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Agricultural intensification has resulted in the depletion of groundwater resources in many regions of the world. A prime example is Saudi Arabia, which witnessed dramatic agricultural expansion since the 1970s. To explore the influence of policy interventions aimed to better manage water resources, accurate information on the changes in the number and acreage of center-pivot fields is required. To quantify these metrics, we apply a hybrid machine learning framework, consisting of Density-Based Spatial Clustering of Applications with Noise, Convolutional Neural Networks, and Spectral Clustering, to the annual maximum Normalized Differential Vegetation Index maps obtained from Landsat imagery collected between 1990 to 2021. When evaluated against more than 28,000 manually delineated fields, the approach demonstrated producer's accuracies ranging from 83.7% to 94.8% and user's accuracies ranging from 90.2% to 97.9%. The coefficient of determination ( R 2 ) between framework-delineated and manually delineated fields was higher than 0.97. Nationally, we found that most fields pre-dated 1990 (covering 8841 km 2 in that year) and were primarily located within the central regions covering Hail, Qassim, Riyadh, and Wadi ad-Dawasir. A small decreasing trend in field acreage was observed for the period 1990–2010. However, by 2015, the acreage had increased to approximately 33,000 fields covering 9310 km 2 . While a maximum extent was achieved in 2016, recent decreases have seen levels return to pre-1990 levels. The gradual decrease between 1990 to 2010 was related to policy initiatives designed to phase-out wheat, while increases between 2010 to 2015 were linked to fodder crop expansion. There is evidence of an agricultural uptick starting in 2021, which is likely in response to global influences such as the COVID-19 pandemic or the conflict in Ukraine. Overall, this work offers the first detailed assessment of long-term agricultural development in Saudi Arabia, and provides important insights related to production metrics such as crop types, crop water consumption, and crop phenology and the overarching impacts of agricultural policy interventions. [ABSTRACT FROM AUTHOR]

Soil moisture is an important precision agricultural variable that can be used to identify optimal growth conditions, infer vegetation stress, and maximize crop yield. However, obtaining soil moisture information with sufficient spatial and temporal resolution for such applications remains a challenge. The optical trapezoid model (OPTRAM), a shortwave infrared transformed reflectance and normalized difference vegetation index (STR-NDVI) method, has previously been used for retrieving soil moisture from optical remote sensing data. However, the capacity of OPTRAM for mapping the high-resolution spatial heterogeneity of soil moisture at individual agricultural field scales has yet to be explored. Here, we advance an approach for retrieving and quantifying the spatial heterogeneity of soil moisture for individual fields using high spatiotemporal resolution Sentinel-2 imagery. We also propose the concept of a dynamic STR-NDVI space for identification of irrigation events and crop growth stages, such as irrigation start and end dates, and dates of initial crop growth, maturity and harvest. Pre-existing OPTRAM parameterization schemes were evaluated for several crops (maize, carrot, alfalfa and Rhodes grass) and a new scheme was proposed. Results show that the original OPTRAM model can derive surface soil moisture (∼ 1 cm in depth) with acceptable coefficients of determination (R2 ≥ 0.43) and root mean square errors (RMSE ≤ 0.09 m3/m3) against ground measurements when uniform dry and wet edge parameters are applied for all crop types. The proposed scheme produced improved soil moisture retrievals with an R2 ≥ 0.65 and RMSE ≤ 0.05 m3/m3 when the specific dry and wet edge parameters were applied for each specific crop type. By analyzing time series of STR-NDVI, we demonstrate that the dynamic STR-NDVI spaces not only traces the coevolution of surface processes but also quantifies the spatial heterogeneity of soil moisture and vegetation status. Thus, the proposed dynamic STR-NDVI spaces based on the high spatiotemporal resolution Sentinel-2 imagery can both advance and extend the application of OPTRAM and improve the interpretation of soil moisture spatial heterogeneity at agricultural field scales, supporting efforts towards irrigation and crop growth monitoring in precision agriculture. • New parametrizations for wet and dry trapezoid edges improved soil moisture estimates. • Sentinel-2 data revealed spatial heterogeneity of soil moisture and vegetation status. • High spatiotemporal STR-NDVI spaces could track soil moisture and vegetation growth. [ABSTRACT FROM AUTHOR]

The performance of the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA)-3B42 version 7 product is assessed over north-eastern Iberia, a region with considerable topographical gradients and complexity. Precipitation characteristics from a dense network of 656 rain gauges, spanning the period from 1998 to 2009, are used to evaluate TMPA-3B42 estimates on a daily scale. A set of accuracy estimators, including the relative bias, mean absolute error (MAE), root mean square error (RMSE) and Spearman coefficient was used to evaluate the results. The assessment indicates that TMPA-3B42 product is capable of describing the seasonal characteristics of the observed precipitation over most of the study domain. In particular, TMPA-3B42 precipitation agrees well with in situ measurements, with MAE less than 2.5 mm.day − 1 , RMSE of 6.4 mm.day − 1 and Spearman correlation coefficients generally above 0.6. TMPA-3B42 provides improved accuracies in winter and summer, whereas it performs much worse in spring and autumn. Spatially, the retrieval errors show a consistent trend, with a general overestimation in regions of low altitude and underestimation in regions of heterogeneous terrain. TMPA-3B42 generally performs well over inland areas, while showing less skill in the coastal regions. A set of skill metrics, including a false alarm ratio [FAR], frequency bias index [FBI], the probability of detection [POD] and threat score [TS], is also used to evaluate TMPA performance under different precipitation thresholds (1, 5, 10, 25 and 50 mm.day − 1 ). The results suggest that TMPA-3B42 retrievals perform well in specifying moderate rain events (5–25 mm.day − 1 ), but show noticeably less skill in producing both light (< 1 mm.day − 1 ) and heavy rainfall thresholds (more than 50 mm.day − 1 ). Given the complexity of the terrain and the associated high spatial variability of precipitation in north-eastern Iberia, the results reveal that TMPA-3B42 data provide an informative addition to the spatial and temporal coverage of rain gauges in the domain, offering insights into characteristics of average precipitation and their spatial patterns. However, the satellite-based precipitation data should be used cautiously for monitoring extreme precipitation events, particularly over complex terrain. An improvement in precipitation algorithms is still needed to more accurately reproduce high precipitation events in areas of heterogeneous topography over this region. [ABSTRACT FROM AUTHOR]

• Multi-indicators were jointly used for maize yield estimation. • Random Forest algorithm had a good performance in estimating maize yield. • Surface temperature had the best accuracy among single indicator in estimating yield. • The yield can be predicted within 24 d before maturity with a good accuracy. • More informative indicators can improve the spatial adaptability of the model. The accurate and timely prediction of crop yield at a large scale is important for food security and the development of agricultural policy. An adaptable and robust method for estimating maize yield for the entire territory of China, however, is currently not available. The inherent trade-off between early estimates of yield and the accuracy of yield prediction also remains a confounding issue. To explore these challenges, we employ indicators such as GPP, ET, surface temperature (Ts), LAI, soil properties and maize phenological information with random forest regression (RFR) and gradient boosting decision tree (GBDT) machine learning approaches to provide maize yield estimates within China. The aims were to: (1) evaluate the accuracy of maize yield prediction obtained from multimodal data analysis using machine-learning; (2) identify the optimal period for estimating yield; and (3) determine the spatial robustness and adaptability of the proposed method. The results can be summarized as: (1) RFR estimated maize yield more accurately than GBDT; (2) Ts was the best single indicator for estimating yield, while the combination of GPP, Ts, ET and LAI proved best when multi-indicators were used (R 2 = 0.77 and rRMSE = 16.15% for the RFR); (3) the prediction accuracy was lower with earlier lead time but remained relatively high within at least 24 days before maturity (R 2 > 0.77 and rRMSE <16.92%); and (4) combining machine-learning algorithms with multi-indicators demonstrated a capacity to cope with the spatial heterogeneity. Overall, this study provides a reliable reference for managing agricultural production. [ABSTRACT FROM AUTHOR]

