Mapping the Variability of Soil Texture-Based on  Vis-NIR Proximal Sensing

Sari Virgawati; Muhjidin Mawardi; Lilik Sutiarso; Sakae Shibusawa; Hendrik Segah; Masakazu Kodaira

doi:10.30871/jagi.v2i1.798

Sari Virgawati Dept. of Agrotechnology, University of Pembangunan Nasional “Veteran” Yogyakarta, Indonesia
Muhjidin Mawardi Dept. of Agricultural and Biosystem Engineering, University of Gadjah Mada, Indonesia
Lilik Sutiarso Dept. of Agricultural and Biosystem Engineering, University of Gadjah Mada, Indonesia
Sakae Shibusawa Dept. of Environmental and Agric. Engineering, Tokyo University of Agric. and Technology, Japan
Hendrik Segah Dept. of Forestry, Faculty of Agriculture, University of Palangka Raya, Indonesia
Masakazu Kodaira Dept. of Environmental and Agric. Engineering, Tokyo University of Agric. and Technology, Japan

DOI: https://doi.org/10.30871/jagi.v2i1.798

Keywords: Vis-NIR, spectroscopy, soil texture, PLSR, IDW

Abstract

Soil texture is one of the soil properties influencing most physical, chemical, and biological soil processes. Information on soil texture is important to support the agronomic decisions for farm management. The problem is how to provide reliable, fast and inexpensive information of soil texture in numerous soil samples and repeated measurement. The objective of this research was to generate the soil texture map based on laboratory Vis-NIR (Visible - Near Infra-Red) spectroscopy and inverse distance weighted (IDW) interpolation method. An ASD Fieldspec 3 with a spectral range from 350 nm to 2500 nm was used to measure the soil reflectance. Pipette method was used to measure the silt, clay and sand fractions. The partial least square regression (PLSR) was performed to establish the prediction model of soil texture. The predicted values were mapped and showing the information of spatial and temporal variability of soil texture.

Keywords: Vis-NIR, spectroscopy, soil texture, PLSR, IDW

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References

Adamchuk, V.I., Allred, B.A., & Viscarra- Rossel, R.A. (2012). Proximal Soil Sensing: Global Perspective. FastTime, 17 (1). Retrieved from http://www.eegs.org.

Baharom, S. N. A., Shibusawa, S., Kodaira, M., & Kanda, R. (2015). Multiple-depth mapping of soil properties using a visible and near infrared real-time soil sensor for a paddy field. Engineering in Agriculture, Environment and Food, 8(1), 13-17.

Andreo, V. (2013). Remote Sensing and Geo-graphic Information Systems in Precision Farming. Retrieved from http://aulavirtual.ig. conae.gov.ar/ moodle/pluginfile.php/513/ mod_ page/content/ 71/seminario_andreo_2013.pdf.

Auernhammer, H., & Demmel, M. (2015). State of the Art and Future Requirements. In Qin Zhang (Ed.): Precision Agriculture Technology for Crop Farming. CRC Press. USA. ISBN: 978-1-4822-5108-1.

BBSDLP. (2016). Datasheet and map files of Nglipar and Dlingo. Soil Map Scale 1:250,000. Balai Besar Sumber Daya Lahan Pertanian, Bogor (via email: 12.13.16).

Broome, S.W. (2016). Soil Physical Properties [Lecture notes]. Retrieved from http://broome. soil.ncsu.edu/ssc012/Lecture/topic8.htm.

Chang, C. W., Laird, D. A., Mausbach, M. J., & Hurburgh, C. R. (2001). Near-infrared reflectance spectroscopy–principal components regression analyses of soil properties. Soil Science Society of America Journal, 65(2), 480-490.

Conforti, M., Froio, R., Matteucci, G., & Buttafuoco, G. (2015). Visible and near infrared spectroscopy for predicting texture in forest soil: an application in southern Italy. iForest-Biogeosciences and Forestry, 8(3), 339.

Curcio, D., Ciraolo, G., D’Asaro, F., & Minacapilli, M. (2013). Prediction of soil texture distributions using VNIR-SWIR reflectance spectroscopy. Procedia Environmental Sciences, 19, 494-503.

Geladi P. & Kowalski, B.R. (1986). Partial Least-squares Regression: A Tutorial. Analytica Chimica Acta, 185, 1-17. https://doi.org/ 10.1016/0003-2670(86)80028-9.

Gholizadeh, A., Amin, M.S.M., Boruvka, L. & Saberioon, M.M. (2014). Models for Paddy Soil Physical Properties Estimation Using Visible and Near Infrared Reflectance Spectroscopy. Journal of Applied Spectroscopy, 81(3), 534-540. DOI: 10.1007/s10812-014-9966-x.

Jury, W.A. & Horton, R. (2004). Soil Physics. 6th ed. New Jersey, USA: John Wiley & Sons, Inc.

Lubis, M. Z., Anurogo, W., Gustin, O., Hanafi, A., Timbang, D., Rizki, F., ... & Taki, H. M. (2017). Interactive modelling of buildings in Google Earth and GIS: A 3D tool for Urban Planning (Tunjuk Island, Indonesia). Journal of Applied Geospatial Information, 1(2), 44-48.

Merkel, A. (2017). Climate Data for Cities Worldwide. Retrieved from http://en.climate-data.org/.
Osman, K.T. (2013). Soils: Principles, Properties and Management. Dordrecht, The Netherland: Springer Science+Business Media. https:// doi.org/ 10.1111/sum.12053.

Prim, Ashok. (2014). Choosing the Right Interpolation Method. GIS Resources: A Knowledge Archive. Retrieved from http:// www.gisresources.com/choosing-the-right-interpolation-method_2/.
Sahu, K.C. (2008). Textbook of Remote Sensing and Geographical Information Systems. India: Atlantic Publisher.

Stenberg, B., Viscarra-Rossel, R.A., Mouazen, A.M., & Wetterlind, J. (2010). Visible and Near Infrared Spectroscopy in Soil Science. Advances in Agronomy, 107, 163-215. http://dx.doi.org/10.1016/S0065-2113(10) 07005-7.

Viscarra-Rossel, R.A., Walvoort, D.J.J., McBratney, A.B., Janik, L.J., & Skjemstad, J.O. (2006). Visible, Near Infrared, Mid Infrared or Combined Diffuse Reflectance Spectroscopy for Simultaneous Assessment of Various Soil Properties. Geoderma, 131 (1-2), 59-75. https://doi.org/10.1016/j.geoderma. 2005. 03.007

Viscarra-Rossel, R.A., Adamchuk, V.I., Sudduth, K.A., McKenzie, N.J., & Lobsey, C. (2011). Proximal Soil Sensing: An Effective Approach for Soil Measurements in Space and Time. Advances in Agronomy, 113, 237-282. doi:10.1016/B978-0-12-386473-4.00010-5

Viscarra-Rossel, R.A., Behrens, T., Ben-Dor, E., Brown, D.J., Demattê, J.A.M., Shepherd, K.D., ... Ji, W. (2016). A Global Spectral Library to Characterize the World's Soil. Earth-Sci. Rev., 155. http://dx.doi.org/10.1016/j.earscirev.2016. 01.012.