Estimate Soil Organic Matter Content Research Articles

Mapping Forest Cover and Estimating Soil Organic Matter by GIS-Data and an Empirical Model at the Subnational Level in Mexico

Forests play an essential role in climate change as they are the terrestrial ecosystems that store the highest C content in their soils and biomass. Despite this, the lack of information at the subnational level hinders their proper management and conservation. This study aimed to identify the extension and distribution of forests and to develop an empirical model for the spatial prediction of soil organic matter (SOM) in Ixtacamaxtitlan, Puebla, Mexico, based on environmental variables generated through Geographical Information Systems. A supervised classification in Landsat 8 images was used to define the forest cover, and environmental variables related to topography, climate and vegetation were generated. Finally, a Multiple Linear Regression model validated with the leave-one-out cross-validation method was used to examine the relationships between the covariates and the SOM and estimate its content in forest. The results show that the forest cover extension is 41%, with an overall accuracy of 97.7%. The model shows a good fit (R2cv = 0.69, RMSEcv = 1.53). The mean of SOM was 5.2%, and upper values were consistent with higher altitude, precipitation and cooler temperature. Estimating SOM content in forest areas is essential in developing planning strategies at the subnational level to mitigate the harmful effects of climate change.

Grey fuzzy prediction model of soil organic matter content using hyper-spectral data

PurposeIn order to overcome the uncertainty and improve the accuracy of spectral estimation, this paper aims to establish a grey fuzzy prediction model of soil organic matter content by using grey theory and fuzzy theory.Design/methodology/approachBased on the data of 121 soil samples from Zhangqiu district and Jiyang district of Jinan City, Shandong Province, firstly, the soil spectral data are transformed by spectral transformation methods, and the spectral estimation factors are selected according to the principle of maximum correlation. Then, the generalized greyness of interval grey number is used to modify the estimation factors of modeling samples and test samples to improve the correlation. Finally, the hyper-spectral prediction model of soil organic matter is established by using the fuzzy recognition theory, and the model is optimized by adjusting the fuzzy classification number, and the estimation accuracy of the model is evaluated using the mean relative error and the determination coefficient.FindingsThe results show that the generalized greyness of interval grey number can effectively improve the correlation between soil organic matter content and estimation factors, and the accuracy of the proposed model and test samples are significantly improved, where the determination coefficient R2 = 0.9213 and the mean relative error (MRE) = 6.3630% of 20 test samples. The research shows that the grey fuzzy prediction model proposed in this paper is feasible and effective, and provides a new way for hyper-spectral estimation of soil organic matter content.Practical implicationsThe research shows that the grey fuzzy prediction model proposed in this paper can not only effectively deal with the three types of uncertainties in spectral estimation, but also realize the correction of estimation factors, which is helpful to improve the accuracy of modeling estimation. The research result enriches the theory and method of soil spectral estimation, and it also provides a new idea to deal with the three kinds of uncertainty in the prediction problem by using the three kinds of uncertainty theory.Originality/valueThe paper succeeds in realizing both the grey fuzzy prediction model for hyper-spectral estimating soil organic matter content and effectively dealing with the randomness, fuzziness and grey uncertainty in spectral estimation.

Combining different pre-processing and multivariate methods for prediction of soil organic matter by near infrared spectroscopy (NIRS) in Southern Brazil

