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• Improved lorenz chaotic systems with higher complexity. • Pixel permutation method based on the image pyramid structure. • Secure and efficient image encryption method. • Galois field multiplication for efficient diffusion of pixels. This paper proposes an improved Lorenz chaotic system and a secure and efficient image encryption method to enhance encryption effectiveness in encrypted images. The proposed improved Lorenz chaotic system addresses the problem of applying the Lorenz chaotic system to image encryption, resulting in weak chaotic characteristics and susceptibility to reconstruction. Dynamic analysis, sensitivity analysis, and randomness testing demonstrate that the improved Lorenz chaotic system exhibits hyperchaotic characteristics, with a maximum Lyapunov exponent of 2.9897. Based on the improved Lorenz chaotic system, this paper proposes an image encryption method that combines image pyramid structure permutation and Galois field diffusion. Unlike most of the current permutation methods limited to a single image layer, this paper proposes a multilayer permutation method based on the image pyramid structure to enhance the permutation effect of image encryption. Although diffusion based on Galois field multiplication operation is efficient and secure, it is less effective in encrypting pixel points with a pixel value of '0′. To address this issue, this paper incorporates DNA computing into diffusion based on Galois field operations, enabling even pure black images to achieve better encryption effectiveness. Experimental results demonstrate that the encryption method proposed in this paper effectively conceals information contained in the plain image. The global Shannon entropy of the encrypted Lena image can reach 7.9975, indicating a high level of randomness and complexity. Notably, even a slight alteration, such as changing a single pixel, results in a significant divergence, with 99.6307 % of the cipher image's pixels being distinct. Moreover, it effectively withstands analysis from various attacks. Therefore, the encryption method proposed in this paper can be effectively applied to grayscale image encryption scenarios requiring relatively high security and encryption efficiency, such as remote sensing image encryption and personal privacy image encryption. [ABSTRACT FROM AUTHOR]

Hyperspectral target detection is challenging in scenarios where spectral variability is high due to noise, spectral redundancy, and mixing. In addition, this spectral variability also creates the need for target detection algorithms to be robust against variations in the detection threshold. To overcome these challenges, this paper proposes a novel two-stage process for improved target detection in hyperspectral data. In the first stage, coarse detection is performed using a detector with a high probability of detection to identify background samples. These background samples are then used for background learning using an adversarial autoencoder (AAE) network, having spectral angle mapper (SAM) and Huber loss functions to minimize the impact of target pixels' contamination. In the second stage, an inference is made using the spectral difference between the hyperspectral data and the output of the learned background model, which helps in reducing the false alarm rate of the first stage. The proposed approach is compared with seven other target detection techniques using multiple datasets and evaluated through several metrics, such as the area under the curve (AUC) and signal-to-noise probability ratio (SNPR). Results reveal that the proposed technique outperforms other detectors in terms of SNPR, indicating improved target detectability, background suppressibility, and more tolerance to variations in the detection threshold. [ABSTRACT FROM AUTHOR]

Cropland non-agriculturalization (CNA) refers to the conversion of cropland into construction land, woodland/garden/grassland, water body, or other non-agricultural land, which ultimately disrupts local agroecosystems and the cultivation and production of crops. Remote sensing technology is an important tool for large-area CNA detection, and remote sensing based methods that can be used for this task include the time-series analysis method and change detection from bi-temporal images. In particular, change detection methods using high-resolution remote sensing imagery have great potential for CNA detection, but enormous challenges do still remain. The large intra-class variance of cropland with different phenological stages and planting patterns leads to cropland areas being difficult to identify effectively, while certain features can be misidentified because they are similar to cropland, resulting in false alarms and missed detections in the results. There is also a lack of large-scale CNA datasets covering multiple change scenarios as data support. To address these problems, a lightweight model focused on CNA detection (CNANet) is proposed in this paper. Specifically, the uniquely crafted represent-consist-enhance (RCE) module is seamlessly integrated between the encoder and decoder components of CNANet to perform a contrast operation on the deep features extracted by the feature extractor. The RCE module is specifically designed to aggregate multiple cropland representations and extend the cropland representations from the confusing background, to achieve the purpose of reducing the intra-class reflectance differences and enhancing the model's perception of cropland. In addition, a large-scale high-resolution cropland non-agriculturalization (Hi-CNA) dataset was built for the CNA identification task, with a total of 6797 pairs of 512 × 512 images with semantic annotations. Compared to the existing datasets, the Hi-CNA dataset has the advantages of multiple phenological stages, multiple change scenarios, and multiple annotation types, in addition to the large data volume. The experimental results obtained in this study show that the benchmark methods tested on the Hi-CNA dataset can all achieve a good accuracy, proving the high-quality annotation of the dataset. The overall accuracy and F1-score of CNANet with the default settings reach 93.81 % and 78.9 %, respectively, achieving a superior accuracy, compared to the other benchmark methods, and demonstrating stronger perception of cropland changes. In addition, in two selected verification regions within the large-scale real-world CNA mapping results, the F1-score is 83.61 % and 50.87 %. The Hi-CNA can be downloaded from http://rsidea.whu.edu.cn/Hi-CNA_dataset.htm. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicle (UAV) has seen a dramatic rise in popularity for remote-sensing image acquisition and analysis in recent years. It has brought promising results in low-altitude monitoring tasks that require detailed visual inspections. Semantic segmentation is one of the hot topics in UAV remote sensing image analysis, as its capability to mine contextual semantic information from UAV images is crucial for achieving a fine-grained understanding of scenes. However, in the remote sensing field, recent reviews have not focused on combining "UAV remote sensing" and "semantic segmentation" to summarize the advanced works and future trends. In this study, we focus primarily on describing various recent semantic segmentation methods applied in UAV remote sensing images and summarizing their advantages and limitations. According to the distinction in modeling contextual semantic information, we have categorized and outlined the methods based on graph-based contextual models and deep-learning-based models. Publicly available UAV-based image datasets are also gathered to encourage systematic research on advanced semantic segmentation methods. We provide quantitative results of representative methods on two high-resolution UAV-based image datasets for fair comparisons and discussions in terms of semantic segmentation accuracy and model inference efficiency. Besides, this paper concludes some remaining challenges and future directions in semantic segmentation for UAV remote sensing images and points out that methods based on deep learning will become the future research trend. • Summarize semantic segmentation methods and datasets for UAV remote sensing images. • Provide fair comparisons of representative methods in accuracy and efficiency. • Identify challenges and future directions for UAV image semantic segmentation. • Semantic segmentation methods based on deep learning will be the mainstream. [ABSTRACT FROM AUTHOR]

Despite significant progress has been made towards crop yield prediction with remote sensing, there exist knowledge gaps on (1) the impacts of temporal resolution of imaging frequencies on yield prediction, (2) transferability of the models among different genotypes and test sites, and (3) translation of these research developments to crop breeding that benefit farmers. Existing research predominantly provides an on-site perspective, frequently missing the complexities of real-world applications. The objectives of this paper are to investigate the transferability and generalization capabilities of yield prediction models for crop breeding across test sites located in North and South Americas. Toward that goal, we tested different machine learning techniques including Random Forest Regressor (RF), Gradient Boosting Regression (GB), and Deep Neural Networks (DNN) for soybean yield prediction with experiments conducted in different climate and growth conditions. A novel transfer learning approach was proposed for genotype selection and categorizing soybean yield for screening high-yield varieties. Furthermore, we studied the effect of temporal resolution on yield prediction, focusing on the critical development stages and optimal aerial survey frequencies for precise yield prediction using large 31,404 sample data. Results demonstrated that the combined dataset of Argentina and United States representing different climate regimes provided the highest performance with an R2 of 0.76 using RF and GB algorithms. The classification approach was proven to be most useful for crop breeding as demonstrated by accurately identifying the high-yielding genotypes. Increasing temporal sampling of key phenological stages significantly improved yield prediction. Although transfer learning yielded promising outcomes across trials within Argentina the efficacy of transferring models from Argentina to the United States was limited, attributed to significant seasonal and climate variations. This study pioneered the use of transfer learning for model adaptability in real-world breeding scenarios, training and transferring models within the South and North Americas, providing actionable insights and strategies for the breeding community, aiming to facilitate improved decision-making for agricultural productivity. [ABSTRACT FROM AUTHOR]

[Display omitted] The increasing availability of remote sensing imagery led to the emergence of numerous burned area mapping products and methodologies, thus, the necessity of spatial and temporally validating them. The main objective of this paper is to conduct a global analysis and comparison of MODIS and VIIRS active fire dates with dating provided by moderate resolution burned area mapping products. As reference burned area maps, we used the Burned Area Reference Database (BARD) to analyze and compare the performances of MODIS and VIIRS active fires. For 316 path-rows, we calculated the temporal gap between the dates of BARD pixels and those of the five nearest active fires. Olson terrestrial biomes were taken into account in the analysis of results. The agreement rates between the dates of burned areas and of active fires varied based on their geographical distance, fire size and biome. The highest agreement rates were observed for smaller distances (<500 m) and larger fires (>100 ha). Boreal and Mediterranean biomes exhibited the highest values of date percent agreement between burned area pixels and active fires, while the lowest values were found in biomes encompassed by grasslands and savannas. [ABSTRACT FROM AUTHOR]