Summary Current snowmelt parameterisation schemes are largely untested in warmer maritime snowfields, where physical snow properties can differ substantially from the more common colder snow environments. Physical properties such as snow density influence the thermal properties of snow layers and are likely to be important for snowmelt rates. Existing methods for incorporating physical snow properties into temperature-index models (TIMs) require frequent snow density observations. These observations are often unavailable in less monitored snow environments. In this study, previous techniques for end-of-season snow density estimation (Bormann et al., 2013) were enhanced and used as a basis for generating daily snow density data from climate inputs. When evaluated against 2970 observations, the snow density model outperforms a regionalised density-time curve reducing biases from −0.027 g cm −3 to −0.004 g cm −3 (7%). The simulated daily densities were used at 13 sites in the warmer maritime snowfields of Australia to parameterise snowmelt estimation. With absolute snow water equivalent (SWE) errors between 100 and 136 mm, the snow model performance was generally lower in the study region than that reported for colder snow environments, which may be attributed to high annual variability. Model performance was strongly dependent on both calibration and the adjustment for precipitation undercatch errors, which influenced model calibration parameters by 150–200%. Comparison of the density-based snowmelt algorithm against a typical temperature-index model revealed only minor differences between the two snowmelt schemes for estimation of SWE. However, when the model was evaluated against snow depths, the new scheme reduced errors by up to 50%, largely due to improved SWE to depth conversions. While this study demonstrates the use of simulated snow density in snowmelt parameterisation, the snow density model may also be of broad interest for snow depth to SWE conversion. Overall, the study responds to recent calls for broader testing of TIMs across different snow environments, improves existing snow modelling in Australia and proposes a new method for introducing physically-based constraints on snowmelt rates in data-poor regions. [ABSTRACT FROM AUTHOR]

Accurate crop modeling at the field-level is important for yield prediction and agricultural risk mitigation, but is often hindered by the lack of information on field management as well as crop phenology of different cultivars. This study aims to develop a data assimilation framework for field-level crop modeling without management or crop phenology information for potential remote sensing applications. To do this, we first present a Monte Carlo simulation-based approach to estimating planting date and quasi-calibrated phenological parameters. Second, a simplified fertility stress scheme is developed for the AquaCrop model. The aim here is not necessarily to improve the AquaCrop model but to facilitate ensemble simulation when the field-level fertility stress condition is unknown. Finally, in situ soil moisture, canopy cover and biomass measurements are assimilated into the model to estimate crop yield, with the potential for yield prediction also explored. The experiments were performed for a rainfed maize field over 9 growing seasons, with each using a different maize cultivar. Results suggest that the planting dates can be accurately estimated (RMSE = 7.1 days, MAE = 5.4 days), and that the simplified fertility stress scheme adequately approximates the biomass and yield estimates from the original AquaCrop model under different fertility stress conditions. Data assimilation improves yield estimation, with an RMSE of 0.97 Mg/ha compared to 2.14 Mg/ha from the no-assimilation case. Yield prediction experiments reveal that the method is able to predict yield within 15% of the observed values up to 3 months before harvest. The proposed methodology does not rely on field-based information (e.g., planting date, plant density, crop phenology, fertility condition), and illustrates the potential for field-level crop modeling and yield forecasting using remote sensing data. • A new method is proposed to estimate field-level planting date. • AquaCrop is coupled with a simplified fertility stress module. • Soil moisture, canopy cover and biomass data are assimilated. • Maize yield can be predicted up to 3 months before harvest. • The methodology does not rely on field-based information. [ABSTRACT FROM AUTHOR]

Given its high nutritional value and capacity to grow in harsh environments, quinoa has significant potential to address a range of food security concerns. Monitoring the development of phenotypic traits during field trials can provide insights into the varieties best suited to specific environmental conditions and management strategies. Unmanned aerial vehicles (UAVs) provide a promising means for phenotyping and offer the potential for new insights into relative plant performance. During a field trial exploring 141 quinoa accessions, a UAV-based multispectral camera was deployed to retrieve leaf area index (LAI) and SPAD-based chlorophyll across 378 control and 378 saline-irrigated plots using a random forest regression approach based on both individual spectral bands and 25 different vegetation indices (VIs) derived from the multispectral imagery. Results show that most VIs had stronger correlation with the LAI and SPAD-based chlorophyll measurements than individual bands. VIs including the red-edge band had high importance in SPAD-based chlorophyll predictions, while VIs including the near infrared band (but not the red-edge band) improved LAI prediction models. When applied to individual treatments (i.e. control or saline), the models trained using all data (i.e. both control and saline data) achieved high mapping accuracies for LAI (R2 = 0.977–0.980, RMSE = 0.119–0.167) and SPAD-based chlorophyll (R2 = 0.983–0.986, RMSE = 2.535–2.861). Overall, the study demonstrated that UAV-based remote sensing is not only useful for retrieving important phenotypic traits of quinoa, but that machine learning models trained on all available measurements can provide robust predictions for abiotic stress experiments. [ABSTRACT FROM AUTHOR]

Aim Vegetation optical depth ( VOD) is an indicator of the water content of both woody and leaf components in terrestrial aboveground vegetation biomass that can be derived from passive microwave remote sensing. VOD is distinct from optical vegetation remote sensing data such as the normalized difference vegetation index in that it is: (a) less prone to saturation in dense canopies; (b) sensitive to both photosynthetic and non-photosynthetic biomass; and (c) less affected by atmospheric conditions. Our primary objective was to analyse a recently developed long-term VOD record and investigate how the vegetation water content of various land-cover types responded to environmental changes and human influences from 1988 to 2008. Location Global. Methods We first conducted Mann- Kendall trend tests on annual average VOD to identify regions with significant changes over the period 1988-2008. To diagnose the underlying cause of the observed changes, patterns for these identified regions were further compared with independent datasets of precipitation, crop production, deforestation and fire occurrence. Results (1) Over grassland and shrubland, VOD patterns corresponded strongly to temporal precipitation patterns. (2) Over croplands, annual average VOD showed a general increase that corresponded to reported crop production patterns and was attributed to a combination of precipitation patterns and agricultural improvements. (3) Over humid tropical forest, the spatial pattern of VOD decline agrees well with deforestation patterns; the 2005 Amazon drought corresponded with a temporary VOD decrease. (4) Over boreal forests, regional VOD declines are attributed to a combination of fires and clear cutting. Main conclusions Passive microwave remote sensing of VOD can be used to monitor global changes in total aboveground vegetation water content and biomass over various land-cover types. This new observational record can help in hydrological, agricultural, ecological and climate change studies, and provides new insights into large-scale vegetation change and its drivers. [ABSTRACT FROM AUTHOR]

Capturing and identifying field-based agricultural activities, such as the start, duration and end of irrigation, together with crop sowing/germination, growing period and time of harvest, offer informative metrics that can assist in precision agricultural activities in addition to broader water and food security monitoring efforts. While optically based band-ratios, such as the normalized difference vegetation index (NDVI) and normalized difference water index (NDWI), have been used as descriptors for monitoring crop dynamics, data are not always available due to the influence of clouds and other atmospheric effects on optical sensors. Satellite-based microwave systems, such as the synthetic aperture radar (SAR), offer an all-weather advantage in monitoring soil and crop conditions. In this paper, we leverage the relative strengths of both optical- and microwave-based approaches by combining high resolution Sentinel-1 SAR and Sentinel-2 optical imagery to monitor irrigation events and crop dynamics in a dryland agricultural landscape. A microwave backscatter model was used to analyze the responses of simulated backscatters to soil moisture, NDVI and NDWI (both are correlated with vegetation water content and can be regarded as vegetation descriptors), allowing an empirical relationship between these two platforms. A correlation analysis was also performed using Sentinel-1 SAR and Sentinel-2 optical data over crops of maize, alfalfa, carrot and Rhodes grass in Al Kharj farm of Saudi Arabia to identify an appropriate SAR-based vegetation descriptor. The results illustrate the relationship between SAR and both NDVI and NDWI and demonstrated the relationship between the cross-polarization ratio (VH/VV) and the two optical indices. We explore the capacity of this multi-platform and multi-sensor approach to inform on the spatio-temporal dynamics of a range of agricultural activities, which can be used to facilitate field-based management decisions. [ABSTRACT FROM AUTHOR]

