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Accurate maps of subsurface tile drainage lines are needed for agronomic and environmental research studies and the maintenance of current tile drainage systems. In this study, tile lines at the Purdue University Agronomy Center for Research and Education near West Lafayette, Indiana were located using a combination of visual aerial photo interpretation, expert knowledge, and paper construction drawings. The mapping accuracy was assessed at 27 locations where tile lines were located physically using a tile probe. Tile lines were correctly predicted 89% of the time with an average spatial accuracy of ±1.23 m of the true tile locations. This approach was better than a previous tile line location map prepared using an automated remote sensing method, which had an average spatial accuracy of ±2.12 m. Core Ideas: Tile lines were located based on visual aerial photo interpretation, paper maps, and expert knowledge.Photo interpretation was a useful method to map unknown tile lines and provided better results than remote sensing.Accurate location of tile lines is vital for agronomic and environmental research studies. [ABSTRACT FROM AUTHOR]

Ion-selective electrodes (ISEs) are widely used tools for fast and accurate ion sensing. Herein their design is simplified by embedding a potentiometric cell into paper, complete with an ISE, a reference electrode, and a paper-based microfluidic sample zone that offer the full function of a conventional ISE setup. The disposable planar paper-based ion-sensing platform is suitable for low-cost point-of-care and in-field testing applications. The design is symmetrical and each interfacial potential within the cell is well defined and reproducible, so that the response of the device can be theoretically predicted. For a demonstration of clinical applications, paper-based Cl− and K+ sensors are fabricated with highly reproducible and linear responses towards different concentrations of analyte ions in aqueous and biological samples. The single-use devices can be fabricated by a scalable method, do not need any pretreatment prior to use, and only require a sample volume of 20 μL. [ABSTRACT FROM AUTHOR]

This document is a guest editorial for a special issue of the CAAI Transactions on Intelligence Technology journal focused on intelligence technology for remote sensing image analysis. The editorial highlights various research papers that cover topics such as scene interpretation, target detection, hyperspectral classification, and multi-modal analysis. The papers discuss different methods and techniques to improve the accuracy and efficiency of remote sensing tasks, including privacy-preserving recognition, knowledge distillation, optimisation strategies, intelligent anchor learning, deep learning methods, and fusion approaches. The editorial acknowledges the authors and reviewers for their contributions and hopes that the special issue will inspire further research in remote sensing scene interpretation. [Extracted from the article]

Remote sensing applications have a long history in treeline research. Recent reviews have examined the topic mainly from a methodological point of view. Here, we propose a question‐oriented review of remote sensing in treeline ecology to relate remote sensing methodologies to key ecological metrics and identify knowledge gaps and promising areas for future research. We performed a meta‐analysis to assess the role of remote sensing as a tool for measuring spatial patterns and dynamics of alpine and Arctic treeline ecotone globally. We assessed the geographic distribution, scale of analysis, and relationships between remote sensing techniques and treeline ecological metrics through co‐occurrence mapping and multivariate statistics. Our analysis revealed that only 10% of treeline ecology studies applied remote sensing tools, often associated with the keyword 'climate change'. Monitoring studies adopted coarser spatial resolutions over longer temporal extents in comparison with other treeline studies. A multiscale and multi‐sensor spatial approach was implemented in just 19% of papers. Long‐term research commonly relied on aerial and oblique photography to measure treeline shifts through photointerpretation within a multidisciplinary framework. More recent treeline dynamics were often quantified using greenness trends derived from the pixel‐based classification of satellite images. Many recent short‐term studies focused on delineating tree scale metrics derived from the object‐based classification of uncrewed aerial vehicle (UAV) images or LiDAR data. Over the past decade, high‐resolution and low‐cost UAV remote sensing has emerged as an interesting opportunity to fill the gap between local‐scale ecological patterns and coarse‐resolution satellite sensors. Additionally, treeline remote sensing applications would strongly benefit from multidisciplinary frameworks that integrate field studies in ecology and environmental science. The multi‐dimensional structural complexity of treelines typically responds to environmental drivers over multiple scales and thus is best described with multiscale and multi‐sensor approaches. [ABSTRACT FROM AUTHOR]

Due to the inadequate utilization of data correlation and complementarity in the feature extraction process of multimodal remote sensing images, the paper proposes a deep learning semantic segmentation algorithm based on the Dual Channel Attention Mechanism (DCAM). This algorithm uses U‐Net as the backbone, combining the RGB remote sensing image as one input channel with the Convolutional Block Attention Module to extract colour space features. Simultaneously, it utilizes near‐infrared (NIR) as another input channel with the Self‐Attention Module (SAM) to extract shape space features. Finally, by concatenating the multi‐scale attention features of the RGB remote sensing image channel and the NIR remote sensing image channel, it achieves the correlation and complementarity of contextual features between the two modal remote sensing images. Experimental results on the GID‐15 dataset demonstrate that the DCAM algorithm significantly improves the segmentation accuracy, edge segmentation quality, and object segmentation integrity for various types of targets compared to current mainstream segmentation methods. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) have been widely used in satellite remote sensing. However, satellites in orbit with limited resources and power consumption cannot meet the storage and computing power requirements of current million‐scale artificial intelligence models. This paper proposes a new generation of high flexibility and intelligent CNNs hardware accelerator for satellite remote sensing in order to make its computing carrier more lightweight and efficient. A data quantization scheme for INT16 or INT8 is designed based on the idea of dynamic fixed point numbers and is applied to different scenarios. The operation mode of the systolic array is divided into channel blocks, and the calculation method is optimized to increase the utilization of on‐chip computing resources and enhance the calculation efficiency. An RTL‐level CNNs field programable gate arrays accelerator with microinstruction sequence scheduling data flow is then designed. The hardware framework is built upon the Xilinx VC709. The results show that, under INT16 or INT8 precision, the system achieves remarkable throughput in most convolutional layers of the network, with an average performance of 153.14 giga operations per second (GOPS) or 301.52 GOPS, which is close to the system's peak performance, taking full advantage of the platform's parallel computing capabilities. [ABSTRACT FROM AUTHOR]

Cloud removal algorithm is a crucial step of remote sensing image preprocessing. The current mainstream remote sensing image cloud removal algorithms are implemented based on deep learning, and most of them are supervised. A large number of data pairs are required for training to achieve cloud removal. However, real with/without cloud image pairs datasets are difficult to obtain in the real world, and the models obtained by training on synthetic datasets often need to generalize better to natural scenes. And the existing unsupervised thin cloud removal methods based on Cycle‐GAN framework with considerable model complexity and unstable training are not an excellent solution to the problem of lack of paired datasets. Based on this, in this paper, the authors propose an unsupervised remote sensing image thin cloud removal method based on contrastive learning—GAN‐UD. It is a network consisting of a frequency‐spatial attention generator and a discriminator. In addition, the authors introduce local contrastive loss and global content loss to constrain the content of the generated images to ensure that the generated cloud‐free images are consistent with the input cloud images in terms of image content. Experimental results show that the proposed method in this paper can still effectively remove thin clouds from remote sensing images without paired training datasets, outperforms current unsupervised cloud removal methods, and achieves comparable performance to supervised methods. [ABSTRACT FROM AUTHOR]

ABSTRACT Since Monteith published his seminal paper on evaporation and introduced the canopy resistance 50 years ago, evaporation research has shown great progress, such that his contribution describing evaporation as the delicate interplay between supply, demand and control, needs a tribute. We first identify the key contributions of John Monteith to this development and then identify a new set of challenges in the field of ecohydrology made possible by his breakthrough. These relate to adequate prediction of the occurrence, frequency and severity of drought, in relation to vegetation water use and resilience, and the interplay between the carbon and water cycles. Importantly, since Monteith published his paper, development of satellite remote sensing has made it possible to generalize site level processes and to investigate vegetation, carbon and water relations at global scale. This emerging field of research, tentatively called global ecohydrology, is set to address key questions related to the resilience and vulnerability of the world's ecosystems to their changing environment. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The challenges of Reproducibility and Replicability (R & R) in computer science experiments have become a focus of attention in the last decade, as efforts to adhere to good research practices have increased. However, experiments using Deep Learning (DL) remain difficult to reproduce due to the complexity of the techniques used. Challenges such as estimating poverty indicators (e.g., wealth index levels) from remote sensing imagery, requiring the use of huge volumes of data across different geographic locations, would be impossible without the use of DL technology. To test the reproducibility of DL experiments, we report a review of the reproducibility of three DL experiments which analyze visual indicators from satellite and street imagery. For each experiment, we identify the challenges found in the data sets, methods and workflows used. As a result of this assessment we propose a checklist incorporating relevant FAIR principles to screen an experiment for its reproducibility. Based on the lessons learned from this study, we recommend a set of actions aimed to improve the reproducibility of such experiments and reduce the likelihood of wasted effort. We believe that the target audience is broad, from researchers seeking to reproduce an experiment, authors reporting an experiment, or reviewers seeking to assess the work of others. Plain Language Summary: This paper aims to help researchers understand the challenges of reproducing Deep Learning (DL) publications, mitigate reproducibility gaps, and make their own work more reproducible. We build on the work of others and add recommendations organized by (a) the quality of the data set (and associated metadata), (b) the DL methodology, (c) the implementation methodology, and the infrastructure used. To our knowledge, this is the first initiative of its kind to address the problem of reproducibility in remote sensing imagery and DL problems for real‐world tasks. We hope this paper lowers the barrier to entry for the DL community to improve research. Following the lifecycle mantra: reproduce!, then replicate! With the goal of improving reproducibility! Key Points: We discuss the reproducibility challenges faced in research by Deep Learning approaches using Big DataWe provide advice for pre‐screening papers (before experiments) to avoid poorly invested effortWe present a recipe with a set of mitigation strategies to address common errors users (researchers, authors, reviewers) may encounter [ABSTRACT FROM AUTHOR]

