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Due to the significant disparities in feature spaces of multi-source images, change detection (CD) of heterogeneous remote sensing images (HRSIs) remains a highly challenging problem. Currently, CD methods based on domain transfer networks (DTNs) have garnered significant attention. However, the computer scientists underutilize knowledge in the field of CD during DTNs design, and the existing CD methods do not fully utilize the heterogeneous complementary features contained in HRSIs. Therefore, this study proposes a novel CD method based on multiple pseudo-homogeneous image pairs. First, a cycle-consistent generative adversarial network with knowledge constraints (named as KCGAN) was designed for obtaining good pseudo-homogeneous images. In detail, both the domain knowledge that there are land cover changes in multi-temporal images and that the objects in an image can be described from different scales were well integrated into the design of KCGAN. Then, a multi-modal difference Siamese fusion network (named as MDSiamF) was proposed to extract change information from the multiple pseudo-homogeneous image pairs generated with KCGAN. Experiments conducted on three datasets showed that: 1) compared to existing domain transfer methods, the unchanged areas in the pseudo-homogeneous images obtained by KCGAN exhibit better feature consistency (with a peak signal-to-noise ratio higher than 20.85 and a PHash value higher than 0.9); 2) compared to state-of-the-art methods for CD of HRSIs, the proposed method shows stable and good CD performance (with an overall accuracy higher than 0.98 and a F1 Score higher than 0.78).

Modeling the gravitational field of irregular celestial bodies is a common problem currently faced by the researchers in the fields of Earth science and deep space exploration. This paper reviews the developmental history of gravitational field modeling theory in these two fields. It summarizes research progress in the last decade, focusing on multipole expansion and potential-flow simulation theories. The gravitational field multipole expansion theory introduces the complex algebraic function commonly used in electromagnetism and quantum mechanics to the gravitational field modeling research field, which can express the (internal and external) gravitational field of celestial bodies more concisely. The calculation rules and mathematical tools from the complex function theory make solving the gravitational dynamics problems in Earth and planetary science more convenient. The potential-flow simulation theory is proposed on top of the equivalence of the Earth's external gravitational vector field and the potential-flow velocity field. That is, both are source- and curl-free vector fields. It introduces the numerical algorithms of computational fluid dynamics to model the external gravitational fields of celestial bodies. Because the potential-flow simulation theory is not established based on the perturbation theory, it can overcome the divergence problem of conventional methods, especially when inverting for the gravitational field of an irregularly shaped celestial body. The two theories expand the theoretical research of gravitational field modeling by using seemingly irrelevant knowledge from other research fields, and such methodology is instructive for Earth or deep-space science researchers to make subsequent theoretical improvements. Moreover, we also systematically categorize, compare, and analyze the existing gravitational field modeling methods based on the mathematical definition of the gravitational field, as well as its variants, and summarize their advantages and disadvantages, helping to select proper gravitational field modeling methods for the Earth and deep-space science researchers.

Abstract In GNSS (Global Navigation Satellite Systems) meteorology, the accuracy of precipitable water vapor (PWV) retrieved from the tropospheric delay of GNSS signals is affected by the conversion factor. Compact VMF1 product (known as GGOS Atmosphere data) provides high‐accuracy global grid‐wise weighted mean temperature (Tm) values, which can be utilized to calculate the conversion factor. However, the Tm provided in the compact VMF1 data are solely ground surface values. To enhance the performance of compact VMF1 product, a new Tm lapse rate model for each grid point was developed for the purpose of reducing its surface Tm to the elevation of the GNSS site. Then the reduced Tm values over the neighboring grid points together with horizontal interpolation were used to obtain the interpolated Tm for the GNSS station. The sample data for the development of the new model were the Tm profiles obtained from ERA5 monthly averaged data spanning 2009–2018. To assess the model's performance, global radiosonde data at 504 radiosonde stations spanning 2019–2021 were employed. Results demonstrated that implementing the Tm lapse rate model significantly enhanced the accuracy of interpolating Tm values for GNSS stations with substantial height disparities from adjacent grid points, thereby improving PWV conversion accuracy. This indicates that employing the new Tm lapse rate model to adjust surface Tm data in the compact VMF1 product holds promise for enhancing its utility in GNSS meteorology.

