In recent years, there has been a growing use of unmanned aerial vehicle (UAV) based light detection and ranging (LiDAR) data for mapping plant area index (PAI) in orchards. However, using LiDAR time-series collected throughout the growing season to assess PAI variations in response to phenology, represents an understudied area of investigation. Furthermore, establishing the optimal spatial resolution for mapping biophysical variables of tree crops from LiDAR point cloud data remains poorly defined. Here, we assess the capability of a UAV-based LiDAR system to characterize cherry trees throughout the growing season, with a focus on monitoring PAI and the vertical structure of individual trees. A time-series of 14 point cloud acquisitions with a density of 3300 points/m2 was collected between February and December 2022, covering all phenological stages of a cherry orchard in southern France. A voxel-based method was applied to create a three-dimensional grid within which PAI was estimated for each voxel. PAI was mapped by accumulating the individual voxel-based PAI values within each vertical voxel column. The results demonstrate that a voxel size of at least 0.7 m is required to retrieve reliable PAI estimates (RMSE = 0.58 m2.m−2, MAE = 0.48 m2.m−2, bias = 0.19 m2.m−2, rRMSE = 23 This research presents an automated LiDAR-based computational approach to assess within-field variability in orchards, including plant area index, vertical structure, and canopy condition.

Abstract Developed by the King Abdullah University of Science and Technology (KAUST) to support research in atmospheric science and remote sensing, KAUSTSat represented Saudi Arabia's (and the Middle East's) first research‐focused hyperspectral CubeSat mission for Earth observation. The primary payload consisted of the Simera HyperScape50, a miniaturized hyperspectral sensor operating in the visible to near‐infrared range. The sensor was equipped with a custom continuous variable filter that collected imagery at 30 m spatial resolution with a 120 km swath. The HyperScape50 allowed for up to 32 user‐defined spectral bands to be selected per acquisition from a total of 442 programmable channels between 442 and 884 nm, including a panchromatic band. These capabilities enable detailed observations of vegetation, soil, coastal zones, and other surface features relevant to applications in agriculture, biodiversity, resource management, and disaster response. In this paper, we provide an overview of the mission architecture, sensor design, acquisition strategy, and data structure. The hyperspectral data sets acquired over the 14‐month mission lifetime will also be presented, with a particular focus on the Arabian Peninsula and RadCalNet calibration sites. The KAUSTSat mission serves as a demonstration case of the viability of academic‐driven CubeSat platforms for delivering targeted, high‐quality environmental data, and represents a valuable reference for future small satellite Earth observation programs.

Hyperspectral unmixing aims to decompose each pixel in a hyperspectral image into a set of constituent endmembers and their corresponding abundances. Recent deep learning based approaches have demonstrated strong performance in capturing both spectral and spatial features. However, obtaining reliable per-pixel abundance ground truth in real hyperspectral scenes is generally infeasible, which motivates unsupervised and self-supervised unmixing strategies. In this work, we propose S3ViT, a self-supervised Spectral Vision Transformer designed for pixel-wise hyperspectral unmixing. The transformer captures spectral and spatial dependencies by applying self-attention over the full sequence of pixel tokens (1×1) augmented with learnable positional embeddings. It operates without ground-truth annotations by generating pseudo labels through an unsupervised process: first, Singular Value Decomposition (SVD) is used to estimate the number of endmembers based on a thresholded singular value spectrum; then, k-means clustering provides cluster-derived priors that are used to form a contextual token and initialize spectral prototypes, without being treated as true abundance supervision. To guide training, two initialization tokens, one from Vertex Component Analysis (VCA) and one from the k-means cluster-derived priors, are embedded alongside patch tokens into the transformer. The model learns to estimate abundance maps while enforcing the Abundance Non-negativity and Sum-to-One constraints through ReLU and Softmax layers. Endmember spectra are later estimated from pixels with high predicted abundances. We evaluated S3ViT on the Samson, Jasper Ridge and Washington DC Mall benchmark datasets and compared it to state-of-the-art geometrical and deep learning methods. Our model achieves superior or comparable performance in both RMSE and SAD metrics, with up to 31% improvement in SAD and 25% in RMSE. These results indicate that a compact pixel-token ViT, guided by weak spectral priors and optimized via reconstruction losses, can achieve competitive unmixing performance on standard benchmarks.

