Mobile Proximal Sensing with Visible and Near Infrared Spectroscopy for Digital Soil Mapping

The objective of this study was to estimate multiple soil property local regression models, confirm the accuracy of the predicted values using visible near-infrared subsurface diffuse reflectance spectra collected by a mobile proximal soil sensor, and show that digital soil maps predicted by multiple soil property local regression models are able to visualize empirical knowledge of the grower. The parent materials in the experimental fields were light clay, clay loam, and sandy clay loam. The study was conducted in Saitama Prefecture, Japan. To develop local regression models for the 30 chemical and 4 physical properties, a total of 231 samples were collected; to evaluate accuracy of prediction, 65 samples were collected. The local regression models were developed using 2nd derivative pretreatment by the Savitzky–Golay algorithm and partial least squares regression. The local regression models were evaluated using the coefficient of determination (R2), residual prediction deviation (RPD), range error ratio (RER), and the ratio of prediction error to interquartile range (RPIQ). The R2 accuracy of the 34 local regression models was 0.81 or higher. In the predicted values for 65 unknown samples, the local regression models could ‘distinguish between high and low’ for 3 of the 34 soil properties, but were ‘not useful’ as absolute quantitative values for the other 31 soil properties. However, it was confirmed that the predicted values followed the transition in measured values, and thus that the developed 34 regression models could be used for generating digital soil maps based on relative quantitative values. The grower changed the ridge direction in the field from east–west to north–south just looking at the digital soil maps.

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Modelling of soil texture and its verification with related soil properties

Soil Research ◽

10.1071/sr17252 ◽

2018 ◽

Vol 56 (4) ◽

pp. 421 ◽

Cited By ~ 2

Author(s):

M. Shahadat Hossain ◽

G. K. M. Mustafizur Rahman ◽

M. Saiful Alam ◽

M. Mizanur Rahman ◽

A. R. M. Solaiman ◽

...

Keyword(s):

Electrical Conductivity ◽

Soil Properties ◽

Soil Texture ◽

Cation Exchange Capacity ◽

Soil Property ◽

Exchange Capacity ◽

Strong Correlations ◽

Soil Maps ◽

Hypothetical Data ◽

Digital Soil Maps

Soil texture is an independent and innate soil property and other dynamic soil properties such as electrical conductivity (EC), organic carbon (OC) content and cation exchange capacity (CEC) are mostly dependent on it. An attempt was made to develop a model for numerically simulating soil texture and also to construct relationships of the developed model with other soil properties. Hypothetical data of particle size distribution and our data were used to justify and validate the newly defined indices. Scatter diagrams showed good association between the indices and hypothetical data of soil separates. Moreover, similar trends were observed between the line charts of USDA soil textural class codes and the indices. Strong correlations (r = 0.78–0.96) were found between the indices and soil separates (sand, silt and clay) for our data. However, the indices demonstrated moderate correlations (r = –0.34 to –0.55) with EC and OC of the soils. Strong nonlinear relationships were found between CEC and the three indices (R2 = 0.699, R2 = 0.732 and R2 = 0.672 (all P < 0.001). Furthermore, the variability of EC, OC and CEC within a single USDA textural class and customised textural index groups were described using the developed model. The developed indices showed excellent fitness for simulation of soil texture and demonstrated an extended applicability in terms of their relationships with soil properties related to soil texture, which will help in constructing digital soil maps.

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Covariates selection assessment for field scale digital soil mapping in the context of precision fertilization management

10.5194/egusphere-egu2020-4904 ◽

2020 ◽

Author(s):

Nada Mzid ◽

Stefano Pignatti ◽

Irina Veretelnikova ◽

Raffaele Casa

Keyword(s):

