جستجوی مقالات مرتبط با کلیدواژه « رگرسیون خطی چندگانه گام به گام » در نشریات گروه « آب و خاک »

Over the last decades, soil spectral data as a rapid, low-cost, and non-destructive method has been widely applied to estimate basic soil properties. In this study, the feasibility of using spectrotransfer functions (STFs) and pedotransfer functions (PTFs) was explored to estimate the parameters of fractal and experimental models of Soil Water Retention Curve (SWRC). For this purpose, a number of 100 soil samples were collected and their spectral reflectance over 350-2500 nm region were measured using a handheld spectroradiometer apparatus. Some soil physical properties and parameters obtained from fitting fractal and experimental models of SWRC to the measured data were determined. After spectral preprocessing, stepwise multiple linear regression was applied to derive PTFs and STFs using basic soil properties and soil spectral reflectance as input, respectively. According to the results, the parametric PTFs had high accuracy in estimating the fractal dimension of the soil particle size distribution (Dpsd) (R2 = 0.96), while the derived parametric functions had moderate predictive accuracy in estimating other studied fractal and hydraulic parameters including DSWRC-TW, DSWRC-B, λBC, nvG and bC (R2 = 0.40 – 0.59). The results also showed that the proposed spectral transfer functions (STFs) had moderately accuracy in estimating Dpsd (RPD = 1.40) and had poor accuracy in estimating DSWRC-TW, DSWRC-B, λBC, nvG and bC (RPD = 1.13 – 1.37). Overall, the results of this study showed that despite of the relatively lower accuracy of spectral parametric functions compared to pedotransfer functions, the use of soil spectral data due to simultaneous estimation of several parameters, lower cost, less time and field data (especially with development of soil information databases and spectral libraries), can be used as an indirect, rapid and novel method in estimating parameters of fractal and experimental models of SWRC.

The soil water retention curve (SWRC), as an important hydraulic soil property, is used in modeling water flow and solute transport in the unsaturated zone of the soil. Direct measurements of SWRC are difficult, time-consuming and costly. Hence, researchers have proposed indirect methods such as pedotransfer functions to estimate soil water retention curve using readily available soil data. Over the last decades, soil spectral data as a rapid, low cost, and non-destructive method has been widely applied to estimate basic soil properties. Consequently, in this study, the feasibility of using soil spectral information in the visible and near-infrared region, as input variables for transfer functions, and evaluation its performance was investigated compared to basic soil properties in estimating soil water retention curve.

A number of 100 soil samples were collected and their spectral reflectance over 350-2500 nm region were measured using a handheld spectroradiometer apparatus. Some basic soil properties such as particle size distribution, particle density, bulk density, organic carbon content and calcium carbonate equivalent, and soil moisture content at matric potentials of -10, -33, -50, -100, -300, -500, -1000, and -1500 kPa were also determined with pressure plate - membrane apparatus. Spectral reflectance curves of the samples were recorded using RS3 software on a portable computer connected to a spectroradiometer with 5 readings per soil sample. After spectral preprocessing, the correlation coefficient between absorption features of soil in each wavelength with soil moisture content at different matric potentials were investigated. Stepwise multiple linear regression was applied to derive pedo-transfer functions (PTFs) and spectral transfer functions (STFs) that uses basic soil properties and soil spectral reflectance as input, respectively. The accuracy of the proposed functions were assessed by adjusted coefficient of determination (R2adj), normalized root mean square error (NRMSE), mean error (ME), and the ratio of performance to deviation (RPD).

Pedo-transfer functions (PTFs) provided more accurate estimates at the dry-end of the soil moisture curve than the wet-end, due to the high correlation of soil moisture with soil particle size distribution at the dry-end of the soil moisture curve. The results of the statistical parameters showed that the derived PTFs for estimating soil water retention at 10 to 1500 kPa matric suctions have good prediction accuracy. However, STFs also had reasonable but poorer results than the proposed PTFs in estimating the studied characteristics.

