Evaluating the Potentials of PLSR and SVR Models for Soil Properties Prediction Using Field Imaging,Laboratory VNIR Spectroscopy and Their Combination

Pedo-spectroscopy has the potential to provide valuable information about soil physical,chemical,and biological properties.Nowadays,wemay predict soil properties usingVNIRfield imaging spectra(IS)such as Prisma satellite data or laboratory spectra(LS).The primary goal of this study is to investigate machine learning models namely Partial Least Squares Regression(PLSR)and Support Vector Regression(SVR)for the prediction of several soil properties,including clay,sand,silt,organic matter,nitrate NO3-,and calcium carbonate CaCO_(3),using five VNIR spectra dataset combinations(%IS,%LS)as follows:C1(0%IS,100%LS),C2(20%IS,80%LS),C3(50%IS,50%LS),C4(80%IS,20%LS)and C5(100%IS,0%LS).Soil samples were collected at bare soils and at the upper(0–30 cm)layer.The data set has been split into a training dataset 80%of the collected data(n=248)and a validation dataset 20%of the collected data(n=61).The proposed PLSR and SVR models were trained then tested for each dataset combination.According to our results,SVR outperforms PLSR for both:C1(0%IS,100%LS)and C5(100%IS,0%LS).For Soil Organic Matter(SOM)prediction,it achieves(R^(2)=0.79%,RMSE=1.42%)and(R^(2)=0.76%,RMSE=1.3%),respectively.The data fusion has improved the soil property prediction.The highest improvement was obtained for the SOM property(R^(2)=0.80%,RMSE=1.39)when using the SVR model and applying the second Combination C2(20% of IS and 80%LS).

Application of Geochemistry and VNIR Spectroscopy in Mapping Heavy Metal Pollution of Stream Sediments in the Takab Mining Area, NW of Iran

This study considered the possibility of using visible and near infrared(VNIR) spectral absorption feature parameters(SAFPs) in predicting the concentration and mapping the distribution of heavy metals in sediments of the Takab area. In total, 60 sediment samples were collected along main streams draining from the mining districts and tailing sites, in order to measure the concentration of As, Co, V, Cu, Cr, Ni, Hg, Ti, Pb and Zn and the reflectance spectra(350–2500 nm). The quantitative relationship between SAFPs(Depth500 nm, R610/500 nm, R1344/778 nm, Area500 nm, Depth2200 nm, Area2200 nm, Asym2200 nm) and geochemical data were assessed using stepwise multiple linear regression(SMLR) and enter multiple linear regression(EMLR) methods. The results showed a strong negative correlation between Ni and Cr with Area2200 nm, a significant positive correlation between As and Asym2200 nm, Ni and Co with Depth2200 nm, as well as Co, V and total values with Depth500 nm. The EMLR method eventuated in a significant prediction result for Ni, Cr, Co and As concentrations based on spectral parameters, whereas the prediction for Zn, V and total value was relatively weak. The spatial distribution pattern of geochemical data showed that mining activities, along with the natural weathering of base metal occurrences and rock units, has caused high concentrations of heavy metals in sediments of the Sarough River tributaries.

Determination of soil physicochemical attributes in farming sites through visible,near-infrared diffuse reflectance spectroscopy and PLSR modeling

Introduction:An accurate and reliable detection of soil physicochemical attributes(SPAs)is a difficult and complicated issue in soil science.The SPA may be varied spatially and temporally with the complexity of nature.In the past,SPA detection has been obtained through routine soil chemical and physical laboratory analysis.However,these laboratory methods do not fulfill the rapid requirements.Accordingly,diffuse reflectance spectroscopy(DRS)can be used to nondestructively detect and characterize soil attributes with superior solution.In the present article,we report a study done through spectral curves in the visible(350–700 nm)and near-infrared(700–2500 nm)(VNIR)region of 74 soil specimens which were agglomerated by farming sectors of Phulambri Tehsil of the Aurangabad region of Maharashtra,India.The quantitative analysis of VNIR spectrum was done.Results:The spectra of agglomerated farming soils were acquired by the Analytical Spectral Device(ASD)Field spec 4 spectroradiometer.The soil spectra of the VNIR region were preprocessed to get pure spectra which were the input for regression modeling.The partial least squares regression(PLSR)model was computed to construct the calibration models,which were individually validated for the prediction of SPA from the soil spectrum.The computed model was based on a correlation study between reflected spectra and detected SPA.The detected SPAs were soil organic carbon(SOC),nitrogen(N),soil organic matter(SOM),pH values,electrical conductivity(EC),phosphorus(P),potassium(K),iron(Fe),sand,silt,and clay.The accuracy of the PLSR model-validated determinant(R^(2))values were SOC 0.89,N 0.68,SOM 0.93,pH values 0.82,EC 0.89,P 0.98,K 0.82,Fe 0.94,sand 0.98,silt 0.90,and clay 0.69 with root mean square error of prediction(RMSEP)3.51,4.34,2.66,2.12,4.11,1.41,4.22,1.56,1.89,1.97,and 9.91,respectively.According to the experimental results,the VNIR-DRS was better for detection of SPA and produced more accurate predictions for SPA.Conclusions:In conclusion,the methods examined here offered rapid and novel detection of SPA from reflectance spectroscopy.The outcome of the present research will be apt for precision farming and decision-making.