The Use of Thermal Infrared Wavelengths to Determine the Volcanic Soils
In this study, an application was carried out to determine the Volcanic Soils by using remote sensing. The study area was located on the Golcuk formation in Isparta-Turkey. The thermal bands of Landsat 7 image were used for processing. The implementation of the climate model that was based on the water index was used in ERDAS Imagine software together with pixel based image classification. Soil Moisture Index (SMI) was modeled by using the surface temperature (Ts) which was obtained from thermal bands and vegetation index (NDVI) derived from Landsat 7. Surface moisture values were grouped and classified by using scoring system. Thematic layers were compared together with the field studies. Consequently, different moisture levels for volcanic soils were indicator for determination and separation. Those thermal wavelengths are preferable bands for separation of volcanic soils using moisture and temperature models.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1132541Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 725
 Manchanda, M. L., Kudrat, M., & Tiwari, A. K. (2002). Soil survey and mapping using remote sensing. Tropical ecology, 43(1), 61-74.
 Guerrero, C., Viscarra Rossel, R. A., & Mouazen, A. M. (2010). Diffuse reflectance spectroscopy in soil science and land resource assessment. Special Issue Geoderma, 158, 1−2.
 Rossel, R. V., & Chen, C. (2011). Digitally mapping the information content of visible–near infrared spectra of surficial Australian soils. Remote Sensing of Environment, 115(6), 1443-1455.
 Hoffer, R.M. 1978. Biological and physical considerations in application computer aided analysis techniques to remote sensing. pp 237-286. In: P.H. Swain & S.M. Davis (eds.) Remote Sensing: Quantitative Approach. McGraw-Hill International Book Co.
 Zhang, D., Tang, R., Zhao, W., Tang, B., Wu, H., Shao, K., Li, Z. L., 2014. Surface soil water content estimation from thermal remote sensing based on the temporal variation of land surface temperature. Remote Sens., (6), pp. 3170-3187.
 French, A.N., et al., 2005. Surface energy fluxes with the Advanced Spaceborne Thermal Emission and Reflection radiometer (ASTER) at the Iowa 2002 SMACEX site (USA). Remote Sens. Environ. 99 (1–2),55–65.
 Su, H., McCabe, M.F., Wood, E.F., Su, Z., Prueger, J.H., 2005. Modeling evapotranspiration during SMACEX: comparing two approaches for local- and regional-scale prediction. J. Hydrometeorology 6 (6), 910–922.
 Mulder, V. L., De Bruin, S., Schaepman, M. E., & Mayr, T. R. (2011). The use of remote sensing in soil and terrain mapping—A review. Geoderma, 162(1), 1-19.
 Moran, M.S.; Clarke, T.R.; Inoue, Y.; Vidal, A. Estimating crop water deficit using the relation of between surface air temperature and spectral vegetation index. Remote Sens. Environ. 1994, 49, 246–263.
 Whiting, M.L.; Li, L.; Ustin, S.L. Predicting water content using Gaussian Model on soil spectra. Remote Sens. Environ. 2004, 89, 535–552.
 Petropoulos, G.; Carlson, T.N.; Wooster, M.J.; Islam, S. A review of Ts/VI remote sensing based methods for the retrieval of land surface energy fluxes and soil surface moisture. Prog. Phys. Geog. 2009, 33–250.
 Zeng Y., Feng Z. and Xiang N. (2004). Assessment of soil moisture using Landsat ETM+ temperature/vegetation index in semiarid environment. Geoscience and Remote Sensing Symposium, IGARSS '04. Proceedings. 2004 IEEE International, Volume: 6, 4306 – 4309. doi: 10.1109/IGARSS.2004.1370089.
 Parida B. R., Collado W. B., Borah R., Hazarika M. K., and Samarakoon L. (2008). Detecting Drought-Prone Areas of Rice Agriculture Using a MODIS-Derived Soil Moisture Index. GIScience & Remote Sensing, 45, No. 1, p. 109 – 129. doi: 10.2747/1548-1603.45.1.109.
 Wang L. and Qu J. (2009). Satellite remote sensing applications for surface soil moisture monitoring: A review. Front. Earth Sci. China 3: 23.
 Potić I, Bugarski M, Matić-Varenica J., 2017. Soil Moisture Determination Using Remote Sensing Data For The Property Protection And Increase Of Agriculture Production. 2017 Annual World Bank Conference On Land And Poverty, Washington DC, March 20-24, 2017.
 USGS, 2017. Landsat 7 Science Data Users Handbook. Department of the Interior, U. S. Geological Survey. https://landsat.gsfc.nasa.gov/wp-content/uploads/2016/08/Landsat7_Handbook.pdf.
 Yu, X., Guo, X., & Wu, Z. (2014). Land surface temperature retrieval from Landsat 8 TIRS—Comparison between radiative transfer equation-based method, split window algorithm and single channel method. Remote Sensing, 6(10), 9829-9852.
 Norman, J. M., & Becker, F. (1995). Terminology in thermal infrared remote sensing of natural surfaces. Remote Sensing Reviews, 12(3-4), 159-173).
 Sobrino, J. A., Jiménez-Muñoz, J. C., Sòria, G., Romaguera, M., Guanter, L., Moreno, J., Martínez, P. (2008). Land surface emissivity retrieval from different VNIR and TIR sensors. IEEE Transactions on Geoscience and Remote Sensing, 46(2), 316-327.
 ERDAS, 2002. Basic and advanced user manual.
 ESRI, 2010. User's guide, http://www.esri.com, 2010.
 Dahlgren, R. A., Saigusa, M., & Ugolini, F. C. (2004). The nature, properties and management of volcanic soils. Advances in Agronomy, 82.
 Fidencio, P. H., Poppi, R. J., & de Andrade, J. C. (2002). Determination of organic matter in soils using radial basis function networks and near infrared spectroscopy. Analytica Chimica Acta, 453(1), 125-134.
 Goward, S. and Hope, A., 1989. Evapotranspiration from combined reflected solar and emitted terrestrial radiation: Preliminary FIFE results from AVHRR data. Advances in Space Research, (9), pp. 239‐249.
 Nemani, R., Pierce, L., Running, S. and Goward, S., 1992. Developing satellite‐derived estimates of surface moisture status. Journal of Applied Meteorology, (32), pp.548‐557.
 Goetz, S., 1997. Multi‐sensor analysis of NDVI, surface temperature and biophysical variables at a mixed grassland site. International Journal of Remote Sensing, (18), pp. 71‐94.
 Sandholt, I., Rasmussen, K. and Andersen, J., 2002. A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status. Remote Sensing of Environment., (79), pp. 213‐224.
 Wang, C., Qi, S., Niu, Z. and Wang, J., 2004. Evaluating soil moisture status in China using the temperature‐vegetation dryness index (TVDI). Canadian Journal of Remote Sensing, (30), pp. 671‐679.
 Yue, W., Xu, J., Tan, W. and Xu, L., 2007. The relationship between land surface temperature and NDVI with remote sensing: application to Shanghai Landsat 7 ETM+ data. International Journal of Remote Sensing, (28), pp. 3205‐3226.
 Zhang, F., Gao, Z. and Zuo, L., 2007. Study on relationship of soil moisture and land cover: a case in Lijin County, Shangdong Province. Remote Sensing and Modeling of Ecosystems for Sustainability IV. Ustin, L. (eds). Proceedings of SPIE 6679, 667918, SPIE, Bellingham, WA.