Estimating the Texture of Purple Soils Using Vis-NIR Spectroscopy and Optimized Conversion Models — Oak Academic Publishing
Research ArticleOpen AccessGoogle Scholar indexed
Estimating the Texture of Purple Soils Using Vis-NIR Spectroscopy and Optimized Conversion Models
Chongqing Key Laboratory of Surface Process and Environment Remote Sensing in the Three Gorges Reservoir Area, School of Geography and Tourism, Chongqing Normal University, Chongqing, China
,
Chongqing Field Observation and Research Station of Surface Ecological Process in the Three Gorges Reservoir Area, School of Geography and Tourism, Chongqing Normal University, Chongqing, China
,
Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geographical Sciences, Southwest University, Chongqing, China
,
Chongqing Key Laboratory of Surface Process and Environment Remote Sensing in the Three Gorges Reservoir Area, School of Geography and Tourism, Chongqing Normal University, Chongqing, China
,
Chongqing Key Laboratory of Surface Process and Environment Remote Sensing in the Three Gorges Reservoir Area, School of Geography and Tourism, Chongqing Normal University, Chongqing, China
1 Chongqing Key Laboratory of Surface Process and Environment Remote Sensing in the Three Gorges Reservoir Area, School of Geography and Tourism, Chongqing Normal University, Chongqing, China
2 Chongqing Field Observation and Research Station of Surface Ecological Process in the Three Gorges Reservoir Area, School of Geography and Tourism, Chongqing Normal University, Chongqing, China
3 Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geographical Sciences, Southwest University, Chongqing, China
4 Chongqing Key Laboratory of Surface Process and Environment Remote Sensing in the Three Gorges Reservoir Area, School of Geography and Tourism, Chongqing Normal University, Chongqing, China
5 Chongqing Key Laboratory of Surface Process and Environment Remote Sensing in the Three Gorges Reservoir Area, School of Geography and Tourism, Chongqing Normal University, Chongqing, China
Soil texture is an indicator of soil physical structure which delivers many ecological functions of soils such as thermal regime, plant growth, and soil quality. However, traditional methods for soil texture measurement are time-consuming and labor-intensive. This study attempts to explore an indirect method for rapid estimating the texture of three subgroups of purple soils ( i.e. calcareous, neutral, and acidic). 190 topsoil (0 - 10 cm) samples were collected from sloping croplands in Tongnan and Beibei Districts of Chongqing Municipality in China. Vis-NIR spectrum was measured and processed, and stepwise multiple linear regression (SMLR), partial least squares regression (PLSR), and back propagation neural network (BPNN) models were constructed to inform the soil texture. The clay fractions ranged from 4.40% to 27.12% while sand fractions ranged from 0.34% to 36.57%, hereby soil samples encompass three textural classes ( i.e. silt, silt loam, and silty clay loam). For the original spectrum, the texture of calcareous and neutral purple soils was not significantly correlated with spectral reflectance and linear models (SMLR and PLSR) exhibited low prediction accuracy. The correlation coefficients and the goodness-of-fits between soil texture and the transformed spectra of all soil groups increased by continuum-removal (CR), first-order differential ( R' ), and second-order differential ( R" ) transformations. Among them, the R" had the best performance in terms of improving the correlation coefficients and the goodness-of-fits. For the calcareous purple soil, the SMLR exceeds PLSR and BPNN with a higher coefficient of determination ( R 2 ) and the ratio of performance to inter-quartile distance (RPIQ) values and lower root mean square error of validation (RMSEV), but for the neutral and acidic purple soils, the PLSR model has a better prediction accuracy. In summary, the linear methods (SMLR and PLSR) are more reliable in estimating the texture of the three purple soil groups when using Vis-NIR spectroscopy inversion.