Coal-gangue object detection has attracted substantial attention because it is the core of realizing vision-based intelligent and green coal separation. However, most existing studies have been focused on laboratory datasets and prioritized model lightweight. This makes the coal-gangue object detection challenging to adapt to the complex and harsh scenes of real production environments. Therefore, our project collected and labeled image datasets of coal and gangue under real production conditions from a coal preparation plant. We then designed a one-stage object model, named STATNet, following the “backbone-neck-head” architecture with the aim of enhancing the detection accuracy under industrial coal preparation scenarios. The proposed model utilizes Swin Transformer as backbone module to extract multi-scale features, improved path augmentation feature pyramid network (iPAFPN) as neck module to enrich feature fusion, and task-aligned head (TAH) as head module to mitigate conflicts and misalignments between classification and localization tasks. Experimental results on a real-world industrial dataset demonstrate that the proposed STATNet model achieves an impressive AP50 of 89.27 %, significantly surpassing several state-of-the-art baseline models by 2.02 % to 5.58 %. Additionally, it exhibits stronger robustness in resisting image corruption and perturbation. These findings demonstrate its promising prospects in practical coal and gangue separation applications.

When light passes through air–glass and glass–water interfaces, refraction occurs, which affects the accuracy of stereo vision three-dimensional measurements of underwater targets. To eliminate the impact of refraction, we developed a refractive stereo vision measurement model based on light propagation paths, utilizing the normalized coordinate of the underwater target. This model is rigorous in theory, and easy to understand and apply. Additionally, we established an underwater simulation imaging model based on the principle that light travels the shortest time between two points. Simulation experiments conducted using this imaging model verified the performance of the underwater stereo vision measurement model. The results demonstrate that the accuracy achieved by the new measurement model is comparable to that of the stereo vision measurement model in the air and significantly higher than that of the existing refractive measurement model. This is because the light rays from the camera’s optical center to the refraction point at the air–glass interface do not always intersect. The experiments also indicate that the deviation in the refractive index of water lead to corresponding systematic errors in the measurement results. Therefore, in real underwater measurements, it is crucial to carefully calibrate the refractive index of water and maintain the validity of the calibration results.

Mitochondrial (MITO) dysfunction occurs in the failing heart and contributes to worsening of heart failure (HF). Reduced aldehyde dehydrogenase 2 (ALDH2) in left ventricular (LV) myocardium of diabetic hearts has been implicated in MITO dysfunction through accumulation of toxic aldehydes including and elevated levels of 4-hydroxy-2-nonenal (4HNE). This study examined whether dysregulation of MITO ALDH2 (mALDH2) occurs in mitochondria of the failing LV and is associated with increased levels of 4HNE.LV tissue from 7 HF and 7 normal (NL) dogs was obtained. Protein quantification of total mitochondrial ALDH2 (t-mALDH2), phosphorylated mALDH2 (p-mALDH2), total MITO protein kinase c epsilon (t-mPKCε), phosphorylated mPKCε (p-mPKCε) was performed by Western blotting, and total mALDH2 enzymatic activity was measured. Protein adducts of 4HNE-MITO and 4HNE-mALDH2 were also measured in MITO fraction by Western Blotting.Protein level of t-mALDH2 was decreased in HF compared with NL dogs (0.63 ± 0.07 vs 1.17 ± 0.08, p

There has been substantial research on multi-medium visual measurement in fields such as underwater three-dimensional reconstruction and underwater structure monitoring. Addressing the issue where traditional air-based visual-measurement models fail due to refraction when light passes through different media, numerous studies have established refraction-imaging models based on the actual geometry of light refraction to compensate for the effects of refraction on cross-media imaging. However, the calibration of refraction parameters inevitably contains errors, leading to deviations in these parameters. To analyze the impact of refraction-parameter deviations on measurements in underwater structure visual navigation, this paper develops a dual-media stereo-vision measurement simulation model and conducts comprehensive simulation experiments. The results indicate that to achieve high-precision underwater-measurement outcomes, the calibration method for refraction parameters, the distribution of the targets in the field of view, and the distance of the target from the camera must all be meticulously designed. These findings provide guidance for the construction of underwater stereo-vision measurement systems, the calibration of refraction parameters, underwater experiments, and practical applications.

