Olive water use and crop coefficients from energy balance and radiometric canopy temperatures

Biophysical and meteorological variables as well as radiometric canopy temperatures were collected in an intensive orchard near Évora, Portugal, with 28% ground cover by canopy and combined in a simplified two-source energy balance model (STSEB) to independently calculate the olive tree transpiration (T_STSEB) component of the total evapotranspiration (ETc). Sap flow observations were simultaneously taken in the same orchard allowing also for independent calculations of tree transpiration (T_SF). Model water use results were compared with water use estimates from the sap flow measurements. Good agreement was observed (R2=0.86, RMSE=0.20 mm d-1), with an estimation average absolute error (AAE) of 0.17 mm d-1. From June to August, on average olive water use were 1.92 and 1.89 mm d-1 for sap flow and STSEB model respectively, and 1.38 and 1.58 mm d-1 for the month of September. Results were also used to assess the olive basal crop coefficients (Kcb). Kcb estimates of 0.33 were obtained for sap flow and STSEB model, respectively, for June to August, and of 0.44 and 0.53 for the month of September. Basal crop coefficients were lower than the suggested FAO56 average Kcb values of 0.65 for June to August, the crop mid-season growth stage, and of 0.65 for the month of September, the end-season.

Assessing the role of Mediterranean evergreen oaks canopy cover in land surface albedo and temperature using a remote sensing-based approach

Modifications in vegetation cover can have an impact on the climate through changes in biogeochemical and biogeophysical processes. In this paper, the tree canopy cover percentage of a savannah-like ecosystem (montado/dehesa) was estimated at Landsat pixel level for 2011, and the role of different canopy cover percentages on land surface albedo (LSA) and land surface temperature (LST) were analysed. A modelling procedure using a SGB machine-learning algorithm and Landsat 5-TM spectral bands and derived vegetation indices as explanatory variables, showed that the estimation of montado canopy cover was obtained with good agreement (R2 = 78.4%). Overall, montado canopy cover estimations showed that low canopy cover class (MT_1) is the most representative with 50.63% of total montado area. MODIS LSA and LST products were used to investigate the magnitude of differences in mean annual LSA and LST values between contrasting montado canopy cover percentages. As a result, it was found a significant statistical relationship between montado canopy cover percentage and mean annual surface albedo (R2 = 0.866, p < 0.001) and surface temperature (R2 = 0.942, p < 0.001). The comparisons between the four contrasting montado canopy cover classes showed marked differences in LSA (χ2 = 192.17, df = 3, p < 0.001) and LST (χ2 = 318.18, df = 3, p < 0.001). The highest montado canopy cover percentage (MT_4) generally had lower albedo than lowest canopy cover class, presenting a difference of −11.2% in mean annual albedo values. It was also showed that MT_4 and MT_3 are the cooler canopy cover classes, and MT_2 and MT_1 the warmer, where MT_1 class had a difference of 3.42 °C compared with MT_4 class. Overall, this research highlighted the role that potential changes in montado canopy cover may play in local land surface albedo and temperature variations, as an increase in these two biogeophysical parameters may potentially bring about, in the long term, local/regional climatic changes moving towards greater aridity.