The negative impact humans have on the environment directly affects the environmental security. Over the years, it has been proven that wars make drastic and sometimes unrecoverable environmental damage. Remote sensing has played a main role in providing necessary data for spatiotemporal analysis. This paper reviews remote sensing implementation of war activities for environmental monitoring. This review is timely due to the exponentially increasing number of works published in recent years. This paper's main objective is to locate the papers and find geographic link, sensor use, and environmental degradation type. Following a discussion of remote sensing's capabilities, this overview illustrates numerous environmental damages caused by military operations in various world places. Our study found that wars have a detrimental influence on the ecosystem across the world, with major reasons being forest loss, oil spills, and urban growth. According to the findings, remote sensing, particularly middle‐resolution satellite images, is extensively and successfully employed for environmental security monitoring. The rehabilitation of a deteriorated environment should be one of the key areas of future study. [ABSTRACT FROM AUTHOR]

Effective extraction of building from remote sensing images requires both global and local information. Despite convolutional neural networks (CNNs) excelling at capturing local details, their intrinsic focus on local operations poses challenge in effectively extracting global features, especially in the context of large‐scale buildings. In contrast, transformers excel at capturing global information, but compared to CNNs, they tend to overly rely on large‐scale datasets and pre‐trained parameters. To tackle the challenge, this paper presents the multi‐scale and dual‐branch feature fusion network (MDTrans). Specifically, the CNN and transformer branches are integrated in a dual‐branch parallel manner during both encoding and decoding stages, local information for small‐scale buildings is extracted by utilizing Dense Connection Blocks in the CNN branch, while crucial global information for large‐scale buildings is effectively captured through Swin Transformer Block in the transformer branch. Additionally, Dual Branch Information Fusion Block is designed to fuse local and global features from the two branches. Furthermore, Multi‐Convolutional Block is designed to further enhance the feature extraction capability of buildings with different sizes. Through extensive experiments on the WHU, Massachusetts, and Inria building datasets, MDTrans achieves intersection over union (IoU) scores of 91.36%, 64.69%, and 79.25%, respectively, outperforming other state‐of‐the‐art models. [ABSTRACT FROM AUTHOR]

In this review study, the major available methods for measurement and estimation of evapotranspiration (ET) are discussed briefly while explaining the latest developments. The best available validation methods are also reviewed and explained. It highlights the importance of accurate ET quantification in managing water resources, evaluating climate change impacts, and supporting crop water requirement management. Measurement methods such as scintillometry, lysimetry, and the eddy covariance (EC) flux method are presented. Additionally, hydrological models are discussed as estimation approaches for actual and potential ET. The paper explores various ET estimation products, particularly those based on remote sensing techniques. Specifically, methods like Mapping EvapoTranspiration at high Resolution with Internalized Calibration (METRIC), Simplified Surface Energy Balance Operational (SSEBop ET), Moderate Resolution Imaging Spectroradiometer (MOD16), Surface Energy Balance Algorithm for Land (SEBAL), Global Land Surface Evaporation: Amsterdam Methodology (GLEAM), Satellite Application Facility on Land Surface Analysis (LSA‐SAF), and Global Land Data Assimilation System (GLDAS) are described. The integration of machine learning (ML) with EC and remote sensing is investigated, with a comprehensive discussion of different ML approaches. Validation methods including the EC method, water balance method‐derived ET (WBET), and statistical techniques are explained. Overall, this review paper provides a comprehensive overview of ET quantification, covering measurement techniques, estimation approaches, remote sensing methods, and the integration of ML. The insights gained from this review contribute to a profound knowledge of ET dynamics and helps those sectors dealing with drought monitoring, water resource management and climate change assessments. [ABSTRACT FROM AUTHOR]

Target detection in infrared remote sensing images has important practical applications. Among the current high‐performance methods, the deep learning‐based methods require training samples, and their generalization ability is also limited by the training set. The separation of low‐rank and sparse matrix requires joint processing of multiple images with high computational complexity. The track‐before‐detect algorithms based on particle filtering also have high computational complexity. In this paper, the low‐rank and sparse matrix of a single image are proposed for target detection, and a differentiable objective function is used in the separation. At the same time, an extended multitarget tracking algorithm based on random sets is used for target filtering between frames, and the design of the filters adopts the conjugate distribution under the Bayesian framework. Finally, the practical infrared sequence images containing multiple targets and complex backgrounds were employed to verify the performance of the proposed algorithms by comparing them with state‐of‐the‐art algorithms. [ABSTRACT FROM AUTHOR]

Due to differences in the quantity and size of observed targets, hyperspectral images are characterized by class imbalance. The standard deep learning classification model training scheme optimizes the overall classification error, which may lead to performance imbalance between classes in hyperspectral image classification frameworks. Therefore, a novel factor annealing decoupling compositional training method is proposed in this paper. Without requiring resampling or reweighting, it implicitly modulates the training process, so standard models can sufficiently learn the representation of the minority classes and further be trained as robust classifiers. Specifically, the label‐distribution‐aware margin loss is combined with the error‐rate‐based cross‐entropy loss via combination factor, which considers both imbalanced data representation learning and classifier overall performance. Then, a factor annealing optimization training scheme is designed to adjust the combination factor, which solves the stage division problem of two‐stage decoupling learning. Experimental results on two hyperspectral image datasets demonstrate that, as compared with other competing approaches, the proposed method can continuously and stably optimize the model parameters, achieving improvements in class average metrics and difficult classes without affecting overall classification performance. [ABSTRACT FROM AUTHOR]

High spectral resolution infrared sounders are an integral component of the global observing system and are used in a broad range of applications. This is enabled by their high accuracies which are ensured by rigorous calibration/validation activities. One of these activities is the post‐launch intercomparison with other high spectral resolution infrared sounders using simultaneous nadir overpasses (SNOs). This paper introduces a novel application of the previously developed SNO methodology by including time difference histogram symmetrization and a spatial sampling uncertainty. Where possible, radiometric measurement uncertainties are included and propagated through the statistics. Comparisons of Atmospheric Infrared Sounder (AIRS), METOP‐A/B/C Infrared Atmospheric Sounding Interferometer (IASI), and Cross‐track Infrared Sounder (CrIS) from 1 October 2019, to 1 October 2020, are analyzed. Results show AIRS and IASI differences as well as CrIS and AIRS differences are generally less than 0.4 K across the spectrum, and CrIS and IASI differences are generally less than 0.25 K. Comparison of the Suomi National Polar‐Orbiting Partnership and NOAA‐20 CrIS instruments via IASI and AIRS shows differences are generally less than 0.1 K across all bands and that the two CrIS instruments statistically agree within their radiometric uncertainties except for the narrow 2,370 cm−1 region where artifacts due to brightness temperature conversion are prone to occur. Plain Language Summary: One of the primary sources of information used to monitor Earth's weather patterns are satellite based meteorological instruments that measure radiation in the infrared region with high spectral resolution. Agencies worldwide use data from these types of instruments to provide input into weather prediction models. The long term data sets are also very valuable for multi‐decadal climate studies. To ensure the value of these data, the instruments need to be properly calibrated using external references. This paper details and expands upon the previously established methodology of one commonly used calibration technique—called SNOs. This method compares nadir‐viewing data from different instruments measuring radiation coming from the same mass of atmosphere in time and space. Introduced in this paper are two additions to the methodology: (a) a symmetrization of the time differences between when the instruments measure the specific air mass, and (b) an uncertainty estimate on the comparison based on the spatial coincidence of the measurements. Results from this methodology demonstration show how well the current suite of satellite‐based instrumentation measuring infrared radiation at high spectral resolution agree with each other. Key Points: Simultaneous nadir overpass comparisons include time difference symmetrization, spatial sampling uncertainty, and Cross‐track Infrared Sounder (CrIS) radiometric uncertaintyDifferences between Suomi National Polar‐Orbiting Partnership (SNPP) CrIS and NOAA‐20 CrIS are generally less than 0.1 K and within combined radiometric measurement uncertaintiesDifferences between Atmospheric Infrared Sounder (AIRS) and CrIS or AIRS and Infrared Atmospheric Sounding Interferometer (IASI) generally less than 0.4 K, differences between CrIS and IASI generally less than 0.25 K [ABSTRACT FROM AUTHOR]

ABSTRACT Landscape‐scale LiDAR‐based studies are becoming increasingly prevalent in archaeology, mainly focusing on detecting archaeological sites to create datasets for spatial analysis. However, the representativeness of these datasets in accurately reflecting the surviving distributions of archaeological sites has often been overlooked. This paper discusses issues of sampling and representativeness in LiDAR‐derived datasets, particularly within the scope of large‐scale landscape studies in Mediterranean contexts. Drawing insights from the Ancient Hillforts Survey, which analysed 15 296 km2 in south‐central Italy, the study examines the variability in the visibility of different site typologies in open‐source but low‐resolution LiDAR data. Through an examination of hillforts, platform farms, settlements, field systems, traces of Roman centuriation, and transhumance routes, the paper highlights significant variability in the identification and mapping within and across different site types. Recognizing the need to account for this variability in the development of spatial analysis, the paper discusses the use of sampling areas to address this variability. This approach aims to effectively mitigate potential biases in analysis, emphasizing the necessity for nuanced methodologies in interpreting LiDAR data for archaeological research. [ABSTRACT FROM AUTHOR]

