Remote Sensing-Assisted Estimation of Water Use in Apple Orchards with Permanent Living Mulch

Orchards are complex agricultural systems with various characteristics that influence crop evapotranspiration (ETc), such as variety, tree height, planting density, irrigation methods, and inter-row management. The preservation of biodiversity and improvement of soil fertility have become important goals in modern orchard management. Consequently, the traditional approach to weed control between rows, which relies on herbicides and soil mobilization, has gradually been replaced by the use of permanent living mulch (LM). This study explored the potential of a remote sensing (RS)-assisted method to monitor water use and water productivity in apple orchards with permanent mulch. The experimental data were obtained in the Lis Valley Irrigation District, on the Central Coast of Portugal, where the "Ma & ccedil;& atilde; de Alcoba & ccedil;a" (Alcoba & ccedil;a apple) is produced. The methodology was applied over three growing seasons (2019-2021), combining ground observations with RS tools, including drone flights and satellite images. The estimation of ETa followed a modified version of the Food and Agriculture Organization of the United Nations (FAO) single crop coefficient approach, in which the crop coefficient (Kc) was derived from the normalized difference vegetation index (NDVI) calculated from satellite images and incorporated into a daily soil water balance. The average seasonal ETa (FAO-56) was 824 +/- 14 mm, and the water productivity (WP) was 3.99 +/- 0.7 kg m-3. Good correlations were found between the Kc's proposed by FAO and the NDVI evolution in the experimental plot, with an R2 of 0.75 for the entire growing season. The results from the derived RS-assisted method were compared to the ETa values obtained from the Mapping Evapotranspiration at High Resolution with Internalized Calibration (METRIC) surface energy balance model, showing a root mean square (RMSE) of +/- 0.3 mm day-1 and a low bias of 0.6 mm day-1. This study provided insights into mulch management, including cutting intensity, and its role in maintaining the health of the main crop. RS data can be used in this management to adjust cutting schedules, determine Kc, and monitor canopy management practices such as pruning, health monitoring, and irrigation warnings.