Estimating purple-soil moisture content using Vis-NIR spectroscopy

Soil moisture is essential for plant growth in terrestrial ecosystems.This study investigated the visible-near infrared(Vis-NIR)spectra of three subgroups of purple soils(calcareous,neutral,and acidic)from western Chongqing,China,containing different water contents.The relationship between soil moisture and spectral reflectivity(R)was analyzed using four spectral transformations,and estimation models were established for estimating the soil moisture content(SMC)of purple soil based on stepwise multiple linear regression(SMLR)and partial least squares regression(PLSR).We found that soil spectra were similar for different moisture contents,with reflectivity decreasing with increasing moisture content and following the order neutral>calcareous>acidic purple soil(at constant moisture content).Three of the four spectral transformations can highlight spectral sensitivity to SMC and significantly improve the correlation between the reflectance spectra and SMC.SMLR and PLSRmethods provide similar prediction accuracy.The PLSR-based model using a first-order reflectivity differential(R?)is more effective for estimating the SMC,and gave coefficient of determination(v2),root mean square errors of validation(RMSEV),and ratio of performance to inter-quartile distance(RPIQ)values of 0.946,1.347,and 6.328,respectively,for the calcareous purple soil,and 0.944,1.818,and 6.569,respectively,for the acidic purple soil.For neutral purple soil,the best prediction was obtained using the SMLR method with R?transformation,yieldingv2,RMSEV and RPIQ values of 0.973,0.888 and 8.791,respectively.In general,PLSR is more suitable than SMLR for estimating the SMC of purple soil.

Capability of Visible-Near Infrared Spectroscopy in Estimating Soils Carbon, Potassium and Phosphorus

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.

Estimation of soil organic matter in the Ogan-Kuqa River Oasis, Northwest China, based on visible and near-infrared spectroscopy and machine learning

Visible and near-infrared(vis-NIR)spectroscopy technique allows for fast and efficient determination of soil organic matter(SOM).However,a prior requirement for the vis-NIR spectroscopy technique to predict SOM is the effective removal of redundant information.Therefore,this study aims to select three wavelength selection strategies for obtaining the spectral response characteristics of SOM.The SOM content and spectral information of 110 soil samples from the Ogan-Kuqa River Oasis were measured under laboratory conditions in July 2017.Pearson correlation analysis was introduced to preselect spectral wavelengths from the preprocessed spectra that passed the 0.01 level significance test.The successive projection algorithm(SPA),competitive adaptive reweighted sampling(CARS),and Boruta algorithm were used to detect the optimal variables from the preselected wavelengths.Finally,partial least squares regression(PLSR)and random forest(RF)models combined with the optimal wavelengths were applied to develop a quantitative estimation model of the SOM content.The results demonstrate that the optimal variables selected were mainly located near the range of spectral absorption features(i.e.,1400.0,1900.0,and 2200.0 nm),and the CARS and Boruta algorithm also selected a few visible wavelengths located in the range of 480.0–510.0 nm.Both models can achieve a more satisfactory prediction of the SOM content,and the RF model had better accuracy than the PLSR model.The SOM content prediction model established by Boruta algorithm combined with the RF model performed best with 23 variables and the model achieved the coefficient of determination(R2)of 0.78 and the residual prediction deviation(RPD)of 2.38.The Boruta algorithm effectively removed redundant information and optimized the optimal wavelengths to improve the prediction accuracy of the estimated SOM content.Therefore,combining vis-NIR spectroscopy with machine learning to estimate SOM content is an important method to improve the accuracy of SOM prediction in arid land.

Hyperspectral Inversion Model of Available Potassium Content in Red Soil of Eucalyptus Plantation in Northern Guangxi

Soil information is the basis of soil management and precise variable fertilization. The traditional method of obtaining soil information through chemical detection of laboratory has high cost and poor timeliness, which is difficult to meet the needs of digital forestry, soil monitoring and real-time management of nutrients. Taking red soil of Eucalyptus plantation in northern Guangxi as the research object, the spectral data of samples with different soil available potassium contents were measured, and the spectral characteristics were analyzed, and the inversion model was established by using PLS method. The results showed that the spectral sensitive bands of available potassium content in red soil of the region mainly concentrated in 400-600, 1 450, 2 200 nm and so on. After the first derivative transformation, the redundant information in the original spectral data can be significantly reduced, and the correlation between spectral indexes and soil available potassium content can be improved. The full-band modeling results of R and FDR were better than those of significant bands. The optimal model was full-band-FDR-PLS, R2=0.862, and RMSE=2.718. The results of this study can be used for the application of near-earth remote sensing in Guangxi, such as soil digital mapping, precise variable fertilization and real-time monitoring of soil available potassium.

The application of proximal visible and near-infrared spectroscopy to estimate soil organic matter on the Triffa Plain of Morocco

Soil organic matter (SOM) is a fundamental soil constituent. The estimation of this parameter in the laboratory using the classical method is complex time-consuming and requires the use of chemical re-agents. The objectives of this study were to assess the accuracy of two laboratory measurement setups of the VIS-NIR spectroscopy in estimating SOM content and determine the important spectral bands in the SOM estimation model. A total of 115 soil samples were collected from the non-root zone (0-20 cm) of soil in the study area of the Triffa Plain and then analysed for SOM in the laboratory by the Walkley-Black method. The reflectance spectra of soil samples were measured by two protocols, Contact Probe (CP) and Pistol Grip (PG)) of the ASD spectroradiometer (350-2500 nm) in the laboratory. Partial least squares regression (PLSR) was used to develop the prediction models. The results of coefficient of determination (R2) and the root mean square error (RMSE) showed that the pistol grip offers reasonable accuracy with an R2=0.93 and RMSE=0.13 compared to the contact probe protocol with an R2=0.85 and RMSE = 0.19. The near-Infrared range were more accurate than those in the visible range for pre-dicting SOM using the both setups (CP and PG). The significant wavelengths contributing to the pre-diction of SOM for (PG) setup were at:424, 597, 1432, 1484, 1830,1920, 2200, 2357 and 2430 nm, while were at 433, 587, 1380, 1431, 1929, 2200 and 2345 nm for (CP) setup.

Development of a national VNIR soil-spectral library for soil classification and prediction of organic matter concentrations

Soil visible-near infrared diffuse reflectance spectroscopy(vis-NIR DRS)has become an important area of research in the fields of remote and proximal soil sensing.The technique is considered to be particularly useful for acquiring data for soil digital mapping,precision agriculture and soil survey.In this study,1581 soil samples were collected from 14 provinces in China,including Tibet,Xinjiang,Heilongjiang,and Hainan.The samples represent 16 soil groups of the Genetic Soil Classification of China.After air-drying and sieving,the diffuse reflectance spectra of the samples were measured under laboratory conditions in the range between 350 and 2500 nm using a portable vis-NIR spectrometer.All the soil spectra were smoothed using the Savitzky-Golay method with first derivatives before performing multivariate data analyses.The spectra were compressed using principal components analysis and the fuzzy k-means method was used to calculate the optimal soil spectral classification.The scores of the principal component analyses were classified into five clusters that describe the mineral and organic composition of the soils.The results on the classification of the spectra are comparable to the results of other similar research.Spectroscopic predictions of soil organic matter concentrations used a combination of the soil spectral classification with multivariate calibration using partial least squares regression(PLSR).This combination significantly improved the predictions of soil organic matter(R2=0.899;RPD=3.158)compared with using PLSR alone(R2=0.697;RPD=1.817).