In the face of the climate change crisis, the increase in air temperature negatively impacts rice crop productivity due to stress from water scarcity. The objective of this study was to determine the rice crop water stress index (CWSI) and stomatal conductance (Gs) under different irrigation regimes, specifically continuous flood irrigation treatments (CF) and irrigations with alternating wetting and drying (AWD) at water levels of 5 cm, 10 cm, and 20 cm below the soil surface (${\mathrm{AWD}}_5{, \mathrm{AWD}}_{10}$, and ${\mathrm{AWD}}_{20}$) in an experimental area of INIA-Vista Florida and in six commercial areas of the Lambayeque region using thermal images captured with thermal sensors. The results indicated that AWD irrigation generated more water stress, with CWSI values between 0.4 and 1.0. Despite this, the yields were similar in CF and ${\mathrm{AWD}}_{20}.$In the commercial areas, CWSI values between 0.38 and 0.51 were obtained, with Santa Julia having the highest values. Furthermore, a strong Pearson correlation (R) of 0.91 was established between the CWSI and Gs, representing a reference scale based on Gs values for evaluating water stress levels.

Predicting rice yield accurately is crucial for enhancing farming practices and securing food supplies. This research aims to estimate rice yield in Peru’s Lambayeque region by utilizing spectral and textural indices derived from unmanned aerial vehicle (UAV) imagery, which offers a cost-effective alternative to traditional approaches. UAV data collection in commercial areas involved seven flights in 2022 and ten in 2023, focusing on key growth stages such as flowering, milk, and dough, each showing significant predictive capability. Vegetation indices like NDVI, SP, DVI, NDRE, GNDVI, and EVI2, along with textural features from the gray-level co-occurrence matrix (GLCM) such as ENE, ENT, COR, IDM, CON, SA, and VAR, were combined to form a comprehensive dataset for model training. Among the machine learning models tested, including Multiple Linear Regression (MLR), Support Vector Machines (SVR), and Random Forest (RF), MLR demonstrated high reliability for annual data with an R² of 0.69 during the flowering and milk stages, and an R² of 0.78 for the dough stage in 2022. The RF model excelled in the combined analysis of 2022–2023 data, achieving an R² of 0.58 for the dough stage, all confirmed through cross-validation. Integrating spectral and textural data from UAV imagery enhances early yield prediction, aiding precision agriculture and informed decision-making in rice management. These results emphasize the need to incorporate climate variables to refine predictions under diverse environmental conditions, offering a scalable solution to improve agricultural management and market planning.

In the context of global warming, rising air temperatures are increasing evapotranspiration (${\mathrm{ET}}_{\mathrm{c}}$) in all agricultural crops, including rice, a staple food worldwide. Simultaneously, the occurrence of droughts is reducing water availability, affecting traditional irrigation methods for rice cultivation (flood irrigation). The objective of this study was to determine ${\mathrm{ET}}_{\mathrm{c}}$ (water use) and yield performance in rice crop under different irrigation regimes: treatments with continuous flood irrigation (CF) and irrigations with alternating wetting and drying (${\mathrm{A}\mathrm{W}\mathrm{D}}_5$, ${\mathrm{A}\mathrm{W}\mathrm{D}}_{10}$, and ${\mathrm{A}\mathrm{W}\mathrm{D}}_{20}$) in an experimental area in INIA–Vista Florida. Water balance, rice physiological data, and yield were measured in the field, and local weather data and thermal and multispectral images were collected with a meteorological station and a UAV (a total of 13 flights). ${\mathrm{ET}}_{\mathrm{c}}$ values obtained by applying the METRIC^TM (Mapping Evapotranspiration at High Resolution using Internalized Calibration) energy balance model ranged from 2.4 to 8.9 mm d⁻¹ for the AWD and CF irrigation regimes. In addition, ${\mathrm{ET}}_{\mathrm{c}}$ was estimated by a water balance using the AquaCrop model, previously parameterized with RGB image data and field weather data, soil, irrigation water, and crops, obtaining values between 4.3 and 7.1 mm d⁻¹ for the AWD and CF irrigation regimes. The results indicated that AWD irrigation allows for water savings of 27 to 28%, although it entails a yield reduction of from 2 to 15%, which translates into an increase in water use efficiency (WUE) of from 18 to 36%, allowing for optimizing water use and improving irrigation management.

Accurate rice yield prediction is essential for optimizing water management and supporting decision-making in agricultural systems, particularly in arid environments where irrigation efficiency is critical. This study assessed five machine learning algorithms—Multiple Linear Regression (MLR), Support Vector Regression (SVR, linear and RBF), Partial Least Squares Regression (PLSR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost)—for plot-scale rice yield estimation using Sentinel-2 vegetation indices (VIs) during the 2022 and 2023 seasons in the Chancay–Lambayeque Valley, Peru. VIs sensitive to canopy vigor, water status, and structure were derived in Google Earth Engine and optimized via Sequential Forward Selection to identify the most relevant predictors per phenological stage. Models were trained and validated against field yields using leave-one-out cross-validation (LOOCV). Intermediate stages (Flowering, Milk, Dough) yielded the strongest relationships, with water-sensitive indices (NDMI, MSI) consistently ranked as key predictors. MLR and PLSR achieved the highest generalization (R²_CV up to 0.68; RMSE_CV ≈ 1.3 t ha⁻¹), while RF and XGBoost showed high training accuracy but lower validation performance, indicating overfitting. Model accuracy decreased in 2023 due to climatic variability and limited satellite observations. Findings confirm that Sentinel-2–based VI modeling offers a cost-effective, scalable alternative to UAV data for operational rice yield monitoring, supporting water resource management and decision-making in data-scarce agricultural regions.

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.

Alternate wetting and drying (AWD) irrigation reduces CH₄ emissions from flooded rice but amplifies N₂O pulses; identifying candidate phenological windows for the remote screening of greenhouse gas (GHG) fluxes remains challenging with small datasets. In a single-site, single-season exploratory study at INIA Vista Florida (Lambayeque, Peru), eight UAV flights were paired with eight PlanetScope SuperDove scenes ($|\Delta |\le 1$ d) and closed-chamber CH₄, N₂O and CO₂ fluxes under four water regimes (CF, AWD₅, AWD₁₀, AWD₂₀; 96 sub-plot × date observations). Multivariate explanatory power was assessed by bootstrap Ridge regression on each sensor’s native predictors (VI + GLCM + $T_{\mathrm{mean}}$ for the UAV, VI for PlanetScope). Maximum tillering (79 DAS) emerged as a candidate UAV window, ranking in the top three for all gases through GLCM textures, whereas PlanetScope peaked at Mid-boot and Late-boot (103–107 DAS), with median ${\widetilde{R^2}}_{\mathrm{UAV}}$ at $0.34$–$0.71$ and ${\widetilde{R^2}}_{\mathrm{Planet}}$ at $0.20$–$0.60$. Nested Leave-One-Plot-Out (LOPO) validation gave $R_{\mathrm{CV}}^2$ between $+0.57$ and $+0.69$ for four of six platform × gas combinations (UAV-CH₄ and Planet-N₂O stayed weak), and $T_{\mathrm{mean}}$ was decisive for N₂O on the UAV ($\Delta R^2=+0.48$). Repeating the stage selection inside every LOPO fold preserved the leading combinations and their ranking. These exploratory windows and sensor-native descriptors need multi-site, multi-season validation before operational use.