Overall, the results of this study revealed that, despite the relatively poorer results of STFs than PTF, due to lower costs, time and field data, soil spectral data can be used as an indirect and novel method for estimating volumetric soil moisture content at different matric potentials.

Soil organic carbon (SOC), as a great constitute of soil organic matter (SOM), has an important role in chemical, physical and biological processes of soil. SOM or SOC is a key parameter of soil quality and a soil fertility indicator. SOM has an essential role in formation of soil aggregate and its stability, water and nutrients adsorption, water holding capacity, infiltration of air and water, hydraulic conductivity, soil water repellency and carbon sequestration. Various studies have shown that the quantity and quality of SOM can be affected by anthropogenic activities such as farming practices and other economic development activities. It has also been reported a high rate of SOM loss on eroded lands. Hence, monitoring temporal and spatial variation of SOM is essential for evaluating long-term soil productivity management. However, conventional soil sampling and chemical measurement of SOC, especially in large geographic scale, is tedious, time consuming and expensive. Therefore, rapid and precise assessment of SOC content can be useful in long-term management of soil. The objective of this study was to investigate the ability of soil visible-near infrared (Vis-NIR) spectroscopy for estimating SOC in Zrebar lake watershed of Marivan, Kurdistan province, Iran.

A total of 100 soil samples were collected from the studied region, with an area about 10718 hectares. The spectral reflectance and physicochemical properties of all soil samples were measured under laboratory controlled conditions. After recording of the spectra, different pre-processing methods were applied and compared. Then, pedo-transfer functions (PTFs) and specto-transfer functions (STFs) were developed to estimate SOC content using stepwise multiple linear regression (SMLR). The accuracy and reliability of the derived PTFs and STFs were evaluated using coefficient of determination (R2), normalized root mean square error (NRMSE), mean error (ME), index of agreement (d), and ratio of performance to deviation (RPD) statistics.

Based on the results, soil organic carbon showed high and significant (significance level of 1%) correlations with spectral reflectance values at wavelengths 858 and 1916 nm. The results indicated that the derived PTFs had the higher accuracy (R2avg=0.83, NRMSEavg = 24.55%) to estimate SOC in comparison with the STFs (R2avg=0.44, NRMSEavg= 44.31%). However, SOC could be also fairly estimated by the derived specto-transfer functions (Ravg2=0.52, RPDavg= 1.44).The results also revealed that the Savitzki–Golay smoothing filter with 1st order derivative was the best spectral pre-processing method to reduce the effect of random noise and improve the calibration models.

Overall, the results indicated that although the performance of STFs was not superior to the corresponding PTFs for estimating SOC, but this approach can be used as a reasonable indirect method in case of unavailability of PTFs.

Cation exchange capacity (CEC) is an important soil physicochemical property that has great effect on fertility and soil quality management. Measurement of CEC is difficult, time-consuming and expensive. The objective of this study was to assess whether inclusion of soil spectral data as a unique set of the predictors and alternative to soil basic properties would improve CEC predictions. Consequently, a total of 120 soil samples were collected from surface soil layer. The CEC and easily-determined soil properties were measured by standard laboratory methods. The spectral reflectance of soils over 350 to 2500 nm range were also determined using a handheld spectroradiometer apparatus. Different pre-processing techniques were evaluated after recording the spectra. Stepwise multiple linear regression (SMLR) was used to estimate some soil properties and CEC. Three scenarios including spectrotransfer functions (STF), pedotransfer functions (PTF) and spectropedotransfer functions (SPTF) were investigated. Results showed that STF had higher accuracy (RPD=1.50; RMSE=2.57 cmolc/kg) than the others in predicting soil CEC. PTF (RPD=1.09; RMSE=3.55 cmolc/kg) and SPTF (RPD=0.95; RMSR=4.06 cmolc/kg) provided poor predictions accuracy. These results suggest the efficacy of the spectral data, which can be used as an indirect, simple, and fast method to predict soil cation exchange capacity.