Developing Point Spectro Transfer Functions in Soil Erodibility Prediction in VIS-NIR-SWIR Rang

آب و خاک

Article 2, Volume 31, Issue 3 - Serial Number 53, July 2018, Pages 797-810   

Document Type: Research Article

DOI: 10.22067/jsw.v31i3.52932

Authors

Yaser Ostovari^* ; shoja ghorbani; Hosseinali Bahrami; Mahdi Naderi; mozhgan abasi

Shahrekord University

Abstract

Introduction: Soil erodibility (K factor) is generally considered as soil sensitivity to erosion and is highly affected by different climatic, physical, hydrological, chemical, mineralogical and biological properties. This factor can be directly determined as the mean rate of soil loss from standard plots divided by erosivity factor. Since measuring the erodibility factor in the field especially watershed scale is time-consuming and costly, this factor is commonly estimated by pedotransfer functions (PTFs) using readily available soil properties. Wischmeier and Smith (1978) developed an equation using multiple linear regressions (MLR) to estimate erodibility factor of the USA using some readily available soil properties. This equation has been used to estimate K based on soil properties in many studies. As using PTFs in large sales is limited due to cost and time of collecting samples, recently soil spectroscopy technique has been widely used to predict certain soil properties using Point SpectroTransfer Functions (PSTFs). PSTFs use the correlation between soil spectra in Vis-NIR (350-2500 nm) and certain soil properties. The objective of this study was to develop PSTFs and PTFs for soil erodibility factor prediction in the Simakan watershed Fars, Iran.
Materials and Methods: The Semikan watershed, which mainly has calcareous soil with more than 40% lime (total carbonates), is located in the central of Fars province, between 30°06'-30°18'N and 53°05'-53°18'E (WGS′ 1984, zone 39°N) with an area of about 350 km2. For this study, 40 standard plots, which are 22.1×1.83 m with a uniform ploughed slope of 9% in the upslope/downslope direction, were installed in the slopes of 8-10% and the deposit of each plot was collected after rainfall. From each plot three samples were sampled and some physicochemical properties including soil texture, organic matter, water aggregate stability, soil permeability, pH, EC were analyzed Spectra of the air-dried and sieved soil samples were recorded in the Vis-NIR-SWIR (350 to 2500 nm) range at 1.4- to 2-nm sampling intervals in a standard and controlled dark laboratory environment using a portable spectroradiometer apparatus (FieldSpec 3, Analytical Spectral Device, ASD Inc.). Some bands which had the highest correlation with K factor were chosen as input parameter for developing PSTFs. A stepwise multiple linear regression method was used for developing PTFs and SPTFs. R2, RMSE and ME were used for comparing PTFs and SPTFs.
Results and Discussion: The K values varied from 0.005 to 0.023 t h MJ−1 mm−1 with an average standard deviation of 0.014 and of 0.003 t h MJ−1 mm−1, respectively. The K estimated by Wischmeier and Smith (1978) equation varied from 0.015 to 0.045 t h MJ−1 mm−1 with an average of 0.030 t h MJ−1 mm−1. There was a significant difference (p

Keywords

Spectral Reflection; Simakan Dam; Radiospectrometer; Rainfall Erosivity; USLE

References

1. Abbasi Y., Ghanbarian-Alavijeh B., Liaghat A.M., and Shorafa M. 2011. Evaluation of pedotransfer functions for estimating soil water retention curve of saline and saline-alkali soils of Iran. Pedosphere, 21(2): 230-237.

2. Alexakis D., Diofantos G., and Hadjimitsis A. 2013. Integrated use of remote sensing, GIS and precipitation data for the assessment of soil erosion rate in the catchment area of “Yialias” in Cyprus. Atmospheric Research, 1-13.

3. Arnoldus J.M.J. 1977. Methodology used to determine the maximum potential average annual soil loss due to sheet and rill erosion in Morocco. Food. Agric. Org., Soils Bull, 34: 39–51.

4. Babaeian E., Homaee M., and Norouzi A.A. 2014. Deriving and validating parametric spectrotransfer functions for estimating soil hydraulic properties in VIS-NIR-SWIR range. Soil and Water resource conservation, 3(3) : 21-36. (in Persian with English abstract)

5. Clark R.N., King T.V.V., Klejwa M., Swayze G.A., and Vergo N. 1990. High spectral resolution reflectance spectros- copy of minerals, Journal of Geophysics Research , 95: 12.