Soil Properties ◽

Precision Agriculture ◽

Prediction Models ◽

Central Italy ◽

Soil Mapping ◽

Digital Soil Mapping ◽

Soil Maps ◽

Field Scale ◽

Umbria Region ◽

Digital Soil Maps

The application of digital soil mapping in precision agriculture is extremely important, since an assessment of the spatial variability of soil properties within cultivated fields is essential in order to optimize agronomic practices such as fertilization, sowing, irrigation and tillage. In this context, it is necessary to develop methods which rely on information that can be obtained rapidly and at low cost. In the present work, an assessment is carried out of what are the most useful covariates to include in the digital soil mapping of field-scale properties of agronomic interest such as texture (clay, sand, silt), soil organic matter and pH in different farms of the Umbria Region in Central Italy. In each farm a proximal sensing-based mapping of the apparent soil electrical resistivity was carried out using the EMAS (Electro-Magnetic Agro Scanner) sensor. Soil sampling and subsequent analysis in the laboratory were carried out in each field. Different covariates were then used in the development of digital soil maps: apparent resistivity, high resolution Digital Elevation Model (DEM) from Lidar data, and bare soil and/or vegetation indices derived from Sentinel-2 images of the experimental fields. The approach followed two steps: (i) estimation of the variables using a Multiple Linear Regression (MLR) model, (ii) spatial interpolation via prediction models (including regression kriging and block kriging). The validity of the digital soil maps results was assessed both in terms of the accuracy in the estimation of soil properties and in terms of their impact on the fertilization prescription maps for nitrogen (N), phosphorus (P) and potassium (K).

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Digital soil mapping of soil fertility index for agricultural lands in tropical environment

10.5194/egusphere-egu21-2239 ◽

2021 ◽

Author(s):

Ozias Hounkpatin ◽

Aymar Bossa ◽

Mouinou Igué ◽

Yacouba Yira ◽

Brice Sinsin

Keyword(s):

Random Forest ◽

Soil Fertility ◽

Soil Properties ◽

Management Practices ◽

Fertility Level ◽

Soil Maps ◽

Fertility Index ◽

Bioclimatic Variables ◽

Soil Fertility Index ◽

Digital Soil Maps

Indicators of soil production function such as soil fertility index can potentially be a key decision tool in spatial planning for sustainable land management. The establishment of such soil fertility index requires basic soil properties which can be modelled for spatial mapping. The objective of this study was to take advantage of the soil legacy data of Benin to produce a digital soil map of soil fertility index at a national scale based on 8 soil properties (soil organic carbon matter, nitrogen, pH, exchangeable potassium, assimilable phosphorus, sum of base, cation exchange capacity and base saturation). Speci&#64257;c research aims were: (1) to model and develop digital soil maps; (2) to identify important factors influencing soil nutrients; (3) to establish soil fertility potentials using digital soil maps. For each soil property, modelling procedures involved the use of di&#64256;erent covariates including soil type, topographic, bioclimatic and spectral data along with the comparative assessment of the Cubist and Quantile Random Forest model. Results revealed that apart from N and exchangeable K, significant models can be produced for most of the soil properties with R-square varying between 28% and 72% with the Quantile Random Forest presenting a more accurate prediction interval coverage probability. The analysis revealed that the distance to the nearest stream has strong predictive ability for all the soil properties along with the bioclimatic variables. Visualisation of the soil fertility map showed that most of the soils in Benin have low fertility level suggesting that the use of fertilizers and organic materials will be critical in sustaining crop productivity. A limited number of high and average fertility level soils were found in the low elevation areas of southern Benin and policy could advocate for their sole use for agriculture purpose as well as promote sustainable management practices.

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USING LEGACY SOIL DATA FOR STANDARDIZING PREDICTIONS OF TOPSOIL CLAY CONTENT OBTAINED FROM VNIR/SWIR HYPERSPECTRAL AIRBORNE IMAGES

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-3-w3-439-2015 ◽

2015 ◽

Vol XL-3/W3 ◽

pp. 439-444

Author(s):

C. Gomez ◽

A. Gholizadeh ◽

L. Borůvka ◽

P. Lagacherie

Keyword(s):

