A simulation of soil water content based on remote sensing in a semi-arid Mediterranean agricultural landscape

  1. Sáchez, N.
  2. Martínez Fernández, José
  3. Rodríguez Ruiz, Marta
  4. Torres Álvarez, Enrique
  5. Calera Belmonte, Alfonso
Revista:
Spanish journal of agricultural research

ISSN: 1695-971X,  2171-9292

Año de publicación: 2012

Volumen: 10

Número: 2

Páginas: 521-531

Tipo: Artículo

DOI: 10.5424/SJAR/2012102-611-11 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

Otras publicaciones en: Spanish journal of agricultural research

Resumen

This paper shows the application of a water balance based on remote sensing that integrated a Landsat 5 series from 2009 in an area of 1,300 km2 in the Duero Basin (Spain). The objective was to simulate the daily soil water content (SWC), actual evapotranspiration, deep percolation and irrigation rates. The accuracy of the application is tested in a semi-arid Mediterranean agricultural landscape with crops over natural conditions. The results of the simulated SWC were compared against 19 in situ stations of the Soil Moisture Measurement Stations Network (REMEDHUS), in order to check the feasibility and accuracy of the application. The theoretical basis of the application was the FAO56 calculation assisted by remotely sensed imagery. The basal crop coefficient (Kcb), as well as other parameters of the calculation came from the remote reflectance of the images. This approach was implemented in the computerized tool HIDROMORE+, which integrates various spatial databases. The comparison of simulated and observed values (at different depths and different land uses) showed a good global agreement for the area (R2=0.92, RMSE=0.031 m3 m-3, and bias=-0.027 m3 m-3). The land uses better described were rainfed cereals (R2=0.86, RMSE=0.030 m3 m-3, and bias=-0.025 m3 m-3) and vineyards (R2=0.86, RMSE=0.016 m3 m-3, and bias=-0.013 m3 m-3). In general, an underestimation of the soil water content is noticed, more pronounced into the root zone than at surface layer. The final aim was to convert the application into a hydrological tool available for agricultural water management.

