Research Article | OPEN ACCESS
Generation of Extensive Moisture Content's Map Using Weighted Moisture Values over Multi-Surface Cover Types
1A.A. Hassaballa, 1A.N. Matori and 2H.Z.M. Shafri
1Department of Civil Engineering, Geoinformatics and Highway Cluster, Universiti Teknologi PETRONAS, UTP, Bandar Sri Iskandar, Malaysia
2Department of Civil Engineering, Universiti Putra Malaysia, UPM, Serdang Malaysia
Research Journal of Applied Sciences, Engineering and Technology 2014 8:1603-1611
Received: May 21, 2013 | Accepted: June 15, 2013 | Published: February 27, 2014
Abstract
The study utilized the Thermal Inertia method (TI) for soil surface moisture (θ) estimation, which is based mainly on two sets of parameters. First, the spatial set, which includes the determination of satellite’s surface Temperature (Ts) and the Vegetation Indices (VI). Second, the set of soil parameters, which includes the determination of soil Field Capacity (FC) and the Permanent Wilting Point (PWP) as upper and lower boundaries of the soil water capacity respectively. In this study, MODIS (Aqua/Terra) images were used for estimating the spatial set (Ts and NDVI). Moreover, 3 meteorological stations at 3 different surface cover areas within the study area were selected (Seberang Perak for agricultural land, UTP for multi-cover land and Sitiawan for urban area), then in-situ measurements of FC, PWP, TS and θ were conducted over each station at the time of MODIS satellite overpass. In order to overcome the atmospheric attenuation, the satellite’ Ts was rectified by the field measured Ts through regression plots. After that, the satellite θ over the 3 different locations were generated and then validated using the field measured θ. A good agreement was found between the actual and estimated θ, so that, the R2 over the agricultural area found to be 88%, over the area with multiple surface cover was 81% and over the urban area was 66%. After assuring the validity and the applicability of the used technique, a generalized θ map was generated using weighting factors from three partitions of the surface cover in order to produce an accurate θ map that considers the spectral disparity of variable surface covers within a single pixel.
Keywords:
MODIS applications, moisture content, perak tengah and manjung, remote sensing, spectral disparity, thermal inertia method,
References
-
Beljaars, A.C.M., P. Viterbo, M.J. Miller and A.K. Betts, 1996. The anomalous rainfall over the United States during July 1993: Sensitivity to land surface parameterization and soil moisture anomalies. Monthly Weather Rev., 124: 362-383.
CrossRef
-
Bindlish, R., 2000. Active and passive microwave remote sensing of soil moisture. Ph.D. Thesis, The Pennsylvania State University.
-
Carlson, T.N., R.R. Gillies and E.M. Perry, 1994. A method to make use of thermal infrared temperature and NDVI measurements to infer surface soil water content and fractional vegetation cover. Remote Sens. Rev., 9(1-2): 161-173.
CrossRef
-
Delworth, T. and S. Manabe, 1988. The influence of potential evaporation on the variabilities of simulated soil wetness and climate. J. Climate, 1: 523-547.
CrossRef
-
Entekhabi, D., H. Nakamura and E.G. Njoku, 1994. Solving the inverse problem for soil moisture and temperature profiles by sequential assimilation of multifrequency remotely sensed observations. IEEE T. Geosci. Remote Sens., 32: 438-448.
CrossRef
-
Hassaballa, A.A. and A.B. Matori, 2011. Study on surface moisture content, vegetation cover and air temperature based on NOAA/AVHRR surface temperatures and field measurements. Proceeding of National Postgraduate Conference (NPC), Kuala Lumpur, pp: 1-5.
CrossRef
-
Hassaballa, A.A., O.F. Althuwaynee and B. Pradhan, 2013. Extraction of soil moisture from RADARSAT-1 and its role in the formation of the 6 December 2008 landslide at Bukit Antarabangsa, Kuala Lumpur. Arab. J. Geosci., 1-10: 1866-7511.
-
Koster, R.D., M.J. Suarez, P. Liu, U. Jambor, A. Berg, M. Kistler, R. Reichle, M. Rodell and J. Famiglietti, 2004. Realistic initialization of land surface states: Impacts on subseasonal forecast skill. J. Hydrometeorol., 5: 1049-1063.