Almost 30 years ago, Naomi Miller and Katheryn Gleason edited the influential volume, The Archaeology of Garden and Field, a guide to the identification and interpretation of evidence for past agricultural practice inscribed within the landscape. Here we introduce a new collection of papers that advance both theoretical discourses and methodological approaches to the study of ancient field systems. Contemporary archaeological debates bring new urgency to explorations of relict agricultural features, as they offer powerful perspectives on the entanglements of humans with their environment in the Anthropocene, while also serving to decolonize the past through engagement with Indigenous and traditional ecological knowledge. Although many ancient fields are at dire risk of destruction or have already been lost to modern land‐use changes, an emerging suite of new technologies and innovative methods are now enabling archaeologists to find and interpret past agricultural systems as never before. Herein, we argue for the critical importance of archaeological investigations that prioritize discovery and interpretation of relict fields and their constitution within larger landscapes, both as a means to better understand people in the past as well as our role as a species in shaping global ecosystems. [ABSTRACT FROM AUTHOR]

Remote sensing scene images contain numerous feature targets with unrelated semantic information, so how to extract to the local key information and semantic features of the image becomes the key to achieving accurate classification. Existing Convolutional Neural Networks (CNNs) mostly concentrate on the global representation of an image and lose the shallow features. To overcome these issues, this paper proposes LPNet for remote sensing scene image classification. First, LPNet employs LKConv to extract the semantic features in the image, while using standard convolution to extract local key information in the image. Additionally, the LPNet applies a shortcut residual concatenation branch to reuse features. Then, parameter fusion combines parameters from previous branches, improving the capacity of the model to obtain a more comprehensive and rich feature representation of the image. Finally, considering the relationship between the classification ability of the model and the depth of feature extraction, the Feature Mixture (FM) Block is used to deepen the model for feature extraction. Comparative experiments on four publicly available datasets show that LPNet provides comparable results to other state‐of‐the‐art methods. The effectiveness of LPNet is further demonstrated by visualizing the effective receptive fields (ERFs). [ABSTRACT FROM AUTHOR]

The divergence method, a lightweight approach for estimating emission fluxes from satellite images, rests on a few implicit assumptions. This paper explicitly outlines these assumptions by deriving the method from first principles. The assumptions are: the enhanced mass flux is dominated by advection, normal fluxes vanish at the top and bottom of the atmosphere, steady‐state conditions apply, sources are multiplications of temporal and spatial functions, sinks are described as first‐order reactions, and effective wind fields are concentration‐weighted wind fields. No such assumptions have to be made for the background field. A "topography correction term" does not follow from the theory, but is rather shown to be a practical correction for topography‐dependent effective wind speed errors. The cross‐sectional flux method follows naturally from the derived theory, and the methods are compared. Effects of discrete pixels and finite‐difference operations are explored, leading to recommendations, primarily the recommendation to integrate over small regions only to minimize the influence of noise. Numerical examples featuring Gaussian plumes and COSMO‐GHG simulated plumes are provided. The Gaussian plume example suggests that the divergence method might underestimate emissions when assuming only advection in the presence of cross‐wind diffusion. Conversely, the cross‐sectional flux method remains unaffected, provided fluxes are integrated across the entire plume. The COSMO‐GHG example reveals frequent violations of the steady‐state assumption, although the assumption remains valid proximal to the source (<20 km in this example). It is the hope that this paper provides a solid theoretical foundation for the divergence and cross‐sectional flux methods. Plain Language Summary: Power plants, megacities, and other regions can be places where considerable amounts of gases are emitted into the atmosphere. Satellite data is capable of recording the enhanced concentrations in the atmosphere due to these sources. How does one estimate emissions of these sources based on this data? Typically by running an atmospheric inverse model; but one computationally lightweight method gaining popularity is the "divergence method." With this method, one applies a simple mathematical operation to the satellite image multiplied by the horizontal wind speeds—the result of which is related to the emissions. The method has been used before, but here we systematically state the assumptions involved; factors like how gases are transported horizontally and over time, and what happens when we apply the method on pixelated data (such as satellite images). We find that some other analyses in the literature may be partially incorrect. Another popular method (the "cross‐sectional flux") is easily derived from the same theory. Synthetic examples are used to illustrate that various assumptions are not met in realistic data; although the examples also show that one can work around these limitations by a careful application of the methods, and by acquiring more estimates over many days. Key Points: The divergence method for estimating emissions from satellite images is derived from first principlesAssumptions made on the way are made explicit, highlighting discrepancies with existing literatureA comparison is made against the cross‐sectional flux method which follows from the same theory [ABSTRACT FROM AUTHOR]

Forecasting flood and drought events requires accurate modeling tools. Hydraulic river models are based on estimates of riverbed geometry which are traditionally collected in situ. The novel Ice, Cloud and Land Elevation Satellite 2 [ICESat‐2] lidar altimetry mission with 6 simultaneous high‐resolution laser beams provides the opportunity to define river cross‐section geometries as well as observe water surface elevation [WSE] and water surface slope spatially resolved along the river chainage. This paper describes a method to utilize terrain altimetry and water surface slope estimates to define complete river geometries from ICESat‐2 data products, using the diffusive wave approximation to calculate depth in the submerged section not penetrated by the lidar. Exemplifying the method, cross‐sections are defined for a stretch of the Mekong River. Hydrodynamic model results of the stretch are compared with ICESat‐2 WSE estimates and in situ gauging station time series. Insights in river characteristics from satellite imagery and the ICESat‐2 slope estimates allow for fine‐tuning of the cross‐sections using spatially varying Manning numbers. The final model achieves a root mean square error against the ICESat‐2 WSE of 0.676 m and average Kling‐Gupta Efficiency against gauging station time series of 0.880. The method is limited by the diffusive wave approximation resulting in inaccurate cross‐section estimates in sections with supercritical flow or significant acceleration. Errors can be identified from ICESat‐2 WSE estimates and reduced with additional cross‐sections. Combined with hydrological models, the method will allow for cross‐section definition without in situ data. Plain Language Summary: The depth and width of the river channel are important factors when seeking to predict floods. To predict water level in a river, computer models for flood forecasting must be informed with river channel geometry at cross‐sections along the river. Such cross‐sections are traditionally measured in the field which is expensive and time consuming. This paper describes a method to estimate river cross‐sectional shape from land and water height measurements from the satellite ICESat‐2. When the satellite path crosses the river, the precise laser instrument onboard outlines the surface to indicate the river channel shape, but it does not penetrate the water. We found that it was possible to estimate the shape of the submerged part of the cross‐section using the water surface slope obtained from the satellite's unique instrument. Evaluating the method on a stretch of the Mekong River, we were able to model water level along the river with accuracy sufficient for flood forecasting. In some sections, where the flow speed changes quickly, a hydraulic model with the defined cross‐sections does not reproduce observed water levels. Using satellite crossings from days with steadier flow reduces the error. The method allows for building hydraulic river models without cross‐section surveys. Key Points: Land elevation and water surface slope estimates from the ICESat‐2 lidar altimetry mission are combined to define river cross‐sectionsRMSE of 0.676 m and KGE of 0.88 is achieved for a hydrodynamic model of a stretch of the Mekong River using the derived cross‐sectionsSections with significant flow acceleration lead to inaccurate cross‐sections, but errors are reduced with additional ICESat‐2 crossings [ABSTRACT FROM AUTHOR]

Repeat photography offers distinctive insights into ecological change, with ground‐based oblique photographs often predating early aerial images by decades. However, the oblique angle of the photographs presents challenges for extracting and analyzing ecological information using traditional remote sensing approaches. Several innovative methods have been developed for analyzing repeat photographs, but none offer a comprehensive end‐to‐end workflow incorporating image classification and georeferencing to produce quantifiable landcover data. In this paper, we provide an overview of two new tools, an automated deep learning classifier and intuitive georeferencing tool, and describe how they are used to derive landcover data from 19 images associated with the Mountain Legacy Project, a research team that works with the world's largest collection of systematic high‐resolution historic mountain photographs. We then combined these data to produce a contemporary landcover map for a study area in Jasper National Park, Canada. We assessed georeferencing accuracy by calculating the root‐mean‐square error and mean displacement for a subset of the images, which was 4.6 and 3.7 m, respectively. Overall classification accuracy of the landcover map produced from oblique images was 68%, which was comparable to landcover data produced from aerial imagery using a conventional classification method. The new workflow advances the use of repeat photographs for yielding quantitative landcover data. It has several advantages over existing methods including the ability to produce quick and consistent image classifications with little human input, and accurately georeference and combine these data to generate landcover maps for large areas. [ABSTRACT FROM AUTHOR]

