Search Results (638)

Wheat and corn are two major food crops in Xinjiang. However, the spectral similarity between these crop types and the complexity of their spatial distribution has posed significant challenges to accurate crop identification. To this end, the study aimed to improve the accuracy of crop distribution identification in complex environments in three ways. First, by analysing the kNDVI and EVI time series, the optimal identification window was determined to be days 156–176—a period when wheat is in the grain-filling to milk-ripening phase and maize is in the jointing to tillering phase—during which, the strongest spectral differences between the two crops occurs. Second, principal component analysis (PCA) was applied to Sentinel-2 data. The top three principal components were extracted to construct the input dataset, effectively integrating visible and near-infrared band information. This approach suppressed redundancy and noise while replacing traditional RGB datasets. Finally, the Convolutional Block Attention Module (CBAM) was integrated into the U-Net model to enhance feature focusing on key crop areas. An improved Atrous Spatial Pyramid Pooling (ASPP) module based on deep separable convolutions was adopted to reduce the computational load while boosting multi-scale context awareness. The experimental results showed the following: (1) Wheat and corn exhibit obvious phenological differences between the 156th and 176th days of the year, which can be used as the optimal time window for identifying their spatial distributions. (2) The method proposed by this research had the best performance, with its mIoU, mPA, F1-score, and overall accuracy (OA) reaching 83.03%, 91.34%, 90.73%, and 90.91%, respectively. Compared to DeeplabV3+, PSPnet, HRnet, Segformer, and U-Net, the OA improved by 5.97%, 4.55%, 2.03%, 8.99%, and 1.5%, respectively. The recognition accuracy of the PCA dataset improved by approximately 2% compared to the RGB dataset. (3) This strategy still had high accuracy when predicting wheat and corn yields in Qitai County, Xinjiang, and had a certain degree of generalisability. In summary, the improved strategy proposed in this study holds considerable application potential for identifying the spatial distribution of wheat and corn in arid regions. Full article

Accurate phenological information is crucial for evaluating ecosystem dynamics and the carbon budget. As one of China’s largest terrestrial ecosystem carbon pools, Southwest China plays a significant role in achieving the “dual carbon” goals of carbon peaking and carbon neutrality. However, evergreen forests are widely distributed in this region, and phenology extraction based on vegetation indices has certain limitations, while SIF-based phenology extraction offers a viable alternative. This study first evaluated phenological results derived from three solar-induced chlorophyll fluorescence (SIF) datasets, six curve-fitting methods, and five phenological extraction thresholds at flux sites to determine the optimal threshold and SIF data for phenological indicator extraction. Secondly, uncertainties in phenological indicators obtained from the six fitting methods were quantified at the regional scale. Finally, based on the optimal phenological results, the spatiotemporal variations in phenology in Southwest China were systematically analyzed. Results show: (1) Optimal thresholds are 20% for the start of growing season (SOS) and 30% for the end of growing season (EOS), with GOSIF best for SOS and EOS, and CSIF for the peak of growing season (POS). (2) Cubic Smoothing Spline (CS) has the lowest uncertainty for SOS, while Savitzky–Golay Filter (SG) has the lowest for EOS and POS. (3) Phenology exhibits significant spatial heterogeneity, with SOS and POS generally showing an advancing trend, and EOS and length of growing season (LOS) showing a delaying (extending) trend. This study provides a reference for phenology extraction in regions with frequent cloud cover and widespread evergreen vegetation, supporting effective assessment of regional ecosystem dynamics and carbon balance. Full article

