Tuzlu Topraklarda Toprak Organik Karbon Seviyelerinin NIRS (Near Infrared Spectroscopy) Kullanarak Belirlenmesi (original) (raw)
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Prediction of soil organic matter and clay contents by near-infrared spectroscopy - NIRS
Ciência Rural
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...
Arid Land Research and Management, 2011
The quality of lands may be degraded by the accumulation of salts in soils, which is typically measured as soil Electrical Conductivity (ECe). High-salinity soils developed in low elevation spots in the Harran Plain after the initiation of intensive irrigation and crop production on clayey soils under high evaporation. This study evaluated the feasibility of using hyperspectral Visible and Near Infrared Reflectance Spectroscopy (VNIRRS) as a potentially more cost-effective approach for the characterization of soil salinity. 150 locations were taken at 0–15 and 15–30 cm depths from an area of 1000 ha with salinity levels ranging from none to very high. Sieved soils were measured for ECe using saturation paste and also scanned by VNIRRS in both air dried and oven dry states. For spectral preprocessing, raw reflectance spectra were averaged over 10 nm and a continuum removal (CR) method was applied. Calibration models between spectra and ECe were based on Multiple Adaptive Regression Splines (MARS), Partial Least Square Regression (PLSR), and Classification and Regression Trees (CART, for groupings). The VNIRRS data were also combined with topographical parameters from digital elevation models to improve estimations. Results showed that the estimation quality of ECe varied depending on approaches used, with the best results using continuum removed spectra of oven dried samples using MARS after separating samples containing high amounts of gypsum (R2 = 0.86, RPD = 2.70). Topographical variables with VNIRRS data improved estimations up to 12%. CART analysis showed that soils could be categorized as saline and non-saline based on soil reflectance with 65% accuracy.
2008
The potential of near infrared (NIR) reflectance spectroscopy to predict various physical, chemical and biochemical properties in Mediterranean soils from SE Spain was evaluated. Soil samples (n= 393) were obtained by sampling 13 locations during three years (2003–2005 period). These samples had a wide range of soil characteristics due to variations in land use, vegetation cover and specific climatic conditions. Biochemical properties also included microbial biomarkers based on phospholipid fatty acids (PLFA).
Non-Destructive Assessment of Soil Organic Carbon Using Near Infrared Technology
Zenodo (CERN European Organization for Nuclear Research), 2022
Soil organic carbon (C-organic) is a major component of soil quality that influences the composition of organic materials and the properties of soil mixtures. This C-organic has a practical value and importance in agriculture as well. Normally, conventional and timeconsuming procedures were used to determine C-organic. However, this method is costly, time consuming, involves chemical materials, and may result in pollution. As a result, an alternative fast and environmentally friendly method for determining C-organic in soil is required. The near infrared reflectance spectroscopy (NIRS) technique can be considered for use because it is quick, non-destructive, requires little preparation, and produces no pollution. As a result, the primary goal of this research is to use the NIRS technique to predict C-organics and classify soils based on geographical characteristics. Soil samples were collected from four different site locations, and spectra data were collected in the range of 4000-10 000 cm-1. NIR spectra data and partial least square regression (PLSR) were used to create a C-organic prediction model, while principal component analysis was used to create a classification model (PCA). The results demonstrated that the NIRS technique could predict C-organic with a maximum correlation coefficient (r) of 0.96 and a residual predictive deviation (RPD) index of 4.05, indicating excellent prediction model performance. It is possible to conclude that the NIRS technique can be used to predict C-organic and classify soil characteristics in a quick and non-destructive manner.
Evaluating near infrared spectroscopy for field prediction of soil properties
This paper demonstrates the application of near infrared diffuse reflectance spectroscopy (NIR-DRS) measurements as part of digital soil mapping. We also investigate whether calibration functions developed from a spectral library can be used for rapid characterisation of soil properties in the field. Soil samples were collected along 24 toposequences in the Pokolbin irrigation district,~7 km 2 of predominantly agricultural land in the Hunter Valley, NSW, Australia. Soil samples at 2 depths: 0-0.10 and 0.40-0.50 m were collected. The soil samples were scanned using NIR under 3 different conditions: field condition, dried unground, and dried ground. A separate spectral library containing soil laboratory measurements was used to develop functions to predict 3 main soil properties from NIR spectra (total C content, clay content, and sum of exchangeable cations). The absorbance spectra were found to be different for the 3 soil conditions. The field spectra appear to have higher absorbance, followed by dried unground samples and then dried ground samples. Although most spectral signatures or peaks were similar for the 3 soil conditions, field samples appear to have higher absorbance, particularly at 1400 nm and 1900 nm. The convex hull of the first 2 principal components of the soil spectra is an easy tool to evaluate the similarity of spectra from a calibration set to an observation. For field prediction, samples need to be calibrated using field samples. Finally, this study shows that NIR-DRS measurement is a useful part of digital soil mapping.