The Global Precipitation Measurement (GPM) Mission Core Observatory satellite launched in 2014 as a joint mission between National Aeronautics and Space Administration (NASA) and JAXA. Global Precipitation Measurement (GPM) has, since that time, provided continuous, valuable dual‐frequency radar and passive microwave radiometer observations. Here, we introduce a gridded data set of collocated GPM Core Observatory observational products merged with a reanalysis‐derived Atmospheric river (AR) data set in the North Atlantic and North Pacific sectors. The three data sets that are merged and gridded are: (a) the NASA Goddard Profiling (GPROF) precipitation product, which uses GPM passive microwave radiometer observations to derive surface precipitation rates, (b) a water vapor data product derived from the GPM Core Observatory radiometer, provided by Remote Sensing Systems (RSS), and (c) the Mattingly et al. (2018, https://doi.org/10.1029/2018jd028714) AR data set that is specifically tuned to the high‐latitude regions. This novel merged data set spans from May 2014 to December 2022 with plans to update annually through 2026 at minimum. This gridded product combines RSS passive water vapor and precipitation estimates with coincident AR detection. This data product benefits the scientific community by providing (a) user‐friendly gridded satellite data compared to standard satellite data sets, while maintaining high temporal resolution, and (b) coincident satellite observations to assess the link between ARs and precipitation. Plain Language Summary: This paper summarizes the creation of a new, gridded data set containing precipitation, atmospheric water vapor content, and atmospheric river (AR) information from May 2014 to December 2022. The precipitation and water vapor data are both remotely‐sensed data products using the Global Precipitation Measurement Core Observatory Microwave Imager (GMI). The AR algorithm detection is more sensitive to moisture transport than global algorithms and is thus better suited to colder and drier environments. We temporally match and interpolate all data to a common grid and provide examples of the merged data to illustrate the seasonality and impacts of AR presence on precipitation. Lastly, we demonstrate the utility of this data set by investigating the assumptions used to estimate precipitation from GMI measurements. The data set confirms that ARs are associated with warmer temperatures, higher water vapor content, and more intense precipitation rates. Key Points: A new data set is presented that collocates satellite‐derived precipitation rates and water vapor with reanalysis‐derived Atmospheric rivers (ARs)Higher precipitation rates and water vapor content are coincident with AR events in the high‐latitude regionsAn example application illustrates warmer, more moist conditions during precipitating AR events that are present in the GPROF database [ABSTRACT FROM AUTHOR]

The concept of radial velocity is proposed for high frequency (HF) radar for sea surface current measurement. However, in a spaceborne synthetic aperture radar (SAR) system, the meaning of this concept has changed greatly. Through the evidence and analysis presented in this paper, the radial velocity observed via spaceborne SAR is revealed from the projection of the satellite's velocity at different points along the range direction, and its distribution on the image presents a strip‐like texture, which is very different from the central radial distribution on the velocity image measured via HF radar. The retrieved sea surface velocity field does not exhibit any radial characteristics, and its value is solely the magnitude of the sea surface velocity. Plain Language Summary: Spaceborne synthetic aperture radar (SAR) and high frequency (HF) radar are two instruments for detecting the sea surface current field. Research on HF radar began much earlier than research on spaceborne SAR, so it is generally believed that the velocity measured via spaceborne SAR is also the radial component of the sea surface velocity, which is consistent with the radial sea surface velocity measured via HF radar. Through sufficient evidence, in this paper, it is shown that the sea surface velocity measured via spaceborne SAR is not the radial velocity but rather the magnitude of the sea surface velocity. Only the range satellite velocity measured via spaceborne SAR presents a strip‐like radial feature. Based on the explanation presented in this paper, spaceborne SAR will become the most important technique for sea surface current remote sensing since it can directly obtain the velocity field of the sea surface current. Key Points: The differences between the Doppler measured satellite's velocity and sea surface velocity are compared in detailThe concept of radial velocity with its imaging mechanism is clearly revealed [ABSTRACT FROM AUTHOR]

Hyper‐spectral image (HSI) clustering has become a hot spot in remote sensing image research. However, due to the large amount and high dimension of HSI data, some traditional image clustering methods are no longer suitable, and even lead to poor clustering performance and long computing time. Therefore, this paper proposes a novel clustering method for HSI to make up for the shortcomings of traditional clustering methods. Firstly, a fuzzy similarity matrix is constructed by using the bipartite graph to obtain low‐dimensional hyper‐spectral data, which can reduce the complexity and the operation time of the algorithm. Secondly, fuzzy membership mapping is carried out through the constructed bipartite graph, and a non‐negative regularization term is added to limit ill‐conditioned problems. Thirdly, this paper uses the Geman‐McClure function to optimize Euclidean distance. Finally, in this paper, the spectral information of HSI is considered while the neighbourhood information of HSI is added to achieve the unity of space spectrum, which enhances the robustness of the algorithm and improves the clustering performance of the algorithm. Compared with existing HSI clustering algorithms, the proposed algorithm has higher clustering accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Currently, the opportunistic method to estimate rainfall using commercial microwave links (CMLs) has been shown as an efficient way to complement traditional instruments in terms of spatial‐temporal resolution and coverage. In this paper, we collected data from 26 CMLs in Jiangyin City, Jiangsu Province, and conducted experiments on rainfall field reconstruction and nowcasting. First, the raw CML data were processed to invert the path‐averaged rainfall intensity. Second, the algorithms of inverse distance weighting (IDW) and ordinary kriging (OK) interpolation were employed to reconstruct the rainfall field. Then a 10‐min prediction of the rainfall field was achieved using a nowcasting model based on the long short‐term memory neural network and a setup window was introduced to improve the prediction performance of the first few minutes. The reconstruction results show that the average correlation coefficient (ACC) and the average root mean square error (ARMSE) between the IDW‐based results and daily cumulative rainfall from rain gauges (RGs) are 0.89 and 8.69 mm, respectively, while the ACC and ARMSE between the OK‐based results and RG data are 0.89 and 9.13 mm, respectively. The nowcasting results show that the ACC between the prediction results with a 5‐min setup window and the IDW‐retrieved rainfall fields can reach 0.91 at the first minute and gradually decrease to 0.20 within 10 min. Furthermore, the model has better nowcasting performance for stratiform precipitation and mixed precipitation compared to convective precipitation. Plain Language Summary: Accurate and real‐time rainfall monitoring and forecasting are of great significance for disaster prevention and control, agriculture, meteorological research, and related fields. However, traditional rainfall measurement methods are insufficient for meeting the demands of comprehensive precipitation observations because of poor spatial‐temporal resolution and limited coverage. Currently, measuring rainfall using additional microwave attenuation caused by raindrops has been shown as an efficient way to complement traditional instruments. Based on commercial microwave link (CML) rainfall measurement technology, this paper carries out rainfall field reconstruction and nowcasting experiments in Jiangyin City, China. The results show the CML network enables accurate rainfall field reconstruction and nowcasting. Key Points: Based on the commercial microwave link (CML) rainfall monitoring network in Jiangyin, China, accurate rainfall inversion by CMLs is achievedThe two‐dimensional rainfall fields are accurately reconstructed using the inverse distance weighting and ordinary kriging interpolation algorithmsA long short‐term memory‐based rainfall field nowcasting model is proposed to achieve 10‐min continuous predictions of rainfall fields [ABSTRACT FROM AUTHOR]

This study is part of the requirements consolidation study for the European Copernicus Hyperspectral Imaging Mission for Environment (CHIME). It explores the value added by existing hyperspectral data of similar characteristics to CHIME, namely AVIRIS‐NG and PRecursore IperSpettrale della Missione Applicativa (PRISMA), for detecting topsoil texture properties. The spatial variability is retrieved using the linear spectral mixture analysis, an image‐based algorithm that breaks down the hyperspectral data set into fractional abundance of spectral classes within each pixel. The fractional abundance of image‐based endmembers is broken into categories to find intervals having a spatial relation with texture components in terms of fine (clay and silt) or coarse (sand) abundance. The fraction maps obtained show similar spatial patterns to the USDA soil texture classification, obtained with a geostatistical approach. Specifically, AVIRIS CHIME‐like FAM1 > 0.45 presented an agreement of 86% with clay and/or silt higher than 45% which, according to the United Stated Agriculture Department (USDA) intervals, correspond to loam‐clay loam classes. Similar results are obtained with PRISMA with FAM2 0.20–0.35, overlapping 63% of the kriging‐based USDA clay‐loam class. The fractional abundance categories showing the highest overlap percentages are correlated with the short‐wave infrared spectral range, showing average coefficients of 0.7 where wavelengths are over 1,500 nm. From 1700 nm, CHIME‐like shows values of 0.8. In conclusion, this exploratory research and results leverage the opportunity of extending the processing chain to a larger number of case studies to better understand the physical relation between the spectral reflectance captured by new spaceborne hyperspectral sensors and the spatial patterns of soil texture classes. Plain Language Summary: In the current context led by the constant availability, almost in near‐real time, of remote sensing data with high spatial, spectral and temporal resolutions, the monitoring of soil properties should no longer be solely limited to field data observations. There is a growing need of knowledge for preserving and exploiting soils and the upcoming generation of hyperspectral satellite remote sensing has popped up new opportunities for developing and adapting algorithms that break up the image pixels into several spectral behaviors that correspond to different soil properties. This paper contributes to the application of these image analysis techniques to soil texture classes retrieval considering the United Stated Agriculture Department model. The paper uses the principles of the spectral mixture concept to explore the opportunity of detecting soil textures in two farmland units and provides some highlights on the observational requirements needed to integrate the future satellite missions' architecture within the agriculture and food security application domain. Key Points: Hyperspectral imaging is used to retrieve United Stated Agriculture Department soil texture classification in two farmland units applying linear spectral mixture analysisCopernicus Hyperspectral Imaging Mission for Environment‐like and PRecursore IperSpettrale della Missione Applicativa‐derived fractional abundance intervals are found to spatially overlap the clay‐loam class obtained with field dataFractional abundances categories having the highest overlap percentage are strongly correlated with the Short‐Wave InfraRed > 1,500 nm [ABSTRACT FROM AUTHOR]