KeywordsSoil TextureVis-NIR SpectraStepwise Multiple Linear RegressionPartial Least Squares RegressionBackpropagation Neural Network
Daniels, W.L. (2016) The Nature and Properties of Soils, 15th Edition Ray R. Weil and Nyle C. Brady. Pearson Press, Upper Saddle River NJ, 2017. 1086 p. $164.80. ISBN-10: 0-13-325448-8; ISBN-13: 978-0-13-325448-8. Also Available as eText for $67.99. Soil Science Society of America Journal, 80, 1428. https://doi.org/10.2136/sssaj2016.0005br
Greve, M.H., Kheir, R.B., Greve, M.B. and Bocher, P.K. (2012) Quantifying the Ability of Environmental Parameters to Predict Soil Texture Fractions Using Regression-Tree Model with GIS and LIDAR Data: The Case Study of Denmark. Ecological Indicators, 18, 1-10. https://doi.org/10.1016/j.ecolind.2011.10.006
Brians, A., Debbiel, S. and Sachaj, M. (2009) The Impact of Soil Structure on the Establishment of Winter Wheat (Triticum aestivum). European Journal of Agronomy, 30, 243-257. https://doi.org/10.1016/j.eja.2008.12.002
Shahriari, Delbari, M., Afrasiab, P. and Pahlavan-Rad, M.R. (2019) Predicting Regional Spatial Distribution of Soil Texture in Floodplains Using Remote Sensing Data: A Case of Southeastern Iran. Catena, 182, Article ID: 104149. https://doi.org/10.1016/j.catena.2019.104149
Lakshmi, V., Albertson, J. and Schaake, J. (2001) Land Surface Hydrology, Meteorology and Climate: Observations and Modeling. American Geophysical Union, Washington DC, 77-94. https://doi.org/10.1029/WS003
Zhang, N., Zhang, D.L., Li, L.X. and Qu, Z.Y. (2014) Establishment and Evaluation of Model for Predicting Soil Texture Based on Hyperspectral Data—Case Study of Jiefangzha Irrigation Area in Hetao Irrigation District. Journal of Arid Land Resources and Environment, 28, 67-72. (In Chinese)
X.D., Yao, X.J., Yun, H., Xu, Y.H. and Nan, Y.M. (2022) Rapid Detection of Surface Soil Texture Based on Noise Estimation. China Resources Comprehensive Utilization, 40, 38-40. (In Chinese)
Beuselinck, L., Govers, G., Poesen, J., Degraer, G. and Froyen, L. (1998) Grain-Size Analysis by Laser Diffractometry: Comparison with the Sieve-Pipette Method. Catena, 32, 193-208. https://doi.org/10.1016/S0341-8162(98)00051-4
Douglas, R.K., Nawar, S., Alamar, M.C., Mouazen, A.M. and Coulon, F. (2018) Rapid Prediction of Total Petroleum Hydrocarbons Concentration in Contaminated Soil Using Vis-NIR Spectroscopy and Regression Techniques. Science of the Total Environment, 616-617, 147-155. https://doi.org/10.1016/j.scitotenv.2017.10.323
Mohanty, B., Gupta, A. and Das, B.S. (2016) Estimation of Weathering Indices Using Spectral Reflectance over Visible to Mid-Infrared Regions. Geoderma, 26, 111-119. https://doi.org/10.1016/j.geoderma.2015.11.030
Stenberg, B., Mouazen, A.M., Wetterlind, J. and Rossel, R.A.V. (2010) Visible and Near-Infrared Spectroscopy in Soil Science. Advances in Agronomy, 107, 163-215. https://doi.org/10.1016/S0065-2113(10)07005-7
Buddenbaum, H. and Steffens, M. (2012) The Effects of Spectral Pretreatments on Chemometric Analyses of Soil Profiles Using Laboratory Imaging Spectroscopy. Applied and Environmental Soil Science, 2012, Article ID: 274903. https://doi.org/10.1155/2012/274903
Ba, Y., Liu, J., Han, J. and Zhang, X. (2019) Application of Vis-NIR Spectroscopy for Determining the Content of Organic Matter in Saline-Alkali Soils. Spectrochimica Acta, Part A: Molecular and Biomolecular Spectroscopy, 229, Article ID: 117863. https://doi.org/10.1016/j.saa.2019.117863
Nawar, S., Buddenbaum, H., Hill, J., Kozak, J. and Mouazen A.M. (2016) Estimating the Soil Clay Content and Organic Matter by Means of Different Calibration Methods of Vis-NIR Diffuse Reflectance Spectroscopy. Soil and Tillage Research, 155, 510-522. https://doi.org/10.1016/j.still.2015.07.021
Jia, X., Kuo, B. and Crawford, M.M. (2013) Feature Mining for Hyperspectral Image Classification. Proceedings of the IEEE, 101, 676-697. https://doi.org/10.1109/JPROC.2012.2229082
Bin, J., Fan, W., Zhou, J.H., Li, X. and Liang, Y.Z. (2017) Application of Intelligent Optimization Algorithms to Wavelength Selection of Near-Infrared Spectroscopy. Spectroscopy and Spectral Analysis, 37, 95-102.