Automated extraction of polygonal building contours from high-resolution remote sensing images is important for various applications. However, it remains a difficult task to achieve automated extraction of polygonal buildings at the level of human delineation due to diverse building structures and imperfect image conditions. In this paper, we propose Line2Poly, an end-to-end approach that uses feature lines as geometric primitives to achieve polygonal building extraction by recovering topological relationships among these lines within an individual building. To extract building feature lines with precision, we adopt a two-stage strategy that combines Convolutional Neural Network (CNN) and transformer architectures. A CNN-based module extracts preliminary feature lines, which serve as positional priors for initializing positional queries in the subsequent transformer-based module. For polygonal building contour reconstruction, we devise a learnable polygon topology reconstruction module that predicts adjacency relationships among discrete lines, and integrates lines into building polygons. The resultant building polygons, based on feature lines, exhibit inherent regularity that aligns with manual labeling standards. Extensive experiments on the Vectorizing World Buildings dataset, the WHU aerial building dataset and the WHU-Mix (vector) dataset validate Line2Poly's impressive performance in building feature line extraction and instance-level building detection. Moreover, Line2Poly's predictions exhibit the highest level of concurrence with manual delineations, with over 83% agreement on the WHU aerial building test set and 68.7/59.7% on the WHU-Mix (vector) test set I and II, respectively. [ABSTRACT FROM AUTHOR]

Remote sensing image scene classification (RSI-SC) is crucial for various high-level applications, including RSI retrieval, image captioning, and object detection. Deep learning-based methods can accurately predict scene categories. However, these approaches often require numerous labeled samples for training, limiting their practicality in real-world RS applications with scarce label resources. In contrast, few-shot remote sensing image scene classification (FS-RSI-SC) has garnered substantial research interest owing to its potential to mitigate the need for extensive training samples. In recent years, there has been a surge in studies on FS-RSI-SC. This paper presents a comprehensive overview of FS-RSI-SC research, categorizing existing methods into two groups. The first group comprises approaches based on data augmentation, transfer learning, metric learning, and meta-learning. Our analysis reveals that most existing FS-RSI-SC methods fall into the meta-learning category, employing attention mechanisms, self-supervised learning (SSL), and feature fusion techniques for enhanced performance. Additionally, transfer learning-based methods consistently outperform other approaches in this category. The second group is centered around large-scale pre-training, which has demonstrated remarkable competitiveness across various tasks, including FS-RSI-SC. This special group of methods has shown considerable potential and is expected to attract more attention with the increasing popularity of large-scale pre-training and the unimodal and multimodal foundation models. Moreover, we proposed a pipeline that harnesses the capabilities of powerful large vision-language models (VLMs) as image encoders, establishing new baselines for FS-RSI-SC on commonly used datasets under standard experimental settings. Our empirical results validated the effectiveness of utilizing large VLMs and highlighted their potential for FS-RSI-SC. Through a joint analysis of state-of-the-art methods and our experiments with VLMs, we identified the prevailing challenges in FS-RSI-SC and outlined promising directions for future research. [ABSTRACT FROM AUTHOR]

Geographic spaces is a vast and complex system involving multiple elements and nonlinear interactions of these elements, and rich in geographical phenomena, processes and patterns. Artificial intelligence methods (AI) are increasingly utilized to extract information of interest, patterns and insights from massive remote sensing (RS) images. Among them, two representative paradigms for RS interpretation are knowledge-driven symbolism and data-driven connectionism. Knowledge-driven approaches are certain and theoretically sound, which consider the regularities, formulae with accompanying theorems, and expert knowledge. However, it is difficult to address huge-scale RS data and complex nonlinear problems. The data-driven paradigm has stronger data learning and representation ability, yet poor interpretability, low trustworthiness of interpretation results, and reliance on massive labeled data. To address these limitations, we argue that current RS intelligent interpretation requires the guidance of geographic ideas and a unified theoretical framework rather than the independent use of remotely sensed data, duplicative development of interpretation models, or isolated applications of geographic information extraction. Thus, we introduce the concepts of trustworthy RS interpretation (TRSI) with multi-granularity of space, time, and attribute to facilitate the understanding of the morphology, types, and indicators (visual perception) and the perspective of the structure, evolution, and trends (geographical cognition) of complex geographical space. Second, integrated technologies of the TRSI with multiple scales and stages are developed, including a pixel-level visual perception, a geo-parcel-level quantitative inversion, and a scene-level geographical cognition. Finally, a RS big data interpretation system with quantitative semantic parsing is designed to support precise applications with cloud–edge-end collaboration. Rather than a simple overview and accumulation of traditional RS intelligent interpretation methods, this paper analyzes current challenges in RS interpretation and proposes new concepts, technologies, and applications of TRSI. It will empower us to perceive the complex geographical space comprehensibly and reveal its structural composition and evolutionary mechanism, serving geospatial decision applications such as evaluation, planning, and forecasting. [ABSTRACT FROM AUTHOR]

Land-cover information reflects basic Earth's surface environments and is critical to human settlements. As a well-established deep learning architecture, the fully convolutional network has achieved impressive progress in various land-cover mapping tasks. However, most research has focused on designing powerful encoders, ignoring the exploration of decoders. The existing handcrafted decoders are relatively simple and lack flexibility, limiting the generalizability for complex remote sensing scenes. In this paper, we propose a Land-cOVEr mapping Neural Architecture Search framework (LoveNAS) to automatically find efficient decoders that are compatible with the encoders and tasks. Specifically, LoveNAS introduces a hierarchical dense search space, including densely connected layer-level and multi-scale operation-level search spaces. The search spaces contain independent connection and operation fusion strategies, facilitating sufficient interaction of multi-scale features. After searching based on large-scale datasets, a series of pre-trained encoders and adaptive decoders are obtained. These can be smoothly applied to multi-scene tasks using weight-transfer network training. Experimental results on normal and disaster scenes shows that LoveNAS outperforms 16 handcrafted architectures and NAS methods. Some searched structures coincide with the existing advanced artificial designs, revealing the potential value of LoveNAS in network design and guidance. Group's website: http://rsidea.whu.edu.cn/resource%5fsharing.htm. GitHub page: https://github.com/Junjue-Wang/LoveNAS. [ABSTRACT FROM AUTHOR]

The state-of-the-art methods for cloud detection are dominated by deep convolutional neural networks (DCNNs). However, it is very expensive to train DCNNs for cloud detection and the trained DCNNs do not always perform well as expected. This paper proposes a shallow CNN (SCNN) by removing pooling/unpooling layers and normalization layers in DCNNs, retaining only three convolutional layers, and equipping them with 3 filters of 1 × 1 , 1 × 1 , 3 × 3 in spatial dimensions. It demonstrates that the three convolutional layers are sufficient for cloud detection. Since the label output by the SCNN for a pixel depends on a 3 × 3 patch around this pixel, the SCNN can be trained using some thousands 3 × 3 patches together with ground truth of their center pixels. It is very cheap to train a SCNN using some thousands 3 × 3 patches and to provide ground truth of their center pixels. Despite of its low cost, SCNN training is stabler than DCNN training, and the trained SCNN outperforms the representative state-of-the-art DCNNs for cloud detection. The same resolution of original image, feature maps and final label map assures that details are not lost as by pooling/unpooling in DCNNs. The border artifacts suffering from deep convolutional and pooling/unpooling layers are minimized by 3 convolutional layers with 1 × 1 , 1 × 1 , 3 × 3 filters. Incoherent patches suffering from patch-by-patch segmentation and batch normalization are eliminated by SCNN without normalization layers. Extensive experiments based on the L7 Irish, L8 Biome and GF1 WHU datasets are carried out to evaluate the proposed method and compare with state-of-the-art methods. The proposed SCNN promises to deal with images from any other sensors. The code and data will be open for further research. 1 1 https://github.com/dfchai/SCNN. [ABSTRACT FROM AUTHOR]

• A multimodal computational approach was used for the 3D survey of a panel painting. • Data from close-range photogrammetry and laser microprofilometry were fused. • The warping arrow before and after restoration was quantified on the 3D point cloud. • Multi-temporal monitoring of the painting is computed via Cloud-to-Cloud distance. • Micrometric roughness of paint layers is measured by laser microprofilometry. This paper reports the results obtained from the workflow designed for the 3D survey of a Renaissance panel painting using close-range photogrammetry and conoscopic laser microprofilometry, before and after its restoration. The artwork under examination is the central panel of a Renaissance polyptych realized by a late Gothic painter active in central Italy in the late 15th century. This painting has had a rather troubled conservation history following numerous adverse events that have severely damaged it structurally. In preparation for restoration work, the fusion of point cloud data derived from active and passive sensors served to create a single multi-resolution model on which to conduct advanced metrological analyses for multi-temporal monitoring of the changes undergone by the panel painting during and after structural restoration. [ABSTRACT FROM AUTHOR]

• Extract ancient city wall sites at pixel level from LiDAR remote sensing data based on deep learning. • Improvement strategies in the dataset generation, model training, and post-processing steps. • A significant source of inspiration for the inspection, conservation, and monitoring of cultural heritage. Ancient city walls, one of the most notable distinguishing features of Chinese ancient cities, are military defenses constructed of rammed earth. The ancient city walls have considerable study value because they served as the city's boundary and a symbol of power. However, as a result of natural erosion and human activities, many sites have been ruined. Existing optical remote sensing technologies, LiDAR point cloud processing algorithms, and deep learning methods are inadequate for the extraction and segmentation of ancient city wall sites. The novel semantic segmentation method for ancient city wall sites is described in this paper that extracts sites at the pixel level from LiDAR remote sensing data based on deep learning. To begin, the point cloud data collected by airborne laser scanning is processed into DEM data, and the distribution of ancient city walls in the study area is obtained through archaeological survey and expert interpretation. The dataset for deep learning semantic segmentation is then generated using image cropping and data augmentation techniques. Third, implement a U-Net semantic segmentation framework for microtopographic archaeological sites, and predict ancient city wall sites in the testing region after model training. Finally, the deep learning results are optimized using the connected component analysis method, and prediction mistakes such as holes and noise are removed. Taking Jinancheng, the capital city of the Chu kingdom, as an example, the proposed method process can identify and extract the ancient city wall sites at the pixel level, where the evaluation metrics reach 94.12% (Precision) and 81.38% (IoU). The experiment results are excellent due to improvement strategies in the dataset generation, model training, and post-processing steps. Thus, this study is significant for the current survey and protection of ancient city wall sites. The source code will be freely available at https://github.com/wshunli/Open-CHAI-CityWalls. [ABSTRACT FROM AUTHOR]