Deoxynivalenol (DON) is the most common mycotoxin in food and feed, which can cause undesirable effects, including diarrhea, emesis, weight loss, and growth delay in livestock. Intestinal epithelial cells were the main target of DON, which can cause oxidative stress and inflammatory injury. Tanshinone IIA (Tan IIA) is fat-soluble diterpene quinone, which is the most abundant active ingredient in salvia miltiorrhiza plant with antioxidant and anti-inflammatory characteristics. However, it is not clear whether Tan IIA can protect against or inhibit intestinal oxidative stress and inflammatory injury under DON exposure. This study aimed to explore the protective effect of Tan IIA on DON-induced toxicity in porcine jejunum epithelial cells (IPEC-J2). Cells were exposed to 0, 0.5, 1.0, 2.0 µM DON and/or 45 µg/mL TAN ⅡA to detect oxidative stress indicators. inflammatory cytokines, NF-κB expression, NLRP3 inflammasome and pyroptosis-related factors. In this study, DON exposure caused IPEC-J2 cells oxidative stress by elevating ROS and 8-OHdG content, inhibited GSH-Px activity. Furthermore, DON increased pro-inflammatory factor (TNF-α, IL-1β, IL-18 and IL-6) expression and decreased the anti-inflammatory factor (IL-10) expression, causing inflammatory response via triggering NF-κB pathway. Interestingly, above changes were alleviated after Tan IIA treatment. In addition, Tan IIA relieved DON-induced pyroptosis by suppressing the expression of pyroptosis-related factors (NLRP3, Caspase-1, GSDMD, IL-1β, and IL-18). In general, our data suggested that Tan IIA can ameliorate DON-induced intestinal epithelial cells injury associated with suppressing the pyroptosis signaling pathway. Our findings pointed that Tan IIA could be used as the potential therapeutic drugs on DON-induced enterotoxicity.

Monitoring the surface deformation of filling coal mines regularly and understanding their spatiotemporal evolution characteristics for mining management and disaster warning is greatly significant. However, there is a lack of research on extracting spatiotemporal evolution characteristics of large-scale and high-resolution surface deformation in backfill mining areas and on evaluating the filling effectiveness of subsidence restraint. In this study, we took the Yineng Coal Mine in the Shandong Province of China and the surrounding area of the coal mine as the study area. The advanced distributed scatterer interferometric synthetic aperture radar (DS InSAR) technique was adopted for time-series analysis. The probability integral method (PIM) model for backfilling mining and an arctangent time function were integrated with DS InSAR to overcome the sparsity of InSAR observation points due to the temporal decorrelation caused by vegetation coverage. The results show that the proposed integration strategy is helpful in improving the number of effective monitoring points and obtaining complete spatiotemporal information of the surface deformation of the working face. The whole study area has eight key deformation zones. All six working faces in the Yineng initial minery display different degrees of deformation during the study period. The comparison between the deformation results of backfill mining and the simulated deformation of nonbackfill mining in the CG1312 working face shows that backfilling mining technology effectively reduces the range (22.00%) and magnitude (61.50%) of surface subsidence and significantly reduces the potential threat to surface buildings.

Global navigation satellite systems (GNSS) tropospheric tomography can be used to build a three-dimensional water vapor field. In traditional tomography, the signals crossing from the four sides of the tomographic region are not utilized. To make the best use of these valuable side-crossing signals, an improved tomographic model based on back propagation artificial neural network (BP-ANN) is proposed. In the new tomographic model, the inside part of the slant wet delay (SWD) of the side-crossing signal is divided into two sections: the isotropic and anisotropic components. The former is estimated by the zenith wet delay multiplied by the mapping function multiplied by an isotropic scale factor using a BP-ANN model, and the latter is estimated by horizontal gradients of the SWD multiplied by an anisotropic scale factor using an empirical model. The new tomographic model is experimentally evaluated using the HK CORS network measurements for the period of 21 days from 1 to 21 August 2019. Statistical results show that the root mean square error (RMSE) of slant water vapor reconstructed from the improved model is reduced to 1.35 from 2.85 mm of the traditional model. Compared with the traditional/height factor models, the percentages of the reduction in the RMSE of the tomographic result derived from the new model are 16%/9% and 22%/16%, respectively, using radiosonde and ERA5 data as references. These results suggest a good performance of the new model for GNSS tropospheric tomography.