Journal of Near Infrared Spectroscopy, 2006
Near infrared (NIR) refl ectance spectroscopy has been receiving increased attention for the rapid and inexpensive determination of soil properties and of total carbon (Ct) and nitrogen content (Nt) in particular. However, methodological aspects such as sample grinding and drying or replication have not been addressed extensively. The objectives of the paper were, thus, to assess how NIR predictions of Ct and Nt were affected by sample grinding (2 mm sieving vs. 0.2 mm grinding), drying (air-drying vs oven-drying at 40°C during 24 h) and replication (use of one to six sub-samples to determine average spectra). This was performed on a range of tropical soils that differed widely in mineralogy (low and high activity clay soils, allophanic soils) and texture (sandy to clayey). The accuracy of the NIR predictions of Ct and Nt was higher with oven-dried compared to air-dried samples and, more markedly, with 0.2 mm ground compared to 2 mm sieved samples. Replication had a positive effect on NIR predictions when 2 mm sieved samples were used, especially for air-dried samples, but this effect was not clear with 0.2 mm ground samples. Thus, the most accurate predictions of Ct and Nt were obtained with oven-dried fi nely ground samples, with limited response to sample replication. Accurate predictions were, however, also obtained with four replicates on oven-dried 2 mm sieved samples. Acceptable and less tedious results could, thus, be achieved when replacing fi ne grinding by replication. Even with this procedure, the r² between predicted (NIR) and measured (reference) values was 0.9 and the ratio of standard error of prediction to mean (CV%) was 20% which can be considered satisfactory for the heterogeneous sample set under study.
Journal of Zhejiang University SCIENCE, 2005
Near infrared reflectance (NIR) spectroscopy is as a rapid, convenient and simple nondestructive technique useful for quantifying several soil properties. This method was used to estimate nitrogen (N) and organic matter (OM) content in a soil of Zhejiang Province, Hangzhou County. A total of 125 soil samples were taken from the field. Ninety-five samples spectra were used during the calibration and cross validation stage. Thirty samples spectra were used to predict N and OM concentration. NIR spectra of these samples were correlated using partial least square regression. The regression coefficients between measured and predicted values of N and OM was 0.92 and 0.93, and SEP (standard error of prediction) were 3.28 and 0.06, respectively, which showed that NIR method had potential to accurately predict these constituents in this soil. The results showed that NIR spectroscopy could be a good tool for precision farming application.
Canadian Journal of Soil Science, 2009
limitations of, near infrared reflectance spectroscopy applications in soil analysis: A review. Can. J. Soil Sci. 89: 531Á541. Near infrared reflectance spectroscopy (NIRS) is a cost-and time-effective and environmentally friendly technique that could be an alternative to conventional soil analysis methods. In this review, we focussed on factors that hamper the potential application of NIRS in soil analysis. The reported studies differed in many aspects, including sample preparation, reference methods, spectrum acquisition and pre-treatments, and regression methods. The most significant opportunities provided by NIRS in soil analysis include its potential use in situ, the determination of various biological, chemical, and physical properties using a single spectrum per sample, and an estimated reduction of analytical cost of at least 50%. Contradictory results among studies on NIRS utilisation in soil analysis are partly related to variations in sample preparation and reference methods. The following calibration statistics appear to be most appropriate for comparing NIRS performance across soil attributes: (i) coefficient of determination (r 2 ), (ii) ratio of performance deviation (RPD), (iii) coefficient of regression (b), and (iv) ratio of the standard error of prediction (SEP) to the standard error of the reference method (SER), i.e., the ratio of standard errors (RSE). Further investigations on issues such as (i) RSE guidelines, (ii) correlation between NIRS spectrophotometers, (iii) correlation of different reference methods for a given attribute to soil spectra, (iv) identification of key factors affecting the accuracy of NIRS predictions, and (v) efficient use of spectral libraries are required to enhance the acceptability of NIRS as a soil analysis technique and to make it more user-friendly. Standardized guidelines are proposed for the assessment of the accuracy of NIRS predictions of soil attributes.
2018
Soil organic carbon (C-organic) is one of main of soil quality which affects the assortment of organic materials and mixtures properties of soils. This C-organic also have a practical value and importance in agriculture. To determine C-organic, normally, conventional and laborious procedures were employed. Yet, this method is expensive, time consuming, involve chemical materials and may cause pollution. Thus, alternative fast and environmental friendly method is required to determine C-organic in soil. The near infrared reflectance spectroscopy (NIRS) technique can be considered to be applied, since this method is fast, nondestructive, simple preparation and pollution free. Therefore, the main objective of this present study is apply NIRS technique in predicting C-organics and classifying soils based on geographical characteristics. Soil samples from 4 different site locations were taken spectra data of these samples were acquired in wavenumbers range of 4000-10 000 cm -1 . C-organi...
Optics and Photonics Journal
The spectroscopy technique has many advantages over conventional analytical methods since it is fast and easy to implement and with no use of chemical extractants. The objective of this study is to quantify soil total Carbon (C), available Phosphorus (P) and exchangeable potassium (K) using VIS-NIR reflectance spectroscopy. A total of 877 soils samples were collected in various agricultural fields in Mali. Multivariate analysis was applied to the recorded soils spectra to estimate the soil chemical properties. Results reveal the over performance of the Principal Component Regression (PCR) compared to the Partial Least Square Regression (PLSR). For coefficient of determination (R2), PLSR accounts for 0.29, 0.42 and 0.57; while the PCR gave 0.17, 0.34 and 0.50, respectively for C, P and K. Nevertheless, this study demonstrates the potential of the VIS-NIR reflectance spectroscopy in analyzing the soils chemical properties.