Jahan, N. and Gan, T.Y. (2011) Modeling the Vegetation-Climate Relationship in a Boreal Mixed Wood Forest of Alberta Using Normalized Difference and Enhanced Vegetation Indices. International Journal of Remote Sensing, 32, 313-335. https://doi.org/10.1080/01431160903464146
Thompson, J.A., Pena-Yewtukhiw, E.M. and Grove, J.H. (2006) Soil-Landscape Modeling across a Physiographic Region: Topographic Patterns and Model Transportability. Geoderma, 133, 57-70. https://doi.org/10.1016/j.geoderma.2006.03.037
Xu, C., Zeng, W.Z., Huang, J.S., Wu, J.W. and Willem, V.L. (2016) Prediction of Soil Moisture Content and Soil Salt Concentration from Hyperspectral Laboratory and Field Data. Remote Sensing, 8, 42-62. https://doi.org/10.3390/rs8010042
Recena, R., Fernández-Cabanás, V.M. and Delgado, A. (2019) Soil Fertility Assessment by Vis-NIR Spectroscopy: Predicting Soil Functioning Rather than Availability Indices. Geoderma, 37, 368-374. https://doi.org/10.1016/j.geoderma.2018.09.049
Filla, V.A., Coelho, A.P., Ferroni, A.D., Bahia, A.S.R.D.S. and José, M.J. (2021) Estimation of Clay Content by Magnetic Susceptibility in Tropical Soils Using Linear and Nonlinear Models. Geoderma, 403, Article ID: 115371. https://doi.org/10.1016/j.geoderma.2021.115371
Wang, Z., Xin, Y. and Ren, W. (2015) Nonlinear Structural Model Updating Based on Instantaneous Frequencies and Amplitudes of the Decomposed Dynamic Responses. Engineering Structures, 100, 189-200. https://doi.org/10.1016/j.engstruct.2015.06.002
Breiman, L. (2001) Statistical Modeling: The Two Cultures. Statistical Science, 16, 199-215. https://doi.org/10.1214/ss/1009213726
Lu, Y.Y., Liu, F., Zhao, Y.G., Song, X.D. and Zhang, G.L. (2019) An Integrated Method of Selecting Environmental Covariates for Predictive Soil Depth Mapping. Journal of Integrative Agriculture, 18, 301-315. https://doi.org/10.1016/S2095-3119(18)61936-7
Malone, B.P., Mcbratney, A.B., Minasny, B. and Laslett, G.M. (2009) Mapping Continuous Depth Functions of Soil Carbon Storage and Available Water Capacity. Geoderma, 154, 138-152. https://doi.org/10.1016/j.geoderma.2009.10.007
Zhong, S.Q., Zhong, M., Wei, C.F., Zhang, W.H. and Hu, F.N. (2016) Shear Strength Features of Soils Developed from Purple Clay Rock and Containing Less than Two-Millimeter Rock Fragments. Journal of Mountain Science, 13, 1464-1480. https://doi.org/10.1007/s11629-015-3524-8
Qin, W., Zuo, C.Q., Yan, Q.H., Wang, Z.Y., Du, P.F. and Yan, N. (2015) Regularity of Soil Erosion during Single Rainfall on Red Soil Bare Slope Land. Transactions of the Chinese Society of Agricultural Engineering, 31, 124-132. (In Chinese)
Xu, L., Zhang, D., Xiang, Y., Chen, F. and Huang, T. (2020) Runoff and Sediment Yield Characteristics of Slope Farmland in Purple Soil Region of Lower Jinsha River under Different Cultivation Measures. Journal of Mountain Science, 38, 851-860.
Ma, C., M., Shen, G.R., Wang, Z.J. and Wang, Z. (2015) Analysis of Spectral Characteristics for Different Soil Particle Sizes. Chinese Journal of Soil Science, 46, 292-298. (In Chinese)
Metzger, K., Daly, K., Ward, M. and Zhang, C. (2020) Mid-Infrared Spectroscopy as an Alternative to Laboratory Extraction for the Determination of Lime Requirement in Tillage Soils. Geoderma, 364, Article ID: 114171. https://doi.org/10.1016/j.geoderma.2020.114171
Guan, H., Jia, K., Zhang, Z. and Xin, M.A. (2015) Research on Remote Sensing Monitoring Model of Soil Salinization Based on Spectrum Characteristic Analysis. Remote Sensing, 27, 100-104.