In Brazil, most guidelines for soil nitrogen (N) fertilization are based on the soil organic matter (SOM) content, which is one of the most laborious and expensive parameters in routine soil analysis. Recently, several studies have been made to generate and evaluate alternative methods to estimate SOM content, such as those based on near infrared spectroscopy (NIRS). However, the accuracy of NIRS is highly affected by spectral pre-processing techniques and multivariate methods. Moreover, the models are highly site-specific, indicating the need to develop regional calibrations. The objective of this study was to evaluate the effect of pre-processing and multivariate methods in SOM prediction using NIRS. A total of 2388 soil samples from Southern Brazil were analyzed for SOM content by carbon oxidation with a sulfochromic solution, followed by spectrophotometric colorimeter determination (modified Walkley-Black method) and by a NIR-spectrometer (1200–2400 nm). Seven pre-processing techniques were tested, including: Savitzki-Golay derivative (SGD), continuum removal (CR), detrend (DET), binning (BIN), smoothing (SMO), and the standard normal variate (SNV). The pre-processing techniques were combined with four multivariate models including multiple linear regression (MLR), partial least squares regression (PLSR), principal component regression (PCR), support vector machine (SVM), random forest (RF) and gaussian process regression (GPR). The performance of the predictions was evaluated by the coefficient of determination (R2) and the root mean squared error (RMSE). Moreover, SOM predictions obtained from the models were compared with the wet digestion method in order to evaluate the capability to correctly classify the SOM content for N fertilization. Overall, comparing the multivariate models, the best predictions according to R2 were found for SVM > GPR > PLSR > MLR > PCR > RF, in decreasing order. The best prediction was obtained with the combination of SVM model and SNV spectral pre-processing (R2 = 0.70, RMSE = 0.44, RPIQ = 2.26). For SVM and GPR, only DET, SNV, BIN and CR improved predictions when compared to raw spectra. When using the linear model MLR, the prediction of SOM was improved only with SNV, SMO and DET, compared to raw spectra. For the PLSR model, the prediction of SOM was improved with DET and SNV. Results estimated by NIRS reached 85% accuracy when compared to values of SOM obtained by the modified Walkley-Black method.

Estimation of Soil Organic Matter in Arid Zones with Coupled Environmental Variables and Spectral Features.

The soil organic matter (SOM) content is a key factor affecting the function and health of soil ecosystems. For measurements of land reclamation and soil fertility, SOM monitoring using visible and near-infrared spectroscopy (Vis-NIR) is one approach to quantifying soil quality, and Vis-NIR is important for monitoring the SOM content in a broad and nondestructive manner. To investigate the influence of environmental factors and Vis-NIR spectroscopy in estimating SOM, 249 soil samples were collected from the Werigan–Kuqa oasis in Xinjiang, China, and their spectral reflectance, SOM content and soil salinity were measured. To classify and improve the prediction accuracy, we also take into account the soil salinity content as a variable indicator. Relevant environmental variables were extracted using remote sensing datasets (land-use/land-cover (LULC), digital elevation model (DEM), World Reference Base for Soil Resources (WRB), and soil texture). On the basis of Savitzky–Golay (S-G) smoothing and first derivative (FD) preprocessing of the original spectrum, three clusters were obtained by K-means clustering through the use of Vis-NIR and used as spectral classification variables. Using Vis-NIR as Model 1, Vis-NIR combined with spectral classification as Model 2, environmental variables as Model 3, and the combination of all the above variables (Vis-NIR, spectral classification, environmental variables, and soil salinity) as Model 4, a SOM content estimation model was constructed using partial least squares regression (PLSR). Using the 249 soil samples, the modeling set contained 166 samples and the validation set contained 83 samples. The results showed that Model 2 (validation r2 = 0.78) was better than Model 1 (validation r2 = 0.76). The prediction accuracy for Model 4 (validation r2 = 0.85) was better than Model 2 (validation r2 = 0.78). Among these, Model 3 was the worst (validation r2 = 0.39). Therefore, the combination of environmental variables with Vis-NIR spectroscopy to estimate SOM content is an important method and has important implications for improving the accuracy of SOM predictions in arid regions.

Estimation of soil organic matter content using selected spectral subset of hyperspectral data

Soil organic matter (SOM) content plays an important role in the global carbon cycle and agricultural activities. Reflectance spectroscopy has been recognized as a promising method to rapidly estimate SOM content. However, the existing estimation methods mainly apply partial least squares regression (PLSR) to the entire spectral region of hyperspectral data. Here we proposed a method to extract the informative spectral subset based on spectral characteristics of soil constituents, which was then used to estimate SOM content with PLSR. Genetic algorithm (GA) and variable importance in the projection (VIP) score of PLSR were adopted to further select spectral bands separately. Both laboratory spectra of soil samples collected from an agricultural area and a hyperspectral satellite image were used to evaluate the performance of the method. For the estimations of SOM content using laboratory spectra, compared with the estimation using the entire spectral region of 400–2400 nm, the model accuracy was improved by using the spectral bands associated with clay minerals and the combined spectral bands of organic matter and clay minerals. For the estimations using soil spectra from hyperspectral remote sensing image, the RMSE and R2 values were improved from 0.91% and 0.34 to 0.55% and 0.76 by using the spectral bands associated with organic matter in comparison with the entire spectral region of 390–1029 nm. The estimation model developed with GA-PLSR using soil spectra from the hyperspectral satellite image was applied to map SOM content. Results suggest that estimating SOM content using informative spectral subset is promising and can be transferred to the hyperspectral satellite image to map SOM content.

