Estimation of surface temperature in Hamedan province using SEBAL algorithm

Surface temperature maps are the most important components of the water requirements in basin scale and also are the most difficult to measure. Conventional methods are very local, ranging from region to field scale. Estimates of the Surface temperature and crop density over the entire area, especially for irrigated areas, are essential. Today, surface temperature can be estimated by using satellites and remote sensing (RS) techniques. In order to obtain the surface temperature, a set of satellite images was used. From a set of 12 Landsat 7 images during the 1998-2002, NDVI and SAVI indicators were established. Based on these indicators the surface temperature was estimated using the SEBAL (surface energy balance algorithm for land) algorithm and compared by measured data that reported by meteorological stations in Hamedan province. Results indicated that, no significant difference between surface temperature using remote sensing data and that reported by meteorological stations. Primary results showed that there was a significant relationship between measured and estimated surface temperature. The results of correlation coefficient were 0.75 and Root Mean Square Error (RMSE) was 5.4 c○, respectively.Surface temperature maps are the most important components of the water requirements in basin scale and also are the most difficult to measure. Conventional methods are very local, ranging from region to field scale. Estimates of the Surface temperature and crop density over the entire area, especially for irrigated areas, are essential. Today, surface temperature can be estimated by using satellites and remote sensing (RS) techniques. In order to obtain the surface temperature, a set of satellite images was used. From a set of 12 Landsat 7 images during the 1998-2002, NDVI and SAVI indicators were established. Based on these indicators the surface temperature was estimated using the SEBAL (surface energy balance algorithm for land) algorithm and compared by measured data that reported by meteorological stations in Hamedan province. Results indicated that, no significant difference between surface temperature using remote sensing data and that reported by meteorological stations. Primary results showed that there was a significant relationship between measured and estimated surface temperature. The results of correlation coefficient were 0.75 and Root Mean Square Error (RMSE) was 5.4 c○, respectively.Surface temperature maps are the most important components of the water requirements in basin scale and also are the most difficult to measure. Conventional methods are very local, ranging from region to field scale. Estimates of the Surface temperature and crop density over the entire area, especially for irrigated areas, are essential. Today, surface temperature can be estimated by using satellites and remote sensing (RS) techniques. In order to obtain the surface temperature, a set of satellite images was used. From a set of 12 Landsat 7 images during the 1998-2002, NDVI and SAVI indicators were established. Based on these indicators the surface temperature was estimated using the SEBAL (surface energy balance algorithm for land) algorithm and compared by measured data that reported by meteorological stations in Hamedan province. Results indicated that, no significant difference between surface temperature using remote sensing data and that reported by meteorological stations. Primary results showed that there was a significant relationship between measured and estimated surface temperature. The results of correlation coefficient were 0.75 and Root Mean Square Error (RMSE) was 5.4 c○, respectively.Surface temperature maps are the most important components of the water requirements in basin scale and also are the most difficult to measure. Conventional methods are very local, ranging from region to field scale. Estimates of the Surface temperature and crop density over the entire area, especially for irrigated areas, are essential. Today, surface temperature can be estimated by using satellites and remote sensing (RS) techniques. In order to obtain the surface temperature, a set of satellite images was used. From a set of 12 Landsat 7 images during the 1998-2002, NDVI and SAVI indicators were established. Based on these indicators the surface temperature was estimated using the SEBAL (surface energy balance algorithm for land) algorithm and compared by measured data that reported by meteorological stations in Hamedan province. Results indicated that, no significant difference between surface temperature using remote sensing data and that reported by meteorological stations. Primary results showed that there was a significant relationship between measured and estimated surface temperature. The results of correlation coefficient were 0.75 and Root Mean Square Error (RMSE) was 5.4 c○, respectively.