Soil Moisture Estimation in Rangelands Using Remote Sensing (Case Study: Malayer, West of Iran)
الموضوعات :
Hamid Nouri
1
(Malayer University)
Muhammad Faramarzi
2
(University of Mohaghegh Ardebili)
الکلمات المفتاحية: Rangeland, Soil Moisture, SEBAL Algorithm, Land sat 8, Malayer,
ملخص المقالة :
Soil moisture is generally regarded as the limiting factors in rangeland production. Although many studies have been conducted to estimate soil moisture in semiarid areas, there is little information on mountainous rangelands in west of Iran. The present study aims to investigate the soil moisture estimation in rangelands as compared to the other land uses over a mountainous area in central Zagros, Iran using remote sensing. The Surface Energy Balance Algorithm for Land was used to compute actual evapotranspiration (ET) and soil moisture by the means of Landsat8 images of March, April, May, June, July, August and September 2013 for diverse land uses in Malayer, Iran. SEBAL algorithm estimates the ET using net radiation flux, soil heat flux and sensible heat flux. The results showed that there was no significant difference between daily ET computed by SEBAL method and Penn man Monteith. Mean Bias Error (MBE) and Root Mean Square Error (RMSE) for daily and hourly ET were 0.15 and 0.39, respectively. The spatial regression was used to detect the relationship between soil moisture index (SMI) and Temperature dryness vegetation index (TDVI) as dependent variables and daily evapotranspiration (ET24) as independent variable. The results revealed that the correlation between SMI and ET24 was positively significant (0.78 to 0.49) and between TDVI and ET24 was negatively significant (-0.74 to -0.46) during the period of rangeland vegetation growth in this area (March to June). SMI in rangelands had the strongest correlation as compared to the other land uses. Thus, SEBAL model is a robust tool to calculate the soil moisture in rangelands by the means of remote sensing.
Ahmad, M.D., Bastiaanssen, W.G. and Feddes, R.A., 2005. A new technique to estimate net groundwater use across large irrigated areas by combining remote sensing and water balance approaches, Rechna Doab, Pakistan. Hydrogeology Journal, 13(5–6): 653–664.
Ariapour A. and Nassaji Zavareh M., 2011. Daily evaporation using of artificial neural networks in Broujerd, Iran. Journal of Rangeland Science, 1(2):125-132.
Bastiaanssen, W.G.M., 2000. SEBAL-based sensible and latent heat fluxes in the irrigated Gediz Basin, Turkey. Journal of Hydrology, 229: 87–100.
Bastiaanssen, W.G.M., Ahmad, M.D. and Chemin, Y., 2002. Satellite surveillance of evaporative depletion across the Indus. Water Resources Research, 38(12): 1273:1–9.
Bastiaanssen, W.G.M., Menenti, M., Feddes, R.A. and Holtslag, A.A.M., 1998. A remote sensing surface energy balance algorithm for land (SEBAL), part 1: formulation. Journal of Hydrology, 212–213: 198–212.
Barkhordari J. and Semsar Yazdi A. 2015, Assessment of the monthly water balance in an arid region using TM model and GIS (Case Study: Pishkouh Watershed, Iran), Journal of Rangeland Science, 5(2): 83-93.
Dashtaki, S.G., Homaee, M. and Khodaverdiloo, H. 2010. Derivation and validation of pedotransfer functions for estimating soil water retention curve using a variety of soil data. Soil Use and Management, 26(1): 68-74.
Engman, E.T. and Gurney, R.J. 1991. Remote sensing in hydrology. Chapman and Hall, London, UK.
Farah, H.O. 2001. Estimation of regional evaporation using a detailed agro-hydrological model. Journal of Hydrology, 229(1–2): 50–58.
Enthekabi, D., Nakamura, H. and Njoku, E. 1994. Solving the inverse problem for soil moisture and temperature profiles by sequential assimilation of multi frequency remotely sensed observations. IEEET. Geosci. Remote Sensing, 32 (2): 438–448.
Feddes, R.A., De Rooij, G.H., van Dam, J.C., Kabat, P., Droogers, P., 1993a. Estimation of regional effective soil hydraulic parameters by inverse modeling. In: Russo, D., Dagan, G. (Eds.), Water Flow and Solute Transport in Soils. Advanced Series in Agricultural Sciences. Berlin., pp. 211–233.
Feddes, R., Menenti, M., Kabat, P., Bastiaanssen, W., 1993b. Is large-scale inverse modeling of unsaturated flow with areal average evaporation and surface soil moisture as estimated by remote sensing feasible? Journal of Hydrology, 143: 125–152.
