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.

The use of Unmanned Aerial Vehicles (UAVs) in viticulture permits the capture of aerial Red-Green-Blue (RGB) images with an ultra-high spatial resolution. Recent studies have demonstrated that RGB images can be used to monitor spatial variability of vine biophysical parameters. However, for estimating these parameters, accurate and automated segmentation methods are required to extract relevant information from RGB images. Manual segmentation of aerial images is a laborious and time-consuming process. Traditional classification methods have shown satisfactory results in the segmentation of RGB images for diverse applications and surfaces, however, in the case of commercial vineyards, it is necessary to consider some particularities inherent to canopy size in the vertical trellis systems (VSP) such as shadow effect and different soil conditions in inter-rows (mixed information of soil and weeds). Therefore, the objective of this study was to compare the performance of four classification methods (K-means, Artificial Neural Networks (ANN), Random Forest (RForest) and Spectral Indices (SI)) to detect canopy in a vineyard trained on VSP. Six flights were carried out from post-flowering to harvest in a commercial vineyard cv. Carménère using a low-cost UAV equipped with a conventional RGB camera. The results show that the ANN and the simple SI method complemented with the Otsu method for thresholding presented the best performance for the detection of the vine canopy with high overall accuracy values for all study days. Spectral indices presented the best performance in the detection of Plant class (Vine canopy) with an overall accuracy of around 0.99. However, considering the performance pixel by pixel, the Spectral indices are not able to discriminate between Soil and Shadow class. The best performance in the classification of three classes (Plant, Soil, and Shadow) of vineyard RGB images, was obtained when the SI values were used as input data in trained methods (ANN and RForest), reaching overall accuracy values around 0.98 with high sensitivity values for the three classes.

Aerial and terrestrial thermography has become a practical tool to determine water stress conditions in vineyards. However, for proper use of this technique it is necessary to consider vine architecture (canopy zone analysis) and image thresholding approaches (determination of the upper and lower baseline temperature values). During the 2014–2015 growing season, an experimental study under different water conditions (slight, mild, moderate, and severe water stress) was carried out in a commercial vineyard (Vitis vinifera L., cv. Carménè). In this study thermal images were obtained from different canopy zones by using both aerial (>60 m height) and ground-based (sunlit, shadow and nadir views) thermography. Using customized code that was written specifically for this research, three different thresholding approaches were applied to each image: (i) the standard deviation technique (SDT); (ii) the energy balance technique (EBT); and (iii) the field reference temperature technique (FRT). Results obtained from three different approaches showed that the EBT had the best performance. The EBT was able to discriminate over 95% of the leaf material, while SDT and FRT were able to detect around 70% and 40% of the leaf material, respectively. In the case of canopy zone analysis, ground-based nadir images presented the best correlations with stomatal conductance (gs) and stem water potential (Ψstem), reaching determination coefficients (r2) of 0.73 and 0.82, respectively. The best relationships between thermal indices and plant-based variables were registered during the period of maximum atmospheric demand (near veraison) with significant correlations for all methods.

The main goal of this research was to estimate the actual evapotranspiration (ET_c) of a drip-irrigated apple orchard located in the semi-arid region of Talca Valley (Chile) using a remote sensing-based soil water balance model. The methodology to estimate ET_c is a modified version of the Food and Agriculture Organization of the United Nations (FAO) dual crop coefficient approach, in which the basal crop coefficient (K_cb) was derived from the soil adjusted vegetation index (SAVI) calculated from satellite images and incorporated into a daily soil water balance in the root zone. A linear relationship between the K_cb and SAVI was developed for the apple orchard K_cb = 1.82·SAVI − 0.07 (R² = 0.95). The methodology was applied during two growing seasons (2010–2011 and 2012–2013), and ET_c was evaluated using latent heat fluxes (LE) from an eddy covariance system. The results indicate that the remote sensing-based soil water balance estimated ET_c reasonably well over two growing seasons. The root mean square error (RMSE) between the measured and simulated ET_c values during 2010–2011 and 2012–2013 were, respectively, 0.78 and 0.74 mm·day⁻¹, which mean a relative error of 25%. The index of agreement (d) values were, respectively, 0.73 and 0.90. In addition, the weekly ET_c showed better agreement. The proposed methodology could be considered as a useful tool for scheduling irrigation and driving the estimation of water requirements over large areas for apple orchards.