Prediction of soil textural constituents using reflected spectra in eastern Mazandaran Province

Document Type : Original Article

Authors

1 MSc Student, Department of Soil Science, Sari Agricultural Sciences and Natural Resources University, Sari, Iran

2 Associate Professor, Department of Soil Science, Sari Agricultural Sciences and Natural Resources University, Sari, Iran

3 Assistant Professor, Department of Soil Science, Sari Agricultural Sciences and Natural Resources University, Sari, Iran

4 Professor, Department of Soil Science, Sari Agricultural Sciences and Natural Resources University, Sari, Iran

Abstract

Textural components of soil play an essential role in erodibility and should be considered in many projects of conservation and environmental modeling processes. Traditional methods of determining soil texture are usually laborious, expensive and time consuming along with destructive effects on the environment. Meanwhile, spectroscopic technology using the spectral features and signatures from the whole reflected spectra of soil surface promises a competent method to study soil constituents. To investigate this issue, 113 points were selected and sampled randomly from 0-15 cm of soil surface in eastern parts of Mazandaran Province, Iran. Samples were haphazardly divided into 91 for model building and 22 for final verification and accuracy assessment processes. Applying the enhanced PLS-algorithm plus the FLOOCV approach along with spectral transformations and pre-processing, the modeling of each textural components were accomplished. Spectrally, sand and clay fractions were modeled with high accuracy as: R2c= 0.89, RMSEc= 7.42, SEc= 7.46 for sand and R2c= 0.82, RMSEc= 6.88, SEc= 6.92 for the clay content. Whereas, the silt predictive model was slightly weaker than the other constituents. The most effective spectral ranges involved in the modeling process, were also detected and recognized based on beta & spectral weight analyses and Marten’s uncertainty test. Additionally, the most influential spectroscopic ranges included were the visible, NIR and SWIR regions with the specified wavelengths. In general, the efficacy of spectroscopic technology in soil texture studies has been proven by this research. Using the computed spectral models, we are able to study the soil textural components at large scales faster, safer, timelier and also cheaper. That is absolutely true and applicable using the regionalized remotely sensed data but requires further investigation in different geographical regions.