Bud break is a critical phenological stage in muscadine grapevines, marking the start of the growing season and the increasing need for irrigation management. Real-time bud detection enables irrigation to match muscadine grape phenology, conserving water and enhancing performance. This study presents BudCAM, a low-cost, solar-powered, edge computing camera system based on Raspberry Pi 5 and integrated with a LoRa radio board, developed for real-time bud detection. Nine BudCAMs were deployed at Florida A&M University Center for Viticulture and Small Fruit Research from mid-February to mid-March, 2024, monitoring three wine cultivars (A27, noble, and Floriana) with three replicates each. Muscadine grape canopy images were captured every 20 min between 7:00 and 19:00, generating 2656 high-resolution (4656 × 3456 pixels) bud break images as a database for bud detection algorithm development. The dataset was divided into 70% training, 15% validation, and 15% test. YOLOv11 models were trained using two primary strategies: a direct single-stage detector on tiled raw images and a refined two-stage pipeline that first identifies the grapevine cordon. Extensive evaluation of multiple model configurations identified the top performers for both the single-stage (mAP@0.5 = 86.0%) and two-stage (mAP@0.5 = 85.0%) approaches. Further analysis revealed that preserving image scale via tiling was superior to alternative inference strategies like resizing or slicing. Field evaluations conducted during the 2025 growing season demonstrated the system’s effectiveness, with the two-stage model exhibiting superior robustness against environmental interference, particularly lens fogging. A time-series filter smooths the raw daily counts to reveal clear phenological trends for visualization. In its final deployment, the autonomous BudCAM system captures an image, performs on-device inference, and transmits the bud count in under three minutes, demonstrating a complete, field-ready solution for precision vineyard management. Full article

The Mediterranean fig (Ficus carica L.) is a dioecious fruit tree of high nutritional and economic value in the Mediterranean basin. In northern Morocco, phenological desynchronization between male and female fig trees limits pollination and production. This study aimed to characterize the phenological stages of indigenous fig and caprifig varieties using the BBCH scale and to evaluate the predictive capacity of artificial neural networks (ANNs). This study was conducted in the Bni Ahmed region over two consecutive years (2021 and 2022) at two sites. At each site, a total of 80 female fig trees were selected. Caprifig trees were selected in accordance with their availability (37 trees/site 1; 24 trees/site 2). Local meteorological data were incorporated into the analysis to evaluate the influence of climatic conditions on phenological stages. Our results revealed significant effects of temperature, humidity, and rainfall on phenological dynamics, along with a clear inter-varietal variability and pronounced desynchronization between male and female fig trees. Early-ripening caprifig varieties showed limited pollination efficiency, whereas late-ripening varieties were better synchronized with the longer receptivity period of female fig trees. Importantly, the ANN model demonstrated exceptional predictive performance (R² up to 0.985, RMSE < 1 day), serving as a robust and practical tool for forecasting key phenological stages and minimizing potential yield losses. These findings demonstrate the value of combining phenological monitoring with AI-based modeling to improve adaptive management of fig orchards under Mediterranean climate change. This is the first study in Morocco to implement such an integrated approach to fig and caprifig trees. Full article

Spatializing crop yield statistical data with administrative divisions as the basic unit helps reveal the spatial distribution characteristics of crop yield and provides necessary spatial information to support field management and government decision-making. However, owing to an insufficient understanding of the factors affecting yield, accurately depicting its spatial differences remains challenging. Taking Hailun city, Heilongjiang Province, as an example, this study proposes a yield downscaling method based on the standardized deviation from the mean of the comprehensive crop condition index (CCCI) during key phenological periods of the growing season. First, Sentinel-2 remote sensing data were used to retrieve crop condition parameters during key phenological periods, and the CCCI was constructed using the correlation between crop condition parameters in key phenological periods and statistical yield as the weight. Subsequently, regression analysis and the entropy weight method were applied to determine the spatiotemporal dynamic weights of the CCCI during key phenological stages and to calculate the standardized deviation from the mean. By combining these two components, the comprehensive spatial difference index of the crop growth condition (CSDICGC) was derived, which offered a new way to characterize the discrepancies between the pixel-level yield and statistical yield, thereby downscaling the yield statistical data from the administrative unit to the pixel scale. The results indicated that this method achieved a regional accuracy close to 100%, with a strong fit at the pixel scale. Pixel-level accuracy validation against ground-truth maize yield data resulted in an R² of 0.82 and a mean relative error (MRE) of 4.75%. The novelty of this study was characterized by the integration of multistage crop condition parameters with dynamic spatiotemporal weighting to overcome the limitations of single-index methods. The crop yield statistical data downscaling spatialization method proposed in this paper is simple and efficient and has the potential to be popularized and applied over relatively large regions. Full article

