Abstract
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.
Highlights
Soil water content (SWC) is a highly dynamic ecological variable, but it is one of the most sensitive factors in agricultural yield
Point measurements of SWC are frequent in agricultural areas for controlling irrigation scheduling and water reserves (Bonet et al, 2010), either with measurements at different depths or with a water balance using the crop coefficient-reference evapotranspiration (Kc-ET0) approach (Allen et al, 1998)
The spatial heterogeneity of soil properties makes it difficult to scale up measurements from points to large scales, and spatial monitoring designs are costly and timeconsuming (Western & Grayson, 2000). This is a difficult problem for agricultural water management, and several remote sensing spatial missions have attempted to fill this gap, even though improved process understanding and algorithms are needed to enable the use of these data in the future (Wagner et al, 2007a)
Summary
Soil water content (SWC) is a highly dynamic ecological variable, but it is one of the most sensitive factors in agricultural yield. The spatial heterogeneity of soil properties makes it difficult to scale up measurements from points to large scales, and spatial monitoring designs are costly and timeconsuming (Western & Grayson, 2000). This is a difficult problem for agricultural water management, and several remote sensing spatial missions (such as AMSR-E, ERS, SMOS, and SMAP) have attempted to fill this gap, even though improved process understanding and algorithms are needed to enable the use of these data in the future (Wagner et al, 2007a)
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