Analysis of Different Interpolation Methods for Soil Moisture Mapping Using Field Measurements and Remotely Sensed Data

Year 2016, Volume: 3 Issue: 3, 11 - 25, 30.12.2016

Mehmet Zeki İmamoğlu Elif Sertel

https://doi.org/10.30897/ijegeo.306477

Cited By: 15

Abstract

In this research, we comparatively analyzed different interpolation methods to create soil moisture maps by
using field measurements and remotely sensed data. Impacts of number and distribution of field measurements
on interpolation procedure was also investigated. Soil moisture measurements of 36 different locations
collected from Büyükçekmece water basin and RADARSAT-1 image of the same region obtained
simultaneously on 2nd of September 2010 to create soil moisture maps of the study region. Locations of 36
field measurement points were selected considering land use/cover, soil type, elevation, spatial distribution and
accessibility to transportation lines. 25 of sample points were used as Control Points (CPs) and used for soil
moisture map creation and 11 of them were reserved and used as Independent Check Points (ICPs) to validate
the accuracy of each approach applied. Two different experiments were conducted with 25 and 15 CPs to
analyze the impact of number and spatial distribution on interpolation. Inverse Distance Weighting (IDW),
Global Polynominal Interpolation (GPI), Local Polynominal Interpolation (LPI), Radial Basis Functions
(RBF), Kriging, Cokriging and regeression methods were applied to different combination of data sets to create
soil moisture maps and obtained results were compared.