Neural Radiance Fields (NeRF) is a technique for reconstructing real-world scenes from multiple views. However, existing methods mostly focus on the visual effects of scene reconstruction while neglecting geometric accuracy, which is crucial in photogrammetry and remote sensing. In this paper, we propose a method called LiDeNeRF which uses LiDAR point cloud to provide depth priors for NeRF reconstruction. The goal of LiDeNeRF is to achieve real-time rendering of NeRF scenes and 3D reconstruction results with high geometric accuracy. In this method, first, the LiDAR point cloud is projected onto images to generate a sparse depth map. Then, by triangulating the sparse depth and using a multi-view image depth propagation method, a dense depth map with high accuracy is quickly generated as the depth prior for NeRF. Finally, a new depth correction module is designed and embedded into the NeRF rendering pipeline to improve the accuracy of scene depth estimation. The experimental results evince that our methodology has attained paramount performance in both novel view synthesis and 3D reconstruction tasks. Our source code is made available at https://github.com/WPC-WHU/LiDeNeRF. [ABSTRACT FROM AUTHOR]

Recently, ensemble multiple deep learning (DL) classifiers has been reported to be an effective method for improving remote sensing classification accuracy. Although these approaches still follow the conventional pattern of inputting instance features and outputting corresponding classes, they often overlook the intrinsic relationships between pixels beyond their spatial features. As a result, the diversity in the ensemble classification results primarily relies on different DL models. However, training the DL models consumes a significant amount of time, and training multiple networks not only incurs additional time costs but also affects the overall efficiency. To address this, a new approach has been proposed in this paper, which takes advantage of the relationships between pixels and their combinations to generate diverse classification results. It's a novel ensemble classification framework, termed as the Doublet-Based Ensemble Classification Framework (DBECF), which eliminates the need for multiple classifiers. The DBECF starts by utilizing the training set to combine different samples to generate doublets. Then, features are assigned to these doublets through an exponentiation operation, resulting in a doublet training set. Using both the original training set and the derived doublet datasets, the DBECF is trained. For each input pixel, the DBECF produces multiple classification results, which are then integrated to obtain a more accurate output. To validate the proposed approach, experiments were conducted on three datasets, including multispectral images, hyperspectral images, and time series images. The maximum accuracies achieved by DBECF on the three datasets are 87.80 %, 97.71 %, and 83.51 %, respectively. In comparison to the contrastive methods, the incremental improvements in accuracy are 3.73 %, 7.66 %, and 9.16 %, respectively. The experimental results indicate that no matter using DL or non-deep learning for training, our proposed framework achieves progress on accuracy improvement outperforming classifications using comparative approach that based on single instance. This research provides a new perspective on the combination of DL and ensemble learning, highlighting its important implications and practical value in enhancing classification accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Newly constructed areas (NCA) are continuously emerging with the development of urbanization, which, however, triggered a series of ecological and environmental issues. Therefore, monitoring NCA is of great significance for sustainable urbanization. Extraction of NCA from remote sensing images involves semantic segmentation of constructed areas and change detection. However, the acquisition of samples for training change detection models is time-consuming. In this study, we proposed a multi-task learning framework including unsupervised change detection and supervised semantic segmentation for NCA extraction. The main contributions of this study are: (1) A deep multivariate alternation detection (DMAD) algorithm was proposed for unsupervised change detection of hyperspectral images. By introducing the optimization objective of Multivariate Alternation Detection (MAD) in the loss functions, the features of changed areas can be extracted more effectively; (2) A multi-task learning framework was proposed for the joint training of DMAD change detection and supervised semantic segmentation, where the two modules were mutually optimized via semantic masks and consistency loss. Based on Orbita Hyperspectral Satellites (OHS) images, we conducted experiments in two cities (Qinzhou and Wuzhou) of Guangxi province. The experimental results indicated that the proposed method can achieve the F1-score better than existing unsupervised change detection methods (e.g., DCCA and DSFA) by an average of 15%, and the OA better than existing semantic segmentation methods (e.g., U-Net and FreeNet) by an average of 1%. The F1-score and Kappa for NCA extraction reached above 0.80 for both study areas. The implementation of this paper will be made available at https://github.com/tulilin/Multitask_NCA. [ABSTRACT FROM AUTHOR]

Multimodal change detection (CD) is an increasingly interesting yet highly challenging subject in remote sensing. To facilitate the comparison of multimodal images, some image regression methods transform one image to the domain of the other image, allowing for images comparison in the same domain as in unimodal CD. In this paper, we begin by analyzing the limitations of previous image structure based regression models that only rely on similarity relationships. Then, we highlight the significance of incorporating dissimilarity relationships as a complementary approach to more comprehensively characterize and utilize the image structure. In light of this, we propose a novel method for multimodal CD called Similarity and Dissimilarity induced Image Regression (SDIR). Specifically, SDIR construct a similarity based k-nearest neighbors (KNN) graph and a dissimilarity based k-farthest neighbors (KFN) graph, where the former mainly characterizes the low-frequency information and the latter captures the high-frequency information in spectral domain. Therefore, the proposed SDIR that incorporates similarity (low-frequency) and dissimilarity (high-frequency) relationships enables to achieve better regression performance. After completing the image regression, we utilize a Markovian based fusion segmentation model to combine the change fusion and change extraction processes for improving the final CD accuracy. The proposed method's effectiveness is demonstrated through experiments on six real datasets and compared with eleven advanced and widely used methods, achieving 5.6% improvements in the average Kappa coefficient. The source code is accessible at https://github.com/yulisun/SDIR. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle-based hyperspectral imaging (HSI) and machine learning (ML) techniques are applied for crop field data collection such as yield, nitrogen content, leaf chlorophyll, biomass estimation, leaf area index (LAI), and biotic and abiotic stress. It is very difficult to identify with certainty best practices for specific applications and for targeted crop species because the results are frequently contradictory; models or indices sometimes perform well, but other times they do not. ML and HSI are useful and sophisticated techniques, but treating them as first-option methods may be detrimental in the long term. Favoring HSI in situations where a multispectral sensor performs equally well only delays end-user adoption of technology owing to cost and complexity. The past few years have seen increased interest in unmanned aerial vehicle (UAV)-based hyperspectral imaging (HSI) and machine learning (ML) in agricultural research, concomitant with an increase in published research on these topics. We provide an updated review, written for agriculturalists, highlighting the benefits in the retrieval of biophysical parameters of crops via UAVs relative to less sophisticated options. We reviewed >70 recent papers and found few consistent results between similar studies. Owing to their high complexity and cost, especially when applied to crops of low value, the benefits of most of the research reviewed are difficult to explain. Future effort will be necessary to distill research findings into lower-cost options for end-users. [ABSTRACT FROM AUTHOR]

Despite the remarkable progress of deep learning-based object extraction in revealing the number and boundary location of geo-objects for high-resolution satellite imagery, it still faces challenges in accurately extracting blurry dense objects. Unlike general objects, blurry dense objects have limited spatial resolution, leading to inaccurate and connected boundaries. Even with the improved spatial resolution and recent boundary refinement methods for general object extraction, connected boundaries may remain undetected in blurry dense object extraction if the gap between object boundaries is less than the spatial resolution. This paper proposes a blurry dense object extraction method named the buffer parsing network (BPNet) for satellite imagery. To solve the connected boundary problem, a buffer parsing module is designed for dense boundary separation. Its essential component is a buffer parsing architecture that comprises a boundary buffer generator and an interior/boundary parsing step. This architecture is instantiated as a dual-task mutual learning head that co-learns the mutual information between the interior and boundary buffer, which estimates the dependence between the dual-task outputs. Specifically, the boundary buffer head generates a buffer region that overlaps with the interior, enabling the architecture to learn the dual-task bias and assign a reliable semantic in the overlapping region through high-confidence voting. To alleviate the inaccurate boundary location problem, BPNet incorporates a high-frequency refinement module for blurry boundary refinement. This module includes a high-frequency enhancement unit to enhance high-frequency signals at the blurry boundaries and a cascade buffer parsing refinement unit that integrates the buffer parsing architecture coarse-to-fine to recover the boundary details progressively. The proposed BPNet framework is validated on two representative blurry dense object datasets for small vehicle and agricultural greenhouse object extraction. The results indicate the superior performance of the BPNet framework, achieving 25.25% and 73.51% in contrast to the state-of-the-art PointRend method, which scored 21.92% and 63.95% in the A P 50 s e g m metric on two datasets, respectively. Furthermore, the ablation analysis of the super-resolution and building extraction methods demonstrates the significance of high-quality boundary details for subsequent practical applications, such as building vectorization. The code is available at: https://github.com/Dingyuan-Chen/BPNet. [ABSTRACT FROM AUTHOR]