Satellite remote sensing has great potential to deliver on the promise of a data-driven agricultural revolution, with emerging space-based platforms providing spatiotemporal insights into precision-level attributes such as crop water use, vegetation health and condition and crop response to management practices. Using a harmonized collection of high-resolution Planet CubeSat, Sentinel-2, Landsat-8 and additional coarser resolution imagery from MODIS and VIIRS, we exploit a multi-satellite data fusion and machine learning approach to deliver a radiometrically calibrated and gap-filled time-series of daily leaf area index (LAI) at an unprecedented spatial resolution of 3 m. The insights available from such high-resolution CubeSat-based LAI data are demonstrated through tracking the growth cycle of a maize crop and identifying observable within-field spatial and temporal variations across key phenological stages. Daily LAI retrievals peaked at the tasseling stage, demonstrating their value for fertilizer and irrigation scheduling. An evaluation of satellite-based retrievals against field-measured LAI data collected from both rain-fed and irrigated fields shows high correlation and captures the spatiotemporal development of intra- and inter-field variations. Novel agricultural insights related to individual vegetative and reproductive growth stages were obtained, showcasing the capacity for new high-resolution CubeSat platforms to deliver actionable intelligence for precision agricultural and related applications. [ABSTRACT FROM AUTHOR]

Coastal water flows facilitate important nutrient exchanges between mangroves, seagrasses and coral reefs. However, due to the complex nature of tidal interactions, their spatiotemporal development can be difficult to trace via traditional field instrumentations. Unmanned aerial vehicles (UAVs) serve as ideal platforms from which to capture such dynamic responses. Here, we provide a UAV-based approach for tracing coastal water flows using object-based detection of dye plume extent coupled with a regression approach for mapping dye concentration. From hovering UAV images and nine subsequent flight surveys covering the duration of an ebbing tide in the Red Sea, our results show that dye plume extent can be mapped with low omission and commission errors when assessed against manual delineations. Our results also demonstrated that the interaction term of two UAV-derived indices may be employed to accurately map dye concentration (coefficient of determination = 0.96, root mean square error = 7.78 ppb), providing insights into vertical and horizontal transportation and dilution of materials in the water column. We showcase the capabilities of high-frequency UAV-derived data and demonstrate how field-based dye concentration measurements can be integrated with UAV data for future studies of coastal water flow dynamics. [ABSTRACT FROM AUTHOR]

Developments in computer vision, such as structure from motion and multiview stereo reconstruction, have enabled a range of photogrammetric applications using unmanned aerial vehicles (UAV)-based imagery. However, some specific cases still present reconstruction challenges, including survey areas composed of steep, overhanging, or vertical rock formations. Here, the suitability and geometric accuracy of four UAV-based image acquisition and data processing scenarios for topographic surveying applications in complex terrain are assessed and compared. The specific cases include the use of: 1) nadir imagery; 2) nadir and oblique imagery; 3) nadir and façade imagery; and 4) nadir, oblique, and façade imagery to reconstruct a topographically complex natural surface. Results illustrate that including oblique and façade imagery to supplement the more traditional nadir collections significantly improves the geometric accuracy of point cloud data reconstruction by approximately 35% when assessed against terrestrial laser scanning data of near-vertical rock walls. Most points (99.41%) had distance errors of less than 50 cm between the point clouds derived from the nadir imagery and nadir–oblique–façade imagery. Apart from delivering enhanced spatial resolution in façade details, the geometric accuracy improvements achieved from integrating nadir, oblique, and façade imagery provide value for a range of applications, including geotechnical and geohazard investigations. Such gains are particularly relevant for studies assessing rock integrity and stability, and engineering design, planning, and construction, where information on the position of rock cracks, joints, faults, shears, and bedding planes may be required. [ABSTRACT FROM AUTHOR]

• Key parameters were determined in 3 dryland areas for 84 growing seasons. • Key influential parameters are different for crop yield and transpiration. • Key influential parameters depend on the wetness condition. • Parameters' impacts on canopy cover and biomass are stage-dependent. • Parameter sensitivities also vary with AquaCrop model version. The application of crop models towards improved local scale prediction and precision management requires the identification and description of the major factors influencing model performance. Such efforts are particularly important for dryland areas which face rapid population growth and increasing constraints on water supplies. In this study, a global sensitivity analysis on crop yield and transpiration was performed for 49 parameters in the FAO-AquaCrop model (version 6.0) across three dryland farming areas with different climatic conditions. The Morris screening method and the variance-based Extended Fourier Amplitude Sensitivity Test (EFAST) method were used to evaluate the parameter sensitivities of several staple crops (maize, soybean or winter wheat) under dry, normal and wet scenarios. Results suggest that parameter sensitivities vary with the target model output (e.g., yield, transpiration) and the wetness condition. By synthesizing parameter sensitivities under different scenarios, the key parameters affecting model performance under both high and low water stress were identified for the three crops. Overall, factors relevant to root development tended to have large impacts under high water stress, while those controlling maximum canopy cover and senescence were more influential under low water stress. Parameter sensitivities were also shown to be stage-dependent from a day-by-day analysis of canopy cover and biomass simulations. Subsequent comparison with AquaCrop version 5.0 suggests that AquaCrop version 6.0 is less sensitive to uncertainties in soil properties. [ABSTRACT FROM AUTHOR]

Satellite based remote sensing offers one of the few approaches able to monitor the spatial and temporal development of regional to continental scale droughts. A unique element of remote sensing platforms is their multi-sensor capability, which enhances the capacity for characterizing drought from a variety of perspectives. Such aspects include monitoring drought influences on vegetation and hydrological responses, as well as assessing sectoral impacts (e.g., agriculture). With advances in remote sensing systems along with an increasing range of platforms available for analysis, this contribution provides a timely and systematic review of multi-sensor remote sensing drought studies, with a particular focus on drought related datasets, drought related phenomena and mechanisms, and drought modeling. To explore this topic, we first present a comprehensive summary of large-scale remote sensing datasets that can be used for multi-sensor drought studies. We then review the role of multi-sensor remote sensing for exploring key drought related phenomena and mechanisms, including vegetation responses to drought, land-atmospheric feedbacks during drought, drought-induced tree mortality, drought-related ecosystem fires, post-drought recovery and legacy effects, flash drought, as well as drought trends under climate change. A summary of recent modeling advances towards developing integrated multi-sensor remote sensing drought indices is also provided. We conclude that leveraging multi-sensor remote sensing provides unique benefits for regional to global drought studies, particularly in: 1) revealing the complex drought impact mechanisms on ecosystem components; 2) providing continuous long-term drought related information at large scales; 3) presenting real-time drought information with high spatiotemporal resolution; 4) providing multiple lines of evidence of drought monitoring to improve modeling and prediction robustness; and 5) improving the accuracy of drought monitoring and assessment efforts. We specifically highlight that more mechanism-oriented drought studies that leverage a combination of sensors and techniques (e.g., optical, microwave, hyperspectral, LiDAR, and constellations) across a range of spatiotemporal scales are needed in order to progress and advance our understanding, characterization and description of drought in the future. • Multi-sensor observations provide advantages in addressing knowledge gaps. • Drought related remote sensing datasets are identified for multi-sensor studies. • Key drought phenomena and mechanisms benefiting from multi-sensors are highlighted. • Recent advances for multi-sensor drought monitoring models are synthesized. • Challenges and opportunities using multi-sensors for drought studies are provided. [ABSTRACT FROM AUTHOR]

Earth observation has traditionally required a compromise in data collection. That is, one could sense the Earth with high spatial resolution occasionally; or with lower spatial fidelity regularly. For many applications, both frequency and detail are required. Precision agriculture is one such example, with sub-10 m spatial, and daily or sub-daily retrieval representing a key goal. Towards this objective, we produced the first cloud-free 3 m daily evaporation product ever retrieved from space, leveraging recently launched nano-satellite constellations to showcase this emerging potential. Focusing on three agricultural fields located in Nebraska, USA, high-resolution crop water use estimates are delivered via CubeSat-based evaporation modeling. Results indicate good model agreement (r2 of 0.86–0.89; mean absolute error between 0.06 and 0.08 mm/h) when evaluated against corrected flux tower data. CubeSat technologies are revolutionizing Earth observation, delivering novel insights and new agricultural informatics that will enhance food and water security efforts, and enable rapid and informed in-field decision making. [ABSTRACT FROM AUTHOR]

