Capability of Visible-Near Infrared Spectroscopy in Estimating Soils Carbon, Potassium and Phosphorus (original) (raw)
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Evaluation of soil quality for agricultural production using visible–near-infrared spectroscopy
Soil quality (SQ) assessment has numerous applications for agricultural management. Conventional quantification of SQ is based on laboratory analysis and integrative indices that can be costly and time consuming to obtain. A rapid, quantitative method using soil spectra, following the successful process of soil characterization by visible (VIS)-near infrared (NIR) spectroscopy, can provide a robust approach for soil monitoring. Objective: To predict specific soil indicator properties and soil quality indices for the productive function of the soil using VIS-NIR spectroscopy, and to evaluate the suitability of spectral data for assessing and monitoring the impact of arable and grassland management in a temperate maritime climate. Methods: The study used 40 sites in Ireland under both arable (n = 20) and grassland (n = 20) management systems. Specific indicators and soil quality indices (SQIs) identified by Askari and Holden and Askari were used as the reference standard for estimation using VIS-NIR spectra. Partial least-squares regression was used to predict the indicators and SQIs. SQI was predicted from both spectrally derived indicator values and directly from the soil spectra, and accuracy was assessed by comparison with laboratory and field derived measurements. Results: The indicators of SQ could be predicted with excellent (soil organic carbon and carbon to nitrogen ratio in grassland soils; total nitrogen, carbon to nitrogen ratio, extractable magnesium and aggregate size distribution in arable soils), good (bulk density of b 2 mm fraction in grassland soils) and moderate (penetration resistance, soil respiration and bulk density in arable soils) accuracy. The SQIs were predicted directly with excellent accuracy under grassland (RPD = 3.04, R 2 = 0.92, RMSE = 0.03) and arable (RPD = 2.78, R 2 = 0.89, RMSE = 0.04) management. Soil structural quality class, management type and management intensity were differentiable by their characteristic reflectance. Conclusion: The reliability of SQI and key indicators of soil quality, and the ability to differentiate by management practices and soil structural quality confirmed the efficiency of VIS-NIR spectroscopy for monitoring and evaluating SQ as a reliable alternative to conventional laboratory methods. Practice: Spectroscopy has the potential to provide a reliable approach that will allow rapid, low cost, high frequency SQ monitoring for multiple purposes, and can play an important role in sustainable land management. Implications: VIS-NIR spectroscopy was shown to be suitable for quantitative assessment of soil quality, thus paving the way for development of an applied tool that can be used for agricultural management on the context of soil security, soil health, soil protection and soil quality.
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2019
Soil organic carbon (SOC) is a crucial indicator of soil fertility, maintaining soil health and sustaining the productivity of agroecosystem. Rapid, reliable and cost effective assessment of soil properties specially for SOC is important for monitoring soil fertility status along with soil health. Conventional chemical analysis of any soil property is hazardous, tedious and time consuming. So, the visible near infrared (VIS-NIR) reflectance spectroscopy can provide an effective alternative technique for rapid and ecofriendly measurement of soil properties. In view of this, a key soil fertility parameter SOC was examined through diffuse reflectance spectroscopy. Georeferenced surface soil samples (0-15cm) were collected from a rice-wheat field of the study area for both chemical and spectral analysis. A viable statistical technique namely partial least square regression (PLSR) technique were used to correlate the measured properties with soil reflectance spectra and for developing spectral model. The predictive performance of newly developed spectral model was evaluated through different reliable indices like root mean square of error of prediction (RMSEP), coefficient of determination (R 2) and ratio of performance deviation (RPD). The result showed that the R 2 value for SOC is 0.44, RMSEP is 0.07 and the RPD value is 1.57 in the validation dataset. The RPD value indicating that SOC can be reliably predicted using the hyperspectral model or reflectance analysis. So, this hyperspectral modeling technique can be successfully employed for monitoring soil health as well as for sustainable agriculture.