The desert riparian forest oasis, dominated by Populus euphratica and Tamarix chinensis, is an important barrier to protect the economic production and habitat of the Tarim River Basin. However, there is still a lack of high-precision spatial distribution data of desert ri-parian forest species below 10 m. The recently launched PlanetScope CubeSat constella-tion, which provides daily earth observation imagery with a resolution of 3 m, offers a highly favorable dataset for mapping the high-resolution distribution of P. euphratica and T. chinensis and an unprecedented opportunity to explore the optimal phenology window to distinguish between them. In this study, time-series PlanetScope images were first used to extract phenological metrics of P. euphratica, dividing the annual life cycle into four phenology windows: duration of leaf expansion (DLE), duration of leaf maturity (DLM), duration of leaf fall (DLF), and duration of the dormancy period (DDP). The random forest model was used to obtain the classification accuracy of 16 phenological window combinations. Results indicate that after gap filling of vegetation index time series, the identification accuracy for P. euphratica and T. chinensis exceeded 0.90. Among individual phenology windows, the DLE window exhibited the highest classification accuracy (average F1-score 0.87). Among the two phenology window combinations, the DLE-DLF and DLE-DLM windows have the highest classification accuracy (average F1-score 0.90). Among the three phenology window combinations, DLE-DLM-DLF displayed the highest classification accuracy (average F1-score 0.91). Nevertheless, the inclusion of features within the DDP window led to a decrease in accuracy by 1–2% points, which was unfavorable for discriminating tree species. Additionally, features observed during the phenology asynchrony period were found to be more valuable for distinguishing between tree species. Our findings highlight the potential of PlanetScope constellation imagery in tree species classification, offering guidance for selecting optimal image acquisition timing and identifying the most valuable images within time series data for future large-scale tree mapping. Full article

Vegetation phenology serves as a crucial indicator reflecting vegetation responses to the growth environment and climate change. Existing studies have demonstrated that in permafrost regions, the impact of frozen soil changes on vegetation phenology is more direct and pronounced compared to climate factors. Amid the slowdown of global warming in the 21st century, permafrost dynamics continued to drive uncertain variations in vegetation phenological stages across the Qinghai–Tibet Plateau (QTP). Using MODIS Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) data during 2001–2024, this study derived vegetation phenological parameters and analyzed their spatiotemporal patterns on the QTP. The results indicate that overall, the start of growing season (SOS) was advanced, the end of growing season (EOS) was delayed, and the length of growing season (LOG) was extended throughout the study period. Additionally, divergent phenological trends were observed across three distinct phases, and regarding frozen soil types, vegetation phenology in permafrost and seasonally frozen ground regions exhibited distinct characteristics. From 2001 to 2024, both permafrost and seasonally frozen ground regions showed an advanced SOS and prolonged LOG, but significant differences were observed in EOS dynamics. For vegetation types, alpine meadow displayed advanced SOS and EOS, alongside an extended LOG. The alpine steppe exhibited advanced SOS and delayed EOS with an extended LOG. Alpine desert displayed SOS advancement and EOS delay, alongside LOG extension. These findings revealed variations in vegetation phenological changes under different frozen soil types and highlighted divergent responses of distinct frozen soil types to climate change. They suggested that the influence of frozen soil types should be considered when investigating vegetation phenological dynamics at the regional scale. Full article