Estimating soil moisture based on synthetic aperture radar (SAR) data remains challenging due to the influences of vegetation and surface roughness. Here we present an algorithm that simultaneously retrieves soil moisture, surface roughness and vegetation water content by jointly using high-resolution Sentinel-1 SAR and Sentinel-2 multispectral imagery, with an application directed towards the provision of information at the precision agricultural scale. Sentinel-2-derived vegetation water indices are investigated and used to quantify the backscatter resulting from the vegetation canopy. The proposed algorithm then inverts the water cloud model to simultaneously estimate soil moisture and surface roughness by minimizing a cost function constructed by model simulations and SAR observations. To examine the performance of VV- and VH-polarized backscatters on soil moisture retrievals, three retrieval schemes are explored: a single channel algorithm using VV (SCA-VV) and VH (SCA-VH) polarizations and a dual channel algorithm using both VV and VH polarizations (DCA-VVVH). An evaluation of the approach using a combination of a cosmic-ray soil moisture observing system (COSMOS) and Soil Climate Analysis Network measurements over Nebraska shows that the SCA-VV scheme yields good agreement at both the COSMOS footprint and single-site scales. The features of the algorithms that have the most impact on the retrieval accuracy include the vegetation water content estimation scheme, parameters of the water cloud model and the specification of initial ranges of soil moisture and roughness, all of which are comprehensively analyzed and discussed. Through careful consideration and selection of these factors, we demonstrate that the proposed SCA-VV approach can provide reasonable soil moisture retrievals, with RMSE ranging from 0.039 to 0.078 m3/m3 and R2 ranging from 0.472 to 0.665, highlighting the utility of SAR for application at the precision agricultural scale. [ABSTRACT FROM AUTHOR]

Australia is one of the world's largest producers of macadamia nuts. As macadamia trees can take up to 15 years to mature and produce maximum yield, it is important to optimize tree condition. Field based assessment of macadamia tree condition is time-consuming and often inconsistent. Using remotely sensed imagery may allow for faster, more extensive, and more consistent assessment of macadamia tree condition. To identify individual macadamia tree crowns, high spatial resolution imagery is required. Hence, the objective of this work was to develop and test an approach to map the condition of individual macadamia tree crowns using both multi-spectral Unmanned Aerial Vehicle (UAV) and WorldView-3 imagery for different macadamia varieties and three different sites located near Bundaberg, Australia. A random forest classifier, based on all available spectral bands and selected vegetation indices was used to predict five condition categories, ranging from excellent (category 1) to poor (category 5). Various combinations of the developed models were tested between the three sites and over time. The results showed that the multi-spectral WorldView-3 imagery produced the lowest out of bag (OOB) classification errors in most cases. However, for both the UAV and the WorldView-3 imagery, more than 98.5% of predicted macadamia condition categories were either correctly mapped or offset by a single category out of the five condition categories (excellent, good, moderate, fair and poor) for trees of the same variety and at one point in time. Multi-temporally, the WorldView-3 imagery performed better than the UAV data for predicting the condition of the same macadamia tree variety. Applying a model from one site to another site with the same macadamia tree variety produced OOB classification between 31.20 and 42.74%, but with >98.63% of trees predicted within a single condition category. Importantly, models trained based on one type of macadamia tree variety could not be successfully applied to a site with another variety. The developed classification models may be used as a decision and management support tool for the macadamia industry to inform management practices and improve on-demand irrigation, fertilization, and pest inspection at the individual tree level. [ABSTRACT FROM AUTHOR]

The interplay between soil moisture and evapotranspiration modulates the water available to sustain soil evaporation and influences canopy stomatal conductance controls on vegetation transpiration. Modeling this behavior remains challenging. Indeed, satellite remote sensing based Penman-Monteith (PM) ET models tend not to directly consider soil moisture constraints on evaporation and transpiration due to a lack of consistent soil moisture data. To address this issue, we modified a PM model to include satellite enhanced surface and root zone soil moisture from the National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) mission. The resulting model was used to produce global 9-km daily ET estimates, including contributing fluxes from soil evaporation, transpiration and evaporation of canopy-intercepted precipitation. The global PM ET estimates were assessed using in situ sap flow and AmeriFlux measurements, upscaled FLUXCOM latent heat flux, as well as against other independent global ET data products (GLEAM, GLDAS, SSEBop, and LandFlux-EVAL). The modelled transpiration showed similar seasonal variation and positive correlation to in situ sap flow measurements available from several forest sites (R2 = 0.85; p < 0.01). When compared against AmeriFlux data, the PM ET estimates showed favorable agreement with annual ET measurements extracted from 34 diverse sites (R2 = 0.58; p < 0.01; RMSE = 227 mm yr−1). The PM 8-day ET results also reflected a similar hemispheric seasonality as the global FLUXCOM record (R2 = 0.94–0.98; p < 0.01), while comparisons against other global ET products showed moderate mean differences of between 49 and 107 mm yr−1 (11–25%) over the global domain. Our PM ET estimates varied up to 52% in response to SMAP surface soil moisture dynamics, displaying stronger surface (0–5 cm depth) than root zone (0–100 cm) soil moisture sensitivity. While PM ET sensitivity to soil moisture was greater in arid climate regions, it was also significant in humid climate zones, with analysis indicating that the inclusion of soil moisture predominantly acts as a sustaining influence on ET, especially in moisture limited drylands. PM ET sensitivity to temperature was stronger in humid forest regions relative to other climate and land cover regimes. Overall, the model results clarify the influence of soil moisture heterogeneity on the global ET pattern as informed by satellite-based estimates of surface and root zone soil moisture. Potential enhancements to the spatial and vertical resolution of soil moisture inputs are expected to enable further ET improvements through more realistic model representation of soil and plant available water. • SMAP surface and root-zone soil moisture are used to improve global daily evapotranspiration estimates. • Global ET is partitioned into transpiration, soil evaporation and evaporation of canopy-intercepted precipitation. • The influence of soil moisture heterogeneity on global ET pattern is clarified in different land cover and climate regimes. [ABSTRACT FROM AUTHOR]

The water uptake rate is a key parameter for improving irrigation efficiency and represents an indicator of tree health and yield. Here, we investigate the potential of C-band Synthetic Aperture Radar (SAR) data acquired by Sentinel-1 to estimate and map the water uptake rate across a very high-density olive orchard (5.3 km2 of olive plots) in the hot and arid desert climate of Saudi Arabia. The SAR predictor variables for water uptake rate estimation were the difference between the SAR backscattering of a given image acquired during the year (t i) and the average SAR backscattering in the second-half of January (t 0), which corresponds to the point when the uptake rate was closest to zero. Random forest regression and multi-linear regression models were used to explore the relationship between the SAR predictor variables and in situ water uptake rate, inferred from twelve sap flow meters that were installed across six plots and collected over two years (2020 and 2021). The trained random forest regression model provided an improved estimate of water uptake rate with correlation coefficient (R2) of 0.87 and root mean square error (RMSE) of 0.13 L.h−1 compared with the multi-linear regression model (R2 = 0.74, RMSE = 0.18 L.h−1). Using the random forest regression, the water uptake rate was mapped at the plot level for three years (2019, 2020, and 2021) at a temporal resolution of 6 days. Consistent with expectations, the results show that the average uptake rate over the mapped area co-varied with vapor pressure deficit (R2 = 0.82). The analysis provides a first exploration of exploiting SAR data to infer the water uptake rate across commercial scale orchards, offering important insights that can be useful for improved water management and irrigation control. • Variation in SAR backscatter time-series is a predictor of olive tree water uptake. • Random forest provides an estimation of tree water uptake with a RMSE = 0.13 L.h−1. • SAR backscattering time-series allow mapping the water uptake rate at plot level. • The mapped water uptake rate values co-varied with the vapor pressure deficit. [ABSTRACT FROM AUTHOR]

Satellite based retrievals of evapotranspiration (ET) are widely used for assessments of global and regional scale surface fluxes. However, the partitioning of the estimated ET between soil evaporation, transpiration, and canopy interception regularly shows strong divergence between models, and to date, remains largely unvalidated. To examine this problem, this paper considers three algorithms: the Penman-Monteith model from the Moderate Resolution Imaging Spectroradiometer (PM-MODIS), the Priestley-Taylor Jet Propulsion Laboratory model (PT-JPL), and the Global Land Evaporation Amsterdam Model (GLEAM). Surface flux estimates from these three models, obtained via the WACMOS-ET initiative, are compared against a comprehensive collection of field studies, spanning a wide range of climates and land cover types. Overall, we find errors between estimates of field and remote sensing-based soil evaporation (RMSD = 90–114%, r 2  = 0.14–0.25, N = 35), interception (RMSD = 62–181%, r 2  = 0.39–0.85, N = 13), and transpiration (RMSD = 54–114%, r 2  = 0.33–0.55, N = 35) are relatively large compared to the combined estimates of total ET (RMSD = 35–49%, r 2  = 0.61–0.75, N = 35). Errors in modeled ET components are compared between land cover types, field methods, and precipitation regimes. Modeled estimates of soil evaporation were found to have significant deviations from observed values across all three models, while the characterization of vegetation effects also influences errors in all three components. Improvements in these estimates, and other satellite based partitioning estimates are likely to lead to better understanding of the movement of water through the soil-plant-water continuum. [ABSTRACT FROM AUTHOR]

