Estimation of Energy Balance Components over a Drip-Irrigated Olive Orchard Using Thermal and Multispectral Cameras Placed on a Helicopter-Based Unmanned Aerial Vehicle (UAV)

Abstract

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 1 S; 71˝44 1 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 cmˆ6 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.

Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications

“…Using these UAS, higher spatial and temporal data resolution can be achieved, which makes possible precision agriculture applications to the submeter resolution per pixel. This allows research and practical applications applied to growth and vigor dynamic assessment, plant water status sensing for irrigation scheduling applications, and evapotranspiration modelling, among others [4][5][6][7][8][9].…”

Estimation of evapotranspiration of temperate grassland based on high-resolution thermal and visible range imagery from unmanned aerial systems

“…precision agriculture applications (Berni et al 2009; Brosy et al 2017; Zhang and Kovacs 2012; Candiago et al 2015; Reineman et al 2013; Link, Senner, and Claupein 2013; Lelong et al 2008; Turner et al 2014; Stefano et al 2017; Vázquez-Tarrío et al 2017). However, studies using UAS-based TIR sensors to map LST and subsequently derive surface turbulent heat fluxes are still rare (Hoffmann et al 2016b; Ortega-Farías et al 2017; Ortega-Farías et al 2016; Brenner et al 2017). In view of the ease of use of UAS and the flexibility in mission planning and operation, simple approaches that facilitate operational ET monitoring in near real time would perfectly complete the asset of UAS.…”

Optimizing the Processing of UAV-Based Thermal Imagery

“…In agriculture, UAV-based thermal remote sensing has been successfully applied for disease detection [20], high-throughput phenotyping in plant breeding [21], or for mapping drought stressed areas e.g., [22][23][24]. New developments also include the estimation of transpiration [25][26][27] or stomatal conductance [28]. In forestry and in ecological studies, the applications so far are less numerous, although the first studies indicate the usefulness of UAV-based thermal remote sensing in areas including fire detection [29], wildlife monitoring [30,31], assessing within-ecosystem variability in water availability [32], or in estimating plant growth and productivity [14].…”