Improving Soil Texture Digital Mapping Using Landsat 8 Satellite Imageries in Calcareous Soils of Southern Iran

Document Type : Original Research

Authors
1 Department of Soil Science, School of Agriculture, Shiraz University, Islamic Republic of Iran.
2 Assistant Prof. Soil and Water Research Institute, Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran
Abstract
Over the last three decades, there has been a general tendency for changing research methods on soil resource management from conventional and mainly qualitative methods to quantitative ones based on spatial correlation models, which are called Digital Soil Mapping (DSM). The present study was carried out in Shabankareh Plain (15,000 ha) with different physiographic units, in Bushehr Province, southern Iran. The target sites (172 points) were selected for soil sampling at depths of 0-30 cm. Soil texture classes DSM was produced by two methods. The first method was Conventional DSM, in which data of soil particles was obtained from laboratory analysis for each sampling point along with their geographical location. Also, the study area boundary was added to ArcGIS software in UTM format and was analyzed by operating Kriging or IDW estimators. The map produced by this method was a low quality digital map containing extra and scattered texture classes with unrealistically sharp boundaries. The second method used CoKriging of L8 multispectral imagery data (OLI bands) and soil samples analysis was operated. Results showed that using B1 band (0.433-0.453 µm) of Landsat 8 satellite imageries of the study area in April 2020 produced high quality digital maps. In this method, soil textures were the same as the ones in the study area. Salt accumulation and water content of surface soil were possible reasons indicating why satellite imageries in the other periods of year were not suitable for DSM. The highest and the lowest ranges of influence among soil texture parameters were 684 meters and 388 meter for clay and sand particles, respectively.

