Estimating the measure of the soil’s lime in dust’s centers by using of VINR spectroscopy and satellite images of OLI
Subject Areas : Geospatial systems developmentMousa Ghazi 1 , Hosseinali Bahrami 2 , Ali Darvishi Boloorani 3 , Saham Mirzaei 4
1 - MSc. Student of Soil Science, Tarbiat Modares University
2 - Assoc. Prof. College of Agriculture, Tarbiat Modares University
3 - Assis. Prof. College of Geography, University of Tehran
4 - Ph.D. Student of Remote Sensing and GIS, University of Tehran
Keywords: Soil’s lime, VNIR spectroscopy, Landsat, indicator, PLSR (Partial least square regression),
Abstract :
In the present age, one of the most important challenges is soil erosion and consequently land degradation. One of the reasons of soil erosion in the source areas of dust is the low quality of nourishing the soil at the base of growth and development of vegetation. Lime is one of the main factors of decreasing the quality of nourishing the soil. Soil’s lime measuring by laboratory method is time consuming and expensive, thus developing the non-destructive and fast methods like the satellite and VNIR spectrometry data is necessary. In this study 29 intact soil samples have been collected on the same day of Landsat 8 satellite’s overpass from two sources. The spectroscopy has been done on these samples in three modes: IMS, IDS, and SMD. The surface and mixed samples lime have been measured in the laboratory. The soil index and PLSR methods have been used for processing data. The results obtained from PLSR method for SMD mode were R2=0.30 and RMSe=1.84 and for IDS and IMS modes were R2=0.13, 0.08 and RMSe=0.85, 0.87 respectively. The results of the RI index for SMD, IDS, and IMS were R2=0.56, 0.29, 0.19 and RMSe=1.41, 0.75, 0.80 respectively, that the results for SMD mode were acceptable. The results of image in PLSR method were R2=0.84 and RMSe=0.34. But the results related to using RI, DI, and NDI indices (R2=0.28, 0.08, 0.31 and RMSe=0.75, 0.86, 0.74, respectively), were unacceptable and weaker than PLSR method. Based on these results the lime map has been produced by using PLSR method.
10. مهندسین مشاور رویان. 1388. دستورالعمل تجزیههای آزمایشگاهی نمونههای خاک و آب. معاونت برنامهریزی و نظارت راهبردی رئیس جمهور، مرکز دادهورزی و اطلاع رسانی. چاپ اول، نشریه شماره 467، 255 صفحه.
11. Alavipanah SK, Zehtabian GR. 2001. Remote sensing and GIS tools for land use planning and management. Proceedings of the FIG working week 2001, Seoul, Korea.
12. Ben-Dor E, Banin A. 1995. Near-infrared analysis as a rapid method to simultaneously evaluate several soil properties. Soil Sci. Soc.Am. J, 59: 364-372.
13. Blanco M, Coello J, Iturriaga H, Maspoch S, De La Pezuela C. 1996. Quantitation of the active compound and major excipients in a pharmaceutical formulation by near infrared diffuse reflectance spectroscopy with fibre optical probe. Analytica Chimica Acta, 333: 147-156.
14. Bashour II, Sayegh AH. 2007. Methods of analysis for soils of arid and semi-arid regions. 1st ed. FAO, USA.
15. Chang CW, Laird D, Mausbach MJ, Hurburgh CR. 2001. Near-infrared reflectance spectroscopy–principal components regression analyses of soil properties. Soil Science Society of America Journal, 65(2): 480-490.
16. Cozzolino D, Moron A, 2003. The potential of near-infrared reflectance spectroscopy to analyse soil chemical and physical characteristics. Journal of Agricultural Science, 140: 65–71.
17. Cho MA, Skidmore A, Corsi F, van Wieren SE, Sobhan I. 2007. Estimation of green grass/herb biomass from airborne hyperspectral imagery using spectral indices and partial least squares regression. International Journal of Applied Earth Observation and Geoinformation, 9(4): 375–391.
18. Dunn BW, Beecher HG, Batten GD, Ciavarella S. 2002. The potential of nearinfrared reflectance spectroscopy for soil analysis – a case study from the Riverine Plain of south-eastern Australia. Australian Journal of Experimental Agriculture, 42: 607–614.
19. Geladi P, Kowalski BR. 1986. Partial least-squares regression: a tutorial. Analytica Chimica Acta, 185: 1–17.
20. Gaffey SJ. 1987. Spectral reflectance of carbonate minerals in the visible and near infrared (0.35-2.55 μm): Anhydrous carbonate minerals. Journal of Geophysical Research, 92: 1429-1440.
