Glacial retreat is a major global challenge, particularly in arid and semi-arid regions where glaciers serve as critical water sources. This research focuses on glacial retreat and its impact on land cover and land use changes (LULC) in the Barroso Mountain range, Tacna, Peru, which is a critical area for water resources in the hyperarid Atacama Desert. Employing advanced remote sensing techniques through the Google Earth Engine (GEE) cloud computing platform, we analyzed historical trends (1985–2022) using Landsat satellite imagery. A normalized index classification was carried out to generate LULC maps for the years 1986, 2001, 2012, and 2022. Future projections until 2042 were developed using Cellular Automata–Markov (CA–Markov) modeling in QGIS, incorporating six predictive environmental variables. The resulting maps presented an overall accuracy (OA) greater than 83%. Historical analysis revealed a dramatic glacier reduction from 44.7 km² in 1986 to 7.4 km² in 2022. In contrast, wetlands expanded substantially from 5.70 km² to 12.14 km², indicating ecosystem shifts potentially driven by glacier meltwater availability. CA–Markov chain modeling projected further glacier loss to 3.07 km² by 2042, while wetlands are expected to expand to 18.8 km² and bodies of water will reach 4.63 km². These future projections (with accuracies above 84%) underline urgent implications for water management, environmental sustainability, and climate adaptation strategies, particularly with regard to downstream hydrological risks and ecosystem resilience.

Accurate estimation of evapotranspiration (ET) is critical for precision irrigation in hyper-arid perennial systems. This study quantified ET in an 8 ha olive orchard in La Yarada–Los Palos (Tacna, Peru) by integrating the METRIC satellite-based energy-balance model (Landsat 8/9, Google Earth Engine) with the process-based AquaCrop model, using ET_FAO-56 as an empirical benchmark. Sixteen cloud-free Landsat scenes from two contrasting seasons—2021–2022 (high-yield) and 2023–2024 (water-limited)—were processed to derive daily ET maps and model simulations aligned with satellite overpasses. Results revealed marked intra-parcel heterogeneity and clear seasonal dynamics. METRIC detected local ET peaks of ~6–7 mm d⁻¹ in densely vegetated central blocks and orchard-mean values up to 4.25 ± 1.76 mm d⁻¹. During the high-yield season, ET_METRIC and ET_AQUACROP showed excellent agreement (R² = 0.94; RMSE = 0.21 mm d⁻¹; bias μ = 0.11 mm d⁻¹), whereas FAO-56 consistently underestimated ET (R² = 0.88; RMSE = 0.82 mm d⁻¹). Under water-limited conditions, model correspondence remained strong but attenuated (ET_METRIC–ET_AQUACROP: R² = 0.75; RMSE = 0.64 mm d⁻¹; ET_METRIC–ET_FAO-56: R² = 0.95; RMSE = 0.59 mm d⁻¹), with METRIC exhibiting a persistent positive bias (μ = 0.43–0.56 mm d⁻¹) attributable to localized soil evaporation and micro-advection. Overall, METRIC provided high-resolution spatial diagnostics of canopy stress, while AquaCrop offered daily continuity and explicit evaporation/transpiration (E/Tr) partitioning, enabling a coherent multiscale assessment of ET. The integrated framework enhances operational monitoring of water use and supports deficit-irrigation optimization in hyper-arid olive systems.

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.