Keywords

Geoistatistic, GIS, Interpolation, Radarsat-1

References

• Algan, O., Yalçın, M.N., Özdoğan, M., Yılmaz, Y., Sarı, E., Kırcı-Elmas, E., Yılmaz,İ., Bulkan,Ö., Ongan, D., Gazioğlu, C., Nazik, A., Polat, M.A., and Meriç, E. (2011). Holocene coastal change in the ancient harbor of Yenikapı–İstanbul and its impact on cultural history. Quaternary Research, Vol .76 (1), pp.30-45.
• Alpar, B., Burak, S. and Gazioğlu, C. (1997). Effect of weather system on the regime of sea level variations in İzmir Bay, Turkish Journal of Marine Sciences, 3 (1997), pp. 83–92
• Alvarez-Mozos,J., Casalí, J., González-Audícana, M., Verhoest N. E. C., (2006). Assessment of the Operational Applicability of RADARSAT-1 Data for Surface Soil Moisture Estimation. IEEE Transactions on Geoscience and Remote Sensing 44, 913-924.
• Baghdadi, N. Aubert, M. Cerdan, O., Franchistéguy, L., Viel, C., Martin, E., Zribi, M., Desprats, J.F. (2007). Operational Mapping of Soil Moisture Using Synthetic Aperture Radar Data: Application to the Touch Basin (France), Sensor.
• Baxter, B. J.C. (1992). The Interpolation Theory Of Radial Basis Functions.
• Beven, K.J.; Fisher, J. (1996). Remote sensing and scaling in hydrology. In Scaling up hydrology using remote sensing; Stewart, J.B., Engman, E.T., Fedes, R.A., Kerr, Y., Eds.; Wiley Press: Chichester, UK.
• Brocca, L., Moramarco, T., Melone, F., Wagner, W., Hasenauer, S., Hahn, S., (2012). Assimilation of surface- and root-zone ASCAT soil moisture products intorainfall—runoff modeling. IEEE Trans. Geosci. Remote Sens., 2542–2555.
• Delhomme, J.P.,(1978). Kriging in the Hydrosciences, Advances in Water Resources, 5, 251-266.
• Eldeiry AA, Garcia LA (2010). Comparison of ordinary kriging, regression kriging, and cokriging techniques to estimate soil salinity using LANDSAT images. J.Irrig Drain ASCE 136: 355–364.
• Gazioğlu, C. Burak, S.Z., Alpar, B., Türker, A. and Barut I.F. (2010). Foreseeable impacts of sea level rise on the southern coast of the Marmara Sea (Turkey), Water Policy 12 (6), 932-943
• Gevaerta, A.I. Parinussab, R.M. Renzulloc, L.J. Dijkd, A.I.J.M. V., and Jeu. R.A.M. D. (2016). Spatio-temporal evaluation of resolution enhancement for passivemicrowave soil moisture and vegetation optical depth. International Journal of Applied Earth Observation and Geoinformation 45, 235–244.
• Grady B. Wright (2003). Radial Basis Function Interpolation, Numerical and Analytical Developments. Imamoglu,M.Z., Sertel, E., Kurucu,Y., Örmeci, C., 2011. Mapping Of Different Soil Proporties By Means Of Geostatistical Methods and Analysis With GIS, II. Soil and Water Supply Congress, 2011, Ankara, Turkey.
• Hejmenowska, B. And Mularz, S. (2000). Integratıon of multitemporal Ers Sar And Landsat TM data for soil moisture assesment, IAPRS, Vol, XXXIII, Amsterdam.
• IPCC, (2013). Summary for Policymakers. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report.
• Jaroslaw Kaya Zawadzki, Mateusz K˛edzior, (2016). Soil moisture variability over Odra watershed: Comparison betweenSMOS and GLDAS data. International Journal of Applied Earth Observation and Geoinformation 45 (2016) 110–124
• Kaya., H. and Gazioğlu, C. (2015). Real Estate Development at Landslides, International Journal of Environment and Geoinformatics Vol:2(1), 62-71.
• Laiolo, P., et al., (2015). Impact of different satellite soil moisture products on the predictions of a continuous distributed hydrological model. Int. J. Appl. EarthObserv. Geoinf.
• Laurent L, Boucard P, Soulier B. (2013). Generation of a cokriging metamodel using a multiparametric strategy. Comput Mech 51: 151–169.
• M.J.Smith, M.F.Goodchild, P.A.Longley, (2007), Geospatial Analysis: Comprehensive Guide to Principles, Techniques and Software Tools.
• Matheron G. (1963). Principles of geostatistics. Econ Geol 58:1246–1266.
• Musaoglu, N., Kaya, S., Seker, D. Z. and Goksel, C. (2002). A case study of using Remote Sensing Data and GIS for Land Management, Catalca Region. FLG XXII International Congress USA, Washington D.C. USA, April 19-26, 2002.
• Olea, R.A. (1982). Optimization of the High Plains Aquifer Observation Network, Kansas. Kansas Geological Survey, Grandwater Series, No. 7, Lawrence, -Kansas.
• Panegrossi, G., Ferretti, R., Pulvirenti, L., Pierdicca, N. (2011). Impact of ASAR soilmoisture data on the MM5 precipitation forecast for the Tanaro flood event ofApril 2009. Nat. Hazards Earth Syst. Sci. 11, 3135–3149.
• Qiang Wang, Rogier van der Velde, Zhongbo Su, Jun Wen. (2016). Aquarius L-band scatterometer and radiometer observations over aTibetan Plateau site. International Journal of Applied Earth Observation and Geoinformation 45, 165–177
• Şeker, D.Z., Direk, Ş., Musaoğlu, N. and Gazioğlu, C. (2013). Determination of Effects of Coastal Deformation Caused by Waves and Storms at Black Sea Coast of Turkey utilizing InSAR Technique, AGU Fall Meeting Abstracts, Vol. 1, 1629
• Seneviratne, S.I., Corti, T., Davin, E.L., Hirschi, M., Jaeger, E.B., Lehner, I., Orlowsky,B., Teuling, A.J. (2010). Investigating soil moisture-climate interactions in achanging climate: a review. Earth-Sci. Rev. 99, 125–161.
• Sertel E, Demirel H and Kaya Ş, (2012). Predictive Mapping Air Pollutantas: A Spatial Approach, Proceedings CD of the Fifth International Spatial Data Quality Symposium, ITC, CD Nm.17, Enschede, Netherland.
• Sertel E, Kutoglu SH and Kaya Ş. (2007). Geometric correction accuracy of different satellite sensor images: application of figure condition. Int J Remote Sens 28(20):4685–4692. doi:10.1080/01431160701592452
• Simav, Ö.; Şeker, D.Z. and Gazioǧlu, C. (2013). Coastal inundation due to sea level rise and extreme sea state and its potential impacts: Çukurova delta case. Turk. J. Earth Sci., 22, 671–680.
• Tobler, W. (1970). A Computer Movie Simulating Urban Growth in the Detroit Region. Economic Geography, 46(2): 234-240.
• URL 1: http://pro.arcgis.com/en/pro-app/help/analysis/geostatistical-analyst/how-global-polynomial-interpolation-works.htm.
• Vieira, S.R., Hatfield, J.L., Nielsen, D.R., Biggar, J.W. (1983). Geoistatistical Theory and Application to Variabilitiy of Some Agronomical Properties, Hilgardia, 51, 3, 1-75.
• Western, A.W. and Blöschl, G. (1999). On the spatial scaling of soil moisture, Journal of Hydrology, 217: 203-224.
• Xiaoqing Z, Miao L, Shuying Z. (2013(. Spatial Interpolation of the Chlorophyll-a Concentration in Zhalong Wetland Based on Cokringing. Chin.Agric.Sci.Bull 29: 160–164.