improved much by including more of the set and were less stable, probably because of atypical samples. At the 20% rate, organic carbon predictions over the validation set (80% of the total) yielded mean R², standard error of prediction (SEP) and RPD (ratio of standard deviation to SEP) of 0.89, 6.7 g kg^?1 and 3.0, respectively; inorganic carbon predictions yielded 0.97, 2.8 g kg^?1 and 5.6, respectively. This seemed appropriate for large‐scale soil inventories and mapping studies but not for accurate carbon monitoring, possibly because carbonate soils were included. More work is needed on organic carbon calibrations for large‐scale soil libraries.     

黄土高原吕梁山不同撂荒年限土壤团聚体稳定性及有机碳分布特征

为探究不同撂荒年限土壤结构及有机碳分布特征,试验选取黄土高原吕梁山自然撂荒1、2、3、5、10、15、20a枣园土壤为研究对象,以清耕作业下的枣园土壤为对照(CK),利用干筛和湿筛法测定并分析各样地0～20 cm 土层中土壤团聚体稳定性、团聚体有机碳与土壤总有机碳含量及其相关性.结果表明:撂荒初期,土壤团聚体含量呈波动...     

采用无人机载高分辨率光谱仪反演土壤有机碳含量

小型无人机(Unmanned Aerial Vehicle,UAV)平台与土壤高光谱技术的有机结合可作为一种快速、准确获取高分辨率土壤有机碳(Soil Organic Carbon,SOC)空间信息的手段,适用于精准农业管理和土地监测,但目前该方面应用不多.该研究选取中国东北黑土和比利时黄土研究区,通过构建与UAV兼容...     

VNIR Soil Spectroscopy for Field Soil Analysis

The advent of affordable, ground-based, global positioning information (GPS)–enabled sensor technologies provides a new method to rapidly acquire georeferenced soil datasets in situ for high-resolution soil attribute mapping. Our research deployed vehicle-mounted electromagnetic sensor survey equipment to map and quantify soil variability (?50 ha per day) using apparent electrical conductivity as an indirect measure of soil texture and moisture differences. A portable visible–near infrared (VNIR) spectrometer (350–2500 nm) was then used in the field to acquire hyperspectral data from the side of soil cores to a specified depth at optimized sampling locations. The sampling locations were derived by statistical analysis of the electromagnetic survey dataset, to proportionally sample the full range of spatial variability. The VNIR spectra were used to predict soil organic carbon (prediction model using field-moist spectra: R² = 0.39; RPD = 1.28; and air-dry spectra: R² = 0.80; RPD = 2.25). These point values were combined with the electromagnetic survey data to produce a soil organic carbon map, using a random forest data mining approach (validation model: R² = 0.52; RMSE = 3.21 Mg C/ha to 30 cm soil depth; prediction model: R² = 0.92; RMSE = 1.53 Mg C/ha to 30 cm soil depth). This spatial modeling method, using high-resolution sensor data, enables prediction of soil carbon stocks, and their spatial variability, at a resolution previously impractical using a solely laboratory-based approach.     

Rapid determination of lime requirement by mid‐infrared spectroscopy: A promising approach for precision agriculture

Mid‐infrared spectroscopy (MIRS) has proven to be a cost‐effective, high throughput measurement technique for soil analysis. After multivariate calibration mid‐infrared spectra can be used to predict various soil properties, some of which are related to lime requirement (LR). The objective of this study was to test the performance of MIRS for recommending variable rate liming on typical Central European soils in view of precision agriculture applications. In Germany, LR of arable topsoils is commonly derived from the parameters organic matter content (SOM), clay content, and soil pH (CaCl₂) as recommended by the Association of German Agricultural Analytical and Research Institutes (VDLUFA). We analysed a total of 458 samples from six locations across Germany, which all revealed large within‐field soil heterogeneity. Calcareous topsoils were observed at some positions of three locations (79 samples). To exclude such samples from LR determination, peak height at 2513 cm^?1 of the MIR spectrum was used for identification. Spectra‐based identification was accurate for carbonate contents > 0.5%. Subsequent LR derivation (LR_SPP) from MIRS‐PLSR predictions of SOM, clay, and pH (CaCl₂) for non‐calcareous soil samples using the VDLUFA look‐up tables was successful for all locations (R² = 0.54–0.82; RMSE = 857–1414 kg CaO ha^?1). Alternatively, we tested direct LR prediction (LR_DP) by MIRS‐PLSR and also achieved satisfactory performance (R² = 0.52–0.77; RMSE = 811–1420 kg CaO ha^?1; RPD = 1.44–2.08). Further improvement was achieved by refining the VDLUFA tables towards a stepless algorithm. It can be concluded that MIRS provides a promising approach for precise LR estimation on heterogeneous arable fields. Large sample numbers can be processed with low effort which is an essential prerequisite for variable rate liming in precision agriculture.     