6. Ghorbani-Dashtaki S., Homaee M., and Khodaberdiloo H. 2010. Derivation and validation of pedotransfer functions for estimating soil water retention curve using a variety of Soil data. Soil Use Management, 26: 68–74.

7. Gomez C., Rossel V., and McBratney B. 2008. Soil organic carbon prediction by hyperspectral remote sensing and field vis-NIR spectroscopy: An Australian case study. Geoderma, 146 : 403–411.

8. Hui L., Xiaoling C., Lim C., and Xiaobin C. 2010.Assessment of soil erosion and sediment yield in Liao watershed, Jiangxi Province, China, using USLE, GIS, and RS. Journal of Earth Science, 2(6): 941–953.

9. Irvem A., Topalog˘l.F., and Uygur V. 2007. Estimating spatial distribution of soil loss over Seyhan River Basin in Turkey. Journal of Hydrology , 336: 30– 37.

10. Khayamim F., Wetterlind J., Khademi H., Robertson J., Canod A., and Stenberg B.2015. Using visible and near infrared spectroscopy to estimate carbonates and gypsum in soils in arid and subhumid regions of Isfahan, Iran. Journal of Near Infrared Spectroscopy, 23: 155–165

11. Kouli M., Soupios P., and Vallianatos F .2009. Soil erosion prediction using the revised universal soil loss equation (RUSLE) in a GIS framework, Chania, Northwestern Crete, Greece. Environment Geology, 57:483–497.

12. Ostovari Y., Asgari K., Cornelis W., and Beigi-Harchegani H. 2015. Simple methods for estimating field capacity using Mamdani inference system and regression tree. Archives of Agronomy and Soil Science. http://dx.doi.org/10.1080/03650340.2014.957687.

13. Refahi H. 2005. Water erosion and conservation. Tehran University, 680 pages.

14. Santos F.L., Reis J.L., Martins O.C., Castanheira N.L., and Serralheiro R.P. 2003. Comparative assessment of infiltration, runoff and erosion of sprinkler irrigated soils. Biosystems Engineering, 86:355–364.

15. Schmid T., and Palacios-Orueta A. Chabrillat S. BendorE and Plaza A. 2012. Spectral characteristic of land surface composition to determination soil erosion within semiarid rained cultivated areas. IGARSS, 2012: 7082-7084.

16. StatSoft .2007.STATISTICA(data analysis software system). Version 8. www.statsoft.com.

17. Tejada M., and Gonzalez J.L. 2006. The relationships between erodibility and erosion in a soil tratedwith two organic amendments. Soil and Tillage Research, 91: 186–198.

18. Thine C., Shephered K., Walsh M., Coe R. and Okwach G. 2004. Application of GIS and remote sensing in characterization of soil hydraulic properties for soil physical quality assessment. University of Nairobi, Kenya, pp:1-18.

19. Vaezi A., Bahrami H.A., Sadeghi S.H., and Mahdian M. H. 2010. Spatial variability of soil erodibility factor (K) of the USLE in North West of Iran. Journal of Agricuthre Science Technology, 12: 241-252.

20. Vaezi A.R, Sadeghi S.H.R, Bahrami H.A, and Mahdian M.H. 2008. Modeling the USLE K-factor for calcareous soils in northwestern Iran. Geomorphology, 97 : 414–423.

21. Vendramea P.R.S., March R.L., Brunetc D., and Becquerc T. 2012. The potential of NIR spectroscopy to predict soil texture and mineralogy in Cerrado Latosols. European Journal of Soil Science, 1-11.

22. Visscara-Rossel R., Walvoortb D.J.J., McBratneya A.B., Janikc L.J., and Skjemstadc J.O. 2006. Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties. Geoderma, 131 : 59–75.

23. Walky A. and Black I.A. 1934. An examination of Degtgareff method for determining soil organic matter and a proposed modification of the chromic acid in soil analysis. 1. Experimental. Soil Science Society of America Journal, 79: 459-465.

24. WischmeierW.H., and Smith, D.D .1978. Predicting rainfall erosion losses: a guide to conservation planning. USDA, Agriculture Handbook No. 537, Washington, DC

25. Yu D.S., Shi X. Z., and Weindorf D.C. 2006. Relationship between permeability and erodibility of cultivated Acrisols and Cambisols in subtropical China. Pedosphere 16: 304–311.