Soil Properties ◽

Spectral Index ◽

Near Infrared ◽

Soil Property ◽

Clay Content ◽

Short Wave ◽

Soil Samples ◽

Spectral Bands ◽

Large Sets ◽

The One

Mapping of topsoil properties using Visible, Near-Infrared and Short Wave Infrared (VNIR/SWIR) hyperspectral imagery requires large sets of ground measurements for calibrating the models that estimate soil properties. To avoid collecting such expensive data, we proposed a procedure including two steps that involves only legacy soil data that were collected over and?or around the study site: 1) estimation of a soil property using a spectral index of the literature and 2) standardisation of the estimated soil property using legacy soil data. This approach was tested for mapping clay contents in a Mediterranean region in which VNIR/SWIR AISA-DUAL hyperspectral airborne data were acquired. The spectral index was the one proposed by Levin et al (2007) using the spectral bands at 2209, 2133 and 2225 nm. Two legacy soil databases were tested as inputs of the procedure: the Focused-Legacy database composed of 67 soil samples collected in 2000 over the study area, and the No-Focused-Legacy database composed of 64 soil samples collected between 1973 and 1979 around but outside of the study area. The results were compared with those obtained from 120 soil samples collected over the study area during the hyperspectral airborne data acquisition, which were considered as a reference. Our results showed that: 1) the spectral index with no further standardisation offered predictions with high accuracy in term of coefficient of correlation r (0.71), but also high bias (−414 g/kg) and SEP (439 g/kg), 2) the standardisation using both legacy soil databases allowed an increase of accuracy (r = 0.76) and a reduction of bias and SEP and 3) a better standardisation was obtained by using the Focused-Legacy database rather than the No-Focused-Legacy database. Finally, the clay predicted map obtained with standardisation using the Focused-Legacy database showed pedologically-significant soil spatial structures with clear short-scale variations of topsoil clay contents in specific areas. This study, associated with the coming availability of a next generation of hyperspectral VNIR/SWIR satellite data for the entire globe, paves the way for inexpensive methods for delivering high resolution soil properties maps.

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Soil Properties Prediction for Precision Agriculture Using Visible and Near-Infrared Spectroscopy: A Systematic Review and Meta-Analysis

Agronomy ◽

10.3390/agronomy11030433 ◽

2021 ◽

Vol 11 (3) ◽

pp. 433

Author(s):

Arman Ahmadi ◽

Mohammad Emami ◽

Andre Daccache ◽

Liuyue He

Keyword(s):

Systematic Review ◽

Soil Properties ◽

Precision Agriculture ◽

Literature Search ◽

Near Infrared ◽

Soil Property ◽

Meta Analysis ◽

Reflectance Spectroscopy ◽

Evidence Based ◽

Property Prediction

Reflectance spectroscopy for soil property prediction is a non-invasive, fast, and cost-effective alternative to the standard laboratory analytical procedures. Soil spectroscopy has been under study for decades now with limited application outside research. The recent advancement in precision agriculture and the need for the spatial assessment of soil properties have raised interest in this technique. The performance of soil spectroscopy differs from one site to another depending on the soil’s physical composition and chemical properties but it also depends on the instrumentation, mode of use (in-situ/laboratory), spectral range, and data analysis methods used to correlate reflectance data to soil properties. This paper uses the systematic review procedure developed by the Centre for Evidence-Based Conservation (CEBC) for an evidence-based search of soil property prediction using Visible (V) and Near-InfraRed (NIR) reflectance spectroscopy. Constrained by inclusion criteria and defined methods for literature search and data extraction, a meta-analysis is conducted on 115 articles collated from 30 countries. In addition to the soil properties, findings are also categorized and reported by different aspects like date of publication, journals, countries, employed regression methods, laboratory or in-field conditions, spectra preprocessing methods, samples drying methods, spectroscopy devices, wavelengths, number of sites and samples, and data division into calibration and validation sets. The arithmetic means of the coefficient of determination (R2) over all the reports for different properties ranged from 0.68 to 0.87, with better predictions for carbon and nitrogen content and lower performance for silt and clay. After over 30 years of research on using V-NIR spectroscopy to predict soil properties, this systematic review reveals solid evidence from a literature search that this technology can be relied on as a low-cost and fast alternative for standard methods of soil properties prediction with acceptable accuracy.