• Earthquake anomalies are analyzed for Mw 6.5 Idaho Earthquake America. • Significant anomalies occur in LST, AT, RH, AP and OLR. • Anomalies are studied using statistical, NARX, MLP and CWT methods. • NARX predicted anomalies are more clear than other methods. Remote sensing has became a powerful tool for identifying lithosphere and atmosphere anomalies associated with the impending Earthquakes (EQs) in the vicinity of seismic breeding zone. In this paper, Land Surface Temperature (LST) of both the daytime and nighttime from the Moderate Resolution Imaging Spectroradiometer (MODIS) along with Air Temperature (AT), Relative Humidity (RH), Air Pressure (AP) and Outgoing Longwave Radiations (OLR) are studied for the 2020 Idaho (USA) EQ of Mw 6.5. We found the EQ induced surface and atmospheric parameters anomalies just one day prior to EQ main shock using statistical analysis. Moreover, we observed a sharp increment in LST and AT followed by the drop in both AP and RH, which are responsible for cooling the hot gases emitted from epicenter during preparation period. Also, we observed a large increase in OLR on the same day confirming these anomalous variations to be related with the main shock. Furthermore, these abrupt variations are also confirmed using neural networks (nonlinear autoregressive network with exogenous inputs (NARX), multilayer perceptron (MLP) and continuous wavelet transformation (CWT). These multi-parameter and multi-technique analyses can contribute to assist in the main shock forecasting in future with an enhanced cluster of satellite observations. [ABSTRACT FROM AUTHOR]

In the context of Earth observation, change detection boils down to comparing images acquired at different times by sensors of possibly different spatial and/or spectral resolutions or different modalities (e.g., optical or radar). Even when considering only optical images, this task has proven to be challenging as soon as the sensors differ by their spatial and/or spectral resolutions. This paper proposes a novel unsupervised change detection method dedicated to images acquired by such so-called heterogeneous optical sensors. It capitalizes on recent advances which formulate the change detection task into a robust fusion framework. Adopting this formulation, the work reported in this paper shows that any off-the-shelf network trained beforehand to fuse optical images of different spatial and/or spectral resolutions can be easily complemented with a network of the same architecture and embedded into an adversarial framework to perform change detection. A comparison with state-of-the-art change detection methods demonstrates the versatility and the effectiveness of the proposed approach. • Detecting changes between images acquired by heterogeneous sensors is formulated as a robust fusion problem. • The method is able to handle multi-band images with possibly different spatial and/or spectral resolutions. • The method is unsupervised, i.e., it does not need a train set composed of image pairs with associated binary change maps. • Any pretrained deep network designed to fuse heterogeneous images can be reused to perform change detection. • When compared to conventional approaches, the strategy appears to be more flexible while competing favorably. [ABSTRACT FROM AUTHOR]

• A bibliometric and content analysis was conducted to comprehensively review and analyze deep learning research in cropland field identification. • The paper discusses the challenges of deep learning-based research on cropland field identification, providing readers with insights and directions for future research in the field • This study fills the gap by providing a systematically summarized review of this research area. The cropland field (CF) is the basic unit of agricultural production and a key element of precision agriculture. High-precision delineations of CF boundaries provide a reliable data foundation for field labor and mechanized operations. In recent years, with the dual advancements in remote sensing satellite technology and artificial intelligence, enabling the extraction of CF information on a wide scale and with high precision, research on CF identification based on deep learning (DL) has emerged as a highly esteemed direction in this field. To comprehend the developmental trends within this field, this study employs bibliometric and content analysis methods to comprehensively review and analyze DL research in the field of CF identification from various perspectives. Initially, 93 relevant literature pieces were retrieved and screened from two databases, the Web of Science Core Collection and the Chinese Science Citation Database, for review. The previous studies underwent quantitative analysis using bibliometric software across five dimensions: publication year, literature type and publication journal, country, author, and keyword. Subsequently, we analyze the current status and trends of employing DL in the field of CF identification from four perspectives: remote sensing data sources, DL models, types of CF extraction results, and sample datasets. Simultaneously, we combed through current publicly available sample datasets and data products that can be referenced to produce sample datasets for CFs. Finally, the challenges and future research focus of DL-based CF identification research are discussed. This paper provides both qualitative and quantitative analyses of research on DL-based CF identification, elucidating the current status, development trends, challenges, and future research focuses. [ABSTRACT FROM AUTHOR]

Remote sensing scene (RSS) image classification plays a vital role in various fields such as urban planning and environmental protection. However, due to higher inter-class similarity and intra-class variability, achieving accurate classification for RSS images poses a considerable challenge for current convolutional neural networks (CNNs)-based and visual transformer (ViT)-based methods. To address these issues, this paper proposes a novel dual-encoding method named master–slave encoding network (MSE-Net) from two perspectives of feature extraction and fusion. The master encoder, based on ViT, extracts higher-level semantic features, while the slave encoder, based on CNN, captures relative lower-level spatial structure information. Secondly, to integrate feature information from the two encoders effectively, this paper further develop two fusion strategies. The first strategy involves the auxiliary enhancement units (AEUs), which eliminates semantic divergence between the two encoders, enhances spatial context awareness of the slave encoder and promotes effective feature learning. The interactive perception unit (IPU), as the second strategy, facilitates interaction and integration of the two encoders' representations to extract more discriminative feature information. In addition, we conducted comparative experiments on four widely-used RSS datasets, including RSSCN7, SIRI-WHU, the aerial image dataset (AID) and NWPU-RESISC45 (NWPU45), to verify the effectiveness of MSE-Net. The experimental results demonstrate that MSE-Net achieved state-of-the-art (SOTA) performance across all the datasets. [Display omitted] [ABSTRACT FROM AUTHOR]

• This paper presents a novel technique which will significantly reduce the operating time of the distance relay for second zone internal faults over the conventional schemes. • The proposed method is capable of selecting single pole as well as three-pole breaker operation according to the faulted phases. • The proposed method has the capability to sense RCBO even during occurrence of a pole jam situation in case of three-pole breaker operation. • The technique is validated by performing Hardware-in-Loop using real time digital simulator and real time automatic controller (RTAC). The proposed algorithm is programmed in RTAC using IEC 61,131–3 industrial automation programming language. An algorithm for second zone acceleration of the distance relay utilising the sequence voltages along with healthy phases impedances is presented in this paper. Specifically, the algorithm employs variation in sequence voltage ratio indices to sense the single-pole remote circuit breaker operation (RCBO), while utilising both magnitude and angle of the healthy phase impedance for sensing three-pole RCBO operation. Additionally, the developed algorithm can also identify the faulty phases based on variations in impedance magnitude. To avoid false-trip during transient faults, impedance of the faulted phase before RCBO is compared with its value after RCBO. Performance of the proposed approach is validated for various internal and external faults covering addition of parallel link, presence of noise, different load angle, fault location, fault inception angle, fault impedance, source impedance and errors in measurement/transmission line parameters on an existing 230 kV transmission line modelled in PSCAD/EMTDC software. The results, obtained from both simulation as well as Hardware in Loop (HIL) simulation through laboratory prototype, designate that the proposed approach performs appropriately regardless of power flow direction, fault resistance magnitude, and light load conditions. The results also reveal that the suggested approach is able to accelerate trip within a cycle after RCBO event, which is comparable to or faster than many existing methods. Compared to several existing approaches in the literature, it also outperforms in terms of sensitivity, reliability and selectivity. [ABSTRACT FROM AUTHOR]

Precision agriculture has emerged as a dominant force in the United States, with widespread adoption of advanced technologies and decision support systems (DSS) since the 1980s. Key tools such as variable rate application (VRA), autopilot systems, and remote sensing have become integral for U.S. farmers, offering invaluable insights from crop, soil, and weather information to optimize agricultural production while minimizing environmental impact. To synthesize and categorize the extensive research available on precision agriculture, a systematic review protocol has been designed. Our objective is to offer clear and authoritative insights into the nature, scope, and volume of this field. Implementing a rigorous search strategy, we utilized renowned databases such as Scopus® and Web of ScienceTM to gather relevant and significant materiality. The retrieval process involved the use of indexing terms and Boolean operators, with a focus on 'precision agriculture' and 'precision farming', striking a balance between specificity and comprehensiveness. To ensure the credibility of our findings, only peer-reviewed papers authored by individuals affiliated with U.S. institutions have been included. Expert reviewers with deep knowledge in the field independently assessed the selected papers, thoroughly evaluating titles, abstracts, keywords, methods, conclusions, and declarations. Consistency and eligibility were paramount in determining which papers met the criteria for inclusion. Any discrepancies or disagreements were resolved through rigorous consensus-building discussions among the reviewers. Through this comprehensive meta-review, we provide a scientific contribution that enhances our understanding of precision agriculture, highlighting focus areas for further research and development (R&D). By synthesizing and categorizing the existing literature, we offer authoritative insights into the research landscape, informing future investigations and fostering innovation. Focusing specifically on the U.S., we shed light on the unique aspects and pioneering advancements in precision agriculture within the country. Ultimately, our findings have the potential to drive progress, contributing to sustainable development, increased productivity, enhanced environmental sustainability, and responsible agricultural practices. [ABSTRACT FROM AUTHOR]

Object detection, as one of the most fundamental and essential tasks in the field of computer vision, has been the focus of unremitting efforts by researchers, who are committed to modifying the neural network structure in order to improve the accuracy of object detection and expedite task execution. As the application scope continues to expand, small object detection has gradually emerged as a crucial branch in the field of object detection. In this paper, the development history of object detection algorithms is introduced, the concept of small objects is introduced, and the current problems and challenges faced by small object detection are outlined. In this paper, the network structure is disassembled from a macroscopic point of view, and improved algorithms such as enhanced data augmentation, improved feature extraction, superior feature fusion, and refined loss functions are described in detail. Furthermore, the paper explores a series of emerging and improved algorithms for small object detection. It encompasses the introduction of advanced strategies such as unsupervised learning, end-to-end training, density cropping, transfer learning, and anchor-free approaches. The paper provides a comprehensive list of commonly used general-purpose datasets and domain-specific datasets for small object detection tasks, offering performance comparisons of the mentioned improved algorithms. In conclusion, the paper summarizes and provides an outlook on current small object detection algorithms, furnishing the reader with a thorough understanding of the field and insights into future directions. • GAN can improve the resolution of images containing small objects. • The receptive field can be expanded by dilated convolution. • Multi-scale feature extraction is often used in small object detection. • Small object datasets are mostly concentrated in remote sensing. [ABSTRACT FROM AUTHOR]