Soil characterization by near-infrared spectroscopy and principal component analysis
REVISTA CIÊNCIA AGRONÔMICA, 2021
This research aimed to use principal component analysis (PCA) as an exploratory method for spectral data of soil absorbance from the Baturité Massif and Central Hinterland (Ceará State, Brazil) to verify the potential of the technique in soil characterization. We analyzed 46 soil samples from different areas (native and cultivated). Each sample was analyzed in two particle sizes: 2 and 0.2 mm. We obtained spectral data by near-infrared spectroscopy (NIR), selecting the 1,360-2,260 nm range (2,376 variables). We evaluated three data pretreatment methods: multiplicative scatter correction (MSC), first derivative, and second derivative of the Savitzky-Golay filter. The absorption bands observed were: 1,414 nm (C-H stretching and deformation combination), 1,450 nm (O-H associated with the carbon chain), 1,780 nm (second overtone of C-H), 1,928 nm (O-H associated with molecular water), and 2,208 nm (C-H stretch and C=O combination). The best pretreatment was verified using only the multiplicative scatter correction (MSC). Two principal components explained 98% of the data variability, being the first principal component (PC1) related to the characteristic band of moisture, with negative values in the 1,928 nm region, while the second principal component (PC2) was related to the total organic matter (OM) originating from the C-H, C=O, and N-H bonds, wavelength region 1,414 nm. The PCA allowed characterizing the samples in terms of moisture and OM contents, with emphasis on soils under irrigated agroforestry system with higher values of moisture and OM, while the soil in degradation process presented lower values for these attributes. The NIR spectroscopy, associated with data processing methods (PCA and MSC), allows identifying changes in soil attributes, such as moisture and OM.
Journal of Arid Environments, 2010
Reflectance spectroscopy can be used to nondestructively characterize materials for a wide range of applications. In this study, visible-near infrared reflectance spectroscopy (VNIR) was evaluated for prediction of diverse soil properties related to four different soil series of the Entisol soil group within a single field in northern Turkey. Soil samples were collected from 512 locations in a 25 Â 25 m sampling grid over a 32 ha (800 Â 400 m) area. Air-dried soil samples were scanned at 1 nm resolution from 350 to 2500 nm, and calibrations between soil physical and chemical properties and reflectance spectra were developed using cross-validation under partial least squares regression (PLSR) and multivariate adaptive regression splines (MARS). Raw reflectance and first derivative reflectance data were used separately and combined for all samples in the data set. Data were additionally divided into two random subsets of 70 and 30% of the full data, which were each used for calibration and validation. Overall, MARS provided better predictions when under cross-validation. However, PLSR and MARS results were comparable in terms of prediction accuracy when using separate data sets for calibration and validation. No improvement was obtained by combining first derivative and raw data. Strongest correlations were obtained with exchangeable Ca and Mg, cation exchange capacity, and organic matter, clay, sand, and CaCO 3 contents. When soil data were classified into groups, VNIR spectroscopy estimated class memberships well, especially for soil texture. In conclusion, VNIR spectroscopy was variably successful in estimating soil properties at the field scale, and showed potential for substituting laboratory analyses or providing inexpensive co-variable data.
Korean Journal of Soil Science and Fertilizer, 2014
This study investigates the prediction of soil chemical properties (organic matter (OM), pH, Ca, Mg, K, Na, total acidity, cation exchange capacity (CEC)) on 688 Korean soil samples using the visible-near infrared reflectance (VIS-NIR) spectroscopy. Reflectance from the visible to near-infrared spectrum (350 to 2500 nm) was acquired using the ASD Field Spec Pro. A total of 688 soil samples from 168 soil profiles were collected from 2009 to 2011. The spectra were resampled to 10 nm spacing and converted to the 1st derivative of absorbance (log (1/R)), which was used for predicting soil chemical properties. Principal components analysis (PCA), partial least squares regression (PLSR) and regression rules model (Cubist) were applied to predict soil chemical properties. The regression rules model (Cubist) showed the best results among these, with lower error on the calibration data. For quantitatively determining OM, total acidity, CEC, a VIS-NIR spectroscopy could be used as a routine method if the estimation quality is more improved.
2020
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 coefficient 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 pr...
Quantifying Soil Chemical Properties Using Near Infrared Reflectance Spectroscopy
Journal of the Arkansas Academy of Science, 2007
Methodologies for determining soil chemical properties have evolved dramatically during the past century. Early geochemical analyses were conducted exclusively through the use of wet chemistry techniques that were relatively reliable but painstaking and subject to errors at various stages of analysis. Near infrared reflectance spectroscopy (NIRS) has emerged as a new approach for rapidly analyzing a variety ofmaterials including soils. In this study soil samples were taken from eight study areas across the Ozark Highlands of Arkansas, and NIRS calibration models were developed to determine the accuracy of using NIRS to analyze soils compared with standard soil chemical analysis protocols. Multivariate regression models were highly effective for analyzing several important elements. C and N models explained 92% and 88% of their variation, respectively, and Ca, Mg, P, and Mn models explained 72-88% ofthe variability in these elements. Models for C:N and pH explained 82% and 86% oftheir variability, respectively. Models for micronutrients Cu and Zn did not fit as well with 22% and 40% of their variability explained, respectively. Our findings suggest that additional NIRS calibration and modeling is promising for rapidly analyzing the chemical composition of soils, and it is desirable to develop model libraries that are calibrated for the soils of a given region.