Extreme heat and rapid urbanization are converging challenges for the Arabian Peninsula, yet their fine-grained interactions remain poorly understood. We present a high-resolution assessment of land-cover land-use change, population growth, and land surface temperature across 2000-2020. We found newly urbanized areas converted from desert exhibited significantly lower warming ( + 1.78 °C) than existing urban areas ( + 2.39 °C) and unchanged desert ( + 2.97 °C). However, these newly urbanized areas maintained higher surface temperature than existing urban areas by 2020 (42.68 °C versus 41.29 °C, on average), creating a thermal situation where 13.1 million new residents live in places with relative cooling yet higher absolute surface temperature exposure. We identified distinct population-thermal pathways: small Gulf states achieved population growth with minimal warming through urban densification, while larger countries showed sprawl-dominated patterns with varied thermal outcomes. Our findings demonstrate that desert cities experience fundamentally different thermal dynamics than temperate regions and require revised adaptation frameworks accounting for urban cooling potential and extreme baseline temperatures. From 2000-2020, 13.1 million new residents settled in newly urbanized areas in Arabian Peninsula that cooled relative to deserts yet remained hotter than existing cities, according to high-resolution analysis combined land-cover change, population growth, and land surface temperature data.

Ongoing climate warming may profoundly impact terrestrial gross primary productivity (GPP), a key component of the global carbon cycle. However, uncertainty in the relative roles of atmospheric water demand (vapor pressure deficit, VPD) and root-zone soil moisture (SM) limits predictions of drought impacts on GPP. Here, we show that growing-season GPP was more strongly constrained by VPD than SM globally, based on observation-constrained model estimates, satellite retrievals and Dynamic Global Vegetation Model simulations. The importance of VPD increased with higher temperatures and more severe and prolonged droughts. This pattern reflects VPD's critical role in regulating stomatal conductance and plant hydraulic function, both of which are essential for preventing xylem embolism. We further found that P50, the water potential at 50% loss of hydraulic conductivity, was the strongest predictor of their relative influence. Collectively, rising VPD may accelerate declines in terrestrial carbon storage under future warming.

Fractional Vegetation Cover (FVC) is a key ecological variable for monitoring ecosystem health, land degradation, and vegetation dynamics in dryland environments. While satellite and UAV observations enable scalable FVC estimation over large spatial extents, the accuracy and robustness of these models remain strongly dependent on high-quality field-based reference data for calibration and validation. Traditional in-situ methods, including visual estimates using transect-based surveys, remain widely used but are labor-intensive and inherently subjective. Digital photography has emerged as a practical alternative, typically analyzed using index-based computer vision techniques or deep learning models. However, these methods are highly sensitive to background variability and therefore rely on massive labeled datasets. Recent advances in multimodal large language models (MLLMs) suggest a potential paradigm shift, as these models combine visual perception with high-level reasoning and benefit from diverse pre-training that enables conceptual knowledge transfer across tasks. In this study, we evaluate the feasibility of using MLLMs for direct estimation of FVC from ground-level photographs without task-specific training. We collected and compiled a dataset of more than 1,100 quadrat pictures from across 26 dryland sites in Saudi Arabia, spanning a wide range of surface conditions from bare soil to sparsely vegetated rangelands. Each picture corresponded to a 1 m × 1 m quadrat with FVC estimated independently by two experts, whose average was used as reference data for assessment of model predictions. Six state-of-the-art multimodal large language models, including Qwen2.5-VL, Mistral-Small-3.2, LLaMA-4-Maverick, LLaMA-4-Scout, and two Gemma-3 variants, were evaluated using four prompt designs that varied in length, ecological context, and methodological detail. Across all models and prompts, MLLMs achieved a mean absolute error of approximately 7.8%, demonstrating competitive performance relative to traditional image-based methods. The best-performing model-prompt combinations achieved mean absolute error values below 5%, with low systematic bias. Short and ecologically explicit prompts consistently outperformed more complex prompt designs, achieving an average reduction in mean absolute error (MAE) of approximately 1.3–1.4 percentage points compared to visually guided or highly structured prompts (MAE ≈ 6.9% versus 8.2–8.4%). Overall performance was more sensitive to model choice than to prompt structure, with mean MAE varying from approximately 5.6% to 10.0% across models, compared to a narrower range across prompts. The highest accuracy was obtained using the Qwen2.5-VL model with an ecologically detailed prompt, which achieved a mean absolute error of 4.9%, near-zero bias, and an RMSE of 8.4%. Across all prompt designs, Qwen2.5-VL and Mistral-Small-3.2 consistently delivered the best overall performance, both maintaining mean MAE values below 6% and exhibiting stable behavior across prompt variations, indicating robustness to prompt design. These results demonstrate that MLLMs can provide accurate and scalable FVC estimates directly from field photographs, without requiring specialized training datasets. This approach offers a promising alternative for rapid field surveys and reference data generation, particularly in dryland ecosystems where background complexity and data scarcity limit the effectiveness of conventional methods.