• Thermal infrared (TIR) remote sensing provides a diagnostic of surface energy balance. • The Two-Source Energy Balance (TSEB) model uses TIR to map evapotranspiration. • This paper describes the development and applications of TSEB from field to globe. • Applications include drought monitoring, yield prediction, and water management. Thermal infrared (TIR) remote sensing of the land-surface temperature (LST) provides an invaluable diagnostic of surface fluxes and vegetation state, from plant and sub-field scales up to regional and global coverage. However, without proper consideration of the nuances of the remotely sensed LST signal, TIR imaging can give poor results for estimating sensible and latent heating. For example, sensor view angle, atmospheric impacts, and differential coupling of soil and canopy sub-pixel elements with the overlying atmosphere can affect the use of satellite-based LST retrievals in land-surface modeling systems. A concerted effort to address the value and perceived shortcomings of TIR-based modeling culminated in the Workshop on Thermal Remote Sensing of the Energy and Water Balance, held in La Londe les Maures, France in September of 1993. One of the outcomes of this workshop was the Two-Source Energy Balance (TSEB) model, which has fueled research and applications over a range of spatial scales. In this paper we provide some historical context for the development of TSEB and TSEB-based multi-scale modeling systems (ALEXI/DisALEXI) aimed at providing physically based, diagnostic estimates of latent heating (evapotranspiration, or ET, in mass units) and other surface energy fluxes. Applications for TSEB-based ET retrievals are discussed: in drought monitoring and yield estimation, water and forest management, and data assimilation into – and assessment of – prognostic modeling systems. New research focuses on augmenting temporal sampling afforded in the thermal bands by integrating cloud-tolerant, microwave-based LST information, as well as evaluating the capabilities of TSEB for separating ET estimates into evaporation and transpiration components. While the TSEB has demonstrated promise in supplying water use and water stress information down to sub-field scales, improved operational capabilities may be best realized in conjunction with ensemble modeling systems such as OpenET, which can effectively combine strengths of multiple ET retrieval approaches. [ABSTRACT FROM AUTHOR]

As mankind continues to exploit marine resources, there is a growing need to use unmanned surface vessel (USV) platforms for maritime security and surveillance of the operational environment at sea. Satellite remote sensing imagery is now the most common means used for maritime surface security surveillance. Satellite images are susceptible to cloud cover, while camera-equipped USVs can prevent the effects caused by cloud cover. However, there are virtually no vision-based target tracking solutions for the USV perspective. This paper proposes a tracking solution for multiple target tracking at sea named Sea-IouTracker. It can address the potential problems when the USVs are at sea, such as the target loss, anchor frame drift and even serious Re-ID phenomena due to wind and waves and other environmental effects. We chose Sort as the basic tracking framework and we picked Buffered-IoUs based on two different buffer scales instead of traditional IoUs for matching targets in neighbouring frames. And the innovation of the neighbouring frame matching mechanism lies on that the position of the Buffered-IoU is predicted using a motion prediction model and matched with the next detection frame because both matches are unsuccessful. This method increases the stability of the tracking model and reduces the phenomenon of re-ID. We have also developed a detector that adaptively fuses image features for the tracking scheme. The detector proposed is based on the ASFF (Adaptively Spatial Feature Fusion) detector head, CBAM (Convolutional Block Attention Module) and Yolov7 baseline for maritime target detection. This paper verifies that the Sea-IoUTracker can achieve MOTA = 77.5, ID SW = 1.5% when tracking multiple targets on the MOT17 dataset. We have also proved our detector proposed is superior to other SOTA detectors in accuracy when detecting sea surface targets in the Singapore Maritime Dataset (SMD). We have also verified that the target tracking scheme proposed in this paper outperforms to the current state-of-the-art(SOTA) tracker C-BIoU (Yang et al., 2022) Tracker and ByteTrack based on four tracking video sequences in SMD. In this case, the accuracy is improved by about 2% with respect to the latter, while the ID SW phenomenon is reduced by about 20%. • The target tracking scheme proposed in this paper is a maritime target tracking scheme for maritime security requirements. • After two failed B-IoU based matches, we add a re-matching mechanism between the predicted target anchor and the target detection frame based on the buffered area to increase the probability of re-matching the lost target and thus improve the tracking effect. • We propose a detector which outperforms than other SOTA detectors when detecting targets with multi-scale features at sea. [ABSTRACT FROM AUTHOR]

Remote sensing of urban environments has unveiled a significant shift from single-city investigations to the inclusion of multiple cities. Originated from the ideas of the Remote Sensing of Environment special issue entitled "Remote Sensing of the Urban Environment: Beyond the Single City," this paper offers a comprehensive examination of the state of the science in multi-city remote sensing, and aims at fostering the rapid advancement of this emerging field to address global sustainability challenges and support knowledge development needed for a new discipline – urban sustainability science (USS). Through a synthesized review of eight key research fields within urban remote sensing [i.e., land use and land cover (LULC) and change, urban vertical structure, urban heat islands, hazards, energy use and emissions, air quality, carbon budgets, and green space], the paper provides insights into the underlying rationale for conducting multi-city studies, the criteria employed in the selection of cities, the societal applications, as well as the opportunities and future directions for expanding the scope of assessments in multi-city remote sensing. • We provide a comprehensive review of multi-city remote sensing. • We systematically analyze eight key research fields in urban remote sensing. • We provide insights into the underlying rationale behind conducting multi-city studies. • We summarize the criteria employed for city selection and the societal applications. • We further discusses opportunities and future directions in multi-city remote sensing. [ABSTRACT FROM AUTHOR]

Intelligent grazing is a new livestock husbandry development mode based on obtaining the multi-source information, ecological balance is the goal. This paper focuses on intelligent grazing, reviews from grass remote sensing and aerial seeding, unmanned aerial vehicles (UAVs) monitoring and intelligent grazing robot, and summarizing the development of intelligent grazing elements at this stage, exploring the new development direction of automatic grazing management with grazing robot at this stage, and proposes a novel intelligent grazing strategy based on remote sensing, herd perception and UAVs monitoring. Furthermore, focusing on the intelligent grazing needs, the digital twin pasture is built in this paper mainly serves the technical verification and data acquisition based on machine vision, simulating the behavior of the herd during grazing. • Reviewing from UAVs grazing, pasture remote sensing, livestock monitoring. • Proposing a grazing strategy with remote sensing, herd perception and UAVs monitoring. • Constructing a digital twin pasture to simulate the grazing herds. [ABSTRACT FROM AUTHOR]

Imaging spectroscopy integrates traditional computer vision and spectroscopy into a single system and has gained widespread acceptance as a non-destructive scientific instrument for a wide range of applications. The current state of imaging spectroscopy spans diverse applications including but not limited to air-borne and ground-based computer vision systems. This paper presents the current state of research and industrial applications including precision agriculture, material classification, medical science, forensic science, face recognition and document image analysis, environment monitoring, and remote sensing, which can be aided through imaging spectroscopy. In this regard, we further discuss a comprehensive list of applications of imaging spectroscopy, pre-processing techniques, and spectral image acquisition systems. Likewise, publicly available databases and current software tools for spectral data analysis are also documented in this review. This review paper, therefore, could potentially serve as a reference and roadmap for people looking for literature, databases, applications, and tools to undertake additional research in imaging spectroscopy. • A survey on the current state of imaging spectroscopy. • Pre-processing techniques and data acquisition methods are highlighted. • We provide applications of imaging spectroscopy in various domains. • Publicly available databases and software tools for spectral data are presented. [ABSTRACT FROM AUTHOR]

• A review has been conducted on the application of ML and RS in hydrology domain. • Analyses were conducted on surface hydrology, hydro-climatic extremes and GWM & WQ. • GIS and ML algorithms prove valuable in the realm of hydrological investigations. • State-of-the-art approach is attributed to hydrology and water resources. Water, one of the most valuable resources on Earth, is the subject of the study of hydrology, which is of utmost importance. Satellite remote sensing (RS) has emerged as a critical tool for comprehending Earth and atmospheric dynamics, including hydrology. With the assistance of satellite RS, the scientific community has achieved significant progress in recent years. Since machine learning (ML) and RS techniques were initially applied to the study of hydrology, there has been a tremendous increase in interest in studying potential areas for future advancements in hydrology. The growth can see in the publications of related papers. Considering these initiatives, the current review paper attempts to give a thorough analysis of the function of ML and RS techniques in four fields of hydrology. This review study considers hydrological topics of streamflow, rainfall-runoff, groundwater modelling and water quality, and hydroclimatic extremes. The use of learning strategies in the hydrological sciences is examined in all reviews and research papers. Several databases were utilised for this purpose, including Scopus-index, science direct, Web of Science, and Google Scholar. The overall results of this study show that employing RS techniques, ML and ensemble approaches is incomparably superior to using traditional methods in hydrological studies. [ABSTRACT FROM AUTHOR]