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Predicting the lignin H/G ratio of Pinus sylvestris L. wood samples by PLS-R models based on near-infrared spectroscopy

Holzforschung ◽

10.1515/hf-2019-0186 ◽

2020 ◽

Vol 74 (7) ◽

pp. 655-662 ◽

Cited By ~ 1

Author(s):

Ana Alves ◽

Rita Simões ◽

José Luís Lousada ◽

José Lima-Brito ◽

José Rodrigues

Keyword(s):

Pinus Sylvestris ◽

Near Infrared ◽

External Validation ◽

Reference Method ◽

Nir Spectroscopy ◽

Coefficient Of Determination ◽

Least Squares Regression ◽

Predicted Values ◽

G Ratio

AbstractSoftwood lignin consists mainly of guaiacyl (G) units and low amounts of hydroxyphenyl (H) units. Even in a small percentage, the ratio of H to G (H/G) and the intraspecific variation are crucial wood lignin properties. Analytical pyrolysis (Py) was already successfully used as a reference method to develop a model based on near-infrared (NIR) spectroscopy for the determination of the H/G ratio on Pinus pinaster (Pnb) wood samples. The predicted values of the Pinus sylvestris (Psyl) samples by this model were well correlated (R = 0.91) with the reference data (Py), but with a bias that increased with increasing H/G ratio. Partial least squares regression (PLS-R) models were developed for the prediction of the H/G ratio, dedicated models for Psyl wood samples and common models based on both species (Pnb and Psyl). All the calibration models showed a high coefficient of determination and low errors. The coefficient of determination of the external validation of the dedicated models ranged from 0.92 to 0.96 and for the common models ranged from 0.83 to 0.93. However, the comparison of the predictive ability of the dedicated and common models using the Psyl external validation set showed almost identical predicted values.

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Prediction of soil properties with machine learning models based on the spectral response of soil samples in the near infrared range

Soil Science Annual ◽

10.2478/ssa-2019-0027 ◽

2019 ◽

Vol 70 (4) ◽

pp. 298-313 ◽

Cited By ~ 1

Author(s):

Stanisław Gruszczyński

Keyword(s):

Soil Properties ◽

Near Infrared ◽

Soil Analysis ◽

Spectral Response ◽

Research Centre ◽

Infrared Range ◽

Soil Maps ◽

Soil Features ◽

The Impact ◽

The Relationship

Abstract One of the basic methods for soil analysis time and cost reduction is using soil sample spectral response in laboratory conditions. The problem with this method lies in determining the relationship between the shape of the soil spectral response and soil physical or chemical properties. The LUCAS soil database collected by the EU’s ESDAC research centre is good material to analyse the relationship between the soil properties and the near infrared (NIR) spectral response. The modelling described in the paper is based on these data. The analysis of the impact of soil properties configuration on absorbance levels in various NIR spectrum ranges was conducted using the stepwise regression models with the properties, properties squared and products of properties being explanatory variables. The analysis of partial correlation of soil properties values with absorbance values and absorbance derivative in the entire spectral range was conducted in order to evaluate the impact of the absorbance transformation (the first derivative of absorbance vector) on the change of significance of relationship with properties values. The Multi Layer Perceptron (MLP) models were used to estimate the absorbance relationship with single soil features. Soil property modelling based on the selection and transformation algorithm of raw values and first and second absorbance derivatives was also conducted along with the suitability evaluation of such models in building digital soil maps. The absorbance is affected by a limited number of tested soil features like pH, texture, content of carbonates, SOC, N, and CEC; P and K contents have, in case of this research, a negligible impact. The NIR methodology can be suitable in conditions of limited soil variation and particularly in development of thematic soil maps.

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Applying the Method of Lagrange Multipliers to Derive an Estimator for Unsampled Soil Properties

Scientific Research Journal ◽

10.24191/srj.v11i1.5416 ◽

2014 ◽

Vol 11 (1) ◽

pp. 15

Author(s):

Set Foong Ng ◽

Pei Eng Ch’ng ◽

Yee Ming Chew ◽

Kok Shien Ng

Keyword(s):

Soil Properties ◽

Lagrange Multipliers ◽

Unbiased Estimator ◽

Soil Property ◽

Minimum Variance ◽

Distance Method ◽

True Value ◽

A Site ◽

Method Of Lagrange Multipliers ◽

Inverse Distance

Soil properties are very crucial for civil engineers to differentiate one type of soil from another and to predict its mechanical behavior. However, it is not practical to measure soil properties at all the locations at a site. In this paper, an estimator is derived to estimate the unknown values for soil properties from locations where soil samples were not collected. The estimator is obtained by combining the concept of the ‘Inverse Distance Method’ into the technique of ‘Kriging’. The method of Lagrange Multipliers is applied in this paper. It is shown that the estimator derived in this paper is an unbiased estimator. The partiality of the estimator with respect to the true value is zero. Hence, the estimated value will be equal to the true value of the soil property. It is also shown that the variance between the estimator and the soil property is minimised. Hence, the distribution of this unbiased estimator with minimum variance spreads the least from the true value. With this characteristic of minimum variance unbiased estimator, a high accuracy estimation of soil property could be obtained.