Near-surface air temperature (NSAT) is crucial for climate and hydrological studies, making the development of accurate estimation models along with high-resolution datasets essential. Although remote sensing (RS) technology combined with machine learning (ML) has been widely applied to the retrieval of land surface variables, several challenges remain in estimating NSAT: (1) the optimal algorithm for different land cover types still unclear; (2) the differences in the contributions of various predictors to NSAT across different land cover types are not well understood; (3) there is still a lack of high-resolution, spatiotemporally continuous NSAT datasets. To address these, we developed an NSAT estimation framework for mainland China using 831 meteorological stations and nine RS predictors, integrating stacking ensemble learning with SHAP analysis. We systematically compared stratified modeling by land cover type (StraM) with integrated modeling (IntM). Results show: (1) NSAT estimation accuracy varies by ML algorithm and land cover type. StraM with stacked optimal algorithms achieved the best performance (NSATmin: RMSE = 2.647 degrees C; NSATave: RMSE = 1.883 degrees C; NSATmax: RMSE = 2.681 degrees C); (2) The StraM method consistently achieved significantly higher estimation accuracy across all land cover types compared to the IntM. (3) The contributions of predictors to NSAT estimation varied among land cover types, with land surface temperature remaining the most important variable. The generated NSAT dataset aligns with existing datasets and ground observations in capturing the spatiotemporal characteristics of NSAT over China, while demonstrating improved capability in representing spatial details. This study provides a robust foundation for developing representative NSAT products applicable to ecological monitoring and climate research.

IntroductionIn arid and hyper-arid regions, agriculture depends heavily on irrigation, making crop type monitoring important for water allocation, monitoring crop management policies, and providing the information required to forecast food supply. However, field labels are often scarce, and crop calendars can shift due to locally managed planting, harvest, and irrigation decisions, complicating mapping at field-scale.MethodsWe present a seasonal crop type mapping approach applied to Wadi Al-Dawasir, Saudi Arabia, generating maps for 2020–2024 from biweekly optical and radar satellite time series. The method learns representations from unlabeled imagery through self-supervised pretraining and fine-tunes a segmentation model using a small set of field observations with pseudo-label augmentation from unsupervised clustering. We mapped four classes: fallow, cereal, vegetables, and forage, and evaluated performance using overall accuracy, F1-score, and mean intersection-over-union (mIoU).ResultsThe configuration combining self-supervised pretraining, pseudo-label augmentation, and optical-radar inputs achieved an mIoU of 0.80, an F1-score of 0.88, and an overall accuracy of 0.97. In contrast, using optical information alone substantially reduced accuracy, with an mIoU of 0.35, an F1-score of 0.32, and an overall accuracy of 0.64. Fallow and forage produced the highest mapping accuracies, with mIoU values of 0.97 and 0.85, respectively, while vegetables had the lowest accuracy among the four crop groups, with an mIoU of 0.52.DiscussionThese results show that self-supervised temporal pretraining combined with pseudo-label augmentation can support efficient multi-season, field-scale crop mapping using limited labels in irrigation-driven arid regions. The lower accuracy for vegetables highlights the continued challenge of mapping heterogeneous crop groups with overlapping phenological patterns.