Xylella fastidiosa was first detected in Portugal in 2019 in Lavandula dentata. In response, the national plant health authorities promptly established a Demarcated Zone in the affected area and implemented a series of eradication and control measures, including the systematic removal and destruction of infected and host plants. This study analyzes the economic and operational impacts of these eradication efforts in the northern region of Portugal, with a focus on Demarcated Zones such as the Porto Metropolitan Area, Sabrosa, Alijó, Baião, Mirandela, Mirandela II, and Bougado between 2019 and June 2023. During this period, about 412,500 plants were uprooted. The majority were Pteridium aquilinum (bracken fern), with 360,324 individuals (87.3%), reflecting its wide distribution and the large area affected. Olea europaea (olive tree) was the second most common species removed, with 7024 plants (1.7%), highlighting its economic relevance. Other notable species included Quercus robur (3511; 0.85%), Pelargonium graveolens (3509; 0.85%), and Rosa spp. (1106; 0.27%). Overall, destruction costs were estimated at about EUR 1.04 million, with replanting costs of roughly EUR 6.81 million. In parallel, prospection activities—conducted to detect early signs of infection and monitor disease spread—generated expenses of roughly EUR 5.94 million. While prospecting represents a significant financial investment, the results show that it is considerably more cost-effective than large-scale eradication. Prospection enables early detection and containment, preventing the widespread destruction of vegetation and minimizing disruption to agricultural production, biodiversity, and local communities. Importantly, our findings reveal a sharp decline in confirmed cases in the initial outbreak area—the Porto Demarcated Zone—from 124 cases in 2019 to just 5 in 2023, indicating the effectiveness of the eradication and monitoring measures implemented. However, the presence of 20 active Demarcated Zones across the country as of 2023 highlights the continued risk of spread and the need for sustained vigilance. The complexity of managing Xylella fastidiosa across ecologically and logistically diverse territories justifies the high costs associated with surveillance and targeted interventions. This study reinforces the strategic value of prospection as a proactive and sustainable tool for plant health management. Effective surveillance requires the integration of advanced methodologies aligned with the phenological stages of host plants and the biological cycle of vectors. Targeting high-risk locations, optimizing sample numbers, ensuring diagnostic accuracy, and maintaining continuous training for field teams are critical for improving efficiency and reducing costs. Ultimately, the findings underscore the need to refine and adapt monitoring and eradication strategies to contain the pathogen, safeguard agricultural systems, and prevent Xylella fastidiosa from becoming endemic in Portugal. Full article

The aim of this study is to determine the reliability of regular and spatial cross-validation methods in predicting subfield-scale maize yields using phenological measures derived by Sentinel-2. Three maize fields from eastern Croatia were monitored during the 2023 growing season, with high-resolution ground truth yield data collected using combine harvester sensors. Sentinel-2 time series were used to compute two vegetation indices, Enhanced Vegetation Index (EVI) and Wide Dynamic Range Vegetation Index (WDRVI). These features served as inputs for three machine learning models, including Random Forest (RF) and Bayesian Generalized Linear Model (BGLM), which were trained and evaluated using both regular and spatial 10-fold cross-validation. Results showed that spatial cross-validation produced a more realistic and conservative estimate of the performance of the model, while regular cross-validation overestimated predictive accuracy systematically because of spatial dependence among the samples. EVI-based models were more reliable than WDRVI, generating more accurate phenomenological fits and yield predictions across parcels. These results emphasize the importance of spatially explicit validation for subfield yield modeling and suggest that overlooking spatial structure can lead to misleading conclusions about model accuracy and generalizability. Full article

Accurate assessment of a crop’s water requirement is essential for optimising irrigation scheduling and increasing the sustainability of water use. The crop coefficient (K_c) is a dimensionless factor that converts reference evapotranspiration (ET₀) into actual crop evapotranspiration (ET_c) and is widely used for irrigation scheduling. The K_c reflects canopy cover, phenology, and crop type/variety, but is difficult to measure directly in heterogeneous perennial systems, such as vineyards. Remote sensing (RS) products, especially open-source satellite imagery, offer a cost-effective solution at moderate spatial and temporal scales, although their application in vineyards has been relatively limited due to the large pixel size (~100 m²) relative to vine canopy size (~2 m²). This study aimed to improve grapevine K_c predictions using vegetation indices derived from harmonised Sentinel-2 imagery in combination with spectral unmixing, with ground data obtained from canopy light interception measurements in three winegrape cultivars (Shiraz, Cabernet Sauvignon, and Chardonnay) in the Barossa and Eden Valleys, South Australia. A linear spectral mixture analysis approach was taken, which required estimation of vine canopy cover through beta regression models to improve the accuracy of vegetation indices that were used to build the K_c prediction models. Unmixing improved the prediction of seasonal K_c values in Shiraz (R² of 0.625, RMSE = 0.078, MAE = 0.063), Cabernet Sauvignon (R² = 0.686, RMSE = 0.072, MAE = 0.055) and Chardonnay (R² = 0.814, RMSE = 0.075, MAE = 0.059) compared to unmixed pixels. Furthermore, unmixing improved predictions during the early and late canopy growth stages when pixel variability was greater. Our findings demonstrate that integrating open-source satellite data with machine learning models and spectral unmixing can accurately reproduce the temporal dynamics of K_c values in vineyards. This approach was also shown to be transferable across cultivars and regions, providing a practical tool for crop monitoring and irrigation management in support of sustainable viticulture. Full article