Comparative Analysis for Grey Relation Estimation Models of Soil Organic Matter based on Hyperspectral Data

Rapid and accurate acquisition of farmland soil organic matter content is of great significance for the rapid monitoring soil fertility and the development of precision agriculture. Based on the hyperspectral data and organic matter content data of 74 soil samples in Zhangqiu District, Jinan City, Shandong Province, first transform soil spectral reflectance by the logarithm of the first-order differential, square, reciprocal, logarithm and square root, the estimation factors were selected according to the principle of maximum relation. Second, the hyperspectral estimation of soil organic matter was carried out by using six models such as grey close relation degree, and the estimation results of different methods were combined and analyzed. The results show that the combined grey relation model can effectively improve the estimation accuracy, and the average relative error of 16 test samples is 8.931%. Studies have shown that using the combined model of grey relation degree to estimate soil organic matter content with hyperspectral data is valid.

Improving estimation of soil organic matter content by combining Landsat 8 OLI images and environmental data: A case study in the river valley of the southern Qinghai-Tibet Plateau

The Qinghai-Tibet Plateau (QTP) is a typical ecologically fragile area. Once the surface vegetation degenerates, it may not be restored. This requires the development of soil organic matter (SOM) monitoring method without destroying the surface, so as to ensure the sustainable development of plateau agriculture. This work investigated the environmental factors that are significantly related to SOM content in the river valley of the southern QTP. These environmental factors include soil hydrothermal factors (soil moisture content and soil temperature), topographic factors (elevation and slope) and vegetation factor (NDVI). The original band reflectivity (OR) of Landsat 8 OLI images and the band reflectivity after the first-order derivative (FDR) and the second-order derivative (SDR) processing were combined with the above environmental factors to estimate SOM content. The results showed that the accuracy of the model was improved obviously by adding environmental factors. The estimation effect of back propagation neural network (BPNN) model was better than that of geographically weighted regression (GWR) model, partial least squares regression (PLSR) model and multivariable linear regression (MLR) model. GWR model can also meet the estimation requirements, while PLSR and MLR models cannot achieve effectively the estimation of SOM content. FDR-BPNN model considering environmental factors was the best model for estimating SOM content, with R2 being 0.947, RMSEC being 4.701 g·kg−1 and MAEV being 5.485 g·kg−1. Moreover, the model had the lowest uncertainty and the highest stability. This study will provide a good insight for the monitoring of SOM content in the future, and provide basic data support for the implementation of precision agriculture in the QTP.

Estimating and mapping of soil organic matter content in a typical river basin of the Qinghai-Tibet Plateau

Spectroscopy is a fast, non-destructive, and cheap method, which has been widely used in the estimation of soil organic matter (SOM) concentration. This study presented a methodology to estimate and map SOM content by crop canopy reflectance spectra combining with land parameters in the Yarlung Zangbo River (YZR) basin. The reflectance spectra of the oat canopy were collected in the field, and then were processed by savitzky-golay filtering (S-G), continuous removal (CR), and first derivative of reflectivity (FDR). The principal components were extracted from the processed spectral data. Land parameters such as elevation, slope, NDVI, and land surface temperature (LST) were selected to establish 18 models to estimate SOM content. The results showed that the estimation accuracy of SOM content could be improved by combining crop canopy reflectance spectra with land parameters. FDR-BPNN model with land parameters had the best estimation effect (R2 = 0.973, MAE = 0.847 g·kg−1).