Radar sounding of Mars' south polar ice cap has revealed portions of its base with stronger radar returns than its surface. These have been used to estimate high real permittivity values consistent with liquid water. However, the thermal, chemical, and geological conditions required to sustain such water bodies are challenging to achieve in the contemporary Martian environment. Three recent papers address this conflict. Khuller and Plaut (2021, https://doi.org/10.1029/2021GL093631) find similar radar signatures across the ice cap, expanding the range conditions associated with water candidates. Bierson et al. (2021, https://doi.org/10.1029/2021GL093880) show that, for non‐zero conductivity, a wider range of materials could produce the observed echoes. Similarly, Smith et al., (2021, https://doi.org/10.1029/2021GL093618) use modeling, laboratory tests, and remote sensing data to propose smectite deposits as their specific source. While not incompatible with bodies of liquid brines, these papers provide potential alternative sources for the bright radar observations. Plain Language Summary: Bright radar reflections from beneath the ice cap on the south pole of Mars provided tantalizing evidence for liquid water bodies beneath the ice. However, three recent papers present new challenges for this interpretation. The first shows that similar bright reflections are widespread across much of the ice cap. The environment on Mars already makes it difficult for water to remain stable and these echoes mean that it would have to do so across an even wider range of challenging temperatures and pressures. The two other papers show that other materials, like salty ice and clay, could also produce the bright reflections. Although these new papers do not rule out the possibility of very salty bodies of liquid water beneath the ice cap, they do raise the possibility of alternative sources for the bright reflections. Key Points: Bright radar reflections are widespread across the base of Mar's south polar ice capThe brightness of these reflections could result, in part, from the electrical conductivity of the base of the ice capPotential conductive materials include liquid brines, saline ice, and conductive deposits such as hydrous smectite clays [ABSTRACT FROM AUTHOR]

The salinity of the salt lake is an important factor to evaluate whether it contains some mineral resources or not, the fault buried in the salt lake could control the abundance of the salinity. Therefore, it is of great geological importance to identify the fault buried in the salt lake. Taking the Gasikule Salt Lake in China for example, the paper established a new method to identify the fault buried in the salt lake based on the multi‐source remote sensing data including Landsat TM, SPOT‐5 and ASTER data. It includes the acquisition and selection of the multi‐source remote sensing data, data preprocessing, lake waterfront extraction, spectrum extraction of brine with different salinity, salinity index construction, salinity separation, analysis of the abnormal salinity and identification of the fault buried in salt lake, temperature inversion of brine and the fault verification. As a result, the study identified an important fault buried in the east of the Gasikule Salt Lake that controls the highest salinity abnormal. Because the level of the salinity is positively correlated to the mineral abundance, the result provides the important reference to identify the water body rich in mineral resources in the salt lake. [ABSTRACT FROM AUTHOR]

Deep convolutional neural networks based remote sensing change detection has recently shown significant performance improvement. However, small region changes and global‐local features in high‐resolution remote sensing images are not fully explored. This paper introduces a hybrid U‐shaped and transformer network for change detection in high‐resolution remote sensing images. Specifically, a UNet++‐based backbone to facilitate feature learning across different scales. In addition, we introduce a transformer‐based feature fusion module for extracting long‐range dependencies, which can enhance the representation ability of the network. Furthermore, the introduced efficient channel attention mechanism can efficiently calibrate the feature representation and concentrate on more important feature information. Thanks to the above designs, the proposed method enjoys a strong ability to extract local and global features for remote sensing change detection. Extensive experimental results on different remote sensing images show that our method can achieve superior performance in comparison with state‐of‐the‐art change detection methods. [ABSTRACT FROM AUTHOR]

Traditional marine monitoring can be a resource‐intensive process that often covers a network of sampling stations where data are collected manually by divers, or discretely using in situ water samples at different depths at fixed positions followed by laboratory analysis. As such, environmental status is often reported after a delay of months or years. However, things are set to change for the better. Recent advances in technologies, such as remote sensing, machine learning techniques, modeling for non‐experts, acoustic monitoring, and intelligent integration of modeling and sensor measurements will revolutionize the future of marine environmental monitoring and monitoring systems. This special series touches upon some of the new technologies and models that may be an integrated part of ecosystem assessment and management in the future. Although technologies are being developed and integrated for marine monitoring around the world, the integration with ecosystem models is still in the early days. Still, this series highlights inspirational examples of the time ahead of us. Integr Environ Assess Manag 2022;18:888–891. © 2022 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC). Key Points: Papers in the special series show advances in various technologies. [ABSTRACT FROM AUTHOR]

Our changing climate is having effects on freshwater ecosystems in all seasons, especially winter. High latitude lakes, wetlands, and rivers are experiencing shorter periods of ice cover, and lower latitudes systems that used to freeze are experiencing open water conditions throughout the winter. A 2019 AGU Chapman conference convened aquatic scientists to examine these changes and address the implications of changing winters to aquatic life, chemistry, and physics. Several studies demonstrate decreased ice cover duration than in the past. The removal of an ice "lid" from lakes and rivers impacts the exchange of gases with the atmosphere and the predominant types of metabolism occurring in the waters below, with the potential for more photosynthesis and an increase in oxic versus anoxic metabolism when the lid is removed. Multiple studies indicated an increase in the interannual variability of winters, especially in terms of ice‐cover duration and ice quality. Increased variability may simply be an outcome of a more variable winter climate or small differences in environmental conditions such as temperature that can have strong effects on gas exchange, light transmission, and turbulence when ice forms. A question that merits further consideration is whether and how winters of shorter duration and severity will change the dynamics of freshwater systems. Are there memory or legacy effects that carry over to the next season or year? There is much work to be done to understand how changing winters will impact the biogeochemical behavior of lakes and rivers in the coming decades. Plain Language Summary: Winters at mid‐high latitudes are changing rapidly with important implications for freshwater ecosystems. An AGU Chapman conference, convened in 2019, brought together scientists to outline what we know about the changes that are occurring and what questions need further study. This special issue is a collection of the papers that developed from that workshop. These studies demonstrate that lakes and rivers at mid‐high latitudes are experiencing less ice cover duration and greater variability in year‐to‐year ice cover. Changing ice and snow cover impacts processes occurring under ice in winter by altering the light regime, mixing, biota present, and the availability of oxygen. There are many important research questions regarding winter dynamics that still need to be addressed but one of the most important ones has to do with how changing winters might influence the biota in other seasons. Key Points: Several of the papers point to interannual variability in winter and seasonal ice cover along with decreased ice cover durationAvailable winter datasets have grown supporting conceptual developments, but increased efforts focused on ecological responses are neededFuture research should examine connections between seasons, deploy submersed sensors year‐round, and use remote sensing approaches [ABSTRACT FROM AUTHOR]

Water vapor plays an extremely important role in the monitoring and prediction of weather, and GNSS tomography can obtain 3D spatiotemporal change information and reliable water vapor profiles. In this paper, an improved global navigation satellite system (GNSS) tropospheric tomography technique using an ERA5 (the fifth generation ECMWF reanalysis) product is developed. First, the ERA5 product was adopted to analyze the spatiotemporal distribution of water vapor, and a water vapor density threshold defining the top of the tomography was determined; then, the height of the grid top (GT) of different seasons was obtained through linear fitting; finally, the water vapor value between GT and tropopause is constrained using the data of the ERA5 product as the initial value. The new method for using the ERA5 product to determine the height of the GT of the tomographic grid reduces the height of the top layer of the grid and increases the number of effective GNSS rays. Data from nine CORS stations in Hong Kong in 2019 were selected for experiments. The results showed that the new algorithm increased the number of effective satellite signals by 14%. In addition, the ERA5 data, the radiosonde data, and the COSMIC‐2 data were used as reference values. The accuracy of the water vapor density obtained by the algorithm was improved by 25%, 17% and 9%, respectively. [ABSTRACT FROM AUTHOR]

Remote sensing (RS) and geospatial sciences already amount to a long history of fostering research in topics related to ecology. Data and methods have mainly been subject to research and experiments, but trends are now emerging that suggest the use of RS in practical applications like nationwide monitoring programs and assisting global conservation goals. However, use of active remote sensing for ecological and conservation is in its infancy, and the implications of active sensor data, including light detection and ranging and radio detection and ranging that mostly deliver three‐dimensional (3D) information, are still relatively primitive and have largely been limited to indirect use of their extracted proxies for ecological modelling.This cross‐journal special feature between Methods in Ecology and Evolution, Journal of Animal Ecology, Journal of Applied Ecology and Journal of Ecology includes 18 papers that include full research papers, reviews and technical applications. They are mostly novel in either or both their interpretation of proxies derived from active RS data and the direct usage of 3D RS techniques (terrestrial, airborne, UAV borne and spaceborne) to address ecological topics.We categorized the published contributions into the following thematic groups, with some degree of overlap: (i) ecosystem structural analysis by active data (nine studies); (ii) response of animal populations to climate dynamics as shown by active data; (iii) interactive effects of forest structure and wildlife monitoring (five studies); (iv) forest inventories assisted by active data (one study) and (v) tree type classification by active data (one study).Synthesis. The studies in this Special Feature and trends shown by other recent works at the interface of ecology and active RS confirm the ongoing shift from indirect and solely proxy‐based approaches to direct and more data‐science driven methods in approaching ecology and conservation problems by means of active sensors. Relatively affordable and accessible drone and citizen science‐based on‐demand active RS data acquisition are becoming common practice, and the future of sensor development is hypothesized to go beyond the current domination of very high spatial resolution data and towards multiple spaceborne platforms. These tools and methods will support spatial upscaling, uncertainty analysis, large‐scale mapping and monitoring of wildlife dynamics, among other topics that can take advantage of multitemporal/time series data. Nevertheless, access to demanding and costly very high‐resolution data sources may still be maintained and optimized by establishing international and public–private partnered data pools. [ABSTRACT FROM AUTHOR]