Keywords


Abdollahi S, Pourghasemi H R, Ghanbarian G A, and Safaeian R. 2019. Prioritization of effective factors in the occurrence of land subsidence and its susceptibility mapping using an SVM model and their different kernel functions. Bulletin of Engineering Geology and the Environment, 78(6), 4017-4034.
Ahmed Z, Iqbal J (2014). Evaluation of Landsat TM5 multispectral data for automated mapping of surface soil texture and organic matter in GIS. European Journal of Remote Sensing, 47(1), 557-573.‏
Alaboz P., Demir S., and Dengiz O. (2020). Determination of spatial distribution of soil moisture constant using different interpolation model case study, Isparta Atabey plain. Journal of Tekirdag Agricultural Faculty. 17(3), 432-444.‏
Arabameri, A., Pradhan B. and Bui D.T. 2020. Spatial modelling of gully erosion in the Ardib River Watershed using three statistical-based techniques. Catena, 190, 104545.‏
Bui, E.N., Searle R.D., Wilson P.R., Philip S.R., Thomas M., Brough D. and Van Gool D. (2020). Soil surveyor knowledge in digital soil mapping and assessment in Australia. Geoderma Regional, e00299.‏
Cambardella, C.A., Moorman T.B., Parkin T.B., Karlen D.L., Novak J.M., Turco R.F. and Konopka A.E. (1994). Field-scale variability of soil properties in central Iowa soils.‏
Design G., (2004). Geostatistics for the environmental science version 7. Gamma Design, USA, 159.‏ ‏
Ersahin S., (2003). Comparing ordinary kriging and cokriging to estimate infiltration rate. Soil Science Society of America Journal, 67(6), 1848-1855.‏
FAO-UNESCO, (1988) Soil map of the world, revised legend. World Soil Resources Report,
Rome, 60 pp
Galbraith J.M., Stolt M.H., Rabenhorst M.C. and Ransom M.D. (2018). Impacts of fundamental changes to Soil Taxonomy. South African Journal of Plant and Soil, 35(4), 263-267.‏
Gee G.W., Bauder J.W. (1986). Particle-size analysis. p. 383–409. A. Klute (ed.) Methods of soil analysis. Part 1. Agron. Monogr. 9. ASA and SSSA, Madison, WI. Particle-size analysis. p. 383–409. In A. Klute (ed.) Methods of soil analysis. Part 1. 2nd ed. Agron. Monogr. 9. ASA and SSSA, Madison, WI.‏
Hong S.Y., Sudduth K.A., Kitchen N.R., Drummond S.T., Palm H.L. and Wiebold W.J. (2002, November). Estimating within-field variations in soil properties from airborne hyperspectral images. In Pecora 15/Land Satellite Information IV/ISPRS Commission I/FIEOS 2002 Conference Proceedings.‏
Khali R.Z., Khalid W. and Akram M. (2016). Estimating of soil texture using Landsat imagery: A case study of Thatta Tehsil, Sindh. In 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (pp. 3110-3113). IEEE.‏
Kidd D., Searle R., Grundy M., McBratney A., Robinson N., O'Brien L. and Jones E. (2020). Operationalising digital soil mapping–Lessons from Australia. Geoderma Regional.‏ https://doi.org/10.1016/j.geodrs.2020.e00335.
Li S. and Chen X. (2014). A new bare-soil index for rapid mapping developing areas using Landsat 8 data. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 40(4), 139.‏
Manchanda M.L., Kudrat M. and Tiwari A.K. (2002). Soil survey and mapping using remote sensing. International Society for Tropical Ecology. 43(1), 61-74.‏ ISSN 0564-3295.
Mondejar J.P. and Tongco A.F. (2019). Estimating topsoil texture fractions by digital soil mapping-a response to the long outdated soil map in the Philippines. Sustainable Environment Research, 29(1), 1-20.‏
Ostovari Y., Moosavi A.A., Mozaffari H. and Pourghasemi H.R. (2021). RUSLE model coupled with RS-GIS for soil erosion evaluation compared with T value in Southwest Iran. Arabian Journal of Geosciences, 14(2), 1-15.‏
Robinson N.J., Dahlhaus P.G., Wong M., MacLeod A., Jones D. and Nicholson C. (2019). Testing the public–private soil data and information sharing model for sustainable soil management outcomes. Soil Use and Management, 35(1), 94-104.‏
Robinson T.P. and Metternicht G. (2006). Testing the performance of spatial interpolation techniques for mapping soil properties. Computers and electronics in agriculture, 50(2), 97-108.‏
Rossiter D. (2005). Digital soil mapping; towards a multiple-use Soil Information System. Análisis Geográficos (Revista del Instituto Geográfico" Augusín Codazzi"), 32(1), 7-15.‏
Sanchez P.,A., Ahamed S., Carré F., Hartemink A.E., Hempel J., Huising J. and Minasny B. (2009). Digital soil map of the world. Science, 325(5941), 680-681.‏
Seyedmohammadi J., Navidi M.N. and Esmaeelnejad L. (2019). Geospatial modeling of surface soil texture of agricultural land using fuzzy logic, geostatistics and GIS techniques. Communications in Soil Science and Plant Analysis, 50(12), 1452-1464.‏
Shahriari M., 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, 104149.‏
Tashayo B., Honarbakhsh A., Akbari M. and Ostovari Y. (2020). Digital mapping of Philip model parameters for prediction of water infiltration at the watershed scale in a semi-arid region of Iran. Geoderma Regional, 22, e00301.‏
Turan İ. D., Dengiz O., and Özkan B. (2019). Spatial assessment and mapping of soil quality index for desertification in the semi-arid terrestrial ecosystem using MCDM in interval type-2 fuzzy environment. Computers and Electronics in Agriculture. 164, 104933.‏
Vrbik J., (2020). Deriving cdf of kolmogorov-smirnov test statistic. Applied Mathematics, 11(3), 227-246.‏
Xing-Yi Z.S., Yue-Yu Z., Xu-Dong M., Kai S. and Herbert J. ( 2007). Spatial variability of nutrient properties in black soil of northeast China. Pedosphere 17 (1):19–29. doi:10.1016/S1002-0160(07)60003-4.