A field experiment was carried out to implement a remote sensing energy balance (RSEB) algorithm for estimating the incoming solar radiation (Rsi), net radiation (Rn), sensible heat flux (H), soil heat flux (G) and latent heat flux (LE) over a drip-irrigated olive (cv. Arbequina) orchard located in the Pencahue Valley, Maule Region, Chile (35°25′S; 71°44′W; 90 m above sea level). For this study, a helicopter-based unmanned aerial vehicle (UAV) was equipped with multispectral and infrared thermal cameras to obtain simultaneously the normalized difference vegetation index (NDVI) and surface temperature (T_surface) at very high resolution (6 cm × 6 cm). Meteorological variables and surface energy balance components were measured at the time of the UAV overpass (near solar noon). The performance of the RSEB algorithm was evaluated using measurements of H and LE obtained from an eddy correlation system. In addition, estimated values of Rsi and Rn were compared with ground-truth measurements from a four-way net radiometer while those of G were compared with soil heat flux based on flux plates. Results indicated that RSEB algorithm estimated LE and H with errors of 7% and 5%, respectively. Values of the root mean squared error (RMSE) and mean absolute error (MAE) for LE were 50 and 43 W m⁻² while those for H were 56 and 46 W m⁻², respectively. Finally, the RSEB algorithm computed Rsi, Rn and G with error less than 5% and with values of RMSE and MAE less than 38 W m⁻². Results demonstrated that multispectral and thermal cameras placed on an UAV could provide an excellent tool to evaluate the intra-orchard spatial variability of Rn, G, H, LE, NDVI and T_surface over the tree canopy and soil surface between rows.

A field experiment was carried out to calibrate and evaluate the METRIC (Mapping EvapoTranspiration at high Resolution Internalized with Calibration) model for estimating the spatial and temporal variability of instantaneous net radiation (Rn_i), soil heat flux (G_i), sensible heat flux (H_i), and latent heat flux (LE_i) over a drip-irrigated apple (Malus domestica cv. Pink Lady) orchard located in the Pelarco valley, Maule Region, Chile (35°25′20′′LS; 71°23′57′′LW; 189 m.a.s.l.). The study was conducted in a plot of 5.5 hectares using 20 satellite images (Landsat 7 ETM+) acquired on clear sky days during three growing seasons (2012/2013, 2013/2014 and 2014/2015). Specific sub-models to estimate G_i, leaf area index (LAI) and aerodynamic roughness length for momentum transfer (Z_om) were calibrated for the apple orchard as an improvement to the standard METRIC model. The performance of the METRIC model was evaluated at the time of satellite overpass using measurements of H_i and LE_i obtained from an eddy correlation system. In addition, estimated values of Rn_i, G_i and LAI were compared with ground-truth measurements from a four-way net radiometer, soil heat flux plates and plant canopy analyzer, respectively. Validation indicated that LAI, Z_om and G_i were estimated using the calibrated functions with errors of +2%, +6% and +3% while those were computed using the standard functions with error of +59%, +83%, and +12%, respectively. In addition, METRIC using the calibrated functions estimated H_i and LE_i with error of +5% and +16%, while using the original functions estimated H_i and LE_i with error of +29% and +26%, respectively.

We present the detection of the signatures of land use/land cover (LULC) changes on the regional climate of the US High Plains. We used the normalized difference vegetation index (NDVI) as a proxy of LULC changes and atmospheric CO₂ concentrations as a proxy of greenhouse gases. An enhanced signal processing procedure was developed to detect the signatures of LULC changes by integrating autoregression and moving average (ARMA) modeling and optimal fingerprinting technique. The results, which are representative of the average spatial signatures of climate response to LULC change forcing on the regional climate of the High Plains during the 26 years of the study period (1981–2006), show a significant cooling effect on the regional temperatures during the summer season. The cooling effect was attributed to probable evaporative cooling originating from the increasing extensive irrigation in the region. The external forcing of atmospheric CO₂ was included in the study to suppress the radiative warming effect of greenhouse gases, thus, enhancing the LULC change signal. The results show that the greenhouse gas radiative warming effect in the region is significant, but weak, compared to the LULC change signal. The study demonstrates the regional climatic impact of anthropogenic induced atmospheric-biosphere interaction attributed to LULC change, which is an additional and important climate forcing in addition to greenhouse gas radiative forcing in High Plains region.

