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Estimating purple-soil moisture content using vis-nir spectroscopy

Journal of Mountain Science 2020年第9期17卷 2214-2223页

作者： GOU Yu WEI Jie LI Jin-lin HAN Chen TU Qing-yan LIU Chun-hong School of Geography and Tourism Science Chongqing Normal UniversityChongqing 401331China Chongqing Key Laboratory of Surface Process and Environment Remote Sensing in the Three Gorges Reservoir Area Chongqing 401331China

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.

关键词： Purple soil Soil moisture vis-nir spectroscopy Stepwise multiple linear regression Partial least squares regression

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

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Journal of Arid Land 2023年第2期15卷 191-204页

作者： ZHOU Qian DING Jianli GE Xiangyu LI Ke ZHANG Zipeng GU Yongsheng College of Geography and Remote Sensing Science Xinjiang UniversityUrumqi 830046China Xinjiang Key Laboratory of Oasis Ecology Xinjiang UniversityUrumqi 830046China Key Laboratory of Smart City and Environment Modelling of Higher Education Institute Xinjiang UniversityUrumqi 830046China

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.

关键词： soil organic matter content vis-nir spectroscopy random forest Boruta algorithm machine learning

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

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International Soil and Water Conservation Research 2020年第2期8卷 195-204页

作者： Ayoub Lazaar Abdul Mounem Mouazen Kamal EL Hammouti Michael Fullen Biswajeet Pradhan Muhammad Sohail Memon Karim Andich Abdelilah Monir Laboratory of Applied Geosciences Department of GeologyFaculty of ScienceMohammed First UniversityOujdaMorocco Division of Mechatronics Biostatistics and Sensors(MeBioS)Faculty of Bioscience EngineeringKasteelpark Arenberg 30B-3001 HeverleeBelgium Faculty of Science and Engineering University of WolverhamptonWolverhamptonWV11LYUnited Kingdom Center for Advanced Modelling and Geospatial Information Systems(CAMGIS) Faculty of Engineering and Information TechnologyUniversity of Technology SydneySydney2007NSWAustralia Faculty of Agricultural Engineering Sindh Agriculture UniversityTandojam70060Pakistan Laboratory of Applied Geomatics and Soil Science National Institute of Agronomic Research(INRA)OujdaMorocco Department of Mathematics EDP and Scientific Computing TeamMoulay Ismail UniversityMéknesMorocco

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.

关键词： Soil organic matter vis-nir spectroscopy Reflectance spectra SOM estimation SOM analysis

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