Irrigation management with remote sensing: Evaluating irrigation requirement for maize under Mediterranean climate condition

tWater use control methods and water resources planning are of high priority. In irrigated agriculture, theright way to save water is to increase water use efficiency through better management. The present workvalidates procedures and methodologies using remote sensing to determine the water availability in thesoil at each moment, giving the opportunity for the application of the water depth strictly necessaryto optimise crop growth (optimum irrigation timing and irrigation amount). The analysis is applied tothe Irrigation District of Divor, Évora, using 7 experimental plots, which are areas irrigated by centre-pivot systems, cultivated to maize. Data were determined from images of the cultivated surface obtainedby satellite and integrated with atmosphere and crop parameters to calculate biophysical indicatorsand indices of water stress in the vegetation—Normalized Difference Vegetation Index (NDVI), Kc, andKcb. Therefore, evapotranspiration (ETc) was estimated and used to calculate crop water requirement,together with the opportunity and the amount of irrigation water to allocate. Although remote sensingdata available from satellite imagery presented some practical constraints, the study could contribute tothe validation of a new methodology that can be used for irrigation management of a large irrigated area,easier and at lower costs than the traditional FAO recommended crop coefficients method. The remotesensing based methodology can also contribute to significant saves of irrigation water.

Estimation of water use and crop coefficients for an intensive olive orchard using sap flow measurements and modeled data

Olive tree sap flow measurements were collected in an intensive orchard near Évora, Portugal, during the irrigation seasons of 2013 and 2014, to calculate daily tree transpiration rates (T_SF). Meteorological variables were also collected to calculate reference evapotranspiration (ETo). Both data were used to assess values of basal crop coefficient (Kcb) for the period of the sap flow observations. The soil water balance model SIMDualKc was calibrated with soil, biophysical ground data and sap flow measurements collected in 2013. Validated in 2014 with collected sap flow observations, the model was used to provide estimates of dual e single crop coefficients for 2014 crop growing season. Good agreement between model simulated daily transpiration rates and those obtained with sapflow measurements was observed for 2014 (R2=0.76, RMSE=0.20 mm d-1), the year of validation, with an estimation average absolute error (AAE) of 0.20 mm d-1. Olive modeled daily actual evapotranspiration resulted in atual ETc values of 0.87, 2.05 and 0.77 mm d-1 for 2014 initial, mid- and end-season, respectively. Actual crop coefficient (Kc act) values of 0.51, 0.43 and 0.67 were also obtained for the same periods, respectively. Higher Kc values during spring (initial stage) and autumn (end-stage) were published in FAO56, varying between 0.65 for Kc ini and 0.70 for Kc end. The lower Kc mid value of 0.43 obtained for the summer (mid-season) is also inconsistent with the FAO56 expected Kc mid value of 0.70 for the period. The modeled Kc results are more consistent with the ones published by Allen & Pereira [1] for olive orchards with effective ground cover of 0.25 to 0.5, which vary between 0.40 and 0.80 for Kc ini, 0.40–0.60 for Kc mid with no active ground cover, and 0.35–0.75 for Kc end, depending on ground cover. The SIMDualKc simulation model proved to be appropriate for obtaining evapotranspiration and crop coefficient values for our intensive olive orchard in southern Portugal.

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.