Monitoring crop conditions is crucial for effective crop management and provides valuable insights into soil-plant-atmosphere interactions. While some studies have used unmanned aerial vehicle (UAV)-based light detection and ranging (LiDAR) data for mapping plant area index (PAI) in orchards, LiDAR-based time-series analysis to assess PAI variations with phenology throughout the growing season represents a significant gap in knowledge. Tracking PAI dynamics across phenological stages reflects canopy development and leaf expansion, which are directly linked to yield formation. Furthermore, the optimal spatial resolution for mapping biophysical variables of tree crops from LiDAR point clouds is yet to be determined. This study aimed to demonstrate the potential of UAV-derived LiDAR time-series to monitor the PAI and tree vertical profiles at high spatial resolution throughout the growing season of a cherry orchard located in southeastern France. A time series of 14 point cloud acquisitions with a density of 3300 points/m² was collected between February and December 2022, with at least one acquisition per month, covering all phenological stages of the cherry orchard. Field measurements were collected on May 30, and October 6, to measure the PAI at twilight using an LAI-2200C Plant Canopy Analyzer (LI-COR Biosciences, Lincoln, NE, USA), with 248 trees sampled. A voxel-based method was applied on the LiDAR point cloud data to create a three-dimensional grid within which PAI was estimated for each voxel. The results showed that a voxel size of at least 70 cm is required to retrieve reliable PAI estimates, while a voxel size of 100 cm produced the most accurate PAI estimates (RMSE = 0.5 m2.m-2, bias = 0.07, R2 = 0.59), when assessed against in-situ PAI measurements. The temporal variation of canopy PAI illustrated the progression of the phenological stages, including flowering, leaf development, ripening and senescence, and the response of the canopy to drought stress (reduction in PAI due to leaf rolling) during the summer. The maps of PAI successfully described the variations in leaf canopy density for different cherry varieties and allowed assessment of the vertical PAI profile at the individual tree level. The LiDAR-derived PAI maps and vertical profiles were able to detect trees exhibiting poor leaf development, which is an important health indicator for effective crop management in orchard settings. Future work should focus on applying UAV-derived observations to optimize crop models to enhancing decision-making tools for effective orchard management.

Integrating data from both unmanned aerial vehicle (UAV) and satellite platforms, UAV-satellite synergies can effectively leverage multi-scale crop information extraction. Although previous reviews have discussed UAV-satellite synergies, they have either adopted a broader scope beyond crop monitoring or focused mainly on specific aspects such as optical data or data fusion methods. This paper provides an updated and crop monitoring specific synthesis by systematically analyzing peer-reviewed studies and offering a structured framework of data sources, synergy strategies, applications, key challenges, and future directions. Our findings reveal a strong concentration of optical sensor combinations and model-based learning strategies, particularly for crop trait estimation and classification. However, the synergy field remains in its infancy, facing persistent challenges in multi-source data matching, strategy design, and practical deployment. Spatial, temporal, and signal mismatches hinder data compatibility and model transferability, while many current strategies rely on empirical routines with limited generalization. To address these limitations, we propose three future directions: enriching multi-source and multimodal observations, enabling modular and adaptive synergies for complex applications, and integrating emerging and cross-domain technologies such as UAV swarms, autonomous deployment systems, and precision agriculture infrastructures. These insights provide a theoretical foundation and practical guidance for advancing UAV-satellite synergies specially for agricultural remote sensing.

Crop disease presents significant threats to global food security and agricultural sustainability. Traditional monitoring methods, reliant on visual inspections and laboratory analyses, are labor intensive and unsuitable for large-scale implementation. Hyperspectral remote sensing has emerged as a promising tool for operational crop disease monitoring. Here, we provide a broad review, starting with a hyperspectral-based description of observable symptoms of common crop disease and then examining hyperspectral features, including spectral and textural features, pigment light absorption, solar-induced chlorophyll fluorescence (SIF), temporal information, and auxiliary data. We also analyze the algorithms used for disease detection, including traditional statistical methods, machine learning (ML)-based methods, and physically based methods. The review highlights the effectiveness of these methods in distinguishing various stressors, detecting early disease, assessing crop resistance, and monitoring large-scale disease. Additionally, we present two case studies of uncrewed aerial vehicle (UAV)-based hyperspectral imaging for maize leaf spot monitoring. Based on a quantitative literature review, we summarize current research trends. Future research should emphasize integrating physical models with deep learning (DL), ensuring the sensitivity and robustness of spectral features and promoting international data sharing.

