| Summary: | The water uptake rate is a key parameter for improving irrigation efficiency and represents an indicator of tree health and yield. Here, we investigate the potential of C-band Synthetic Aperture Radar (SAR) data acquired by Sentinel-1 to estimate and map the water uptake rate across a very high-density olive orchard (5.3 km2 of olive plots) in the hot and arid desert climate of Saudi Arabia. The SAR predictor variables for water uptake rate estimation were the difference between the SAR backscattering of a given image acquired during the year (ti) and the average SAR backscattering in the second-half of January (t0), which corresponds to the point when the uptake rate was closest to zero. Random forest regression and multi-linear regression models were used to explore the relationship between the SAR predictor variables and in situ water uptake rate, inferred from twelve sap flow meters that were installed across six plots and collected over two years (2020 and 2021). The trained random forest regression model provided an improved estimate of water uptake rate with correlation coefficient (R2) of 0.87 and root mean square error (RMSE) of 0.13 L.h−1 compared with the multi-linear regression model (R2 = 0.74, RMSE = 0.18 L.h−1). Using the random forest regression, the water uptake rate was mapped at the plot level for three years (2019, 2020, and 2021) at a temporal resolution of 6 days. Consistent with expectations, the results show that the average uptake rate over the mapped area co-varied with vapor pressure deficit (R2 = 0.82). The analysis provides a first exploration of exploiting SAR data to infer the water uptake rate across commercial scale orchards, offering important insights that can be useful for improved water management and irrigation control.
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