Estimating Soil Organic Matter Content Using Sentinel-2 Imagery by Machine Learning in Shanghai

Soil organic matter (SOM) plays an important role in the field of climate change and terrestrial ecosystems. SOM in large areas, especially in urban areas, is difficult to monitor and estimate by traditional methods. Urban land structure is complex, and soil is a mixture of organic and inorganic constituents with different physical and chemical properties. Previous studies showed that remote sensing techniques that provide diverse data in the visible-near-infrared (VNIR)-shortwave infrared (SWIR) spectral range, are promising in the prediction of SOM content on a large scale. Sentinel-2 covers the important spectral bands (VNIR-SWIR) for SOM prediction with a short revisit time. Thus, this article aimed to evaluate the capacity of Sentinel-2 for SOM prediction in an urban area (i.e., Shanghai). 103 bare soil samples filtrated from 398 soil samples at a depth of 20 cm were selected. Three methods, partial least square regression (PLSR), artificial neural network (ANN), and support vector machine (SVM), were applied. The root mean square error (RMSE) of modelling (mRMSE) and the coefficient of determination ( $R^{2}$ ) of modelling $(mR^{2})$ were used to reflect the accuracy of the model. The results show that PLSR has the poorest performance. ANN has the highest modelling accuracy (mRMSE = 7.387 g kg $^{-1}$ , $mR^{2}=0.446$ ). The ANN prediction accuracy of RMSE (pRMSE) is 4.713 g kg $^{-1}$ and the prediction accuracy of $R^{2}~(pR^{2})$ is 0.723. For SVR, the pRMSE is 4.638 g kg $^{-1}$ , and the $pR^{2}$ is 0.732. The prediction accuracy of SVR is slightly higher than that of ANN. The spatial distribution of SOM demonstrates that the value obtained by ANN is the closest to the range of the bare soil samples, and ANN performs better in vegetation-covered areas. Therefore, Sentinel-2 can be used to estimate SOM content in urban areas, and ANN is a promising method for SOM estimation.

Predikcija sadržaja organske tvari tla pomoću boje tla na tri lokacije s različitim načinom korištenja zemljišta u Zagrebu (Hrvatska)

Soil organic matter (SOM) plays a key role in ecosystems. Reduction of its content due to land-use changes has a negative impact on the soil, but also on the wider environment. Accordingly, SOM content is routinely analyzed in the laboratory. As these are expensive and/or time-consuming, indirect ones are also tested. The aim of this study was to examine the possibility of predicting SOM content by linear regression using soil color as the predictor, at three locations in Zagreb (Croatia), with different soil types (eutric cambisol anthropogenic, humofluvisol, pseudogley) and different land uses (plough land, meadow, forest, respectively). At each location, 5 samples of the surface soil layer were taken. Soil color was determined using the Munsell system, and the hue was 2.5Y and 10YR in dry and moist soil, respectively. Laboratory analyzes showed that the soils are very acid to neutral silt loams. In line with the land-use, they differed significantly in SOM content and were poorly humic (plough land), moderately to highly humic (meadow), and highly humic (forest). Correlation between soil color dimensions and SOM content was significant only for the dry samples, between chroma and SOM and between value/chroma ratio and SOM. Regression analysis showed high coefficients of determination for these two relationships (R2 = 0.88 for chroma-SOM, R2 = 0.76 for value/chroma-SOM). The results suggest that visual soil color determination can be used to estimate SOM content, but only in dry soil. The model calibrated in this paper needs to be validated using samples of other (different) soils.

Water-based measured-value fuzzification improves the estimation accuracy of soil organic matter by visible and near-infrared spectroscopy

Visible and near-infrared (Vis-NIR) reflectance spectroscopy continues to emerge as a rapid and effective approach for estimating several soil physical and chemical properties including soil organic matter (SOM), but its accuracy is restricted by many factors including soil water. This study proposed the water-based measured-value fuzzification (WMF) method to decrease the influence of soil water, and combined with the partial least squares regression (PLSR) to develop SOM models. Vis-NIR spectral data was measured by an ASD FieldSpec 3 spectrometer. After WMF analysis, the PLSR method was used to develop SOM models. By comparison with the PLSR model, the WMF-PLSR model produced markedly better results (root mean square error of validation [RMSEV] = 2.776 g/kg, mean relative error of validation [MREV] = 8.111%, and ratio of performance to interquartile range [RPIQv] = 4.729). With these, the WMF method combined with PLSR shows the potential for estimating SOM content and expands the range of observation methods.