Unprecedented droughts hit southern Amazonia in 2005 and 2010, causing a sharp increase in tree mortality and carbon loss. To better predict the rainforest's response to future droughts, it is necessary to understand its behavior during past events. Satellite observations provide a practical source of continuous observations of Amazonian forest. Here we used a passive microwave-based vegetation water content record (i.e., vegetation optical depth, VOD), together with multiple hydrometeorological observations as well as conventional satellite vegetation measures, to investigate the rainforest canopy dynamics during the 2005 and 2010 droughts. During the onset of droughts in the wet-to-dry season (May–July) of both years, we found large-scale positive anomalies in VOD, leaf area index (LAI) and enhanced vegetation index (EVI) over the southern Amazonia. These observations are very likely caused by enhanced canopy growth. Concurrent below-average rainfall and above-average radiation during the wet-to-dry season can be interpreted as an early arrival of normal dry season conditions, leading to enhanced new leaf development and ecosystem photosynthesis, as supported by field observations. Our results suggest that further rainfall deficit into the subsequent dry season caused water and heat stress during the peak of 2005 and 2010 droughts (August–October) that exceeded the tolerance limits of the rainforest, leading to widespread negative VOD anomalies over the southern Amazonia. Significant VOD anomalies were observed mainly over the western part in 2005 and mainly over central and eastern parts in 2010. The total area with significant negative VOD anomalies was comparable between these two drought years, though the average magnitude of significant negative VOD anomalies was greater in 2005. This finding broadly agrees with the field observations indicating that the reduction in biomass carbon uptake was stronger in 2005 than 2010. The enhanced canopy growth preceding drought-induced senescence should be taken into account when interpreting the ecological impacts of Amazonian droughts. [ABSTRACT FROM AUTHOR]

The trade-off between increasing agricultural production and groundwater conservation, which is often the only irrigation source in arid regions, is a major challenge for water managers, decision-makers and the scientific community. Monitoring of the water use efficiency (WUE) provides a measure of the water lost from a canopy to the atmosphere relative to the biomass produced by the plants, and can be used to inform efforts that seek to reduce irrigation without impacting upon the yield. In this study, WUE was computed as the ratio between the gross primary productivity (GPP) and the evapotranspiration (ET). Both GPP and ET can be measured at the local scale using eddy covariance (EC) systems. However, in order to spatially evaluate WUE for larger areas or regions, local measurements are rarely representative. The spatiotemporal covariation of ET, GPP and WUE remains understudied, particularly at the scale of operational agroecosystems. In this study, Sentinel-2 optical bands were used to provide high spatiotemporal resolution estimates of ET via application of the Priestley-Taylor Jet Propulsion Laboratory (PT-JPL) model. A key soil water stress constraint in PT-JPL was linked to the Modified Soil-Adjusted Vegetation Index (MSAVI), while estimates of the GPP were delivered via the Vegetation Photosynthesis Model (VPM). In order to evaluate the water consumption and irrigation efficiency over an irrigated plantation of very high-density olive trees, ET, GPP and WUE were computed for a 1.5 year period, covering January 2019 to June 2021. Model estimates of both ET and GPP were compared to the EC measurements, with results providing a root mean square error (RMSE) of 63.3 W/m2 and a coefficient of determination (R2) of 0.74 for ET and 2.3 g C/m2/day and 0.73 for GPP, respectively. Based on these estimates, WUE was then calculated as the ratio of the GPP and ET. Relative to in situ measured data, the results were biased, with ET overestimated and GPP underestimated. An alternative measure for WUE was determined based on a linear regression model of meteorological data derived from ERA5-Land and vegetation indices derived from Sentinel-2 data. Overall, the best results were identified using an NDVI-based linear regression model, with an RMSE of 0.9 g C/Kg H 2 O and an R2 of 0.76. These results show the potential of high-resolution satellite data to assess WUE over agroecosystems, helping to overcome the limitations presented by in-situ measurements and supporting agricultural production and water resources management in water-limited regions. • Spatiotemporal patterns of ET, GPP and WUE were determined for a high-density olive tree plantation. • WUE seasonal trends suggest that the irrigation in summer exceeds the water needs in the study area. • PT-JPL model fed by sentinel-2 data provide accurate ET estimation over plot scale. • VPM-based GPP values are well correlated with EC-based GPP measurements. [ABSTRACT FROM AUTHOR]

Accurate measurements of energy fluxes between land and atmosphere are important for understanding and modeling climatic patterns. Several methods are available to measure heat fluxes, and scintillometers are becoming increasingly popular because of their ability to measure sensible ( H ) and latent ( L v E ) heat fluxes over large spatial scales. The main motivation of this study was to test the use of different methods and technologies to derive surface heat fluxes. Measurements of H and L v E were carried out with an eddy covariance (EC) system, two different makes of optical large aperture scintillometers (LAS) and two microwave scintillometers (MWS) with different frequencies at a pasture site in a semi-arid environment of New South Wales, Australia. We used the EC measurements as a benchmark. Fluxes derived from the EC system and LAS systems agreed ( R 2 > 0.94), whereas the MWS systems measured lower H (bias ∼60 W m −2 ) and larger L v E (bias ∼65 W m −2 ) than EC. When the scintillometers were compared against each other, the two LASs showed good agreement of H ( R 2 = 0.98), while MWS with different frequencies and polarizations led to different results. Combination of LAS and MWS measurements (i.e., two wavelength method) resulted in performance that fell in between those estimated using either LAS or MWS alone when compared with the EC system. The cause for discrepancies between surface heat fluxes derived from the EC system and those from the MWS systems and the two-wavelength method are possibly related to inaccurate assignment of the structure parameter of temperature and humidity. Additionally, measurements from MWSs can be associated with two values of the Bowen ratio, thereby leading to uncertainties in the estimation of the fluxes. While only one solution has been considered in this study, when L v E was approximately less than 200 W m −2 , the alternate solution may be more accurate. Therefore, for measurements of surface heat fluxes in a semi-arid or dry environment, the optical scintillometer is recommended, whereas further work will be required to improve the estimation of surface heat fluxes from microwave systems. [ABSTRACT FROM AUTHOR]

Summary The stable isotopes of water are useful tracers of water sources and hydrological processes. Stable water isotope-enabled land surface modeling is a relatively new approach for characterizing the hydrological cycle, providing spatial and temporal variability for a number of hydrological processes. At the land surface, the integration of stable water isotopes with other meteorological measurements can assist in constraining surface heat flux estimates and discriminate between evaporation (E) and transpiration (T). However, research in this area has traditionally been limited by a lack of continuous in-situ isotopic observations. Here, the National Centre for Atmospheric Research stable isotope-enabled Land Surface Model (ISOLSM) is used to simulate the water and energy fluxes and stable water isotope variations. The model was run for a period of one month with meteorological data collected from a coastal sub-tropical site near Sydney, Australia. The modeled energy fluxes (latent heat and sensible heat) agreed reasonably well with eddy covariance observations, indicating that ISOLSM has the capacity to reproduce observed flux behavior. Comparison of modeled isotopic compositions of evapotranspiration (ET) against in-situ Fourier Transform Infrared spectroscopy (FTIR) measured bulk water vapor isotopic data (10 m above the ground), however, showed differences in magnitude and temporal patterns. The disparity is due to a small contribution from local ET fluxes to atmospheric boundary layer water vapor (∼1% based on calculations using ideal gas law) relative to that advected from the ocean for this particular site. Using ISOLSM simulation, the ET was partitioned into E and T with 70% being T. We also identified that soil water from different soil layers affected T and E differently based on the simulated soil isotopic patterns, which reflects the internal working of ISOLSM. These results highlighted the capacity of using the isotope-enabled models to discriminate between different hydrological components and add insight into expected hydrological behavior. [ABSTRACT FROM AUTHOR]