Referencias bibliográficas

  • Allen RG, 2000. Using the FAO-56 dual crop coefficient method over an irrigated region as part of an evapotranspiration intercomparison study. J Hydrol 229(1-2): 27-41. http://dx.doi.org/10.1016/S0022-1694(99)00194-8
  • Allen RG, Bastiaanssen WGM, 2005. Editorial: Special issue on remote sensing of crop evapotranspiration for large regions. Irrig Drain Syst 19(3-4): 207-210. http://dx.doi.org/10.1007/s10795-005-5185-1
  • Allen RG, Pereira LS, Raes D, Smith M, 1998. Crop evapotranspiration. FAO, Rome. 300 pp.
  • Bausch WC, Neale CU, 1987. Crop coefficients derived from reflected canopy radiation: a concept. T Am Soc Agric 30(3): 703-709.
  • Bonet L, Ferrer P, Castel JR, Intrigliolo DS, 2010. Soil capacitance sensors and stem dendrometers. Useful tools for irrigation scheduling of commercial orchards? Span J Agric Res 8(S2): S52-S65.
  • Calera A, González-Piqueras J, Meliá J, 2004. Monitoring barley and corn growth from remote sensing data at field scale. Int J Remote Sens 25(1): 97-109. http://dx.doi.org/10.1080/0143116031000115319
  • Campos I, Neale CMU, Calera A, Balbontín C, González-Piqueras J, 2010. Assessing satellite-based basal crop coefficients for irrigated grapes (Vitis vinifera L.). Agr Water Manage 98(1): 45-54. http://dx.doi.org/10.1016/j.agwat.2010.07.011
  • Ceballos A, Scipal K, Wagner W, Martínez-Fernández J, 2005. Validation of ERS scatterometer-derived soil moisture data in the central part of the Duero Basin, Spain. Hydrol Process 19(8): 1549-1566. http://dx.doi.org/10.1002/hyp.5585
  • Chavez PS Jr, 1989. Radiometric calibration of Landsat Thematic Mapper multispectral images. Photogramm Eng Rem S 55(9): 1285-1294.
  • Diekküger B, Söndgerath D, Kersebaum KC, Mcvoy CW, 1995. Validity of agroecosystem models—a comparison of results of different models applied to the same data set. Ecol Modelling 81(1-3): 3-29. http://dx.doi.org/10.1016/0304-3800(94)00157-D
  • Earls J, Dixon B, Candade N, 2006. A comparative study of Landsat 5 TM land use classification methods including unsupervised classification, artificial neural network and support vector machine for use in a simple hydrologic budget model. ASPRS 2006 Ann Conf, Reno, NV, USA, May, 1-5.
  • Eitzinger J, Marinkovic D, Hösch J, 2002. Sensitivity of different evapotranspiration calculation methods in different crop-weather models. Proc. First Biennial Meeting of the International Environmental Modelling and Software Society, Manno, Switzerland, June, 24-27. pp: 395-400.
  • Er-Raki S, Chehbouni A, Guemouria N, Duchemin B, Ezzahar J, Hadria R, 2007. Combining FAO-56 model and ground-based remote sensing to estimate water consumptions of wheat crops in a semi-arid region. Agr Water Manage 87(1): 41-54. http://dx.doi.org/10.1016/j.agwat.2006.02.004
  • Er-Raki S, Chehbouni A, Duchemin B, 2010. Combining satellite remote sensing data with the FAO-56 dual approach for water use mapping in irrigated wheat fields of a semi-arid region. Remote Sens 2(1): 375-387. http://dx.doi.org/10.3390/rs2010375
  • Gonzalez-Dugo MP, Mateos L, 2008. Spectral vegetation indices for benchmarking water productivity of irrigated cotton and sugarbeet crops. Agr Water Manage 95(1): 48-58. http://dx.doi.org/10.1016/j.agwat.2007.09.001
  • Grayson RB, Moore I, McMohan, TA, 1992a. Physically based hydrologic modeling 1. A terrain-based model for investigative purposes. Water Resour Res 28(10): 2639-2658. http://dx.doi.org/10.1029/92WR01258
  • Grayson RB, Moore I, McMohan TA, 1992b. Physically based hydrologic modeling 2. Is the concept realistic? Water Resour Res 28(10): 2659-2666. http://dx.doi.org/10.1029/92WR01259
  • Hunsaker DJ, Pinter PJ Jr, Kimball BA, 2005. Wheat basal crop coefficients determined by normalized difference vegetation index. Irrig Sci 24(1): 1-14. http://dx.doi.org/10.1007/s00271-005-0001-0
  • Jalota SK, Arora VK, 2002. Model-based assessment of water balance components under different cropping systems in north-west India. Agr Water Manage 57(1): 75-87. http://dx.doi.org/10.1016/S0378-3774(02)00049-5
  • Ju W, Gao P, Wanga J, Zhou Y, Zhang X, 2010. Combining an ecological model with remote sensing and GIS techniques to monitor soil water content of croplands with a monsoon climate. Agr Water Manage 97(8): 1221-1231. http://dx.doi.org/10.1016/j.agwat.2009.12.007
  • Liu Y, Luo Y, 2010. A consolidated evaluation of the FAO-56 dual crop coefficient approach using the lysimeter data in the North China Plain. Agr Water Manage 97(1): 31-40. http://dx.doi.org/10.1016/j.agwat.2009.07.003