The application of proximal visible and near-infrared spectroscopy to estimate soil organic matter on the Triffa Plain of Morocco

Soil organic matter (SOM) is a fundamental soil constituent. The estimation of this parameter in the laboratory using the classical method is complex time-consuming and requires the use of chemical reagents. The objectives of this study were to assess the accuracy of two laboratory measurement setups of the VIS-NIR spectroscopy in estimating SOM content and determine the important spectral bands in the SOM estimation model. A total of 115 soil samples were collected from the non-root zone (0–20 cm) of soil in the study area of the Triffa Plain and then analysed for SOM in the laboratory by the Walkley–Black method. The reflectance spectra of soil samples were measured by two protocols, Contact Probe (CP) and Pistol Grip (PG)) of the ASD spectroradiometer (350–2500 nm) in the laboratory. Partial least squares regression (PLSR) was used to develop the prediction models. The results of coefficient of determination (R2) and the root mean square error (RMSE) showed that the pistol grip offers reasonable accuracy with an R2 = 0.93 and RMSE = 0.13 compared to the contact probe protocol with an R2 = 0.85 and RMSE = 0.19. The near-Infrared range were more accurate than those in the visible range for predicting SOM using the both setups (CP and PG). The significant wavelengths contributing to the prediction of SOM for (PG) setup were at: 424, 597, 1432, 1484, 1830,1920, 2200, 2357 and 2430 nm, while were at 433, 587, 1380, 1431, 1929, 2200 and 2345 nm for (CP) setup.

Prediction of soil organic matter and clay contents by near-infrared spectroscopy - NIRS

ABSTRACT: Among the soil constituents, special attention is given to soil organic matter (SOM) and clay contents, since, among other aspects, they are key factors to nutrient retention and soil aggregates formation, which directly affect the crop production potential. The methods commonly used for the quantification of these constituents have some disadvantages, such as the use of chemical reactants and waste generation. An alternative to these methods is the near-infrared spectroscopy (NIRS) technique. The aim of this research is to evaluate models for SOM and clay quantification in soil samples using spectral data by NIRS. A set (n = 400) of soil samples previously analyzed by traditional methods were used to generate a NIRS calibration curve. The clay content was determined by the hydrometer method while SOM content was determined by sulfochromic solution. For calibration, we used the original spectra (absorbance) and spectral pretreatment (Savitzky-Golay smoothing derivative) in the following models: multiple linear regression (MLR), partial last squares regression (PLSR), support vector machine (SVM) and Gaussian process regression (GPR). The curve validation was performed with the SVM model (best performance in the calibration based on R² and RMSE) in two ways: with 40 random samples from the calibration set and another set with 200 new unknown samples. The soil clay content affects the predictive ability of the calibration curve to estimate SOM content by NIRS. Validation curves showed poorer performance (lower R² and higher RMSE) when generated from unknown samples, where the model tends to overestimate the lower levels and to underestimate the higher levels of clay and SOM. Despite the potential of NIRS technique to predict these attributes, further calibration studies are still needed to use this technique in soil analysis laboratories.

Prediction of soil organic matter in northwestern China using fractional-order derivative spectroscopy and modified normalized difference indices