Dust plays a key role in many Earth system processes and is ubiquitous in the Martian atmosphere. Various intensive field campaigns, laboratory analyses, space-based remote sensing missions, and global modeling efforts aim to characterize dust optical properties. This is a bountiful time for dust scientists, and yet the interpretation of retrievals and comparison to models remains complicated by various conflicting assumptions that are part of each algorithm. For example, the conversion of satellite radiance measurements into products like aerosol optical depth for model evaluation depends upon aerosol properties like particle size and shape that are often prescribed and not part of the retrieval. Conversely, the model calculation of aerosol optical depth often uses different assumptions. The goal of this review is to first document algorithmic assumptions by various satellite retrieval products and models, and identify where there is consistency and where there are differences. In general, the differences documented in this paper reflect uncertainties resulting from incomplete observational characterization of dust aerosols and limitations in our understanding. Second, we note what observations might reduce uncertainties in our knowledge and bring greater consistency to retrievals and models, allowing for a more rigorous and harmonious comparison. The lack of comprehensive and realistic shape models for dust is an outstanding issue, such that closure between forward modeling from particle refractive index, shape, and size and observed optical properties cannot be achieved. Limitations in the computational methods that must be applied to model scattering from complex shapes also makes accurate optical modeling for dust challenging. Field observations indicate the persistence of coarse and giant dust particles at higher altitudes and farther downwind from their source than previously expected. Remote sensing retrieval algorithms based on observations at visible wavelengths have limited sensitivity to these particles and generally do not consider them, although a recent product based on longwave radiances is encouraging. Current measurements of the refractive index of bulk dust and fundamental dust minerology components such as hematite vary widely, inhibiting attempts to represent the variability in dust optical properties and forcing, as expected from different major dust source regions on Earth that have varying mineralogical composition. Some remote sensing retrieval algorithms allow for limited refractive index variability in their inversion solutions through mixing with other fine mode aerosol models, or optimizing the single scattering albedo, but Earth system models surveyed for this paper assume a globally uniform, size-invariant refractive index. Although no Martian dust samples have yet been returned to Earth, remote sensing observations indicate that Martian dust is globally homogenous in composition, and a single spectral refractive index assumption has been widely adopted to represent Martian dust. The lack of comprehensive, statistically representative measurements of dust particle microphysical properties (size distribution, morphology, complex index of refraction spectra, internal structure heterogeneity), and the resulting optical properties, limits our ability to verify the fidelity of these assumptions. A chain of measurements is needed, ranging from characterizing individual dust mineralogy components (e.g., pure hematite and goethite) to in situ sampling of complex atmospheric aerosol mixtures. Such results could be applied to both remote sensing retrievals that characterize the optical properties of the total aerosol burden in the atmosphere from total radiance measurements, and to global models that represent the total aerosol burden in the atmosphere by building it up from the balance of individual aerosol sources and sinks. • Algorithmic assumptions by various satellite retrieval products and models • No closure between forward modeled and observed dust optical properties. • Retrieval algorithms using visible channels have low sensitivity to large particles. • Field observations indicate the persistence of coarse and giant dust particles. • Retrieval algorithms and global models make different refractive index assumptions. [ABSTRACT FROM AUTHOR]

Using Economic Irrigation Water Productivity (EIWP) as an indicator for on-farm irrigation decision-making is of utmost importance in addressing the challenges posed by poor policies in managing agricultural water use, particularly in water-scarce basins. Various modeling systems are available for quantifying crop actual evapotranspiration (ET a) and biomass needed for measuring the economic output obtained from each unit of irrigation water utilized. The difference in ET a and biomass estimates between the modeling systems could translate into a difference in EIWP outcomes, which influences the basin-wide irrigation water management. In this paper we examine the influence of selecting different ET a and biomass models, particularly the hybrid single-source energy balance HSEB, the Global Field-Scale Crop Yield and ET Mapper in Google Earth Engine GYMEE, and FAO's WaPOR V2, on evaluating EIWP on a basin-wide level. The method includes combining remote sensing and economic data to compare variability in ET a , biomass, and EIWP values derived from HSEB, GYMEE, and WaPOR. The approach is demonstrated with field survey data from the upper hydrologic unit of Lebanon's largest catchment, the Litani River Basin, in its three productive districts Baalbak, Zahleh, and West Bekaa for the year 2021. Field-scale mean monthly ET a and biomass estimates for all crops, obtained from both models, are very comparable. On a district level, the results reveal a reasonable model agreement for the four crops in the estimation of ET a with moderate to strong correlation (0.75 < r < 0.95). WaPOR consistently produces slightly higher mean ET a values for potato, wheat, and table grapes when compared to HSEB. Both models reasonably agree when estimating biomass for the four crops with high correlation (r > 0.9). Contrary to the ET a results, the GYMEE model consistently estimates slightly higher mean biomass values for all crops compared to WaPOR. The EIWP values produced by both models consistently indicate that potato holds the highest EIWP across all districts, followed by onion, table grapes, and wheat. The mean district HSEB-GYMEE model derived EIWPs are slightly higher than those derived from the WaPOR model for most crops. For EIWP obtained from HSEB-GYMEE, mean EIWP for potato is 12 times higher than that of wheat. As for that obtained from WaPOR, mean EIWP for potato is 10 times higher than that of wheat. The paper establishes a basis for future research on the application of remote sensing models in addressing water-stressed and socioeconomically challenged basins, with the potential to inform strategic irrigation management decisions based on model selection. ● WAPOR and HSEB models compared for ET and Economic Water Productivity. ● Model choice significantly impacts EIWP outcomes, affecting water management. ● Comparative analysis of ET and biomass enhances policy precision. ● Potato leads in EIWP across districts, optimizing water resource use. [ABSTRACT FROM AUTHOR]

Peanuts (Arachis hypogaea) hold significant global economic and nutritional importance, positioning them as a crucial crop in the realm of sustainable agriculture. In this comprehensive systematic and meta-analytic review, we thoroughly examine the extensive research and innovative strategies related to mechanizing peanut farming, with a specific focus on activities encompassing tilling, sowing, and harvesting. Our primary aim is to provide a meticulous and analytical exploration of the far-reaching implications of mechanization in peanut production. We emphasize the urgent need for cost-effective, socially responsible, and environmentally sustainable practices. Through a rigorous analysis of existing literature, we unveil the multifaceted impacts of mechanization on factors such as yield, quality, and losses in peanut cultivation. To ensure a comprehensive analysis, we primarily concentrated on peer-reviewed articles sourced from distinguished databases such as Scopus® and Web of Science™. Specifically, our review encompasses studies published between 1989 and 2022, covering a diverse collection of bibliographic resources. This range spans from the inception of the term "mechanizable peanut" to the most recent publications in these interdisciplinary databases. To refine our search, we developed research-specific search strings by combining relevant keywords, including "PEANUT", "TILLAGE", "SOWING", "DIGGING", "HARVESTING", and/or "MATURITY", along with appropriate Boolean operators. A team of reviewers meticulously evaluated the readability, consistency, and eligibility of papers by scrutinizing titles, highlights, abstracts, keywords, as well as materials and methods sections. We selectively included papers that aligned with our research scope, while excluding studies related to intercropping, rotation systems, fertilization, pests and diseases, and manual labor-related operations. Our findings highlight the potential for increased productivity and quality in agricultural systems that embrace mechanization, all while minimizing losses and their environmental impact. To optimize yield and quality while reducing losses, it is imperative for stakeholders to collaborate and endorse conservationist tillage practices, precision sowing techniques, and advanced harvesting methods. This meta-review serves as a call to action directed at the scientific community, policymakers, and producers, urging them to prioritize the integration of mechanization into peanut farming. This approach not only advances sustainable agricultural practices but also addresses the growing global demand for this essential crop. By examining advancements in conservationist tillage, precision sowing, and harvesting methodologies, we equip stakeholders with the knowledge required to enhance peanut cultivation. We particularly emphasize the importance of harnessing cutting-edge technologies, such as remote sensing for maturity prediction, to facilitate informed decision-making in the field. [Display omitted] • This paper provides a comprehensive review of the literature on tilling, sowing, and harvesting. • Explore advanced technologies like automation and sensors for enhanced efficiency and productivity. • Analyze the significant contributions of research and innovation to the advancement of precision agriculture. • Gain insights into the operation of agricultural machinery [ABSTRACT FROM AUTHOR]

The role of soil saturation condition on the liquefaction occurrence highlights the need for a tool to track the ground-truth soil moisture content involved in this seismic phenomenon. Soil Moisture Active Passive (SMAP) satellite estimates near-real time surface and root zone soil moisture measurements with global coverage. Typical proxies for soil saturation in liquefaction analysis include average water table depth patterns, mean annual precipitation measurements, and topographic conditions. As an alternative to these proxies, this paper incorporates satellite-based soil moisture data to enhance the understanding of the interrelation between saturation conditions and liquefaction events. Proposing a new approach for sampling non-liquefaction cases, a liquefaction/non-liquefaction database was developed in this paper based on reconnaissance reports of eleven target earthquakes. Well-known geospatial explanatory variables as well as new SMAP-based soil moisture parameters, affecting soil liquefaction, are used to develop a new soil moisture-based global liquefaction model (SMGLM), which is compared with an existing global liquefaction model. Considering the ongoing advancement of earth observing satellites, the results of this paper can build a basis for developing fully satellite-based models that could identify liquefied sites using high resolution near-real time soil moisture data. • The SMAP datasets provide a great opportunity to use near real-time ground-truth soil saturation data in liquefaction models. • SMAP soil saturation data are used to develop a soil moisture-based liquefaction model. • The model lays a basis for future satellite-based systems identifying and predicting liquefied sites using next generation of soil saturation data. [ABSTRACT FROM AUTHOR]