• A first of its kind demonstration of sub-daily high resolution SkySat monitoring. • Dye plume extent could be mapped from SkySat data using object-based detection. • A random forest method could predict dye concentration from SkySat data. • Sub-daily SkySat data could accurately track dye plume extent and concentration. • UAV data bridged the spatial and temporal disconnect between field and SkySat data. Sub-daily tracking of dynamic features and events using high spatial resolution satellite imagery has only recently become possible, with advanced observational capabilities now available through tasking of satellite constellations. Here, we provide a first of its kind demonstration of using sub-daily 0.50 m resolution SkySat imagery to track coastal water flows, combining these data with object-based detection and a machine-learning approach to map the extent and concentration of two dye plumes. Coincident high-frequency unmanned aerial vehicle (UAV) imagery was also employed for quantitative modeling of dye concentration and evaluation of the sub-daily satellite-based dye tracking. Our results show that sub-daily SkySat imagery can track dye plume extent with low omission (8.73–16.05%) and commission errors (0.32–2.77%) and model dye concentration (coefficient of determination = 0.73; root mean square error = 28.68 ppb) with the assistance of high-frequency UAV data. The results also demonstrate the capabilities of using UAV imagery for scaling between field data and satellite imagery for tracking coastal water flow dynamics. This research has implications for monitoring of water flows and nutrient or pollution exchange, and it also demonstrates the capabilities of higher temporal resolution satellite data for delivering further insights into dynamic processes of coastal systems. [Display omitted] [ABSTRACT FROM AUTHOR]

• CubeSat-based LAI maps were integrated into APSIM using a particle filter. • Assimilating cubesat maps enhances APSIM LAI predictive ability by several weeks. • Prediction of within-field crop yield at 3 m spatial resolution was achieved. • The approach overcomes the impacts of cloud-cover of traditional satellite data. Accurate early season predictions of crop yield at the within-field scale can be used to address a range of crop production, management, and precision agricultural challenges. While the remote sensing of within-field insights has been a research goal for many years, it is only recently that observations with the required spatio-temporal resolutions, together with efficient assimilation methods to integrate these into modeling frameworks, have become available to advance yield prediction efforts. Here we explore a yield prediction approach that combines daily high-resolution CubeSat imagery with the APSIM crop model. The approach employs APSIM to train a linear regression that relates simulated yield to simulated leaf area index (LAI). That relationship is then used to identify the optimal regression date at which the LAI provides the best prediction of yield: in this case, approximately 14 weeks prior to harvest. Instead of applying the regression on satellite imagery that is coincident, or closest to, the regression date, our method implements a particle filter that integrates CubeSat-based LAI into APSIM to provide end-of-season high-resolution (3 m) yield maps weeks before the optimal regression date. The approach is demonstrated on a rainfed maize field located in Nebraska, USA, where suitable collections of both imagery and in-situ data were available for assessment. The procedure does not require in-field data to calibrate the regression model, with results showing that even with a single assimilation step, it is possible to provide yield estimates with good accuracy up to 21 days before the optimal regression date. Yield spatial variability was reproduced reasonably well, with a strong correlation to independently collected measurements (R2 = 0.73 and rRMSE = 12%). When the field averaged yield was compared, our approach reduced yield prediction error from 1 Mg/ha (control case based on a calibrated APSIM model), to 0.5 Mg/ha (using satellite imagery alone), and then to 0.2 Mg/ha (results with assimilation up to three weeks prior to the optimal regression date). Such a capacity to provide spatially explicit yield predictions early in the season has considerable potential to enhance digital agricultural goals and improve end-of-season yield predictions. [ABSTRACT FROM AUTHOR]

An integrated data assimilation system is implemented over the Red-Arkansas river basin to estimate the regional scale terrestrial water cycle driven by multiple satellite remote sensing data. These satellite products include the Tropical Rainfall Measurement Mission (TRMM), TRMM Microwave Imager (TMI), and Moderate Resolution Imaging Spectroradiometer (MODIS). Also, a number of previously developed assimilation techniques, including the ensemble Kalman filter (EnKF), the particle filter (PF), the water balance constrainer, and the copula error model, and as well as physically based models, including the Variable Infiltration Capacity (VIC), the Land Surface Microwave Emission Model (LSMEM), and the Surface Energy Balance System (SEBS), are tested in the water budget estimation experiments. This remote sensing based water budget estimation study is evaluated using ground observations driven model simulations. It is found that the land surface model driven by the bias-corrected TRMM rainfall produces reasonable water cycle states and fluxes, and the estimates are moderately improved by assimilating TMI 10.67 GHz microwave brightness temperature measurements that provides information on the surface soil moisture state, while it remains challenging to improve the results by assimilating evapotranspiration estimated from satellite-based measurements. [Copyright &y& Elsevier]

The miniaturization of thermal infrared sensors suitable for integration with unmanned aerial vehicles (UAVs) has provided new opportunities to observe surface temperature at ultra-high spatial and temporal resolutions. In parallel, there has been a rapid development of software capable of streamlining the generation of orthomosaics. However, these approaches were developed to process optical and multi-spectral image data and were not designed to account for the often rapidly changing surface characteristics inherent in the collection and processing of thermal data. Although radiometric calibration and shutter correction of uncooled sensors have improved, the processing of thermal image data remains difficult due to (1) vignetting effects on the uncooled microbolometer focal plane array; (2) inconsistencies between images relative to in-flight effects (wind-speed and direction); (3) unsuitable methods for thermal infrared orthomosaic generation. Here, we use thermal infrared UAV data collected with a FLIR-based TeAx camera over an agricultural field at different times of the day to assess inconsistencies in orthophotos and their impact on UAV-based thermal infrared orthomosaics. Depending on the wind direction and speed, we found a significant difference in UAV-based surface temperature (up to 2 °C) within overlapping areas of neighboring flight lines, with orthophotos collected with tail wind being systematically cooler than those with head wind. To address these issues, we introduce a new swath-based mosaicking approach, which was compared to three standard blending modes for orthomosaic generation. The swath-based mosaicking approach improves the ability to identify rapid changes of surface temperature during data acquisition, corrects for the influence of flight direction relative to the wind orientation, and provides uncertainty (pixel-based standard deviation) maps to accompany the orthomosaic of surface temperature. It also produced more accurate temperature retrievals than the other three standard orthomosaicking methods, with a root mean square error of 1.2 °C when assessed against in situ measurements. As importantly, our findings demonstrate that thermal infrared data require appropriate processing to reduce inconsistencies between observations, and thus, improve the accuracy and utility of orthomosaics. [ABSTRACT FROM AUTHOR]

Parameter calibration is normally required prior to crop model simulation, which can be a time-consuming and data-intensive task. Meanwhile, the growth stages of different hybrids/cultivars of the same crop often show some similarities, which implies that phenological parameters calibrated for one hybrid/cultivar may be useful for the simulation of another. In this study, a data assimilation framework is proposed to reduce the requirement for parameter calibration for maize simulation using AquaCrop. The phenological parameters were uniformly scaled from previous research performed in a different location for a different maize hybrid, and other parameters were taken from default settings in the model documentation. To constrain simulation uncertainties, soil moisture and canopy cover observations were assimilated both separately and jointly in order to update model states. The methodology was tested across a rain-fed field in Nebraska for 6 growing seasons. The results suggested that the under-calibrated model with uniformly scaled phenological parameters captured the temporal dynamics of crop growth, but may lead to large estimation bias. Data assimilation effectively improved model performance, and the joint assimilation outperformed single-variable assimilation. When soil moisture and canopy cover were jointly assimilated, the overall yield estimates (RMSE = 1.24 t/ha, nRMSE = 11.48%, R2 = 0.695) were improved over the no-assimilation case (RMSE = 2.01 t/ha, nRMSE = 18.61%, R2 = 0.338). Sensitivity analyses suggested that the improvement was still evident with temporally sparse soil moisture observations and a small ensemble size. Further testing using observations within 90 days after planting demonstrated that the method was able to predict yield around 3 months before harvest (RMSE = 1.7 t/ha, nRMSE = 15.74%). This study indicated that maize yield can be estimated and predicted accurately by monitoring the soil moisture and canopy status, which has potential for regional applications using remote sensing data. • Maize was simulated with reduced parameter calibration. • Parameter values were from documentation or scaled from previous research. • Soil moisture and/or canopy cover data were assimilated to constrain uncertainty. • Assimilation effectively improved maize simulation based on 6 years of simulation. • The method is able to predict maize yield around 3 months before harvest. [ABSTRACT FROM AUTHOR]