Over the past few decades, the ground-based global navigation satellite systems (GNSSs) tropospheric sounding technique has undergone rapid development and has proven to be highly effective in sensing atmospheric variables. Through the utilization of the maturing data assimilation technique, GNSS products, e.g., precipitable water vapor (PWV), can be effectively integrated into a numerical weather prediction (NWP) model, thereby significantly bolstering its performance. In this study, to further refine this practice, a comprehensive investigation of the optimal spatial resolutions for assimilating near real-time PWV into the Weather Research and Forecasting model to improve the accuracy of atmospheric humidity field was conducted under different weather conditions in the context of Victoria, Australia. Results revealed that the optimal spatial resolutions under the heavy rainfall and normal conditions were 46.40 and 55.10 km, respectively. Therefore, the overall optimal spatial resolution for assimilating PWV was ultimately determined as 46.40 km, which can capture the necessary details and improve the accuracy of humidity field across different weather scenarios. Specifically, by incorporating PWV into an NWP model using the optimal spatial resolution, the accuracy of atmospheric humidity fields under the heavy rainfall and normal conditions were significantly improved by 26.0% and 24.7%, respectively. Therefore, the findings have considerable implications for further advancing the assimilation technique and offer valuable insights for the construction and deployment of GNSS ground infrastructure in future scenarios.

Deoxynivalenol (DON) is the one of the most common mycotoxins, widely detected in various original foods and processed foods. Tanshinone IIA (Tan IIA) is a fat-soluble diterpene quinone extracted from Salvia miltiorrhiza Bunge, which has multi-biological functions and pharmacological effects. However, whether Tan IIA has a protective effect against DON-induced intestinal toxicity is unknown. In this study, the results showed Tan IIA treatment could attenuate DON-induced IPEC-J2 cell death. DON increased oxidation product accumulation, decreased antioxidant ability and disrupted barrier function, while Tan IIA reversed DON-induced barrier function impairment and oxidative stress. Furthermore, Tan IIA dramatically improved mitochondrial function via mitochondrial quality control. Tan IIA could upregulate mitochondrial biogenesis and mitochondrial fusion as well as downregulate mitochondrial fission and mitochondrial unfolded protein response. In addition, Tan IIA significantly attenuated mitophagy caused by DON. Collectively, Tan IIA presented a potential protective effect against DON toxicity and the underlying mechanisms were involved in mitochondrial quality control–mediated mitophagy.

Abstract This paper presents a new model for ionospheric total electron content (TEC) over China. The new model is developed using a hybrid method composed of the particle swarm optimization (PSO) and artificial neural network and long‐term observations from 257 ground‐based global navigation satellite systems (GNSS) stations and space‐borne GNSS radio occultation systems (COSMIC and Fengyun) during the 14‐year period of 2008–2021. The PSO algorithm is used to optimize the traditional back‐propagation neural network (BP‐NN) model by reducing the effects of the local minimum problem. The new model is validated using out‐of‐sample data, and its results are compared to the BP‐NN, IRI‐2016 model, and global ionospheric maps provided by the International GNSS Service. Results show that TEC predicted from the new model agrees better with the reference TEC than the BP‐NN and IRI‐2016 models. The improvements made by the new model over the BP‐NN and IRI‐2016 models in the equinox, summer, and winter seasons of the solar maximum year (2015) are 4%–20%/20%–36%, 9%–21%/26%–42%, and 6%–22%/21%–43%, respectively, and their corresponding results in the solar minimum year (2019) are 12%–24%/41%–59%, 9%–24%/28%–56%, and 10%–26%/53%–72%. Furthermore, the new model well captures the diurnal evolution, seasonal variation, and variations in the ionospheric TEC under different solar activity levels. It also well captures the mid‐latitude summer nighttime anomaly over China, and the diurnal anomaly is more pronounced in the solar minimum year (2019) than in the solar maximum year (2015) in terms of the nighttime‐to‐noontime ratio and the range of months it lasts in a year.