Reservoir sedimentation poses a critical threat to water security by reducing storage capacity and impairing functionality. Despite its importance in water resource management, sedimentation remains poorly quantified at large scales. Here, we present the first nationwide assessment across 574 reservoirs in Saudi Arabia, with a total design capacity of approximately 2.58 billion cubic meters (BCM). We combine long-term water extent trends (1986-2024) from Landsat imagery with sediment yield and reservoir infill time estimates from the Revised Universal Soil Loss Equation (RUSLE). Analysis of 488 reservoirs (>0.1 million m3) with reliable records revealed a median annual water extent decline of -1.5% year-1, with 63% (307 reservoirs) showing decreasing trends. Statistically significant reductions (p<0.05) were found in 30% (147 reservoirs), while only 3% (12 reservoirs) showed significant increasing trends. Furthermore, 26% (126 reservoirs) experienced ≥5 consecutive years of negligible water presence, suggesting severe sediment accumulation. Climate variability was not the dominant driver, as precipitation and evaporation trends were minor and poorly spatially correlated with water extent trends. Reservoir infill times from RUSLE (median 43 years) aligned reasonably with satellite-based estimates (median 66 years), with shorter infill times corresponding to stronger declines. Our estimates indicate that sedimentation may have reduced the total usable storage capacity of Saudi Arabia's reservoirs by approximately 32% to 1.77 BCM, although this does not account for potential sediment management interventions. Given government plans to construct 1,000 additional dams, effective watershed management, sediment removal, and sustainable design are vital for Saudi Arabia's water future.

In line with Saudi Vision 2030 and the ambitions of the Saudi Green Initiative to plant 10 billion trees and protect 30% of Saudi Arabia’s land and sea areas, large parts of Saudi Arabia are being transitioned into nature reserves and undergoing regreening activities, while also fencing off large areas for vegetation restoration and protection. To effectively manage the regreening and restoration initiatives, it is imperative to frequently monitor biomass changes over time and quantify the accumulation of biomass to determine if the restoration and tree-planting initiatives have the intended outcomes. However, landscape responses to regreening and vegetation restoration are poorly understood in hyper-arid rangelands. Environmental processes within different habitat zones might differ, which further complicates biomass monitoring. Here, we present a framework that is currently being applied across multiple hyper-arid rangelands in Saudi Arabia to estimate biomass within newly established nature reserves. The initial work focused on developing a scaling approach between ground, unmanned aerial vehicle (UAV), and satellite image data, including PlanetScope and Sentinel-2 imagery. Field sites were identified using Google Earth imagery and a number of criteria, including a 5-year Sentinel-2 NDVI time-series to identify greening events, terrain characteristics based on DEM data, and differences in vegetation functional types (annual and perennial grass/herbs/forbs, shrubs and trees), soil types, and habitats. Field-based measurements at selected sites focused on determining biomass of different vegetation functional types, using a double-sampling approach, including a limited number of destructive samples, and a large number of biomass estimates based on the structural and dimensional characteristics of the destructive samples. UAV-based light detection and ranging (LiDAR) and multispectral data were obtained for each site to estimate biomass from the field-based samples using different machine and deep learning approaches (random forest, support vector machines, vision transformer, UNet with attention encoder). It was found that LiDAR data provided useful information on vegetation height and volume, which improved the biomass estimates, whereas the multispectral data enabled discrimination between photosynthetic and non-photosynthetic vegetation components. Based on the UAV-derived estimates of biomass, a scaling approach was applied to estimate biomass from both PlanetScope and Sentinel-2 data, with results demonstrating that the higher spatial resolution of the PlanetScope data improved the accuracy due to the very sparse vegetation in most parts of the hyper-arid rangelands of this study. While hyper-arid rangelands are generally underrepresented in research studies worldwide, this research provide a viable framework for assessing vegetation dynamics of biomass both seasonally and annually.

