Estimation of crop water stress in a nectarine orchard using high-resolution imagery from unmanned aerial vehicle (UAV)

Adequate and timely irrigation based on real-time monitoring of crop water status is critical for efficient and sustainable water use. However, detection of water status in large crop fields is not a trivial task as manual inspection can be time consuming and costly. Moreover, the symptoms of water stress are visually detectable only after the crops are already in a significantly water deficient stage. Consequently, capability of monitoring water status in crops on a regular basis could maximize productivity and water use efficiency. As an indicator, the crop water stress index (CWSI) has been widely used to estimate water status in the crop fields. CWSI can be derived from ground-based leaf temperature measurements, however, airborne or UAV-borne high-resolution thermal sensing provides a superior platform to cover large regions within a short time window. In this paper, UAV-borne thermal sensing was conducted to map plant water stress and spatial variability in water control and deficit plots over 1 ha of a nectarine orchard at an altitude of 100 m from ground level. Targets of ground control points (GCPs) were designed to suit the image spatial resolution as well as the visibility in the thermal infrared spectral range. The target was made of aluminium body marked with a black cross, which can be detected as a cool object in the thermal infrared image due to its low emissivity. Thermal infrared images were post-processed to generate single temperature-based orthomosaic image for the entire study field. CWSI map was computed using canopy temperatures at the centre of canopies from the mosaic image. Histogram analysis was used to estimate the lower boundary temperature (T_wet), representing the temperature of fully transpiring leaves. The upper boundary temperature (T_dry) was determined by air temperature + 6 ⁰C. Ground measurements of midday stem water potential (SWP) and stomatal conductance (g_c) were collected concurrently with UAV operation and used to correlate the thermal measurement to crop biophysical parameters. Results showed that CWSI was in good agreement with both SWP and g_c with determination coefficients (R²) of 0.92 and 0.97, respectively. Thus, remotely estimated CWSI from a UAV platform can play an important role in effective mapping of spatial variability of nectarine water stress and subsequently in optimal management of irrigation.