Visible-near-infrared (Vis-NIR) spectroscopy makes it possible to estimate soil organic matter content (SOMC). Spectral pretreatment techniques have important significance in the quantitative analysis of SOMC. A total of 150 soil samples collected in northwestern China were used as data sets for calibration and validation. The SOMC values and reflectance spectra were measured in the laboratory. Fractional-order derivatives (FODs) (intervals of 0.05, range of 0–2) were used for soil spectral pretreatment, and a new three-band index (modified normalized difference index, MNDI) was constructed based on the band-optimization algorithm and the existing two-band exponential form (normalized difference index, NDI). Partial least square-support vector machine (PLS-SVM) models were calibrated using spectral parameters selected based on a single dimension (FOD), two-dimensional index (NDI) and three-dimensional index (MNDI) and subsequently applied to estimate SOMC. Three model evaluation parameters, namely, the coefficient of determination (R2), root mean squared error (RMSE), and ratio of performance to interquartile range (RPIQ), were used to evaluate the estimation accuracy of the models. The results showed that with increased derivative order, the spectral strength gradually decreased, but the spectral detail increased. Furthermore, the correlation between FOD spectra and SOMC was significantly enhanced in the visible region, with the most obvious effect in the 1.05- to 1.45-order range. The PLS-SVM modeling results showed that the sensitivity and estimation accuracy of SOMC increased with increasing spectral synergy (i.e., 1D (FOD) < 2D (NDI) < 3D (MNDI)). Among the models, MNDI exhibited the best model performance, yielding a validation R2 and RPIQ of 0.846 and 3.396, respectively. The combination of FOD and MNDI could weaken the soil noise information and improve the prediction accuracy of SOMC. Furthermore, the three-dimensional index has strong application potential for estimating other biochemical parameters of soil using Vis-NIR spectroscopy.

Estimation of soil organic matter content by modeling with artificial neural networks

Soil organic matter has direct relationship with soil fertility and quality. However, its estimation in laboratory generates chemical residues which can contaminate the environment, and more ecological methods to determine the soil organic matter present high costs to the laboratories. This study aimed to evaluate the accuracy of artificial neural networks (ANNs) in estimating soil organic matter content from soil chemical attributes and to indicate whether network complexity affects estimation accuracy. A database was created containing 8556 samples, and 75% of the data were used for calibration and 25% for validation of the models. The variables used were: pH, potassium, phosphorus, calcium, magnesium and potential acidity. However, potassium and phosphorus were removed from the input variables. The ANNs were from the Multilayer Perceptron class, with two hidden layers, each of which had number of neurons ranging from 4 to 20. The 15 ANNs with lowest root mean square error (RMSE) were randomly presented by the program Statistica7®, and 6 of them were chosen for accuracy assessment. The fits were tested by analysis of variance (F test) and accuracy was assessed based on the coefficient of determination (R2), RMSE, mean error (ME), index of agreement (d) and confidence coefficient (c). The ANNs showed high accuracy to estimate soil organic matter in the phases of both calibration (R2 = 0.92; RMSE = 1.82 g kg−1) and validation (R2 = 0.76; RMSE = 1.98 g kg−1). Less complex networks can be trained and show the same accuracy as more complex networks.

Hyperspectral Estimation Model of Forest Soil Organic Matter in Northwest Yunnan Province, China

Soil organic matter (SOM) is an important index to evaluate soil fertility and soil quality, while playing an important role in the terrestrial carbon cycle. The technology of hyperspectral remote sensing is an important method to estimate SOM content efficiently and accurately. This study researched the best hyperspectral estimation model for SOM content in Shangri-La forest soil. The spectral reflectance of soils with sizes of 2 mm, 1 mm, 0.50 mm, and 0.25 mm were measured indoors. After smoothing and de-noising, the reciprocal reflectance (RR), logarithmic reflectance (LR), first-derivative reflectance (FR), reciprocal first-derivative reflectance (RFR), logarithmic first-derivative reflectance (LFR), and mathematical transformations of the original spectral reflectance (REF) were carried out to analyze the relevance of spectral reflectance and SOM content and extract the characteristic bands. Finally the simple linear regression (SLR), multiple stepwise linear regression (SMLR), and partial least squares regression (PLSR) models for SOM content estimation were established. The results showed that: (1) With the decrease of soil particle size, the spectral reflectance increased. The smaller the soil particle sizes, the more obvious was the increase in spectral reflectance. (2) The sensitive bands of SOM were mainly in the 580–690 nm range (correlation coefficient (R) &gt; 0.6, p-value (p) &lt; 0.01), and the spectral information of SOM could be significantly enhanced by first-order differential transformation. (3) Comparing the three models, PLSR had better estimation ability than SMLR and SLR. The precision of the 0.25 mm soil particle size and the LFR index in the PLSR estimation model of SOM content was the best (coefficient of determination of validation (Rv2) = 0.91, root mean square error of validation (RMSEv) = 13.41, the ratio of percent deviation (RPD) = 3.33). The results provide a basis for monitoring SOM content rapidly in the forests of Northwest Yunnan, and provide a reference for forest SOM estimation in other areas.