Spatial and temporal variability of drought in Oman was assessed for the period 1979–2014 using the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) at both seasonal (3-month) and annual (12-month) timescales. Changes in drought characteristics (e.g. frequency, duration, intensity, severity, and spatial extent) were detected for six distinct climatic regions delineated using a Principal Component Analysis (PCA). Results suggest a statistically significant increase in the frequency and severity of drought during the past four decades. Nonetheless, drought exhibited pronounced decadal variability, with more frequent and severe droughts occurring after 1997/1998. Also, results demonstrate that the areas influenced by extreme and very extreme droughts witnessed a statistically significant increase on the order of 0.13% and 0.18%/decade, respectively, compared to −0.06%/decade for the areas impacted by mild droughts. Spatially, there has been a prevalent drying trend in central and eastern regions of Oman, while a tendency towards wetter conditions was observed in northern Oman (especially in the Batinah plain). This study also investigates the time-varying dependency between climatic drivers of drought and large-scale circulation patterns using the cumulative annual mean (CAM) method. Results indicate an acceleration of drying after 1997/1998, mainly due to the warming sea surface temperature (SST) anomalies over the Atlantic Ocean in recent decades and also the shift of the Pacific Ocean circulation patterns (e.g. PDO and El Nino) from the positive to the negative mode at the end of the 1990s. These configurations are favorable for significant changes in the thermodynamic drivers of drought (i.e. air temperature and relative humidity), but have less influence on aerodynamic drivers (i.e. wind speed). The significant temperature rise in recent decades, combined with a significant decline of rainfall and relative humidity, can explain the prolonged drought episode lasting from 1998 to 2014. A comprehensive assessment of drought variability in a poorly-studied water-stressed region like Oman is of particular concern for stakeholders and policy-makers. This study provides meaningful conclusions and a basis for water resources management, agricultural and energy production, ecological protection, and contribute to more reliable drought mitigation strategies at the regional and national scales. • Drought has been aggravated in Oman over the recent decades, with a significant changing point mostly in 1997/1998. • Extreme and very extreme droughts have become more frequent, compared to mild and moderate droughts. • Droughts occurred largely in central and eastern regions of Oman, while mountainous regions exhibited less drying. • Recent drought intensification can be linked to the significant changes in SST anomalies, mainly over the tropical oceans. [ABSTRACT FROM AUTHOR]

This study assesses the spatial and temporal characteristics of nighttime surface urban heat island (SUHI) effects over Greater Cairo: the largest metropolitan area in Africa. This study employed nighttime land surface temperature (LST) data at 1 km resolution from the Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua sensor for the period 2003–2019. We presented a new spatial anomaly algorithm, which allowed to define SUHI using the most anomalous hotspot and cold spot of LST for each time step over Greater Cairo between 2003 and 2019. Results demonstrate that although there is a significant increase in the spatial extent of SUHI over the past two decades, a significant decrease in the mean and maximum intensities of SUHI was noted. Moreover, we examined the dependency between SUHI characteristics and related factors that influence energy and heat fluxes between atmosphere and land in urban environments (e.g., surface albedo, vegetation cover, climate variability, and land cover/use changes). Results demonstrate that the decrease in the intensity of SUHI was mainly guided by a stronger warming in daytime and nighttime LST in the neighborhood of urban localities. This warming was accompanied by a decrease in surface albedo and diurnal temperature range (DTR) over these areas. Results of this study can provide guidance to local urban planners and decision-makers to adopt more effective mitigation strategies to diminish the negative impacts of urban warming on natural and human environments. [ABSTRACT FROM AUTHOR]

With the increasing role that unmanned aerial systems (UAS) are playing in data collection for environmental studies, two key challenges relate to harmonizing and providing standardized guidance for data collection, and also establishing protocols that are applicable across a broad range of environments and conditions. In this context, a network of scientists are cooperating within the framework of the Harmonious Project to develop and promote harmonized mapping strategies and disseminate operational guidance to ensure best practice for data collection and interpretation. The culmination of these efforts is summarized in the present manuscript. Through this synthesis study, we identify the many interdependencies of each step in the collection and processing chain, and outline approaches to formalize and ensure a successful workflow and product development. Given the number of environmental conditions, constraints, and variables that could possibly be explored from UAS platforms, it is impractical to provide protocols that can be applied universally under all scenarios. However, it is possible to collate and systematically order the fragmented knowledge on UAS collection and analysis to identify the best practices that can best ensure the streamlined and rigorous development of scientific products. [ABSTRACT FROM AUTHOR]

• Implementation of a flood forecasting system based an ensemble Kalman filter. • Forecasting system is validated with a real test case of the Toce river valley. • Assimilation of real data improved the model prediction skills. • Simultaneously estimation of 2D Manning coefficient further improves performances. Accurate water level forecasts during flood events are crucial to mitigate the loss of human lives and economic damages. However, the accuracy of flood models can be affected by various factors, including the complexity of the terrain geometry and bathymetry, imperfect physics as well as uncertainties in the inflows and parameters. This paper describes a practical implementation of an ensemble Kalman filter (EnKF) based data assimilation system that is aimed towards enhancing the forecasting skill of flood models. The system was implemented and tested with a real world dam break flood, based on the experimentally scaled Toce River valley flood that occurred on July 8th, 1996. Water depth data are available for assimilation from a network of 21 sensors distributed across the domain. Our results demonstrate that assimilating data into the flood model significantly improves the model prediction by up to 90% after assimilation and 60% during forecasting. Assimilating the data more frequently significantly enhances the system performances. Estimating the two-dimensional Manning coefficient together with the model's dynamical variables (water depth and velocities) further improves the model prediction skill. Overall, our results suggest that assimilating data into the flood model, while jointly inferring the state and (poorly known) parameters, using an EnKF may provide an efficient framework for developing an operational flood forecasting system. [ABSTRACT FROM AUTHOR]

The rising global occurrence of plant pathogens highlights the need for a thorough reassessment of current disease detection and management schemes. To that end, we review the utility and limitations of the available sensing platforms deployed for phytodiagnostics in the field. We also discuss recent advances in the use of broad-spectrum biomarkers such as phytohormones and volatile organic compounds (VOCs), and assess the feasibility of deploying these platforms on a large scale. Because these platforms are often complementary, we propose a compressed sensing approach that combines several sensing platforms to manage plant pathogens while minimizing additional costs. Finally, we provide an outlook for the potential benefits of integrating new sensing technologies into farming for timely interventions., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Soil salinity is a major environmental stressor affecting agricultural productivity worldwide. Understanding plant responses to salt stress is crucial for developing resilient crop varieties. Wild relatives of cultivated crops, such as wild tomato, Solanum pimpinellifolium, can serve as a useful resource to further expand the resilience potential of the cultivated germplasm, S. lycopersicum. In this study, we employed high-throughput phenotyping in the greenhouse and field conditions to explore salt stress responses of a S. pimpinellifolium diversity panel. Our study revealed extensive phenotypic variations in response to salt stress, with traits such as transpiration rate, shoot mass, and ion accumulation showing significant correlations with plant performance. We found that while transpiration was a key determinant of plant performance in the greenhouse, shoot mass strongly correlated with yield under field conditions. Conversely, ion accumulation was the least influential factor under greenhouse conditions. Through a Genome Wide Association Study, we identified candidate genes not previously associated with salt stress, highlighting the power of high-throughput phenotyping in uncovering novel aspects of plant stress responses. This study contributes to our understanding of salt stress tolerance in S. pimpinellifolium and lays the groundwork for further investigations into the genetic basis of these traits, ultimately informing breeding efforts for salinity tolerance in tomato and other crops., (© 2024 The Author(s). The Plant Journal published by Society for Experimental Biology and John Wiley & Sons Ltd.)