It is often assumed that a homogeneous distribution of soil properties occurs in the topsoil layer, but the vertical variation of soil properties in cultivated soils would mainly depend on the tillage system. In remote sensing, for example, only information strictly limited to the soil surface is accessible, although the interest of the user communities would concern the topsoil rootzone, i.e. until a depth of 30–40 cm. The present study provides an analysis of data collected from studies in which the vertical variation of the main soil properties was reported for four types of tillage systems: i) deep inversion ploughing (DP); ii) subsoiling (SUB); iii) minimum tillage (MT) and no-tillage (NT). To the best of our knowledge, a review about the effects of different tillage systems on soil vertical variation seems to be lacking in the current literature. A keyword-based search followed by a two-stage manual selection provided a total of 43 papers with data sets suitable for the analysis. The soil data, extracted by these papers, were standardized to avoid different measurement units and different site-specific baselines. This method allowed to obtain the soil data extracted by different papers worldwide, comparable to study the vertical gradient. A gradient of soil organic carbon (SOC) was reported in all the 43 selected papers, whereas total nitrogen (TN) in 20 papers, therefore reliable global models of vertical variation could be obtained for these properties. Linear models of SOC vertical variation were obtained for MT and SUB, whereas second order polynomial functions were calculated for DP and NT. The full cross-validation of the models provided R2 higher than 0.8 and errors (RMSE) from 1.55 to 3.23 g.kg−1, with similar efficiency for SOC and TN. Predictive models of vertical variation of C/N ratio and bulk density (BD) were also carried out, whereas data of pH, CEC, and textural fractions were found only in few papers and modelling was not possible. The models proposed by this analytical review might be employed to extend the estimation of surface soil properties to the whole topsoil layer of cultivated soil, according to the adopted tillage method. In addition, the results of the paper highlighted the impact of tillage system on the vertical gradient of SOC, TN, and BD, allowing a better quantification of the carbon and nitrogen stock in agricultural soils. • Predictive models for remote sensing are strictly limited to the soil surface. • Rootzone volume is commonly the layer of interest in most agronomic applications. • Tillage typologies influence the vertical gradients of soil features. • Inversion tillage tend to homogenize the soil features within A horizon. • Surface features can be extended to the A horizon according to the tillage type. [ABSTRACT FROM AUTHOR]

Urban green spaces (UGS) are a central part of cities and offer multiple ecological and social benefits necessary for a higher quality of life of inhabitants. However, rapid unguided urban growth together with planning policies on rezoning can lead to transformation of UGS to commercial, residential, and other uses. These processes of land use change result in inequities in the distribution of UGS in the city. Yet, data and metrics that capture changes in the geography of UGS is scarce within African cities. This paper addresses this need by examining the spatio-temporal geography of UGS at the neighborhood-scale in Kumasi, the next biggest city in Ghana. Five (5) UGS distribution indicators were computed from remote sensing data for 2013 and 2020 and compared across neighborhoods. Further, a GIS network analysis method was deployed to analyse access to parks in Kumasi. The results showed that between 2013 and 2020, green spaces in Kumasi became fragmented and disconnected despite a slight increase in quantity. The level of UGS fragmentation was found to significantly vary among the neighborhoods. Fragmentation and depletion in UGS were profound at the outskirts of Kumasi while majority of the population cannot access the nearest park under 30-min' walk. These findings contribute to a complete and holistic understanding of the distribution and accessibility of UGS at the neighborhood level in cities and disparities in UGS exposure in the Global South. The findings provide important insights for urban policies while the data and methods serve as blueprints for local authorities, particularly in Ghana to track their progress towards achieving Sustainable Development Goal 11, target 7. • This paper deploys remote sensing and network analysis to explore the changing geography of, and accessibility to, urban green spaces (UGS) in Kumasi. • Green spaces in Kumasi became fragmented and disconnected between 2013 and 2020. • Fragmentation and depletion in UGS were profound at the outskirts of Kumasi. • Most (58%) of the inhabitants in Kumasi are without access to parks within 30 min' walk range. • Recommendations to improve UGS provision and preservation are proffered. [ABSTRACT FROM AUTHOR]

This paper focuses on the hypothesis of optimizing time series predictions using fractal interpolation techniques. In general, the accuracy of machine learning model predictions is closely related to the quality and quantitative aspects of the data used, following the principle of garbage-in, garbage-out. In order to quantitatively and qualitatively augment datasets, one of the most prevalent concerns of data scientists is to generate synthetic data, which should follow as closely as possible the actual pattern of the original data. This study proposes three different data augmentation strategies based on fractal interpolation, namely the Closest Hurst Strategy , Closest Values Strategy and Formula Strategy. To validate the strategies, we used four public datasets from the literature, as well as a private dataset obtained from meteorological records in the city of Braşov, Romania. The prediction results obtained with the LSTM model using the presented interpolation strategies showed a significant accuracy improvement compared to the raw datasets, thus providing a possible answer to practical problems in the field of remote sensing and sensor sensitivity. Moreover, our methodologies answer some optimization-related open questions for the fractal interpolation step using Optuna framework. [ABSTRACT FROM AUTHOR]

Weed and crop segmentation is becoming an increasingly integral part of precision farming that leverages the current computer vision and deep learning technologies. Research has been extensively carried out based on images captured with a camera from various platforms. Unmanned aerial vehicles (UAVs) and ground-based vehicles including agricultural robots are the two popular platforms for data collection in fields. They all contribute to site-specific weed management (SSWM) to maintain crop yield. Currently, the data from these two platforms is processed separately, though sharing the same semantic objects (weed and crop). In our paper, we have proposed a novel method with a new deep learning-based model and the enhanced data augmentation pipeline to train field images alone and subsequently predict both field images and UAV images for weed segmentation and mapping. The network learning process is visualized by feature maps at shallow and deep layers. The results show that the mean intersection of union (IOU) values of the segmentation for the crop (maize), weeds, and soil background in the developed model for the field dataset are 0.744, 0.577, 0.979, respectively, and the performance of aerial images from an UAV with the same model, the IOU values of the segmentation for the crop (maize), weeds and soil background are 0.596, 0.407, and 0.875, respectively. To estimate the effect on the use of plant protection agents, we quantify the relationship between herbicide spraying saving rate and grid size (spraying resolution) based on the predicted weed map. The spraying saving rate is up to 90 % when the spraying resolution is at 1.78 × 1.78 cm2. The study shows that the developed deep convolutional neural network could be used to classify weeds from both field and aerial images and delivers satisfactory results. To achieve this performance, it is crucial to perform preprocessing techniques that reduce dataset differences between two distinct domains. [ABSTRACT FROM AUTHOR]

This paper focuses on the hypothesis of optimizing time series predictions using fractal interpolation techniques. In general, the accuracy of machine learning model predictions is closely related to the quality and quantitative aspects of the data used, following the principle of garbage-in, garbage-out. In order to quantitatively and qualitatively augment datasets, one of the most prevalent concerns of data scientists is to generate synthetic data, which should follow as closely as possible the actual pattern of the original data. This study proposes three different data augmentation strategies based on fractal interpolation, namely the Closest Hurst Strategy , Closest Values Strategy and Formula Strategy. To validate the strategies, we used four public datasets from the literature, as well as a private dataset obtained from meteorological records in the city of Braşov, Romania. The prediction results obtained with the LSTM model using the presented interpolation strategies showed a significant accuracy improvement compared to the raw datasets, thus providing a possible answer to practical problems in the field of remote sensing and sensor sensitivity. Moreover, our methodologies answer some optimization-related open questions for the fractal interpolation step using Optuna framework. [ABSTRACT FROM AUTHOR]

The Normalized Difference Vegetation Index (NDVI) is a valuable indicator of plant vigor that is frequently used in agronomic practices to make timely and targeted decisions with the aim of increasing the productivity of the system. NDVI measurements of large-scale fields are typically performed using remote sensing from satellite and aerial imaging devices. However, due to their low spatial and temporal resolution, these technologies may have limitations in precision viticulture. This paper investigates the potential of a proximal sensing system to characterize the vine foliage that makes use of data collected by a farmer robot equipped with an Intel RealSense D435 camera. The camera includes two infrared (IR) sensors in stereoscopic configuration and one RGB sensor, which provide, for each observation, both infrared and visible red channel information, thus making possible pixel-per-pixel NDVI calculation. Solutions to IR filtering and radiometric calibration issues are proposed that significantly improve measurement accuracy and reliability. Since the camera also provides stereo-based 3D scene reconstruction, depth information can be used to separate the plant canopy from the background before NDVI measurement. At the same time, range data can be employed to extract geometric properties of the crop, such as plant height and/or volume. The system is validated in the field in a commercial vineyard at different phenological stages, from the formation of the berries to leaf discoloration and fall. Experimental results show good agreement compared with ground truth provided by a GreenSeeker, with a mean absolute percentage error in the NDVI estimation of 4.6% and a R 2 of 0.87 tested over the whole grapevine cycle. Therefore, the proposed sensing system could be a feasible solution to automated NDVI estimation at plant-scale. [Display omitted] • Consumer-grade imaging system using a RealSense RGB-D camera for NDVI estimation. • Robotic platform for automated in-field monitoring at plant scale in viticulture. • Proximal sensing system to characterize the vine foliage. • Solutions to IR filtering and radiometric calibration issues under natural light. • Validation in the field in a commercial vineyard at different phenological stages. [ABSTRACT FROM AUTHOR]