Severe water scarcity has become a critical constraint to global agricultural development. Enhancing both the timeliness and accuracy of crop evapotranspiration (ET_c) retrieval is essential for optimizing irrigation scheduling. Addressing the limitations of conventional ground-based point-source measurements in rapidly acquiring two-dimensional ET_c information at the field scale, this study employed unmanned aerial vehicle (UAV) remote sensing equipped with multispectral and thermal infrared sensors to obtain high spatiotemporal resolution imagery of a representative citrus orchard (Citrus reticulata Blanco cv. ‘Yichangmiju’) in western Hubei at different phenological stages. In conjunction with meteorological data (air temperature, daily net radiation, etc.), ET_c was retrieved using two established approaches: the Seguin-Itier (S-I) model, which relates canopy–air temperature differences to ET_c, and the multispectral-driven single crop coefficient method, which estimates ET_c by combining vegetation indices with reference evapotranspiration. The thermal-infrared-driven S-I model, which relates canopy–air temperature differences to ET_c, and the multispectral-driven single crop coefficient method, which estimates ET_c by combining vegetation indices with reference evapotranspiration. The findings indicate that: (1) both the S-I model and the single crop coefficient method achieved satisfactory ET_c estimation accuracy, with the latter performing slightly better (accuracy of 80% and 85%, respectively); (2) the proposed multi-source fusion model consistently demonstrated high accuracy and stability across all phenological stages (R² = 0.9104, 0.9851, and 0.9313 for the fruit-setting, fruit-enlargement, and coloration–sugar-accumulation stages, respectively; all significant at p < 0.01), significantly enhancing the precision and timeliness of ET_c retrieval; and (3) the model was successfully applied to ET_c retrieval during the main growth stages in the Cangwubang citrus-producing area of Yichang, providing practical support for irrigation scheduling and water resource management at the regional scale. This multi-source fusion approach offers effective technical support for precision irrigation control in agriculture and holds broad application prospects. Full article

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. Full article

The use of cover crops is one of the most effective practices for maintaining, or even improving, the carbon balance of agricultural soils, while offering various ecosystem benefits. However, replacing bare soil with cover crops can increase transpiration and potentially reduce the water available for subsequent cash crops. The study takes place in southwestern France where it is essential to strike a balance between carbon storage and water availability, and where agroecological practices are encouraged and water resources are limited and expected to diminish with climate change. In this study, estimates of cover crop biomass production, as well as of the components of the water and carbon cycles, are carried out using a hybrid approach, AgriCarbon-EO, combining modeling, remote sensing, and assimilation, with quantification of target variables and their uncertainties at decametric resolution. The SAFYE-CO₂ agrometeorological model used in AgriCarbon-EO is calibrated to represent cover crops development, and simulated variables are compared with CO₂ fluxes and evapotranspiration measured by eddy covariance (for NEE, R² = 0.57, RMSE = 0.97 gC·m⁻²; for ETR, R² = 0.42, RMSE = 0.87 mm), as well as to an extensive above-ground biomass dataset (R² = 0.71, RMSE = 93.3 g·m⁻²). Knowing the local performance of the approach, a large-scale, decametric-resolution modeling exercise was carried out to simulate winter cover crops in southwestern France, over five contrasting fallow periods. The significant variability in cover crop phenology and above-ground biomass was characterized, and estimates of the amount of humified carbon added to the soil by cover crops were quantified at the pixel level. With amounts ranging from 40 to 130 gC·m⁻² for most of the considered pixels, these new SOC values show clear trends as a function of cumulative evapotranspiration. However, the impact of cover crops on soil water content appears to be minimal due to spring precipitation. Full article