A comprehensive analysis of changes in sea surface temperature (SST) was performed across the Red Sea for the period 2003 to 2020 using satellite data from the Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua. Employing a regionalization scheme based on principal component analysis (PCA), five homogenous subregions were identified that explain about 85 % of the total variation in SST across the Red Sea. The results indicate that there is a diverse and complex range of SST variability throughout the Red Sea. Spatially, distinct SST trends were observed between the southern (PC1) and northern (PC3) regions, which show northward enhancement in the rate of SST trends. A zonal contrast in the rate of warming over the western and eastern sectors was also observed, exhibiting more pronounced warming trends along the western coasts. In contrast to the offshore and deep waters, surface warming in shallow waters (depth < 100 m) was more pronounced, which poses detrimental effects (e.g., thermal coral bleaching) on regional marine ecosystems. We found a robust link between spatial patterns of SST anomalies and the phases of the Red Sea Dipole (RSD). This connection was largely regulated by the upwelling associated with the local wind-stress-curl. Further, the spatial and temporal patterns of wind-driven upwelling (i.e., coastal and wind-stress-curl-driven upwelling) were reminiscent of the SST trend, highlighting the significant role of the upwelling mechanism in the SST budget and trend across the Red Sea. The positive phase of the RSD aligns with periods of stronger Toker Jet activity, reinforcing cold SST anomalies in the southern Red Sea due to enhanced upwelling-induced cooling. However, the impacts of winddriven upwelling on local SST differ from region to region, highlighting the need to employ a high-resolution wind dataset in the simulation of SST across the Red Sea. Overall, our findings offer insights into the complex mechanisms and factors influencing SST variability in the Red Sea, thereby contributing to improving coastal zone management and environmental planning efforts.

Current research on livestock movement ecology focuses on quantifying the factors that trigger alterations in movement behavior and understanding hidden mechanisms. Modern tracking technologies and robust statistical analysis models deliver new opportunities for investigating how individual animals cope with the joint effect of biotic and abiotic factors at different time scales. We applied multivariate Hidden Markov Models (HMMs) to characterize the fine-scale movement behavior (30-second intervals) of GPS-tracked domestic Zhongwei goats (Capra aegagrus hircus) for 124 days and analyzed the combined influence of biotic and abiotic factors and specific time of day on their seasonal movement behavioral transition in a predator-free, semi-arid mountain grassland in China. We classified the behaviors of goats into two states: foraging (low step length, varied turning angle) and travelling (long step lengths, small turning angles). The terrain slopes had the most impact on their movement behavioral transition in the full year, spring, autumn, and winter. However, in the summer with hotter temperatures, the specific time of day explains their movement behavior most. Forage resources indicated by the Normalized Difference Vegetation Index (NDVI), and terrain ruggedness measured by the Vector Ruggedness Measure (VRM), had less impact on their behavior transitions compared to terrain slope and specific time of day. Elevation and solar radiation could not explain their movement behavior in different seasons, nor could NDVI in winter or VRM in spring and autumn. Across different seasons, the probability of foraging behavior increased with the later times of day, steeper terrain slopes, and higher NDVI, while it decreased with increasing VRM. The impact of NDVI on the probability of foraging behavior was largest during the early onset of vegetation growth in spring, and lowest in winter coinciding with a lower availability of food resources. The movement speed was lower, and the daily foraging percentage was higher in spring and winter due to lower food resources and shorter daylight hours. In contrast, movement speed was higher, and the daily foraging percentage was lower in summer and autumn with more food resources and longer daylight hours. The percentage of time allocated to foraging increases hourly from 9:00 am to 8:00 pm across various seasons. HMMs were found to be useful for disentangling the movement behavior of goats. Our approach provides new insights into the seasonal and daily behavioral strategies of goats. Results demonstrate that in the mountain region, terrain slopes and specific times of the day more effectively trigger domestic goat behavioral transition from one state to the next compared with biotic factors, represented herein by NDVI, across different seasons. The early onset of vegetation growth and a shorter period of available high-quality forage in spring, significantly influenced goat behavioral transitions. Overall, these results are important for designing appropriate grazing management strategies that satisfy the ecological and socioeconomic demands of semi-arid grassland ecosystems.