Automated avalanche detection has previously relied on satellite imagery, which is typically unsuitable for real-time monitoring due to long revisit times. To address this, we propose automating avalanche detection using photographs taken from the ground. This paper introduces image classification and avalanche segmentation tasks on a publicly-released dataset of 4090 photographs annotated by experts. Using the ResNet and YOLO architectures, we achieve avalanche detection F1 scores of 94.4% per image and 65.4% per avalanche region, demonstrating the potential that this method offers for avalanche monitoring. In contrast with existing approaches, we label images by avalanche type into four distinct categories: glide, loose-snow, slab, and no avalanche. This labelling scheme provides more detail on avalanche events than binary labels and is shown to improve model F1 scores. Moreover, our models do not require a digital elevation model, simplifying application to new areas. Trained models can be used for real-time avalanche monitoring and to gather temporally continuous data for the improvement of existing avalanche forecasting models. The code and dataset are available at github.com/j-f-ox/avalanche-detection. • Deep learning enables automated avalanche detection in webcam images. • Labeling avalanche data by release type improves model results. • ResNet, YOLO, and VGG are utilized to find avalanches in photos taken from the ground. • A dataset of 4090 avalanche photographs annotated by experts is publicly available. • The avalanche dataset contains 7228 outlined avalanches. [ABSTRACT FROM AUTHOR]

Local feature matching enjoys wide-ranging applications in the realm of computer vision, encompassing domains such as image retrieval, 3D reconstruction, and object recognition. However, challenges persist in improving the accuracy and robustness of matching due to factors like viewpoint and lighting variations. In recent years, the introduction of deep learning models has sparked widespread exploration into local feature matching techniques. The objective of this endeavor is to furnish a comprehensive overview of local feature matching methods. These methods are categorized into two key segments based on the presence of detectors. The Detector-based category encompasses models inclusive of Detect-then-Describe, Joint Detection and Description, Describe-then-Detect, as well as Graph Based techniques. In contrast, the Detector-free category comprises CNN Based, Transformer Based, and Patch Based methods. Our study extends beyond methodological analysis, incorporating evaluations of prevalent datasets and metrics to facilitate a quantitative comparison of state-of-the-art techniques. The paper also explores the practical application of local feature matching in diverse domains such as Structure from Motion, Remote Sensing Image Registration, and Medical Image Registration, underscoring its versatility and significance across various fields. Ultimately, we endeavor to outline the current challenges faced in this domain and furnish future research directions, thereby serving as a reference for researchers involved in local feature matching and its interconnected domains. A comprehensive list of studies in this survey is available at https://github.com/vignywang/Awesome-Local-Feature-Matching. • This endeavor is to furnish a comprehensive overview of local feature matching methods. • We analyze various metrics used for performance evaluation of key local feature matching methods. • We endeavor to outline the current challenges faced in this domain and furnish future research directions. [ABSTRACT FROM AUTHOR]

Burning agricultural straw after reaping is a typical farming approach for quicker crop rotation but compromises the sustainability of cultivation practices. Unmanned aerial vehicle (UAV) remote sensing techniques are regarded as a feasible coping strategy to the sustainability dilemmas confronted in the domain. In this paper, we evaluate current multisensor information fusion technologies including both traditional methods and deep learning approaches for remote monitoring in real agricultural scenes and investigate their applicability for detecting burning rice straw. To this end, we collect StrawBurning , a real-world dataset containing fully paired infrared and visible images through mapping ground scenes based on UAV remote sensing. Furthermore, we propose a novel multiscale contrast adaptation (MCA) method for efficient multisensor image fusion and accurate straw burning detection in real farmland scenarios. The MCA remarkably enhanced the detection performance of YOLOv5 on the StrawBurning data by approximately 3%, 2%, and 5%, in terms of recall, mAP@0.5 and mAP@0.5:0.95, respectively. Therefore, our proposed method is demonstrated to obtain the superior performance compared to the single-modal information and other advanced multisensor information fusion methods. Experimental results indicate that neural network models utilizing the proposed multisensor fusion data showed the potential to accurately detect burning rice straw. • A real-world straw burning dataset is published for agricultural sustainability. • Efficient training-free image fusion algorithm based on multiscale contrast adaptation. • Bi-modal inputs outperform the single-modal one in straw burning detection. • Suitable for future UAV on-board processors in smart agriculture. [ABSTRACT FROM AUTHOR]

• Novel U-LSTM and FADA-A modules are introduced into FieldSeg-DA framework. • U-LSTM merges spatiotemporal features in multi-resolution satellite imagery. • FADA-A aligns cross-domain features with guidance of land cover product. • U-LSTM and FADA-A enhance temporal and spatial transferability respectively. Deep learning has become the leading technique for precisely extracting individual arable fields (IAFs) from high-resolution remote sensing images. Maintaining transferability remains a major concern for deep learning methodologies due to the significant cost of acquiring labelled samples for network training. FieldSeg-DA, introduced by Liu et al. (2022) , enhanced network transferability by introducing a finely tuned adversarial domain adaptation module (FADA). An improved version of the FieldSeg-DA framework, FieldSeg-DA2.0, which further enhances transferability through incorporating multisource remote sensing and land cover data is introduced in this paper. First, we introduce a spatiotemporal fusion module, U-LSTM, to extract the IAF extent by merging textural information from the high-resolution image (Gaofen-2) and phenological information from the coarse time-series data (Sentinel-2). Incorporating phenological information mitigates the risk of overfitting associated with the high-resolution imagery acquired in specific seasons, thereby improving the temporal transferability of FieldSeg-DA2.0 compared to FieldSeg-DA. Second, we introduce a novel fine-grained adversarial domain adaptation module with ancillary data (FADA-A) to enhance spatial transferability. FADA-A incorporates prior knowledge from the Dynamic World (DW) land cover dataset to guide adversarial training in the standard FADA, thereby enhancing the robustness of domain adaptation across diverse geographic regions. We evaluate the performance of FieldSeg-DA2.0 through various spatial and temporal transfer experiments utilizing GaoFen-2 and Sentinel-2 data. The results illustrate that the cross-domain performance of FieldSeg-DA2.0 is significantly better than that of the original FieldSeg-DA, highlighting its robust spatiotemporal transferability. FieldSeg-DA2.0 can accurately delineate IAFs across varied regions and seasons without requiring additional training samples, illustrating its considerable potential for large-scale IAF extraction. [ABSTRACT FROM AUTHOR]

• A novel high-performance PSI method called PSI-LGCPFL has been proposed. • PSI-LGCPFL can capture the relationship between intensity image and the polarization information well. • We establish one scattering system for achieving a dataset of polarization scattering images. • Benchmarking results demonstrate that the PSI-LGCPFL achieves competitive performance. Polarization scattering imaging (PSI) is an extremely challenging problem due to the fact that scattering media can lead to severe degradation of the object information. In this paper, we propose a novel high-performance computational method, the PSI based on a well-designed local-global context polarization feature learning (LGCPFL) framework, named PSI-LGCPFL, to efficiently retrieve the target's information. In the LGCPFL framework, the dilated convolutional neural network and Swin Transformer are concurrently introduced to capture local polarization feature information and long-range polarization dependencies in the polarization scattering images, respectively. We in-depth investigate the relationship between the geometry of target, the scattering imaging distance, and the constituent materials and the PSI quality. The performance of our proposed PSI-LGCPFL is benchmarked and evaluated on three testing datasets established in real-life scenarios, i.e. , STR24, DIS50 and MAT18, which cover untrained target's geometry, various untrained targets lying in untrained distances between scattering medium and targets, and diverse untrained target's materials with different background materials, respectively. The experimental results demonstrate that the proposed PSI-LGCPFL has a superior performance on retrieving the target's information with high-generalization abilities and reasonably inferring speed, and outperforms several existing state-of-the-art methods. To our knowledge, PSI-LGCPFL is the first approach to achieve the PSI by polarization characteristics and a deep learning model with Swin Transformer. It also highlights the prospect of accurately reconstruction of remote sensing target's information in scattering medium via using polarization information and deep learning [ABSTRACT FROM AUTHOR]

This paper presents a data-driven model that utilizes artificial neural networks (ANNs) to estimate incident-wave elevation based on the motion and air-gap responses of floating structures. Accurate wave measurements ensure safe and optimized operation of offshore structures. These measurements can be obtained using both in-situ and remote sensing methods, each with its own challenges and limitations. Given these limitations, we proposed a data-driven approach that exploits the wave-induced motion responses of floating structures. Wave information could be extracted from the air-gap responses using the proposed method. An ANN model was trained using the scaled model test data and fine-tuned using Bayesian optimization. The proposed method demonstrated superior robustness in reconstructing the wave-surface elevation around a floating platform, outperforming the motion compensation algorithm and other data-driven models. The wave elevation calculated using the proposed method in both the time and frequency domains effectively minimized low-frequency errors owing to platform motion and those stemming from the wave–structure interaction. The statistical distribution characteristics also demonstrated the effectiveness of the method. • A data-driven model that utilizes Artificial Neural Networks (ANN) to estimate incident wave elevation based on motion and air-gap responses on floating structures. • A scale model test was launched as database for the data-driven model. • The proposed model showed its potential advantages of data-driven models in providing real-time wave measurements for floating structures. [ABSTRACT FROM AUTHOR]

Understanding the sensitivity of remote sensing (RS) observation to land surface parameters (e.g., reflectance and temperature) is very important for estimating the accuracy of RS products and optimizing inversion algorithms. The most precise method for quantifying this sensitivity is physical modelling of derivative propagation in simulated 3D landscapes. However, to our knowledge, present land surface radiative transfer models (RTM) do not simulate derivative propagation. This paper proposes an original "differentiable radiative transfer modelling" that simulates the derivative propagation in natural and urban landscapes, for reflectance. It is integrated in the framework of DART RTM. We validated it both analytically and with a finite difference method applied to a 3D landscape. This new modelling extends the efficiency of 3D RTMs for sensitivity studies. It is implemented in the DART version freely available for research and education (https://dart.omp.eu). • First derivative (Jacobian) propagation modelling in land surface RT models. • Validation of this derivative modelling by the finite difference method. • Discussion of the major factors that influence the derivative. • Outlook of potential applications of the derivative modelling. [ABSTRACT FROM AUTHOR]