Tao, P.F., Wang, J.H., Li, Z.Z., Zhou, P., Yang, J.J. and Gao, F.Q. (2020) Research of Soil Nutrient Content Inversion Model Based on Hyperspectral Data. Geology and resources, 29, 68-75. (In Chinese)
Smith, G.S., Workman, C.I., Protas, H., Su, Y., Savonenko, A., Kuwabara, H., Gould, N.F., Kraut, M., Jin, H.J. and Nandi, A. (2021) Positron Emission Tomography Imaging of Serotonin Degeneration and Beta-Amyloid Deposition in Late-Life Depression Evaluated with Multi-Modal Partial Least Squares. Translational Psychiatry, 11, Article No. 473. https://doi.org/10.1038/s41398-021-01539-9
Yu, D. and Deng, L. (2011) Deep Learning and Its Applications to Signal and Information Processing. IEEE Signal Processing Magazine, 28, 145-154. https://doi.org/10.1109/MSP.2010.939038
Saikia, P., Baruah, R.D., Singh, S.K. and Chaudhuri, P.K. (2020) Artificial Neural Networks in the Domain of Reservoir Characterization: A Review from Shallow to Deep Models. Computers & Geosciences, 135, Article ID: 104357. https://doi.org/10.1016/j.cageo.2019.104357
Gou, Y., Wei, J., Li, J.L., Han, C., Tu, Q.Y. and Liu, C.H. (2020) Estimating Purple-Soil Moisture Content Using Vis-NIR Spectroscopy. Journal of Mountain Science, 17, 2214-2223. https://doi.org/10.1007/s11629-019-5848-2
Kemper, T. and Sommer, S. (2022) Estimate of Heavy Metal Contamination in Soils after a Mining Accident Using Reflectance Spectroscopy. Environmental Science & Technology, 36, 2742-2747. https://doi.org/10.1021/es015747j
Marco, N., Antoine, S., Bas, W., Erick, B., Ronald, V. and Luca, M. (2015) Soil Spectroscopy: An Opportunity to Be Seized. Global Change Biology, 21, 10-11. https://doi.org/10.1111/gcb.12632
Yu, L., Hong, Y.S., Zhou, Y., Zhu, Q., Xu, N., Li, J.Y. and Nie, Y. (2016) Wavelength Variable Selection Methods for Estimation of Soil Organic Matter Content Using Hyperspectral Technique. Transactions of the Chinese Society of Agricultural Engineering, 32, 95-102. (In Chinese)
Shi, T.Z., Cui, L.J., Wang, J.J., Fei, T., Chen, Y.Y. and Wu, G.F. (2013) Comparison of Multivariate Methods for Estimating Soil Total Nitrogen with Visible/Near-Infrared Spectroscopy. Plant Soil, 366, 363-375. https://doi.org/10.1007/s11104-012-1436-8
Wang, H.J., Liu F., Cui, J., Ma, L. and Yunger. J.A. (2019) Fitting Model of Soil Total Nitrogen Content in Different Soil Particle Sizes Using Hyperspectral Analysis. Transactions of the Chinese Society for Agricultural Machinery, 50, 195-204. (In Chinese)
Mouazen, M., Baerdemaeker, J.D. and Ramon, H. (2006) Effect of Wavelength Range on the Measurement Accuracy of Some Selected Soil Properties Using Visual-Near Infrared Spectroscopy. Journal of Near Infrared Spectroscopy, 14, 189-199. https://doi.org/10.1255/jnirs.614
Chang, C.W., Laird, D.A. and Hurburgh, C.R. (2005) Influence of Soil Moisture on Near-Infrared Reflectance Spectroscopic Measurement of Soil Properties. Soil Science, 170, 244-255. https://doi.org/10.1097/00010694-200504000-00003
Ben-Dor, E., Chabrillat, S., Dematte, M.A.J., Taylor, G.R., Hill, J., Whiting, M.L. and Sommer, S. (2009) Using Imaging Spectroscopy to Study Soil Properties. Remote Sensing of Environment, 113, 538-555. https://doi.org/10.1016/j.rse.2008.09.019
Gomez, C.C., Viscarra, R.A. and McBratney, A.B. (2008) Soil Organic Carbon Prediction by Hyperspectral Remote Sensing and Field VIS-NIR Spectroscopy: An Australian Case Study. Geoderma, 146, 403-411. https://doi.org/10.1016/j.geoderma.2008.06.011
Waiser, T.H., Morgan, C.L.S., Brown, D.J., Hallmark, C.T., Waiser, T.H., Morgan, C.L.S. and Hallmark, C.T. (2007) In Situ Characterization of Soil Clay Content with Visible Near-Infrared Diffuse Reflectance Spectroscopy. Soil Science Society of America Journal, 71, 389-396. https://doi.org/10.2136/sssaj2006.0211
Lu, P., Wang, L., Niu, Z., Li, L. and Zhang, W. (2013) Prediction of Soil Properties Using Laboratory VIS-NIR Spectroscopy and Hyperion Imagery. Journal of Geochemical Exploration, 132, 26-33. https://doi.org/10.1016/j.gexplo.2013.04.003
Nawar, S., Buddenbaum, H. and Hill, J. (2015) Digital Mapping of Soil Properties Using Multivariate Statistical Analysis and ASTER Data in an Arid Region. Remote Sensing, 7, 1181-1205. https://doi.org/10.3390/rs70201181
Minasny, B., Mcbratney, A.B., Bellon-Maurel, V., Roger, J.M., Gobrecht, A., Ferrand, L. and Joalland, S. (2011) Removing the Effect of Soil Moisture from NIR Diffuse Reflectance Spectra for the Prediction of Soil Organic Carbon. Geoderma, 167-168, 118-124. https://doi.org/10.1016/j.geoderma.2011.09.008