Detecting crop water status in mature olive groves using vegetation spectral measurements

• Olive water status is estimated from Vegetation Index VI or full spectral regression.
• Water status analysis needs to exclude factors affecting canopy spectral reflectance.
• Leaf water potential and VI are best related with leaf level data in NIR–SWIR region.
• Field spectroscopy and regression analysis are valuable to monitor crop water status.

Full spectral measurements (350–2500 nm) at tree canopy and leaf levels and the corresponding leaf water potentials (LWP) were acquired in an olive grove of Sicily, at different hours of the day, during summer season 2011. The main objective of the work was to assess, on the basis of the experimental data-set, two different approaches to detect crop water status in terms of LWP. Specifically, using existing families of Vegetation Indices (VIs) and applying Partial Least Squares Regression (PLSR) were optimised and tested. The results indicated that a satisfactory estimation of LWP at tree canopy and leaf levels can be obtained using vegetation indices based on the near infrared–shortwave infrared (NIR–SWIR) domain requiring, however, a specific optimisation of the corresponding “centre-bands”. At tree canopy level, a good prediction of LWP was obtained by using optimised indices working in the visible domain, like the Normalized Difference Greenness Vegetation Index (NDGI, RMSE = 0.37 and R2 = 0.57), the Green Index (GI, RMSE = 0.53 and R2 = 0.39) and the Moisture Spectral Index (MSI, RMSE = 0.41 and R2 = 0.48). On the other hand, a satisfactory estimation of LWP at leaf level was obtained using indices combining SWIR and NIR wavelengths. The best prediction was specifically found by optimising the MSI (RMSE of 0.72 and R2 = 0.45) and the Normalized Difference Water Index (NDWI, RMSE = 0.75 and R2 = 0.45). Even using the PLSR technique, a remarkable prediction of LWP at both tree canopy and leaf levels was obtained. However, this technique requires the availability of full spectra with high resolution, which can only be obtained with handheld spectroradiometers or hyper-spectral remote sensors.