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Histamine Control in Raw and Processed Tuna: A Rapid Tool Based on NIR Spectroscopy

Foods ◽

10.3390/foods10040885 ◽

2021 ◽

Vol 10 (4) ◽

pp. 885

Author(s):

Sergio Ghidini ◽

Luca Maria Chiesa ◽

Sara Panseri ◽

Maria Olga Varrà ◽

Adriana Ianieri ◽

...

Keyword(s):

Root Mean Square ◽

Near Infrared ◽

Nir Spectroscopy ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Coefficient Of Determination ◽

Mean Square ◽

Tuna Fish ◽

Mean Square Errors

The present study was designed to investigate whether near infrared (NIR) spectroscopy with minimal sample processing could be a suitable technique to rapidly measure histamine levels in raw and processed tuna fish. Calibration models based on orthogonal partial least square regression (OPLSR) were built to predict histamine in the range 10–1000 mg kg−1 using the 1000–2500 nm NIR spectra of artificially-contaminated fish. The two models were then validated using a new set of naturally contaminated samples in which histamine content was determined by conventional high-performance liquid chromatography (HPLC) analysis. As for calibration results, coefficient of determination (r2) > 0.98, root mean square of estimation (RMSEE) ≤ 5 mg kg−1 and root mean square of cross-validation (RMSECV) ≤ 6 mg kg−1 were achieved. Both models were optimal also in the validation stage, showing r2 values > 0.97, root mean square errors of prediction (RMSEP) ≤ 10 mg kg−1 and relative range error (RER) ≥ 25, with better results showed by the model for processed fish. The promising results achieved suggest NIR spectroscopy as an implemental analytical solution in fish industries and markets to effectively determine histamine amounts.

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Theoretical Simulation of Near-Infrared Spectrum of Piperine: Insight into Band Origins and the Features of Regression Models

Applied Spectroscopy ◽

10.1177/00037028211027951 ◽

2021 ◽

pp. 000370282110279

Author(s):

Justyna Grabska ◽

Krzysztof B. Beć ◽

Sophia Mayr ◽

Christian W. Huck

Keyword(s):

Infrared Spectrum ◽

Regression Models ◽

Near Infrared ◽

Computing Time ◽

Black Pepper ◽

Partial Least Square ◽

Least Square ◽

Complex Nature ◽

Chemical Information ◽

Near Infrared Spectrum

We investigated the near-infrared spectrum of piperine using quantum mechanical calculations. We evaluated two efficient approaches, DVPT2//PM6 and DVPT2//ONIOM [PM6:B3LYP/6-311++G(2df, 2pd)] that yielded a simulated spectrum with varying accuracy versus computing time factor. We performed vibrational assignments and unveiled complex nature of the near-infrared spectrum of piperine, resulting from a high level of band convolution. The most meaningful contribution to the near-infrared absorption of piperine results from binary combination bands. With the available detailed near-infrared assignment of piperine, we interpreted the properties of partial least square regression models constructed in our earlier study to describe the piperine content in black pepper samples. Two models were compared with spectral data sets obtained with a benchtop and a miniaturized spectrometer. The two spectrometers implement distinct technology which leads to a profound instrumental difference and discrepancy in the predictive performance when analyzing piperine content. We concluded that the sensitivity of the two instruments to certain types of piperine vibrations is different and that the benchtop spectrometer unveiled higher selectivity. Such difference in obtaining chemical information from a sample can be one of the reasons why the benchtop spectrometer performs better in analyzing the piperine content of black pepper. This evidenced direct correspondence between the features critical for applied near-infrared spectroscopic routine and the underlying vibrational properties of the analyzed constituent in a complex sample.

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