Summary: As LEO (Low Earth Orbit) high‐resolution Earth observation satellite needs to transmit a large amount of remote sensing data from the satellite to the ground station, higher downlink capacity still poses a severe challenge to LEO. Therefore, variable coding and modulation (VCM) has become an important scheme to optimize the performance of satellite downlink. In this paper, the downlink VCM system of China Earth observation satellite is studied for the first time. By evaluating the free space loss of the satellite downlink path, a VCM‐based satellite downlink system is designed, and a DVB‐S2 BCH‐LDPC encoded variable bit rate high‐order MAPSK modulator is implemented. The maximum channel bit rate can reach 3.5 Gbps. Then, the experimental results of Gaofen‐7 satellite, which was launched in November 2019, are carried out which showed that our design method effectively meets the design requirements, and the performance is verified. [ABSTRACT FROM AUTHOR]

The goal of this paper is to propose a method to achieve a higher classification rate in Polarimetric Synthetic Aperture Radar (PolSAR) image classification. In our work, PolSAR features are extracted from Convolutional Neural Networks (CNNs) and also Graph Convolutional Networks (GCNs). Due to the construction of the adjacency matrix on all the data, traditional GCNs usually suffer from a substantial computational cost, particularly in large‐scale remote sensing (RS) problems. To this end, first of all, we propose a new mini‐batch GCN (called Mini‐GCN), which allows to train large‐scale GCNs in a mini‐batch fashion. More significantly, our designed mini‐GCN is capable of inferring out‐of‐sample data without re‐training networks and improving classification performance. Furthermore, as CNNs and GCNs can extract different types of PolSAR features, an intuitive solution to break the performance bottleneck of a single model is to fuse them. Since mini‐GCNs can perform batch‐wise network training (enabling the combination of CNNs and GCNs), we can use the feature fusion strategy. Therefore, in this paper, a local graph‐based fusion method is proposed to couple dimension reduction and feature fusion of the information extracted from the designed CNN and Mini‐GCN. Experimental results on real PolSAR data are very encouraging. Compared to the methods using only single feature and stacking all the features together, the proposed feature fusion method improves the overall classification accuracy on the real PolSAR data sets for more than 5%. Moreover, the experiments conducted on PolSAR datasets, demonstrate the advantages of the used mini‐GCNs over the traditional GCNs and the superiority of the proposed feature fusion method with regards to the single CNN or GCN models. [ABSTRACT FROM AUTHOR]

Spatial resolution is an important indicator that measures the quality of remote sensing images. Image texture has been successfully recovered by generative adversarial networks in deep learning super‐resolution (SR) methods. However, the existing methods are prone to image texture distortion. To solve the above problems, this paper proposes an improved generative adversarial network to enhance the super‐resolution reconstruction effect of medium‐ and low‐resolution (LR) remote sensing images. This network is based on the Super Resolution Generative Adversarial Network (SRGAN), which makes great improvements in the structure of the connection between the inside and outside of the residual block and the design of the model loss function. At the same time, the G1–G2–G3 structure between residuals effectively combines the image information of the three scales of small, medium and large. The model loss function can be designed based on the Charbonnier loss function to narrow the pixel distance between the reconstructed remote sensing image and the original image. Furthermore, targeted perceptual loss can direct the network to restore the texture details of the image according to the semantic category. The subjective and objective evaluation of the generated images and the ablation experiments prove that compared with SRGAN and other networks, our method can generate more realistic and reliable textures. Additionally, the indicators [peak signal‐to‐noise ratio (PSNR), structural similarity (SSIM), multiscale structural similarity (MS‐SSIM)] used to measure the quality of the reconstructed image obtain improved objective quantitative evaluation. [ABSTRACT FROM AUTHOR]

Aeolian landforms are widespread in our solar system. Understanding the exact nature and processes of formation of these features are challenging tasks necessitating a strong collaboration between scientists with different skills and scientific backgrounds. This paper describes the special issue for the 5th International Planetary Dunes Workshop, which includes 15 research papers and three commentaries. Among the 18 papers included in this collection, 16 cover Martian aeolian science and two Titan aeolian science. The papers presented focus on bedform morphology and dynamics via remote sensing data, modeling, analogs studies and laboratory experiments. Here we put the main results of the papers in their appropriate scientific context and discuss potential future lines of research. Plain Language Summary: Wind‐sculpted (aeolian) landforms are found on many bodies of our solar system. Their study is fundamental to understanding the geology and climate of these bodies and to safely plan extraterrestrial missions. Here we introduce a collection of papers describing aeolian features on Mars and Titan. Collectively, the papers presented in this special issue show the importance of an interdisciplinary approach in comprehending what we are seeing on other planets. The study of wind landforms in extraterrestrial planets is sparking a new interest on terrestrial aeolian geomorphology highlighting the significance of planetary studies in advancing the understanding of our Earth. Key Points: Results from the 5th International Planetary Dunes Workshop are presentedStudy of aeolian landforms can shed light on climatic changes and are key for landing site characterizationCombined use of imaging and high‐resolution flow modeling can provide new insights into the mechanism and processes that form bedforms [ABSTRACT FROM AUTHOR]

The Surface Water and Ocean Topography (SWOT) mission will vastly expand measurements of global rivers, providing critical new data sets for both gaged and ungaged basins. SWOT discharge products (available approximately 1 year after launch) will provide discharge for all river that reaches wider than 100 m. In this paper, we describe how SWOT discharge produced and archived by the US and French space agencies will be computed from measurements of river water surface elevation, width, and slope and ancillary data, along with expected discharge accuracy. We present for the first time a complete estimate of the SWOT discharge uncertainty budget, with separate terms for random (standard error) and systematic (bias) uncertainty components in river discharge time series. We expect that discharge uncertainty will be less than 30% for two‐thirds of global reaches and will be dominated by bias. Separate river discharge estimates will combine both SWOT and in situ data; these "gage‐constrained" discharge estimates can be expected to have lower systematic uncertainty. Temporal variations in river discharge time series will be dominated by random error and are expected to be estimated within 15% for nearly all reaches, allowing accurate inference of event flow dynamics globally, including in ungaged basins. We believe this level of accuracy lays the groundwork for SWOT to enable breakthroughs in global hydrologic science. Plain Language Summary: The Surface Water and Ocean Topography (SWOT) satellite mission was launched on 15 December 2022. SWOT is designed to produce estimates of river discharge on many rivers where no in situ discharge measurements are currently available. This paper describes how SWOT discharge estimates will be created, and their expected accuracy. SWOT discharge will be estimated using simple flow laws that combine SWOT measurements of river water elevation above sea level, river width, and river slope, with ancillary data such as river bathymetry. We expect that discharge uncertainty will be less than 30% for two‐thirds of global reaches and will be dominated by a systematic bias. Temporal variations in river discharge time series are expected to be estimated within 15% for nearly all reaches, thus capturing the response of river discharge to rainfall and snowmelt events, including in basins that are currently ungaged, and providing a new capability for scientists to better track the flows of freshwater water through the Earth system. Key Points: The Surface Water and Ocean Topography satellite mission is designed to enable the estimation of discharge for global rivers wider than 100 mWhen unconstrained by in situ data, discharge uncertainty is expected to be <30% for most reaches, and to be dominated by systematic biasWe expect discharge temporal variations to be estimated to within 15% for nearly all reaches globally [ABSTRACT FROM AUTHOR]

Night‐time light (NTL) satellite imagery can provide unique insights into the energy sector. Nevertheless, there are limited studies that have systematically reviewed the literature on the relationship between electricity consumption and NTL. Therefore, this paper aims to provide a systematic review of studies that have explored this relationship. The review identified over 200 regression models estimating electricity consumption using NTL satellite images. The key finding of the review was that there was a large variability in regression performance for model prediction of electricity consumption from NTL imagery, indicating a need for further work to refine the techniques and approaches in this emerging field of remote sensing research. The level of spatial aggregation had an important influence on model performance with larger geographical areas, such as countries or states, providing better estimations. [ABSTRACT FROM AUTHOR]

Sufficiently energetic breaking ocean waves produce distinctive visible foam signatures on the water surface called whitecaps. The mixture of surface whitecap foam cells, and sub‐surface bubbles, results in the broad‐band scattering of light that allow whitecaps to be measured with optical cameras. In this paper the temporal evolution of whitecap foam area from laboratory and oceanic breaking waves is compared. When appropriately scaled, the foam area time series for both laboratory and oceanic breaking waves follow similar trends, despite occurring in vastly different settings. Distinct similarities of the signature of foam stabilization due to the presence of surfactants in the controlled laboratory experiments are also found in the field suggesting foam stabilization may be a means to remotely sense the presence/absence or concentration of surfactants in the ocean. In addition, probability density distributions of key whitecap variables such as foam area growth and decay timescales and maximum foam area are compared between laboratory and oceanic whitecaps. The oceanic whitecaps are much larger in scale than the laboratory breaking waves, whereas the whitecap growth and decay timescales are similar in magnitude, the latter suggesting that the depths to which bubbles are injected during active air entrainment in the field are relatively shallow. The aggregated whitecap statistics are used to estimate the energy dissipation of individual whitecaps in a novel manner. Plain Language Summary: The breaking of ocean waves is a key process controlling the evolution of oceanic sea states and the exchange of energy, momentum and material across the ocean‐atmosphere interface. Despite their importance, little is known about the energetics of individual breaking waves in the ocean because of the difficulty in precisely measuring the complicated fluid motions they produce. However, when they are sufficiently energetic, they entrain air and appear as white foam patches on the water surface, and are called whitecaps. This means that their occurrence and scale can be measured using relatively affordable optical cameras. In this paper we show that the patterns of foam evolution in laboratory breaking waves and oceanic whitecaps are remarkably similar giving us confidence that what we learn from laboratory experiments can be confidently applied to real oceanic whitecaps. Building on this, we show what parts of the whitecap foam area evolution can be used to learn more about ocean chemistry and breaking wave energetics. We hope that further development of the principles outlined in this paper can transform how oceanic whitecaps are observed in the ocean and ultimately lead to a better understanding of ocean waves and their evolution. Key Points: Foam area evolution from laboratory breaking waves and oceanic whitecaps is comparedSelf‐similar behavior in foam area evolution is evident in laboratory and field data setsEnergy dissipated by individual breaking waves can be estimated from foam area time history [ABSTRACT FROM AUTHOR]