21. Gomez C, Lagacherie P, Coulouma G. 2008. Continuum removal versus PLSR method for clay and calcium carbonate content estimation from laboratory and airborne hyperspectral measurements. Geoderma, 141-148.
22. Islam K, Singh B, McBratney A.2003. Simultaneous estimation of several soil properties by ultra-violet, visible, and near-infrared reflectance spectroscopy. Soil Research, 41(6): 1101-1114.
23. Janik LJ, Merry RH, Forrester ST, Lanyon DM, Rawson A. 2007. Rapid prediction of soil water retention using mid infrared spectroscopy. Soil Science Society of America Journal, 71: 507–514.
24. Knadel M, Stenberg B, Deng F, Thomsen A, Greve MH. 2013. Comparing predictive abilities of three visible-near infrared spectrophotometers for soil organic carbon and clay determination. Journal of near infrared spectroscopy, 21(2): 67-80.
25. Luce MS, Ziadi N, Gagnon B, Karam A. 2017. Visible near infrared reflectance spectroscopy prediction of soil heavy metal concentrations in paper mill biosolid-and liming by-product –amended agricultural soils. Geoderma, 288: 23-36.
26. Nanni MR, Dematte JAM. 2006. Spectral reflectance methodology in comparison with traditional soil analysis. Soil Science Society of America Journal, 70: 393-407.
27. Rossel RAV, Walvoort DJJ, McBratney AB, Janik LJ, Skjemstad JO.2006. Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties. Geoderma, 131(1): 59-75.
28. Rossel RAV, McGlynn RN, McBratney AB.2006. Determining the composition of mineral-organic mixes using UV–vis–NIR diffuse reflectance spectroscopy. Geoderma, 137(1-2): 70-82.
29. Rossel RAV, McBratney AB. 2008. Diffuse reflectance spectroscopy as a tool for digital soil mapping. In Digital Soil Mapping with Limited Data (pp. 165-172). Springer Netherlands.
30. Savitzky A, Golay MJE. 1964. Smoothing and differentiation of data by simplified least squares procedures. Analytical chemistry, 36(8): 1627-1639.
31. Sudduth KA, Hummel JW, Funk RC.1989. NIR soil organic matter sensor. Paper - can Society of Agricultural Engineers, 23 pp.
32. Stenberg BO, Nordkvist E, Salomonsson L. 1995. Use of near infrared reflectance spectra of soils for objective selection of samples. Soil Sci, 159: 109-114.
33. Stenberg BO, Rossel RAV, Mouazen AM, Wetterlind J. 2010. Visible and nearinfrared spectroscopy in soil science. Advances in Agronomy, 107: 163-215.
34. Summers D, Lewis M, Ostendorf B, Chittleborough D. 2011. Visible near-infrared reflectance spectroscopy as a predictive indicator of soil properties. Ecological Indicators, 11(1): 123-131.
35. Usery EL, Pocknee S, Boydell B.1995. Precision farming data management using Geographic Information System. Photogrammetric Engineering and Remote Sensing, 61(11): 1383-1391.
36. Wold S, Sjostrom M, Eriksson L. 2001. PLS-regression: a basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems, 58: 109–130.
37. Williams P. 2004. Implementation of near-infrared technology. In: Williams, P., Norris, K. (Eds.), Near-infrared Technology in the Agricultural and Food Industries, Vol. American Association of Cereal Chemists Inc., St. Paul, MN.
38. Xie HT, Yang XM, Drury CF, Yang GY, Zhang XD. 2011. Predicting soil organic carbon and total nitrogen using mid- and near-infrared spectra for Brookston clay loam soil in Southwestern Ontario, Canada Can. J. Soil Sci, 91(1): 53-63.
39. Yitagesu FA, van der Meer F, van der Werff H, Zigterman W. 2009. Quantifying engineering parameters of expansive soils from their reflectance spectra. Engineering Geology, 105: 151-160.
40. Yao X, Huang Y, Shang G, Zhou C, Cheng T, Tian Y. 2015. Evaluation of six algorithms to monitor wheat leaf nitrogen concentration. Remote Sens,7(1): 14939–14966.
41. Zhai Y, Thomason JA. 2000. Intelligent algorithms distinguish soil patterns from remote sensing data. (No. 003052), ASAE Paper.
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