Assessing Crop Water Stress Index of Citrus Using In-Situ Measurements, Landsat, and Sentinel-2 Data

With the advent of optical sensors, thermal-based indicators can be retrieved at multiscale levels from handheld devices to satellite platforms, providing a low-cost method to mirror plant water status. Here, we measured the canopy temperature of Orange trees subjected to different irrigation levels (100%, 75%, and 50% of crop water requirement) and strategies (regulated deficit irrigation (DI) and partial root drying (PRD)) to determine the crop water stress index (CWSI). Additionally, the CWSI was estimated based on Land Remote-Sensing Satellite (Landsat) thermal data using hot-cold patches (approach 1) and a novel mechanistic method combined with Sentinel-2 data (approach 2). Based on the in-situ measurements, the CWSI non-water stressed baseline was estimated as T (c) - T (a) = -0.57 x (VPD) + 2.31 (N = 370, R (2) = 0.82), defining 'VPD' as 'vapour pressure deficit', and the upper limit was found to be relatively constant (T (c) - T (a) = 3.43 degrees C). The in-field water stress variability among the different irrigation levels was effectively captured using the CWSI; however, the difference between the DI and PRD irrigated trees was only significant at the 50% irrigation level. Considering the remotely-sensed approach, the CWSI from our proposed method (approach 2) resulted in higher accuracy (root mean square error, RMSE = 0.03; mean bias error, MBE = -0.02) compared to approach 1 (RMSE = 0.10, MBE = -0.08). The improved accuracy from our proposed method was attributed to accounting for VPD and net radiation, applying an iterative method to calculate and calibrate aerodynamic resistance, and the use of high-resolution imagery from Sentinel-2 for reducing the soil background impact on canopy temperature.

Automated summary

By measuring canopy temperature of Orange trees, this study determined the crop water stress index (CWSI) under different irrigation levels and strategies. The remotely-sensed CWSI demonstrated higher accuracy in assessing crop water status, particularly when using the approach that combined Sentinel-2 data.