Time‐lapse cameras have been widely used as a tool to monitor the timing of seasonal vegetation growth. These simple, relatively inexpensive systems can provide high‐frequency observations of leaf development and demography which are critical data sets needed to characterize plant phenology from species to landscapes. This is important for understanding how plants are responding to global changes, as well as for validating satellite‐derived phenology products.However, in remote regions including the high‐latitude Arctic, deploying time‐lapse cameras could be challenging. The remoteness and lack of widespread power and telecommunications infrastructure limit options for the installation, maintenance and retrieval of data and equipment, and make it difficult for cameras to survive in extreme weather (e.g. long cold winters). To improve our understanding of Arctic phenology, new technologies are required to address these challenges.Here, we present a novel, low‐power, compact, lightweight time‐lapse camera system, called power‐interval camera automation module (PiCAM). The PiCAM was designed with explicit consideration to simplify deployment (i.e. without a need for external power supplies) of camera systems and to address the challenges of camera survival in harsh Arctic environments. In this paper, we describe the design, setup and technical details of the PiCAM and provide a roadmap for how to build and operate these systems. As proof of concept, we deployed 26 PiCAMs at three low‐Arctic tundra sites on the Seward Peninsula, Alaska in early August 2021 for characterizing Arctic plant phenology.Of the 26 PiCAMs, 70% remained active at the point of our revisit in late July 2022 despite the extreme winter temperatures they experienced (< −30°C, heavy snow cover). We extracted key plant phenology metrics from the PiCAMs and captured strong differences across key Arctic plant species. We showed that the PiCAM has the potential to be widely used for monitoring plant phenology across the broader Arctic region, addressing the need for ground‐based understanding of Arctic phenological diversity to develop knowledge of plant response to climate change and to validate remote sensing products. [ABSTRACT FROM AUTHOR]

This study addresses the issue of locating the scattering points on the sea surface and rectifying the interference caused by the ionosphere phase in the remote sensing of ocean dynamics parameters. Wide‐beam high‐frequency hybrid sky‐surface wave radar (WB‐HFSSWR) was used for this purpose. In order to mitigate the impact of ionosphere phase contamination on the electron density variation along the sky‐wave path of a radio wave, we present two algorithms: the WB‐HFSSWR Single‐Station sea surface scattering point localization algorithm (WB‐SPLA) and the semi‐physical ionosphere phase decontamination algorithm (SP‐IPDA). Compared with the currently employed ionosphere phase decontamination algorithm, the SP‐IPDA algorithm exhibited enhanced physical characteristics. To evaluate the efficacy of the proposed algorithms, we calculate the root‐mean‐square errors (RMSEs) of the elliptical current velocity (vE‐IRI and vE‐GST) of the WB‐HFSSWR, based on the SP‐IPDA and generalized S‐transform algorithm (GST). The results demonstrated that the RMSEs of vE‐IRI and vE‐GST were comparable to the precision of the bistatic surface‐wave radar elliptical current velocity. The data volume of vE‐IRI was 20.5% larger than that of vE‐GST, and vE‐IRI was more precise than vE‐GST, with RMSEs of 11.98 and 12.63 cm/s, respectively. Furthermore, on the common sea surface scattering points, the precision of vE‐IRI was approximately 18.4% higher than that of vE‐GST. Plain Language Summary: In order to solve the problems of difficulty in locating the sea surface scattering points and correcting the ionosphere phase contamination in the remote sensing of ocean dynamics parameters of the wide‐beam high‐frequency hybrid sky‐surface wave radar (WB‐HFSSWR). We propose a single‐station WB‐HFSSWR sea surface scattering point localization algorithm (WB‐SPLA) and a semi‐physical ionosphere phase decontamination algorithm (SP‐IPDA) to extract the ionosphere phase contamination from the variation of electron density on the sky‐wave path of radio wave. Different from the existing ionosphere phase decontamination algorithm based on signal processing, the SP‐IPDA is an attempt to physically correct the ionosphere phase contamination in application. The results comparing with the generalized S‐transform algorithm indicate that the two algorithms proposed in this paper, which have good application value, are more suitable for remote sensing of ocean dynamics parameters of the WB‐HFSSWR. Key Points: Locating the sea surface scattering points with a single‐station wide‐beam high‐frequency hybrid sky‐surface wave radarExtracting the ionosphere phase contamination from the variation of electron density on the sky‐wave path of radio waveThe ionosphere phase contamination calculated by the IRI model has a strong correlation with that obtained by signal processing [ABSTRACT FROM AUTHOR]

With the arrival of new data acquisition platforms derived from the Internet of Things (IoT), this paper goes beyond the understanding of traditional remote sensing technologies. Deep fusion of remote sensing and computer vision has hit the industrial world and makes it possible to apply Artificial intelligence to solve problems such as automatic extraction of information and image interpretation. However, due to the complex architecture of IoT and the lack of a unified security protection mechanism, devices in remote sensing are vulnerable to privacy leaks when sharing data. It is necessary to design a security scheme suitable for computation‐limited devices in IoT, since traditional encryption methods are based on computational complexity. Visual Cryptography (VC) is a threshold scheme for images that can be decoded directly by the human visual system when superimposing encrypted images. The stacking‐to‐see feature and simple Boolean decryption operation make VC an ideal solution for privacy‐preserving recognition for large‐scale remote sensing images in IoT. In this study, the secure and efficient transmission of high‐resolution remote sensing images by meaningful VC is achieved. By diffusing the error between the encryption block and the original block to adjacent blocks, the degradation of quality in recovery images is mitigated. By fine‐tuning the pre‐trained model from large‐scale datasets, we improve the recognition performance of small encryption datasets for remote sensing images. The experimental results show that the proposed lightweight privacy‐preserving recognition framework maintains high recognition performance while enhancing security. [ABSTRACT FROM AUTHOR]

Exposure to environmental hazards is an important determinant of health, and the frequency and severity of exposures is expected to be impacted by climate change. Through a partnership with the U.S. National Aeronautics and Space Administration, the U.S. Centers for Disease Control and Prevention's National Environmental Public Health Tracking Network is integrating timely observations and model data of priority environmental hazards into its publicly accessible Data Explorer (https://ephtracking.cdc.gov/DataExplorer/). Newly integrated data sets over the contiguous U.S. (CONUS) include: daily 5‐day forecasts of air quality based on the Goddard Earth Observing System Composition Forecast, daily historical (1980‐present) concentrations of speciated PM2.5 based on the modern era retrospective analysis for research and applications, version 2, and Moderate Resolution Imaging Spectroradiometer (MODIS) daily near real‐time maps of flooding (MCDWD). Data integrated into the CDC Tracking Network are broadly intended to improve community health through action by informing both research and early warning activities, including (a) describing temporal and spatial trends in disease and potential environmental exposures, (b) identifying populations most affected, (c) generating hypotheses about associations between health and environmental exposures, and (d) developing, guiding, and assessing environmental public health policies and interventions aimed at reducing or eliminating health outcomes associated with environmental factors. Plain Language Summary: The well‐being of people and communities can be impacted by hazards like polluted air and flooding. This paper describes a new partnership to bring more timely information about U.S. air quality and flooding into a centralized online environmental public health platform, the CDC Tracking Network's Data Explorer. Timely monitoring and forecasting of environmental hazards have the potential to reduce harmful exposures by enabling early warnings and other timely public health interventions. Currently, near real‐time data may not be readily accessible to public health practitioners, particularly those that need to react to public health emergencies, because the data are fragmented across multiple systems in formats inconsistent with typical public health uses. Developing data pipelines and processes to host this data in a usable and standardized format and disseminating the data via an existing online platform assists in making important information about environmental health hazards more readily accessible to public health practitioners, decision makers, and partners. Key Points: Timely information can be critical for public health decision making, but environmental hazard data can be fragmented or hard to useThis work brings timely, routinely updated National Aeronautics and Space Administration (NASA) data into CDC Tracking Network's Data Explorer in familiar and accessible formatsFor CONUS, daily NASA air quality forecasts, historical air quality data, and daily flood maps are integrated into the Data Explorer [ABSTRACT FROM AUTHOR]

