Spatial monitoring of olive systems in arid regions is essential for understanding agricultural expansion, water pressure, and productive sustainability. This study aimed to map coverage and estimate olive plantation density (Olea europaea L.) in the Atacama Desert, Tacna (Peru) through the integration of UAV-satellite multispectral images and machine learning algorithms (CART, Random Forest, and Gradient Tree Boosting). Forty-eight optical, radar, and topographic covariates were analyzed. Fifteen were selected for coverage classification and 16 for plantation density, using Pearson’s correlation (|r| > 0.75). The classification maps reported an area of 23,059.87 ha (38.21%) of olive groves, followed by 5352.10 ha (8.87%) of oregano cultivation and 725.74 ha (1.20%) of orange cultivation, with respect to the total study area, with overall accuracy (OA) of 86.6% and a Kappa coefficient of 0.81. Meanwhile, the RF and GTB regression models showed R² ≈ 0.89 and RPD > 2.8, demonstrating excellent predictive performance for estimating tree density (between 1 and 8 trees per 100 m²). Furthermore, the highest concentration of olive trees was found in the central and southern zones of the study area, associated with favorable soil and microclimatic conditions. This work constitutes the first comprehensive approach for olive mapping in southern Peru using UAV–satellite fusion, demonstrating the capability of ensemble models to improve agricultural mapping accuracy and support water and productive management in arid ecosystems.

Water stress in olive orchards is often mapped from the Normalized Difference Vegetation Index (NDVI), although in sparse orchards NDVI also reflects fractional cover and bare soil. We compared an NDVI-based classification with a METRIC-derived evaporative Crop Water Stress Index (${\mathrm{CWSI}}_{\mathrm{ET}}=1-{\mathrm{ET}}_{\mathrm{a}}/{\mathrm{ET}}_{\mathrm{o}}$) over hyper-arid, groundwater-irrigated olive orchards of La Yarada–Los Palos (Peru), using Landsat 5/8/9 imagery from eighteen austral summers (2006–2026). Both classifications rely on operational thresholds that were not physiologically calibrated for olive: the NDVI ranges were defined from field-observed canopy condition, and the CWSI_ET classes are equal-width bins of the reference-normalized evaporative deficit. NDVI placed most of the cropland in severe-to-extreme classes and CWSI_ET mainly in mild-to-moderate classes (medians 0.26–0.41); NDVI was more severe over 89% of the cropland. The divergence grew as cover decreased, persisted at native thermal resolution and was reproduced by surface temperature, although both continuous indices correlated ($r=-0.61$). Without in situ physiological or flux measurements, these results establish a disagreement between indicators, not which of them matches plant water status. The cropland expanded fourfold (98 to 356 km²) with no trend in mean evapotranspiration or CWSI_ET. NDVI-based stress maps should be interpreted with caution in sparse orchards and combined with energy-balance, meteorological or field information.