Semi-Empirical Soil Organic Matter Retrieval Model With Spectral Reflectance

Rapid and accurate monitoring of soil organic matter (SOM) content is of great significance for precision fertilization of farmland. However, the SOM retrieval models are mainly established by statistical methods, which have limited application scope and incomplete theoretical foundation. Moreover, the accuracy of the SOM retrieval models remains raised. In this paper, for the first time, a semi-empirical SOM content retrieval model is constructed, which has certain theoretical basis, strong applicability and higher accuracy than before. Based on the Kubelka-Munk (KM) theory, the SOM retrieval model with the absorption coefficient k and scattering coefficient s related to SOM (r = k/s) is derived. The validity and reliability of the model are confirmed with validation set (n = 26) including three sorts of soils. Results show that the model can estimate SOM content in different sorts of soils with high prediction accuracy and good prediction ability (root mean square errors of prediction (RMSEP), coefficients of determination (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) and relative percentage deviation (RPD) values of 0.18%, 89.9% and 3.2, respectively) in the range of 552-950nm. The model provides an innovative method for predicting SOM content.

Organic matter determination in arid region soils: loss-on-ignition versus wet oxidation

ABSTRACT The precise assessment of soil organic matter (SOM) is required when studying soil pedology, chemistry, physics, and fertility. Besides, it is a key for evaluating soil quality, plant growth, and sustainable land management. This research aims to correlate the SOM resulted from loss-on-ignition (LOI) with those from wet combustion (Walkley–Black, WB). A total of 130 soil samples were collected from Egypt and analyzed using WB and LOI. In LOI, samples exposed to the combustion temperatures of 300, 375, 430, and 550°C for 2 and 4 hours. Using RStudio, simple linear regressions were conducted to estimate the most suitable temperature/time combinations. The results showed that applying lower temperatures (300 and 375°C) for 2 hours provided a strong correlation between LOI and WB with R2 of 93 and 94% for all dataset and sandy soils, respectively. For clay soils the respective R2 values at 300 and 375°C were 83 and 85%. The proposed combinations were valid to estimate SOM content for different soils with correlation up to 0.99 for sandy soils.

Research on estimating soil organic matter content in Northeast China based on CARS-IRIV and neural network optimization algorithm

Research on estimating soil organic matter content in Northeast China based on CARS-IRIV and neural network optimization algorithm

Estimating soil organic matter content from Hyperion reflectance images using PLSR, PCR, MinR and SWR models in semi-arid regions of Iran

Soil organic matter is highly pivotal as it can improve physical, chemical and biological properties of soil through various functions. Direct measurement of soil organic matter at large scales requires a great number of soil samples which is time consuming, tedious and costly. Consequently, alternative methods must be developed to provide a rapid overview of soil organic matter with reasonable accuracy at large scales. Remote sensing can be considered as a non-destructive, rapid and inexpensive method for such purpose. Among different remote sensing features, hyperspectral spectroscopy may produce inexpensive, quick and accurate way of producing soil organic matter maps at large scales. This study aimed to assess the feasibility of providing accurate soil organic matter distribution maps for large semi-arid areas of Iran. Consequently, some Hyperion images were used to develop relationships between spectral bands and soil organic matter with several methods including Stepwise Regression (SWR), Minimum Regression (MinR), Partial Least Square Regression (PLSR) and Principle Component Regression (PCR) models. Models were first calibrated with Hyperion images of the Ivanekey region and then verified by using 9 random samples from the Ivanekey and 23 samples from the Uromia semi-arid regions. Results indicated that of the applied models, SWR and PLSR can provide reasonable accuracy (RMSE) to predict soil organic matter in entire semi-arid region. However, more investigations are needed to improve the accuracy of such predictive models for arid and semi-arid regions with relatively low organic matter content.