The aerosol shortwave, direct radiative effects of smoke plumes from Chilean wildfires in 2017 and 2023 were derived from satellite observations in both cloud‐free and cloud scenes. At the top of the atmosphere, the aerosol DRE changes sign when aerosol overly clouds or open ocean, confirmed by both measurements and a simulation study. The cloud‐free daily‐mean DRE, computed using an offline radiative transfer model (RTM), was 66 W m−2 in 2023 and 42 W m−2 in 2017, due to absorption by smoke. However, the total radiative effects were larger in 2017 due to a larger plume size compared to 2023. The method presented here provides a new conceptual model to quickly assess the radiative effects of wildfire smoke plumes using satellite measurements and pre‐computed RTM results. The presented estimates are strongly affected by the uncertainty of aerosol optical thickness retrievals from satellite, which can be large in the presence of clouds. Plain Language Summary: From 30 January to the end of February 2023, central Chile experienced over 400 individual wildfires, consuming over 430,000 ha of native sclerophyllous forests, with 25 fatalities as of 14 February. Wildfires are common in central Chile during dry periods, but seem intensified due to the mega‐drought since 2010. Next to health effects, the smoke from these wildfires has important climatic impacts through the change of solar insolation in the atmosphere: smoke strongly absorbs solar radiation and heats the atmosphere, changing the vertical stability. In this paper, the horizontal and vertical smoke distribution in the atmosphere is presented using satellite observations, and the radiative effects in the atmosphere and at the surface are quantified for the recent fires and as well as for the record‐breaking wildfires in 2017, which were the most devastating in the modern history of central‐Chile. Our results show that the radiative effects of the smoke from the recent wildfires were stronger in magnitude during the first few days, but confined to a smaller area, reducing their overall effect. Key Points: The radiative effects of the recent 2023 Chilean wildfire smoke plumes are assessed and compared to the 2017 Chilean wildfires smoke plumesPre‐computed radiative transfer model results can provide readily available aerosol direct radiative effects in clear‐sky for smokeSatellite measurements can further improve the aerosol direct effect and direct forcing estimates for both clear sky and cloud scenes [ABSTRACT FROM AUTHOR]

The task of remote sensing image object detection in low latency scenes is of great research significance. To address the problem that the current high‐precision object detection algorithm based on a feature pyramid network is slow due to a large number of parameters and complicated computation, a fast remote sensing image object detection method based on a Single‐scale Feature Transformation Network (SFTN) is proposed. Firstly, based on the single‐scale remote sensing image features, the new channel features are quickly generated by a linear transformation of the original features and convolution kernel clustering optimization using cosine similarity; secondly, in order to obtain multi‐scale receptive fields, a parallel residual hole convolution module is designed to cover multi‐category remote sensing object scales on the feature map; finally, angle variables are introduced and optimized using angle similarity to effectively improve the object orientation accuracy. The experimental results on different datasets show that the method in this paper improves the detection speed rapidly while ensuring the accuracy of remote sensing image object detection, which is better than many remote sensing image object detection methods. The results demonstrate the reliability and robustness of the method. [ABSTRACT FROM AUTHOR]

The survey of extensive and topographically irregular landscapes is not easy. Survey teams often must be creative to cover the largest area possible, in a short time, with limited budgets, without losing quality and effectiveness in their work. The use of techniques employing geographic information system (GIS) tools has tremendously improved the efficiency and quality of the surveys. Such techniques were employed in the Jabal al‐Yamh and Ḥattā Valley (Emirate of Dubai, UAE), and their methodology, implementation and effectiveness are discussed in this paper. The Jabal al‐Yamh Research Project, which began in 2018 to survey, excavate, restore and research the prehistoric tombs in the Jabal al‐Yamh and the surrounding Ḥattā Valley, thus used primarily GIS tools employing remote sensing and probabilistic analysis to identify a large number of tombs in this rugged topography, delimitating areas more likely to contain tombs. On the other hand, identifying tomb distribution and orientation patterns through GIS data queries also allowed us to answer several sociocultural questions posed during the development of this project. This paper will thus discuss two primary purposes of this project's survey: methods for identifying new tombs for further excavation and protection, on the one hand, and the interpretation of those same tombs' emplacement, attending to their distribution and orientation pattern, on the other, thus developing a theoretical and predictive model of the distribution of prehistoric tombs in the area. [ABSTRACT FROM AUTHOR]

This paper outlines the construction of a new Multiplatform Precipitation Feature (MPF) framework designed to facilitate storm‐based analyses of data sets from multiple independent observing platforms for convective studies. The MPF approach is applied to ground‐ and space‐based precipitation observations and retrievals from the Global Precipitation Measurement mission Validation Network with space‐based lightning measurements from the Lightning Imaging Sensor on board the International Space Station to create a prototype MPF database. The data are synthesized in a thunderstorm‐like, feature‐based framework that enables broad statistical analyses of the storm‐based relationships between the microphysics, kinematics, and electrical properties of convection that can contribute insight into their morphological, regional, and seasonal variations. This manuscript details how MPFs are defined from precipitation data and how observations from multiple additional platforms are combined within the context of an MPF thunderstorm boundary and used to characterize the feature. A demonstration of the prototype database as a proof‐of‐concept showcases its flexibility to conduct both large‐scale storm‐based statistical studies as well as MPF‐facilitated thunderstorm case studies. Key Points: Demonstration database of individual thunderstorm‐like features combines independent data from multiple moving and fixed observing platformsPreliminary features include precipitation and lightning data collected from Global Precipitation Measurement and Lightning Imaging Sensor on board the International Space Station satellite instruments and ground‐based radarsDatabase enables cross‐platform, broad statistical thunderstorm‐centric analyses as well as detailed single‐storm case study reconstruction [ABSTRACT FROM AUTHOR]

The task of quantifying spatial and temporal variations in terrestrial water, energy, and vegetation conditions is challenging due to the significant complexity and heterogeneity of these conditions, all of which are impacted by climate change and anthropogenic activities. To address this challenge, Earth Observations (EOs) of the land and their utilization within data assimilation (DA) systems are vital. Satellite EOs are particularly relevant, as they offer quasi‐global coverage, are non‐intrusive, and provide uniformity, rapid measurements, and continuity. The past three decades have seen unprecedented growth in the number and variety of land remote sensing technologies launched by space agencies and commercial companies around the world. There have also been significant developments in land modeling and DA systems to provide tools that can exploit these measurements. Despite these advances, several important gaps remain in current land DA research and applications. This paper discusses these gaps, particularly in the context of using DA to improve model states for short‐term numerical weather and sub‐seasonal to seasonal predictions. We outline an agenda for land DA priorities so that the next generation of land DA systems will be better poised to take advantage of the significant current and anticipated shifts and advancements in remote sensing, modeling, computational technologies, and hardware resources. Plain Language Summary: Satellite remote sensing measurements have enabled the monitoring of the Earth's land surface with unprecedented scale and frequency. These measurements allow us to monitor the changes on the land surface and understand the contribution of human activities toward them. The information from such observations is combined with the modeled estimates through data assimilation (DA) algorithms. This article discusses the progress made in the development of land DA systems and the major gaps that remain. The paper also outlines priorities that we need to consider in the development of next generation land DA systems so that the potential of land remote sensing measurements can be fully realized. Key Points: Land data assimilation has shown significant promise for short‐term forecasting applicationsSignificant gaps remain in the current land data assimilation systems related to observation utilization and modelsCoordinated development with the modeling and observational community and adoption of technological enhancements are needed in the future [ABSTRACT FROM AUTHOR]

Simple models continue to be important for continental‐scale floodwater depth mapping due to the prohibitively expensive cost of calibrating and applying hydrodynamic models. This paper investigates the accuracy of three simple models for floodwater depth estimation from remote sensing derived water extent and/or Digital Elevation Models (DEMs) in semiarid regions. The three models are Height Above Nearest Drainage (HAND; Nobre et al., 2011, https://doi.org/10.1016/j.jhydrol.2011.03.051), Teng Vaze Dutta (TVD; Teng et al., 2013, http://hdl.handle.net/102.100.100/97033?index=1), and Floodwater Depth Estimation Tool (FwDET; Cohen, Brakenridge, et al., 2018, https://doi.org/10.1111/1752-1688.12609). The model accuracy and nature of errors are established using industry's best practice hydrodynamic models as benchmarks in three regions in eastern Australia. The overall results show that FwDET tends to underestimate (by 0.32 m at 50th percentile) while HAND and TVD overestimate floodwater depth for almost all floods (by 0.97 and 0.98 m, respectively). We quantify how switching DEM from 5 m LiDAR to national or global data sets DEM‐H (Gallant et al., 2011, https://ecat.ga.gov.au/geonetwork/srv/eng/catalog.search#/metadata/72759), MERIT (Yamazaki et al., 2019, https://doi.org/10.1029/2019WR024873), or FABDEM (Hawker et al., 2022, https://doi.org/10.1088/1748-9326/ac4d4f) can affect different models differently; and we evaluate model performance against reach geomorphology and magnitude of flood events. The findings emphasize the importance of choosing a model that is fit for the intended application. By describing the applicability, advantages, and limitations of these models, this paper assists practitioners to choose the most appropriate model based on characteristics of their study area, type of problems they try to solve, and data availability. Plain Language Summary: We have assessed the performance of three simple models for estimating floodwater depth by evaluating their accuracy against different Digital Elevation Model inputs, characteristics of study area, and magnitude of flood events. Our results emphasize that selection of an appropriate model depends on the circumstances. This study provides recommendations for future studies that enhance remote sensing‐based flood maps with water depth, making use of the growing number of publicly available remote sensing water extent and digital elevation data sets globally. All the methods have been coded in Python and are freely available at https://github.com/csiro-hydroinformatics/water-depth-estimation. Key Points: We assessed three simple models for estimating floodwater depth by comparing their results with those of hydrodynamic modelsThe model performance was evaluated against reach geomorphology, magnitude of flood events, and different Digital Elevation Model inputsThe results emphasize that a model should be chosen for the intended application. Guidelines provided for possible applications [ABSTRACT FROM AUTHOR]