Accurate estimation of winter wheat yield is essential for ensuring food security. Recent studies on winter wheat yield estimation based on deep learning methods rarely explore the interpretability of the model from the perspective of crop growth mechanism. In this study, a multiscale winter wheat yield estimation framework (called MultiScaleWheatNet model) was proposed, which was based on time series remote sensing data and further takes into account meteorological and soil factors that affect wheat growth. The model integrated multimodal data from different temporal and spatial scales, extracting growth characteristics specific to particular growth stage based on the growth pattern of wheat phenological phase. It focuses on enhancing model accuracy and interpretability from the perspective of crop growth mechanisms. The results showed that, compared to mainstream deep learning architectures, the MultiScaleWheatNet model had good estimation accuracy in both rain-fed and irrigated farmlands, with higher accuracy in rain-fed farmlands (R² = 0.86, RMSE = 0.15 t·ha⁻¹). At the county scale, the accuracy of the model in estimating winter wheat yield was stable across three years (from 2021 to 2023, R² ≥ 0.35, RMSE ≤ 0.73 t·ha⁻¹, nRMSE ≤ 20.4%). Model interpretability results showed that, taking all growth stages together, the remotely sensed indices had relatively high contribution to wheat yield, with roughly equal contributions from meteorological and soil variables. From the perspective of the growth stages, the contribution of LAI in remote sensing factors demonstrated greater stability throughout the growth stages, particularly during the jointing, heading-filling and milky maturity stage; the combined impact of meteorological factors exhibited a discernible temporal sequence, initially dominated by water availability and subsequently transitioning to temperature and sunlight in the middle and late stages; soil factors demonstrated a close correlation with soil pH and cation exchange capacity in the early and late stages, and with organic carbon content in the middle stage. By deeply combining remote sensing, meteorological and soil data, the framework not only achieves high accuracy in winter wheat yield estimation, but also effectively interprets the dynamic influence mechanism of remote sensing data on yield from the perspective of crop growth, providing a scientific basis for precise field water and fertiliser management and agricultural decision-making. Full article

We develop a year-round, field-scale framework to retrieve soil moisture and map irrigation in an arid irrigation district where crop phenology and canopy dynamics undermine static, single-season approaches. However, the currently popular TVDI application is limited during non-growing seasons. To address this gap, we introduce a season-stratified TVDI scheme—based on the LST–EVI feature space with phenology-specific dry/wet edges—coupled with a non-growing-season inversion that fuses Sentinel-1 SAR and Landsat features and compares multiple regressors (PLSR, RF, XGBoost, and CNN). The study leverages 2023–2024 multi-sensor image time series for the Yichang sub-district of the Hetao Irrigation District (China), together with in situ topsoil moisture, meteorological records, a local cropping calendar, and district statistics for validation. Methodologically, EVI is preferred over NDVI to mitigate saturation under dense canopies; season-specific edge fitting stabilizes TVDI, while cross-validated regressors yield robust soil-moisture retrievals outside the growing period, with the CNN achieving the highest accuracy (test R² ≈ 0.56–0.61), outperforming PLSR/RF/XGBoost by approximately 12–38%. The integrated mapping reveals complementary seasonal irrigation patterns: spring irrigates about 40–45% of farmland (e.g., 43.39% on 20 May 2024), summer peaks around 70% (e.g., 71.42% on 16 August 2024), and autumn stabilizes near 20–25% (e.g., 24.55% on 23 November 2024), with marked spatial contrasts between intensively irrigated southwest blocks and drier northeastern zones. We conclude that season-stratified edges and multi-source inversions together enable reproducible, year-round irrigation detection at field scale. These results provide operational evidence to refine irrigation scheduling and water allocation, and support drought-risk management and precision water governance in arid irrigation districts. Full article