The tropospheric tomography is an ill-posed inversion problem due to the sparsity of global navigation satellite systems (GNSS) stations and the limitation on the projection angles of GNSS signals, which in turn affects the stability and robustness of the tomographic solution. To address this, a new tomographic algorithm, named two-step projected iterative algorithm (TSPIA), is proposed. The wet refractivity (WR) field was constructed in two steps: first, an iterative preprocessing for the initial input values was performed, and then its resultant solution was input into the projected iterative method, in which a hypothesis convex set was constructed to constrain the reconstruction based on the classical algebraic iterative reconstruction (AIR) methods. In addition, a two-dimensional normalized cumulative periodogram (2-D-NCP) termination criterion was investigated since the traditional criteria for judging the convergence of iterations use prefixed empirical thresholds, which may lead to excessive iterations and need complicated work. The TSPIA was tested using GNSS data in Hong Kong over a wet period and a dry period. Statistical results showed that, compared to the classical AIR methods, the accuracy of the reconstructed WR field of the TSPIA were improved by about 10% and 15% when radiosonde and ECMWF data were used as the reference, respectively. Moreover, experiments for the proposed 2-D-NCP criterion demonstrated noticeable computational efficiency. These results suggest that the new approaches proposed in this article can improve the performance of the iterative methods for GNSS tropospheric tomography.

Hyperspectral superresolution reconstruction technique obtains a high-resolution hyperspectral image (HR-HSI) by fusing both a low-resolution hyperspectral image (LR-HSI) and a high-resolution multispectral image. Existing methods of hyperspectral superresolution reconstruction are mostly concentrated on the global low-rank property while spatial and spectral information in individual regions is not considered. To address this issue, the decomposition of low-rank and sparse tensor is proposed in this study. First, HR-HSI was decomposed into low-rank and sparse components. The former was further separated into spatial and spectral domains according to the spectral low-rank property, and the latter was used to compensate for information loss caused by the low-rank property. Then, a nonlocal constraint of adaptive manifold extracting structural details by the manifold structure was designed to enforce nonlocal self-similarity of the spatial domain. In order to ensure the same spatial structure of different bands and reduce the false individual regions in the sparse component, a surface-aware regularization combined with group sparsity was utilized. Finally, HR-HSI was constructed by the alternating direction method of multipliers. Experiment results on three datasets show that the proposed method outperforms five common existing methods by means of both visual and quantitative evaluations. It is concluded that the new method by taking into account the low-rank and sparse properties can improve the result of the reconstruction.

T-2 toxin is an unavoidable food and feed contaminant that seriously threatens human and animal health. Exposure to T-2 toxin can cause testosterone synthesis disorder in male animals, but the molecular mechanism is still not completely clear. The MAPK pathway participates in the regulation of testosterone synthesis by Leydig cells, but it is unclear whether the MAPK pathway participates in T-2 toxin-induced testosterone synthesis disorders. In this research, testosterone synthesis capacity, testosterone synthase expression and MAPK pathway activation were examined in male mice and TM3 cells exposed to T-2 toxin. The results showed that T-2 toxin exposure decreased testicular volume and caused pathological changes in the microstructure and ultrastructure of testicular Leydig cells. T-2 toxin exposure also decreased testicular testosterone content and the protein expression of testosterone synthase. In vitro, T-2 toxin inhibited cell viability and decreased the expression of testosterone synthase in TM3 cells, and it decreased the testosterone contents in cell culture supernatants. Moreover, T-2 toxin activated the MAPK pathway by increasing the expression of p38, JNK and ERK as well as the expression of p-p38, p-JNK and p-ERK in testis and TM3 cells. The p38 molecular inhibitor (SB203580) significantly alleviated the T-2 toxin-induced decrease in testosterone synthase expression in TM3 cells and the T-2 toxin-induced reduction in testosterone content in TM3 cell culture supernatants. In summary, p38 mediates T-2 toxin-induced Leydig cell testosterone synthesis disorder.

Accurate and reliable forecasting of PM2.5 and PM10 concentrations is important to the public to reasonably avoid air pollution and for the governmental policy responses. However, the prediction of PM2.5 and PM10 concentrations has great uncertainty and instability because of the dynamics of atmospheric flows, making it difficult for a single model to efficiently extract the spatial–temporal dependences. This paper reports a robust forecasting system to achieve accurate multi-step ahead forecasting of PM2.5 and PM10 concentrations. First, correlation analysis is adopted to screen the spatial information on pollution and meteorology that may facilitate the prediction of concentrations in a target city. Then, a spatial–temporal attention mechanism is used to assign weights to original inputs from both space and time dimensions to enhance the essential information. Subsequently, the residual-based convolutional neural network with feature extraction capabilities is employed to model the refined inputs. Finally, five accuracy metrics and two additional statistical tests are applied to comprehensively assess the performance of the proposed forecasting system. In addition, experimental studies of three major cities in the Yangtze River Delta urban agglomeration region indicate that the forecasting system outperforms various prevalent baseline models in terms of accuracy and stability. Quantitatively, the proposed STA-ResCNN model reduces root mean square error by 5.595 %-15.247 % and 6.827 %-16.906 % for the average of 1–4 h ahead predictions in three major cities of PM2.5 and PM10, respectively, compared to baseline models. The applicability and generalization of the proposed forecasting system are further verified by the extended applications in the other 23 cities in the entire region. The results prove that the forecasting system is promising in the early warning, regional prevention, and control of air pollution.

