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)

Authors

Samuel Ortega-Farias, Universidad de TalcaFollow
Samuel Ortega-Salazar, University of Nebraska - LincolnFollow
Tomas Poblete, Universidad de TalcaFollow
Ayse Kilic, University of Nebraska-LincolnFollow
Richard Allen, University of IdahoFollow
Carlos Poblete-Echeverria, Pontifica Universidad Catolica de ValparaisoFollow
Luis Ahumada-Orellana, Universidad de TalcaFollow
Mauricio Zuniga, Universidad de TalcaFollow
Daniel Sepulveda, Universidad de TalcaFollow

Date of this Version

2016

Citation

Remote Sens. 2016, 8, 638

Comments

Copyright 2016 by the authors.

Open access

doi:10.3390/rs8080638

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'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 (Tsurface) at very high resolution (6 cm x 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 38W 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.