Keywords

References

Bahrami, A., Danesh, M., & Bahrami, M. (2022). Studying sand component of soil texture using the spectroscopic method. Infrared Physics & Technology, 122, 104056.
Bellon-Maurel, V., Fernandez-Ahumada, E., Palagos, B., Roger, J. M., & McBratney, A. (2010). Critical review of chemometric indicators commonly used for assessing the quality of the prediction of soil attributes by NIR spectroscopy. TrAC Trends in Analytical Chemistry, 29 (9), 1073–1081.
Camargo, O. A., Moniz, A. C., Jorge, J. A., & Valadares, J. M. (2009). Methods of Chemical, Mineralogical and Physical Analysis of Soils Used in the Pedology Section (Technical Bulletin No. 106), Instituto Agronômico (IAC), Campinas.
Casa, R., Castaldi, F., Pascucci, S., Palombo, A., & Pignatti, S. (2013). A comparison of sensor resolution and calibration strategies for soil texture estimation from hyperspectral remote sensing. Geoderma, 197, 17–26.
Chang, C. W., & Laird, D. A. (2002). Near-infrared reflectance spectroscopy analysis of soil C and N. Soil Science, 167: 110–116.
Danesh, M., Bahrami, H. A., Darvishzadeh, R., & Noroozi, A. A. (2016). Investigating clay contents using laboratory diffuse reflectance spectroscopy. Iranian Journal of Remote Sensing & GIS, 8(1), 71-94. (In Persian). 
Danesh, M., Bahmanyar, M. A., & Emadi, S. M. (2022). Reflectance study of soil silt using proximal sensing in Northern Iran. Journal of Civil Engineering and Environmental Sciences, 8(1), 048-056.
Danesh, M., & Bahrami, H. A. (2022). Modeling of Soil Sand Particles Using Spectroscopy Technology, Communications in Soil Science and Plant Analysis, 53, 2216-2228.
Emadi, M., Taghizadeh-Mehrjardi, R., Cherati, A., Danesh, M., Mosavi, A., & Scholten, T. (2020). Predicting and mapping of soil organic carbon using machine learning algorithms in Northern Iran. Remote Sensing, 12(14), 2234.
Guo, L., Zhang, H., Shi, T., Chen, Y., Jiang, Q., & Linderman, M. (2019). Prediction of soil organic carbon stock by laboratory spectral data and airborne hyperspectral images. Geoderma, 337, 32-41.
Hong, Y., Chen, S., Liu, Y., Zhang, Y., Yu, L., Chen, Y., & Liu, Y. (2019). Combination of fractional order derivative and memory-based learning algorithm to improve the estimation accuracy of soil organic matter by visible and near-infrared spectroscopy. Catena, 174, 104–116.
Ji, W. J., Li, S., Chen, S. C., Shi, Z., Viscarra Rossel, R. A., & Mouazen, A. M. (2016). Prediction of soil attributes using the Chinese soil spectral library and standardized spectra recorded at field conditions. Soil Tillage & Research, 155, 492–500.
Ji, W. J., Shi, Z., Huang, J. Y., & Li, S. (2014). In situ measurement of some soil properties in paddy soil using visible and near-infrared spectroscopy. PLoS ONE, 9(8), e105708.
Lu, P., Wang, L., Niu, Z., Li, L., & Zhang, W., (2013). Prediction of soil properties using laboratory VIS–NIR spectroscopy and Hyperion imagery, Journal of Geochemical Exploration, 132, 26–33.
Mura, S., Cappai, C., Greppi, G. F., Barzaghi, S., Stellari, A., & Cattaneo, T. M. P. (2019). Vibrational spectroscopy and Aquaphotomics holistic approach to determine chemical compounds related to sustainability in soil profiles. Computers and Electronics in Agriculture, 159, 92–96.
Ostovari, Y., Ghorbani-Dashtaki, S., Bahrami, H. A., Abbasi, M., Dematte, J. A. M., Arthur, E., & Panagos, P. (2018). Towards prediction of soil erodibility, SOM and CaCO3 using laboratory Vis-NIR spectra: A case study in a semi-arid region of Iran. Geoderma, 314, 102-112.
Padarian, J., Minasny, B., & McBratney, A. B. (2019). Using deep learning to predict soil properties from regional spectral data. Geoderma Regional, 16, e00198.
Peng, L., Cheng, H., Wang, L. J., & Zhu, D. (2020). Comparisons the prediction results of soil properties based on fuzzy c-means clustering and expert knowledge from laboratory Vis-NIR spectroscopy data. Canadian Journal of Soil Science, 101(1), 33-44.
Pudełko, A., & Chodak, M. (2020). Estimation of total nitrogen and organic carbon contents in mine soils with NIR reflectance spectroscopy and various chemometric methods. Geoderma, 368, 114306.
Qi, F., Zhang, R., Liu, X., Niu, Y., Zhang, H., Li, H., Li, J., Wang, B., & Zhang, G. (2018). Soil particle size distribution characteristics of different land-use types in the Funiu mountainous region. Soil & Tillage Research, 184, 45-51.
Tumsavas, Z., Tekin, Y., Ulusoy, Y., & Mouazen, A. M. (2018). Prediction and mapping of soil clay and sand contents using visible and near-infrared spectroscopy. Biosystems Engineering. 177, 90-100.
Viscarra Rossel, R. A., & Webster, R. (2012). Predicting soil properties from the Australian soil visible-near infrared spectroscopic database. European Journal of Soil Science, 63, 848–860.
Xia, F., Peng, J., Wang, Q. L., Zhou, L. Q., & Shi, Z. (2015). Prediction of heavy metal content in soil of cultivated land: hyperspectral technology at provincial scale. Journal of Infrared Millim. Waves, 34, 593–605.
Xu, D., Ma, W., Chen, S., Jiang, Q., He, K., & Shi, Z. (2018a). Assessment of important soil properties related to Chinese Soil Taxonomy based on vis–NIR reflectance spectroscopy. Computers and Electronics in Agriculture, 144, 1-8.
Xu, S., Zhao, Y., Wang, M., & Shi, X. (2018b). Comparison of multivariate methods for estimating selected soil properties from intact soil cores of paddy fields by Vis–NIR spectroscopy. Geoderma, 310, 29-43.
Zhao, L., Hong, H., Fang, Q., Algeo, T. J., Wang, C., Li, M., & Yin, K. (2020). Potential of VNIR spectroscopy for prediction of clay mineralogy and magnetic properties, and its paleoclimatic application to two contrasting Quaternary soil deposits. Catena, 184, 104239.
Zeng, R., Rossiter, D. G., Yang, F., Li, D. C., Zhao, Y. G., & Zhang, G. L. (2017). How accurately can soil classes be allocated based on spectrally predicted physico-chemical properties? Geoderma, 303, 78–84.
Journal of Soil Science Society of Iran
Volume 1, Issue 1
December 2022
Pages 23-38

Files

Share

How to cite

Statistics

ارتقاء امنیت وب با وف ایرانی