  • López-Urrea R, Montoro A, González-Piqueras J, López Fuster P, Fereres E, 2009. Water use of spring wheat to raise water productivity. Agr Water Manage 96(9): 1305-1310. http://dx.doi.org/10.1016/j.agwat.2009.04.015
  • Martín De Santa Olalla F, Calera A, Domínguez A, 2003. Monitoring irrigation water use by combining irrigation advisory service and remotely sensed data with a geographic information system. Agr Water Manage 61(2): 111-124. http://dx.doi.org/10.1016/S0378-3774(02)00169-5
  • Martínez-Fernández J, Ceballos A, 2003. Temporal stability of soil moisture in a large-field experiment in Spain. Soil Sci Soc Am J 67(6): 1647-1656. http://dx.doi.org/10.2136/sssaj2003.1647
  • Martínez-Fernández J, Ceballos A, 2005. Mean soil moisture estimation using temporal stability analysis. J Hydrol 312(1-4): 28-38. http://dx.doi.org/10.1016/j.jhydrol.2005.02.007
  • Neale CM, Bausch WC, Heerman DF, 1989. Development of reflectance-based crop cofficients for corn. T ASAE 32(6): 1891-1899.
  • Quanqi L, Baodi D, Yunzhou Q, Mengyu L, Jiwang Z, 2010. Root growth, available soil water, and water-use efficiency of winter wheat under different irrigation regimes applied at different growth stages in North China. Agr Water Manage 97(10): 1676-1682. http://dx.doi.org/10.1016/j.agwat.2010.05.025
  • Rößler O, Löffler J, 2010. Potentials and limitations of modelling spatio-temporal patterns of soil moisture in a high mountain catchment using WaSiM-ETH. Hydrol Process 24(15): 2182-2196.
  • Rouse JW, Haas RH, Shell JA, Deering DW, Harlan JC, 1974. Monitoring the vernal advancement of retrogradation of natural vegetation. Final Report, Type III. Greenbelt, MD, NASA/GSFC. 371 pp.
  • Sánchez N, Martínez-Fernández J, Calera A, Torres EA, Pérez-Gutiérrez C, 2010. Combining remote sensing and in situ soil moisture data for the application and validation of a distributed water balance model (HIDROMORE). Agr Water Manage 98(1): 69-78. http://dx.doi.org/10.1016/j.agwat.2010.07.014
  • Shepard D, 1968. A two-dimensional interpolation function for irregularly-spaced data. Proc. 23rd ACM Nat Conf, NY. pp: 517-524. http://dx.doi.org/10.1145/800186.810616
  • Singh UK, Rena L, Kang SZ, 2010. Simulation of soil water in space and time using an agro-hydrological model and remote sensing techniques. Agr Water Manage 97(8): 1210-1220. http://dx.doi.org/10.1016/j.agwat.2010.03.002
  • Torres EA, Calera A, 2010. Bare soil evaporation under high evaporation demand: a proposed modification to the FAO-56 model. Hydrolog Sci J 55(3): 303-315. http://dx.doi.org/10.1080/02626661003683249
  • Toutin T, 2004. Geometric processing of remote sensing images: models, algorithms and methods (review). Int J Remote Sens 25(10): 1893-1924 http://dx.doi.org/10.1080/0143116031000101611
  • Van Genuchten MT, 1980. A closed form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Sci Soc Am J 44(5): 892 898. http://dx.doi.org/10.2136/sssaj1980.03615995004400050002x
  • Vanclooster M, Boesten J, 2000. Application of pesticide simulation models to the Vredepeel dataset I. Water, solute and heat transport. Agr Water Manage 44(1-3): 105-117. http://dx.doi.org/10.1016/S0378-3774(99)00087-6
  • Vincent S, Pierre F, 2003. Identifying main crop classes in an irrigated area using high resolution image time series. Proc. IEEE Int Geosci Remote Sens Symp, IGARSS 2003, Toulouse, France, July 21-25. pp: 252-254.
  • Wagner W, Blöschl G, Pampaloni P, Calvet JC, Bizzarri B, Wigneron JP, Kerr Y, 2007a. Operational readiness of microwave remote sensing of soil moisture for hydrologic applications. Nord Hydrol 38(1): 1-20. http://dx.doi.org/10.2166/nh.2007.029
  • Wagner W, Naeimi V, Scipal K, De Jeu R, Martínez-Fernández J, 2007b. Soil moisture from operational meteorological satellites. Hydrogeol J 15(1): 121-131. http://dx.doi.org/10.1007/s10040-006-0104-6
  • Western A, Grayson RB, 2000. Soil moisture and runoff processes at Tarrawarra. In: Spatial patterns in catchment hydrology: observations and modelling. Cambridge Univ Press, Cambrigde, UK, pp: 209-246.
  • Wright JL, 1982. New evapotranspiration crop coefficients. J Irrig Drain Div 108(1): 57-74.
  • Zhang Y, Wegehenkel M, 2006. Integration of MODIS data into a simple model for the spatial distributed simulation of soil water content and evapotranspiration. Remote Sens Environ 104(4): 393-408. http://dx.doi.org/10.1016/j.rse.2006.05.011
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