Mangrove forests are recognized as one of the most effective ecosystems for storing carbon. In drylands, mangroves operate at the extremes of environmental gradients and, in many instances, offer one of the few opportunities for vegetation-based sequestering of carbon. Developing accurate and reproducible methods to map carbon assimilation in mangroves not only serves to inform efforts related to natural capital accounting, but can help to motivate their protection and preservation. Remote sensing offers a means to retrieve numerous vegetation traits, many of which can be related to plant biophysical or biochemical responses. The leaf area index (LAI) is routinely employed as a biophysical indicator of health and condition. Here, we apply a linear regression model to UAV-derived multispectral data to retrieve LAI across three mangrove sites located along the coastline of the Red Sea, with estimates producing an R 2 of 0.72 when compared against ground-sampled LiCOR LAI-2200C LAI data. To explore the potential of monitoring carbon assimilation within these mangrove stands, the UAV-derived LAI estimates were combined with field-measured net photosynthesis rates from a LiCOR 6400/XT, providing a first estimate of carbon assimilation in dryland mangrove systems of approximately 3000 ton C km -2  yr -1 . Overall, these results advance our understanding of carbon assimilation in dryland mangroves and provide a mechanism to quantify the carbon mitigation potential of mangrove reforestation efforts., (© 2024. The Author(s).)

Greenhouse gas (GHG) emissions have created a global climate crisis which requires immediate interventions to mitigate the negative effects on all aspects of life on this planet. As current agriculture and land use contributes up to 25% of total GHG emissions, plant scientists take center stage in finding possible solutions for a transition to sustainable agriculture and land use. In this article, the PlantACT! (Plants for climate ACTion!) initiative of plant scientists lays out a road map of how and in which areas plant scientists can contribute to finding immediate, mid-term, and long-term solutions, and what changes are necessary to implement these solutions at the personal, institutional, and funding levels., Competing Interests: Declaration of interests No interests are declared., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Monitoring leaf Chlorophyll (Chl) in-situ is labor-intensive, limiting representative sampling for detailed mapping of Chl variability at field scales across time. Unmanned aeria-l vehicles (UAV) and hyperspectral cameras provide flexible platforms for observing agricultural systems, overcoming this spatio-temporal sampling constraint. Here, we evaluate a customized machine learning (ML) workflow to retrieve multi-temporal leaf-Chl levels, combining sub-centimeter resolution UAV-hyperspectral imagery (400-1,000 nm) with leaf-level reflectance spectra and SPAD measurements, capturing temporal correlations, selecting relevant predictors, and retrieving accurate results under different conditions. The study is performed within a phenotyping experiment to monitor wild tomato plants' development. Several analyses were conducted to evaluate multiple ML strategies, including: (1) exploring sequential versus retraining learning; (2) comparing insights gained from using 272 spectral bands versus 60 pigment-based vegetation indices (VIs); and (3) assessing six regression methods (linear, partial-least-square regression; PLSR, decision trees, support vector, ensemble trees, and Gaussian process; GPR). Goodness-of-fit ( R 2 ) and accuracy metrics (MAE, RMSE) were determined using training/testing and validation data subsets to assess the models' performance. Overall, while equally good performance was obtained using either PLSR, GPR, or random forest, results show: (1) the retraining strategy improved the ability of most of the approaches to model SPAD-based Chl dynamics; (2) comparative analysis between retrievals and validation data distributions informed the models' ability to capture Chl dynamics through SPAD levels; (3) VI predictors slightly improved R 2 (e.g., from 0.59 to 0.74 units for GPR) and accuracy (e.g., MAE and RMSE differences of up to 2 SPAD units) in specific algorithms; (4) feature importance examined through these methods, revealed strong overlaps between relevant bands and VI predictors, highlighting a few decisive spectral ranges and indices useful for retrieving leaf-Chl levels. The proposed ML framework allows the retrieval of high-quality spatially distributed and multi-temporal SPAD-based chlorophyll maps at an ultra-high pixel resolution (e.g., 7 mm)., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Angel and McCabe.)

This study investigates changes in air quality conditions during the restricted COVID-19 lockdown period in 2020 across 21 metropolitan areas in the Middle East and how these relate to surface urban heat island (SUHI) characteristics. Based on satellite observations of atmospheric gases from Sentinel-5, results indicate significant reductions in the levels of atmospheric pollutants, particularly nitrogen dioxide (NO 2 ), sulfur dioxide (SO 2 ), and carbon monoxide (CO). Air quality improved significantly during the middle phases of the lockdown (April and May), especially in small metropolitan cities like Amman, Beirut, and Jeddah, while it was less significant in "mega" cities like Cairo, Tehran, and Istanbul. For example, the concentrations of NO 2 in Amman, Beirut, and Jeddah decreased by -56.6%, -43.4%, and -32.3%, respectively, during April 2020, compared to April 2019. Rather, there was a small decrease in NO 2 levels in megacities like Tehran (-0.9%) and Cairo (-3.1%). Notably, during the lockdown period, there was a decrease in the mean intensity of nighttime SUHI, while the mean intensity of daytime SUHI experienced either an increase or a slight decrease across these locations. Together with the Gulf metropolitans (e.g. Kuwait, Dubai, and Muscat), the megacities (e.g. Tehran, Ankara, and Istanbul) exhibited anomalous increases in the intensity of daytime SUHI, which may exceed 2 °C. Statistical relationships were established to explore the association between changes in the mean intensity and the hotspot area in each metropolitan location during the lockdown. The findings indicate that the mean intensity of SUHI and the spatial extension of hotspot areas within each metropolitan had a statistically significant negative relationship, with Pearson's r values generally exceeding - 0.55, especially for daytime SUHI. This negative dependency was evident for both daytime and nighttime SUHI during all months of the lockdown. Our findings demonstrate that the decrease in primary pollutant levels during the lockdown contributed to the decrease in the intensity of nighttime SUHIs in the Middle East, especially in April and May. Changes in the characteristics of SUHIs during the lockdown period should be interpreted in the context of long-term climate change, rather than just the consequence of restrictive measures. This is simply because short-term air quality improvements were insufficient to generate meaningful changes in the region's urban climate., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Soil and water salinization has global impact on the sustainability of agricultural production, affecting the health and condition of staple crops and reducing potential yields. Identifying or developing salt-tolerant varieties of commercial crops is a potential pathway to enhance food and water security and deliver on the global demand for an increase in food supplies. Our study focuses on a phenotyping experiment that was designed to establish the influence of salinity stress on a diversity panel of the wild tomato species, Solanum pimpinellifolium . Here, we explore how unoccupied aerial vehicles (UAVs) equipped with both an optical and thermal infrared camera can be used to map and monitor plant temperature (T p ) changes in response to applied salinity stress. An object-based image analysis approach was developed to delineate individual tomato plants, while a green-red vegetation index derived from calibrated red, green, and blue (RGB) optical data allowed the discrimination of vegetation from the soil background. T p was retrieved simultaneously from the co-mounted thermal camera, with T p deviation from the ambient temperature and its change across time used as a potential indication of stress. Results showed that T p differences between salt-treated and control plants were detectable across the five separate UAV campaigns undertaken during the field experiment. Using a simple statistical approach, we show that crop water stress index values greater than 0.36 indicated conditions of plant stress. The optimum period to collect UAV-based T p for identifying plant stress was found between fruit formation and ripening. Preliminary results also indicate that UAV-based T p may be used to detect plant stress before it is visually apparent, although further research with more frequent image collections and field observations is required. Our findings provide a tool to accelerate field phenotyping to identify salt-resistant germplasm and may allow farmers to alleviate yield losses through early detection of plant stress via management interventions., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Stutsel, Johansen, Malbéteau and McCabe.)

Characterizing the terrestrial carbon, water and energy cycles depends strongly on a capacity to accurately reproduce the spatial and temporal dynamics of land surface evaporation. For this, and many other reasons, monitoring terrestrial evaporation across multiple space and time scales has been an area of focused research for many decades. Much of this activity has been supported by developments in satellite remote sensing, which have been leveraged to deliver new process insights, model development and methodological improvements. In this Special Issue, published contributions explored a range of research topics directed towards the enhanced estimation of terrestrial evaporation. Here we summarize these cutting-edge efforts and provide an overview of some of the state-of-the-art approaches for retrieving this key variable. Some perspectives on outstanding challenges, issues, and opportunities are also presented., Competing Interests: Conflicts of Interest: The authors declare no conflict of interest.