Cosmic ray neutron sensor (CRNS) has gained popularity in the last decade for its suitability in estimating areaaveraged soil moisture (SM). The presence of fresh biomass influences the CRNS signal due to its water content, introducing bias to soil moisture estimation. Calibration and correction methods have been developed to account for this bias, but they usually require laborious sampling. Here, a novel approach is tested to assess the impact of biomass water equivalent (BWE) on CRNS soil moisture estimation. It was conducted in two contrasting environments from 15/11/21-1/02/23 for an olive orchard in Saudi Arabia, and from 15/02/22-30/03/23 for a cherry orchard in France. Water-uptake rates were monitored using sap flow sensors, as well as actual evapotranspiration (AET) and in-situ SM within the CRNS footprint. Concurrent environmental variables were also measured with a research-grade weather stations. It was found that when vapor pressure deficit (VPD) > 1.8kPa, CRNS-derived SM (CRNS-SM) closely matched in-situ SM measurements, which indicates minimal influence from BWE. Conversely, when VPD is lower than 1.8kPa, CRNS-SM overestimates the in-situ moisture. An optimization approach was used to find a temporally-varying value of N0 parameter that minimizes the difference between soil moisture estimated with CRNS and in-situ sensors. Furthermore, the results showed that the relative change in the optimized value of N-0 (N-0,N-opt) was well correlated with VPD in both orchards (R-2 = 0.66 for olive and R-2 = 0.74 for cherry orchards), indicating a strong correlation between these variables. These findings suggest that integrating VPD and CRNS observations, and using the VPD-N-0,N-opt correlation approach could be a promising way to account for the bias due to biomass dynamics on the estimation of area-averaged SM.

Biomass water equivalent represents the cumulative vegetation water content and biological hydrogen contained within plant tissue and can provide valuable insights into ecosystem-scale water dynamics. Several studies conducted in crop and forest fields have reported that variation in biomass water equivalent can decrease the accuracy of soil moisture estimates when using the Cosmic-Ray Neutron Sensor (CRNS): a novel approach for real-time soil moisture monitoring. Indeed, with an increasing biomass water equivalent, more neutrons at epithermal and thermal energy levels are held by the hydrogen atoms in the vegetation, leading to an overestimation of soil moisture. In this study, we explore the impact of such variations in biomass water equivalent on the estimated soil moisture from CRNS sensors installed in two distinct orchard plantations. The first plantation is an olive orchard located in northern Saudi Arabia (desert climate), while the second is a cherry orchard located in southeastern France (Mediterranean climate). Utilizing a site-specific calibration value (N0), soil moisture was derived from neutron counts, and compared to reference in-situ soil moisture. The estimated and reference soil moisture difference was analyzed as a function of biomass water equivalent variations. As biomass water equivalent measurements were not possible to obtain at equivalent CRNS acquisition rates, the vapor pressure deficit (VPD), a meteorological variable, in plants, it is used to describe the difference in water vapor pressure between the inside of a leaf and the surrounding air, was used as a proxy. An optimization procedure was performed to update N0 (N0.opt) in such a way that the difference between estimated and reference soil moisture is minimized. Variations in N0,opt are subsequently correlated with VPD to confirm the link between neutron count variations and seasonal changes in biomass water equivalent. Results showed that without considering the effects of biomass water equivalent on neutron counts, the estimated soil moisture overestimates the reference soil moisture when the VPD is low (no stress conditions) and matches the reference soil moisture when the VPD is high (water stress conditions). Moreover, the results showed that the change in N0 (1 - N0.opt / N0) correlated well with VPD (R2 = 0.7). An improved understanding of the potential effects of biomass water equivalent on CRNS signals is required for understanding water dynamics in trees and providing insights for optimizing irrigation.