CrossRef
-
Lanicci, J.M., T.N. Carlson and T.T. Warner, 1987. Sensitivity of the Great Plains severe-storm environment to soil-moisture distribution. Monthly Weather Rev., 115: 2660-2673.
CrossRef
-
Martínez-Fernández, J. and A. Ceballos, 2003. Temporal stability of soil moisture in a large-field experiment in Spain. Soil Sci. Soc. Am. J., 67: 1647-1656.
CrossRef
-
Mitra, D.S. and T.J. Majumdar, 2004. Thermal inertia mapping over the Brahmaputra basin, India using NOAA-AVHRR data and its possible geological applications. Int. J. Remote Sens., 25: 3245-3260.
CrossRef
-
Pauwels, V., R. Hoeben, N.E.C. Verhoest, F.P. De Troch and P.A. Troch, 2002. Improvement of TOPLATS-based discharge predictions through assimilation of ERS-based remotely sensed soil moisture values. Hydrol. Process., 16: 995-1013.
CrossRef
-
Pegram, G.G.S., 2009. A nested multisite daily rainfall stochastic generation model. J. Hydrol., 371: 142-153.
CrossRef
-
Shukla, J. and Y. Mintz, 1982. Influence of land-surface evapotranspiration on the Earth's climate. Science, 215: 1498-1501.
CrossRef PMid:17788673
-
Sobrino, J.A. and M.H. El Kharraz, 1999. Combining afternoon and morning NOAA satellites for thermal inertia estimation 2: Methodology and application. J. Geophys. Res., 104: 9455-9465.
CrossRef
-
Stisen, S., I. Sandholt, A. Nørgaard, R. Fensholt and K.H. Jensen, 2008. Combining the triangle method with thermal inertia to estimate regional evapotranspiration: Applied to MSG-SEVIRI data in the senegal river basin. Remote Sens. Environ., 112: 1242-1255.
CrossRef
-
Su, Z., P.A. Troch, F.P. De Troch, L. Nochtergale and B. Cosyn, 1995. Preliminary results of soil moisture retrieval from ESAR (EMAC 94) and ERS-1/SAR, Part II: Soil Moisture Retrieval. Proceedings of the 2nd Workshop on Hydrological and Microwave Scattering Modelling for Spatial and Temporal Soil Moisture Mapping from ERS-1 and JERS-1SAR Data and Macroscale Hydrologic Modeling (EV5V-CT94-0446). Institute National de la Recherche Agronomique, Unité de Science du Sol et de Bioclimatologie, France, pp: 7-19.
-
Thapliyal, P.K., P.K. Pal, M.S. Narayanan and J. Srinivasan, 2005. Development of a time series-based methodology for estimation of large-area soil wetness over India using IRS-P4 microwave radiometer data. J. Appl. Meteorol., 44: 127-143.
CrossRef
-
Tramutoli, V., P. Claps, M. Marella, N. Pergola and C. Sileo, 2000. Feasibility of hydrological application of thermal inertia from remote sensing. Proceeding of 2nd Plinius Conference, pp: 16-18.
-
Verstraeten, W.W., F. Veroustraete, C.J. Van Der Sande, I. Grootaers and J. Feyen, 2006. Soil moisture retrieval using thermal inertia, determined with visible and thermal spaceborne data, validated for European forests. Remote Sensing Environ., 101: 299-314.
CrossRef
-
Wagner, W., G. Lemoine and H. Rott, 1999. A method for estimating soil moisture from ERS scatterometer and soil data. Remote Sensing Environ., 70: 191-207.
CrossRef
-
Wan, Z., 1999. MODIS Land-Surface Temperature Algorithm Theoretical Basis Document (LST ATBD). Institute for Computational Earth System Science, Santa Barbara, 75.
PMid:10553941
-
Xue, Y. and A.P. Cracknell, 1995. Advanced thermal inertia modelling. Remote Sensing, 16: 431-446.
CrossRef
-
Zhang, J. and T.J. Crowley, 1989. Historical climate records in China and reconstruction of past climates. J. Climate, 2: 833-849.
CrossRef
Competing interests
The authors have no competing interests.
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This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
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