Effects of mineral surface iron on the CPMAS 13C-NMR spectroscopic detection of organic matter from soil fractions in an agricultural topsoil with different amendments

The decrease of NMR visibility of the C signal in soil samples due to the association between organic carbon (OC) and the topsoil mineral surface was investigated. CPMAS ¹³C‐NMR spectra were obtained for soil particle‐size fractions (< 2 μm, 2–20 μm, > 20 μm) and bulk soils from an agricultural topsoil (Chernozem) that had received three different amendments (no fertilization, mineral fertilization (NPK), mineral (NPK) and organic (cattle manure) fertilizations) at Bad Lauchstädt, Germany. The soil organic carbon content of the three soils depended on the degree of soil fertilization. There was no constant relationship between the total NMR signal intensity and the total amount of organic carbon (TOC) for all size fractions. Indeed, a key role played in the C signal intensity by the paramagnetic ferric ion from the clay content in soil fractions and bulk soils was confirmed. Thus, we describe the variations of C signal intensity by taking into account the distribution of clay‐associated OC and non‐associated OC pools. Depending on the amendment, the C signal visibility was weakened by a factor of 2–4 for the clay‐associated OC. This estimation was rendered possible by combining mineral specific surface area (SSA) measurements with the N₂ gas adsorption method (BET method) and determination of TOC and iron concentrations. This approach contributes to the quantitative evaluation of the CPMAS ¹³C‐NMR detection.     

Accounting for the effects of water and the environment on proximally sensed vis–NIR soil spectra and their calibrations

Visible–near infrared (vis–NIR) spectroscopy can be used to estimate soil properties effectively using spectroscopic calibrations derived from data contained in spectroscopic databases. However, these calibrations cannot be used with proximally sensed (field) spectra because the spectra in these databases are recorded in the laboratory and are different to field spectra. Environmental factors, such as the amount of water in the soil, ambient light, temperature and the condition of the soil surface, cause the differences. Here, we investigated the use of direct standardization (DS) to remove those environmental factors from field spectra. We selected 104 sensing (sampling) sites from nine paddy fields in Zhejiang province, China. At each site, vis–NIR spectra were recorded with a portable spectrometer. The soils were also sampled to record their spectra under laboratory conditions and to measure their soil organic matter (SOM) content. The resulting data were divided into training and validation sets. A subset of the corresponding field and laboratory spectra in the training set (the transfer set) was used to derive the DS transfer matrix, which characterizes the differences between the field and laboratory spectra. Using DS, we transferred the field spectra of the validation samples so that they acquired the characteristics of spectra that were measured in the laboratory. A partial least squares regression (PLSR) of SOM on the laboratory spectra of the training set was then used to predict both the original field spectra and the DS‐transferred field spectra. The assessment statistics of the predictions were improved from R² = 0.25 and RPD = 0.35 to R² = 0.69 and RPD = 1.61. We also performed independent predictions of SOM on the DS‐transferred field spectra with a PLSR derived using the Chinese soil spectroscopic database (CSSD), which was developed in the laboratory. The R² and RPD values of these predictions were 0.70 and 1.79, respectively. Predictions of SOM with the DS‐transferred field spectra were more accurate than those treated with external parameter orthogonalisation (EPO), and more accurate than predictions made by spiking. Our results show that DS can effectively account for the effects of water and environmental factors on field spectra and improve predictions of SOM. DS is conceptually straightforward and allows the use of calibrations made with laboratory‐measured spectra to predict soil properties from proximally sensed (field) spectra, without needing to recalibrate the models.