Partitioning of evapotranspiration (ET) into soil evaporative (E) and plant transpiration (T) components remains challenging in flux modeling that has particular relevance to crop water use management. Here, we develop an approach to modify the Mapping EvapoTranspiration at high Resolution and with Internalized Calibration model (METRIC) that allows improved partitioning to landscape-scale flux components. Referred to herein as METRIC-2S, the approach introduces a two-source scheme into the original one source model, using soil and vegetation temperatures to drive the partitioning process. These temperatures are used by METRIC to calculate two ET components, one for the soil and another for the vegetation, which are subsequently weighted by the fractional vegetation cover (fc) to compute E and T. Soil and vegetation temperatures are estimated using the hourglass method, which is driven by the surface temperature and fc. ET estimates from the original METRIC and revised METRIC-2S models are intercompared and validated against eddy covariance measurements over three agricultural sites, including an olive orchard, wheat field and a mixed wheat/olive plantation. Overall, METRIC-2S provides considerable improvements in accuracy relative to the original METRIC model over the three sites, with observed decreases in RMSE from 141 to 63 W/m2 at the olive site, 102 to 83 W/m2 over the wheat field and from 180 to 78 W/m2 at the mixed site. To evaluate the performance of the partitioning scheme, transpiration estimates are compared against available sap flow measurements over the olive orchard site for selected dates that coincide with a Landsat overpass, with an RMSE from this reduced sample of approximately 22.3 W/m2. While additional verification and assessment of component values are required, results suggest that the METRIC-2S approach represents a good trade-off between simplicity and improved accuracy.

Soil moisture (SM) plays a central role in water cycle dynamics and land-atmosphere interactions, acting across local and regional scales. Few studies have explored the use of the ground-based global navigation satellite system reflectometry (GNSS-R) interference pattern technique (IPT) for SM estimation. In these studies, SM was estimated from the GPS elevation angle where lower reflectivity occurs (notch), which is difficult to determine in real GNSS-R interference power (IP) acquisitions. This study introduces the use of IP amplitude at vertical polarization (V-pol), readily extracted from the IP oscillations, as an alternative for SM estimation beneath vegetation cover. An empirical model was developed for estimating SM in irrigated grassland using a GNSS-R receiver with a linearly polarized antenna. The experiment, conducted between June 6 and August 8, 2022, covered the grassland's growth phase and preharvesting and postharvesting. The study incorporated normalized difference water index (NDWI) from the Sentinel-2 satellite to account for vegetation's impact on IP amplitude. Results indicated that the IP amplitude at V-pol accurately estimates SM (RMSE =0.04 m3/m3). Moreover, the results show that the vegetation layer mainly attenuates the IP amplitude with a nonsignificant scattered contribution to the IP, allowing for the simplification of the empirical model by ignoring the scattered contribution of vegetation. The simplified empirical model can be numerically resolved to estimate the NDWI if the SM is known. In summary, this study highlights the effectiveness of the ground-based IPT for close-range sensing of SM and a biomass proxy, such as NDWI.

Object-based analysis is widely used for extracting information from satellite data using machine learning, offering reduced sensitivity to fine-scale variability, noise, and computational cost compared to pixel-based methods. However, segmentation algorithms for center-pivot fields often treat fields as single units, neglecting that a field can be subdivided into different sections caused by varied management practices, such as differing planting and harvesting dates, crop types, and rotations. This variability is particularly prevalent in hot, arid regions, such as Saudi Arabia, where precise water and crop management are crucial for sustaining agricultural productivity. However, such subfield division reduces the accuracy of object-based agroinformatics insights and the effectiveness of large-scale analyses. A machine learning-based approach combining Kmeans clustering and cosine similarity was developed to quantify subfield divisions using temporal features derived from Sentinel-2 normalized difference vegetation index (NDVI) time series. The performance of discrete wavelet transformation(DWT) and Savitzky-Golay filtering was compared for processing the NDVI time series. When evaluated against a reference dataset, the approach achieved a maximum accuracy of 93.38% with DWT level 1 decomposition using the "haar" wavelet function. These parameters were applied to map the nationwide center-pivot subfield division dynamics across Saudi Arabia from 2019 to 2023. Results revealed that approximately 20% of center-pivot fields exhibited subfield divisions, ranging from 5740 fields (2083 km$<^>{2}$) in 2020 to 7342 fields (2770 km$<^>{2}$) in 2023. Larger fields were more prone to subfield divisions, with a median acreage of 40 ha compared to 20 ha for undivided fields. Dominant management strategies included half-to-half and 5:3:2 divisions. This approach enhances object-based agroinformatics products and facilitates more accurate food security assessments.