Chinese Journal of Geochemistry, 2006
The ability of obtaining soil properties estimations from time and cost efficient remotely sensed techniques has been identified as a valuable technique with great demand for larger amounts of good quality soil data to be used in environmental monitoring, modelling and precision agriculture. Visible (Vis) and Near Infrared (NIR) spectroscopy provides a good alternative that may be used to enhance or replace conventional methods of soil analysis. This study site comprised of 118 plots (142 ha) of paddy fields in the Tanjung Karang Rice Irrigation Scheme, Malaysia. The aim of this paper is to evaluate the abilities of Vis (350-700 nm) and NIR (700-2500 nm) regions for prediction of selected soil chemical properties in Malaysian paddy soils. Savitzky-Golay algorithm was implemented as spectral pre-processing and applied Stepwise Multiple Linear Regression (SMLR) to construct calibration models. The soil properties examined in this study were soil pH, electrical conductivity (EC), organic carbon (OC), cation exchange capacity (CEC), total nitrogen (N), available phosphorus (P) and exchangeable potassium (K). All the soil samples tested in this study were shown to have similar reflectance spectra and greater numbers of reflectance peaks in the NIR region especially around λ= 1150, λ= 1650 and 2200 nm). The study also revealed the accuracy of SMLR prediction in each of the Vis and NIR spectral regions. The NIR produced more accurate predictions for most of the measured soil properties; however, higher significant correlation was obtained using the Vis for EC and available P. This work demonstrated that Vis and NIR spectroscopy can be considered as a good tool to assess soil chemical properties in Malaysian paddy fields.
Visible near-infrared reflectance spectroscopy as a predictive indicator of soil properties
2011
Reflectance spectroscopy can be used to nondestructively characterize materials for a wide range of applications. In this study, visible-near infrared reflectance spectroscopy (VNIR) was evaluated for prediction of diverse soil properties related to four different soil series of the Entisol soil group within a single field in northern Turkey. Soil samples were collected from 512 locations in a 25 Â 25 m sampling grid over a 32 ha (800 Â 400 m) area. Air-dried soil samples were scanned at 1 nm resolution from 350 to 2500 nm, and calibrations between soil physical and chemical properties and reflectance spectra were developed using cross-validation under partial least squares regression (PLSR) and multivariate adaptive regression splines (MARS). Raw reflectance and first derivative reflectance data were used separately and combined for all samples in the data set. Data were additionally divided into two random subsets of 70 and 30% of the full data, which were each used for calibration and validation. Overall, MARS provided better predictions when under cross-validation. However, PLSR and MARS results were comparable in terms of prediction accuracy when using separate data sets for calibration and validation. No improvement was obtained by combining first derivative and raw data. Strongest correlations were obtained with exchangeable Ca and Mg, cation exchange capacity, and organic matter, clay, sand, and CaCO 3 contents. When soil data were classified into groups, VNIR spectroscopy estimated class memberships well, especially for soil texture. In conclusion, VNIR spectroscopy was variably successful in estimating soil properties at the field scale, and showed potential for substituting laboratory analyses or providing inexpensive co-variable data.
Rapid Assessment of Soil Quality Indices Using Infrared Reflectance Spectroscopy
Proceedings of the Proceeding of the First International Graduate Conference (IGC) On Innovation, Creativity, Digital, & Technopreneurship for Sustainable Development in Conjunction with The 6th Roundtable for Indonesian Entrepreneurship Educators 2018 Universitas Syiah Kuala October, 3-5, 2018 Band, 2019
In this present study, we investigated the use of near infrared reflectance spectroscopy (NIRS) as a rapid and robust method to assess and evaluate soil quality namely soil carbon organic (SOC) and pH. Diffuse reflectance spectral data were acquired and recorded for 20 g soil samples from four different site locations in Aceh province. Spectra data, in the wavelength range of 1000-2500 nm, were corrected and enhanced using de-trending (DT). Actual SOC and pH parameters were measured using standard laboratory procedures whilst prediction models, used to predict SOC and pH of soil samples, were established using integration principal component analysis and multiple linear regression (PCA+MLR) approach. Prediction performances were evaluated and justified based on statistical indicators: coefficient correlation (r), root mean square error (RMSE) and residual predictive deviation (RPD) index. The results showed that both soil quality indices (SOC and pH) can be predicted simultaneously ...