Fotheringham A S., Brunsdon C. and Charlton M., 2002. Geographically weighted regression: The analysis of spatially varying relationships. Wiley publication New York, USA.
Gillies, R. R., Carlson, T. N., Cui, J., Kustas, W. P., and Humes, K. S. 1997. A verification of the ‘triangle’ method for obtaining surface soil water content and energy fluxes from remote measurements of the Normalized Difference Vegetation index (NDVI) and surface radiant temperature. International Jour. Remote Sensing, 18:3145–3166.
Gupta, H.V., Bastidas, L.A., Sorooshian, S., Shuttleworth, W.J., Yang, Z.L., 1999. Parameter estimation of a land surface scheme using multi-criteria methods. J. Geophys. Res. 104: 19491–19504.
Hogue, T.S., Bastidas, L., Gupta, H., Sorooshian, S., Mitchell, K., Emmerich, W., 2005. Evaluation and transferability of the Noah land surface model in semiarid environments. Journal of Hydrology, 6: 68–84.
Ines, A.V.M., Mohanty, B.P., 2008. Near-surface soil moisture assimilation for quantifying effective soil hydraulic properties under different hydroclimatic conditions. Vadose Zone Journal, 7(1): 39–52.
Jhorar, R. K., Bastiaanssen, W. G. M., Feddes, R. A. and Van Dam J. C. 2002. Inversely estimating soil hydraulic functions using evapotranspiration fluxes, Journal of Hydrology, 258: 198-213.
Kustas, W. P., Norman, J. M., 1996. Use of remote sensing for evapotranspiration monitoring over land surfaces. Joural of Science Hydrology, 41: 495-516.
Kustas, W.P., Diak, G.R. and Moran, M.S., 2003. Evapotranspiration, remote sensing of. In: Encyclopedia of Water Science, Marcel Dekker, Inc., New York, pp. 267–274.
Liu, Y., Gupta, H.V., Sorooshian, S., Bastidas, L.A., Shuttleworth, W.J., 2005. Constraining land surface and atmospheric parameters of a locally coupled model using observational data. Journal of Hydrometeorology, 6: 156–172.
Legates, D. R., Mahmood, R., Levia, D. F., DeLiberty, T. D., Quiring, S., Houser, C., and Nelson, F. E., 2011 Soil Moisture: A central and unifying theme in physical geography, Prog. Phys. Geog., 35: 65–86.
Majnooni-Heris A, Sadraddini A A, Nazemi A H, Shakiba M R, Neyshaburi M R, Hakki Tuzel I., 2012. Determination of single and dual crop coefficients and ratio of transpiration to evapotranspiration for canola, Annals Biological Research, 3(4): 1885-1894.
Matinfar. H. 2012. Evapotranspiration estimation base upon SEBAL model and fieldwork. Scholars Research Library. 3(5): 2459-2463.
Mutiga, J., Su, Zh. and Woldai, T., 2010. Using satellite remote sensing to assess evapotranspiration: Case study of the upper Ewaso Ng’iro North Basin, Kenya. International Journal of Applied Earth Observation and Geo information 12:100–108.
Ragab, R. 1995. Towards a continuous operational system to estimate the root-zone soil moisture from intermittent remotely sensed surface. Journal of Hydrology, 173: 1–25.
Sandholt , I., Rasmussen K. and Andersen J., 2002. A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status. In: Remote Sensing of Environment, 79 (2-3): 213-224.
Scott, C.A., Bastiaanssen, W.G.M. and Ahmad, M.D., 2003. Mapping root zone soil moisture using remotely sensed optical imagery. Journal Irrigation Drainage Engineering ASCE, 129(5): 326–335.
Song, C., Woodcock, C.E., Seto, K.C., Lenney, M.P. and Macomber, S.A., 2001. Classification and change detection using Landsat TM data: when and how to correct atmospheric effect. Remote Sensing Environment, 75: 230–244.
Tasumi, M., Trezza, R., Allen, R.G. and Wright, J.L., 2003. U.S. Validation tests on the SEBAL model for evapotranspiration via satellite. ICID Workshop on Remote Sensing of ET for Large Regions, Montpellier, France, 17 Sept. 2003: p 54-68.
Walker, J.P., Willgoose, G.R. and Kalma, J.D., 2001a. One-dimensional soil moisture profile retrieval by assimilation of near-surface measurements: a simplified soil moisture model and field application. Journal of Hydrometeorology, 2: 356–373.
Walker, J.P., Willgoose, G.R. and Kalma, J.D., 2001b. One-dimensional soil moisture profile retrieval by assimilation of near-surface observations: a comparison of retrieval algorithms. Adv. Water Resource. 24: 631–650.