Evapotranspiration (ET) and sensible heat (H) flux play a critical role in climate change; micrometeorology; atmospheric investigations; and related studies. They are two of the driving variables in climate impact(s) and hydrologic balance dynamics. Therefore, their accurate estimate is important for more robust modeling of the aforementioned relationships. The Bowen ratio energy balance method of estimating ET and H diffusions depends on the assumption that the diffusivities of latent heat (K_V) and sensible heat (K_H) are always equal. This assumption is re-visited and analyzed for a subsurface drip-irrigated field in south central Nebraska. The inequality dynamics for subsurface drip-irrigated conditions have not been studied. Potential causes that lead K_V to differ from K_H and a rectification procedure for the errors introduced by the inequalities were investigated. Actual ET; H; and other surface energy flux parameters using an eddy covariance system and a Bowen Ratio Energy Balance System (located side by side) on an hourly basis were measured continuously for two consecutive years for a non-stressed and subsurface drip-irrigated maize canopy. Most of the differences between K_V and K_H appeared towards the higher values of K_V and K_H. Although it was observed that K_V was predominantly higher than K_H; there were considerable data points showing the opposite. In general; daily K_V ranges from about 0.1 m²∙s⁻¹ to 1.6 m²∙s⁻¹; and K_H ranges from about 0.05 m²∙s⁻¹ to 1.1 m²∙s⁻¹. The higher values for K_V and K_H appear around March and April; and around September and October. The lower values appear around mid to late December and around late June to early July. Hourly estimates of K_V range between approximately 0 m²∙s⁻¹ to 1.8 m²∙s⁻¹ and that of K_H ranges approximately between 0 m²∙s⁻¹ to 1.7 m²∙s⁻¹. The inequalities between K_V and K_H varied diurnally as well as seasonally. The inequalities were greater during the non-growing (dormant) seasons than those during the growing seasons. During the study period, K_V was, in general, lesser than K_H during morning hours and was greater during afternoon hours. The differences between K_V and K_H mainly occurred in the afternoon due to the greater values of sensible heat acting as a secondary source of energy to vaporize water. As a result; during the afternoon; the latent heat diffusion rate (K_V) becomes greater than the sensible heat diffusion rate (K_H). The adjustments (rectification) for the inequalities between eddy diffusivities is quite essential at least for sensible heat estimation, and can have important implications for application of the Bowen ratio method for estimation of diffusion fluxes of other gasses.

The objective of this study was to explore the improvement in accuracy of estimates for evapotranspiration (ET) over complete growing seasons and monthly periods, when more frequent Landsat imagery is made available. Conversely, we explored the reduction in accuracy in ET estimates when frequency of Landsat imagery was reduced. The study was implemented by conducting a series of METRIC applications for two Landsat WRS path overlap areas, one in southern Idaho (paths 39 and 40) during 2000, and a second one in Nebraska (paths 29 and 30) during 2002, years when two fully functioning satellites, Landsat 5 and Landsat 7, were in orbit. The results indicated that high frequency imagery provided by two satellites covering a WRS path overlap was more able to capture the impacts of rapid crop development and harvest, and evaporation associated by wetting events. That data set simulated a nominal four-day revisit time. Three-simulated 16-day revisit data sets created using a single Landsat series for a single path were unable to produce monthly and growing season ET due to the lack of sufficient number of images to even begin the time-integration process. This emphasizes the need to maintain two Landsat satellites in orbit and the high value of four-day revisit times. Limiting the data set to one path and two satellites (eight-day revisit) underestimated growing season ET accordingly by about 8% on average. Error in monthly ET was relatively high when image availability was limited to that for an eight-day revisit. This is due to the importance of timing of images to identify key inflection points in the ETrF curves and to capture special events such as wetting events from irrigation and rain or from water stress or cuttings, as in the case of forage crops. Results suggest that a four-day revisit time as represented by the full-run (run 1) of our analysis provides robustness in the development of time-integrated ET estimates over months and growing seasons, and is a valuable backstop for mitigation of clouded images over extended periods.