Missing data in precipitable water vapor derived from global navigation satellite systems (GNSS-PWV) is commonly a large hurdle in climatical applications, since continuous PWV is an important prerequisite. Interpolation using principal component analysis (PCA) is typically used to resolve this problem. However, the popular PCA-based interpolating methods, e.g., rank-deficient least squares PCA (RDPCA) and data interpolating empirical orthogonal function (DINEOF), often lead to unsatisfactory results. This study analyzes the relationship between missing data and PCA-based interpolation results and proposes an improved interpolation-based RDPCA (IRDPCA) that can take into account the PWV derived from ERA5 (ERA-PWV) as an additional aid. Three key steps are involved in the IRDPCA: initially interpolating missing data, estimating principal components through a functional model and optimizing the interpolation through an iterative process. Using a 6-year GNSS-PWV over 26 stations and ERA-PWV in Yunnan, China, the performance of the IRDPCA is compared with the RDPCA and DINEOF using simulation experiments based on both homogeneous data (i.e., interpolating ERA-PWV using available ERA-PWV) and heterogeneous data (i.e., interpolating GNSS-PWV using ERA-PWV). In the case of using homogeneous data, the root mean square (RMS) values of the interpolation errors are 3.45, 1.18 and 1.17 mm for the RDPCA, DINEOF and IRDPCA, respectively; while the values are 3.50, 2.50 and 1.55 mm in the heterogeneous case. These results demonstrate the superior performance of the IRDPCA in both the heterogeneous and homogeneous cases. Moreover, these methods are also applied to the interpolation of the real GNSS-PWV. The RMS, absolute bias and correlation of the GNSS-PWV are calculated by comparison with ERA-PWV. The results reveal that the interpolated GNSS-PWV using the IRDPCA is not impacted by the systematic discrepancies in the ERA-PWV and agrees well with the original data.

Convolutional neural networks (CNNs) have recently been demonstrated to be able to substantially improve the land cover classification accuracy of hyperspectral images. Meanwhile, the rapidly developing capacity for satellite and airborne image spectroscopy as well as the enormous archives of spectral data have imposed increasing demands on the computational efficiency of CNNs. Here, we propose a novel CNN framework that integrates one-dimensional (1D), two-dimensional (2D), and three-dimensional (3D) CNNs to obtain highly accurate and fast land cover classification from airborne hyperspectral images. To achieve this, we first used 3D CNNs to derive both spatial and spectral features from hyperspectral images. Then, we successively utilized a 2D CNN and a 1D CNN to efficiently acquire higher-level representations of spatial or spectral features. Finally, we leveraged the information obtained from the aforementioned steps for land cover classification. We assessed the performance of the proposed method using two openly available datasets (the Indian Pines dataset and the Wuhan University dataset). Our results showed that the overall classification accuracy of the proposed method in the Indian Pines and Wuhan University datasets was 99.65% and 99.85%, respectively. Compared to the state-of-the-art 3D CNN model and HybridSN model, the training times for our model in the two datasets were reduced by an average of 60% and 40%, respectively, while maintaining comparable classification accuracy. Our study demonstrates that the integration of 1D, 2D, and 3D CNNs effectively improves the computational efficiency of land cover classification with hyperspectral images while maintaining high accuracy. Our innovation offers significant advantages in terms of efficiency and robustness for the processing of large-scale hyperspectral images.

In recent years, precipitable water vapor has been widely used in heavy precipitation prediction, which is obtained from a conversion of the zenith total delay (ZTD) of the GNSS signal. Since the parameter directly estimated for the tropospheric delay from Global Navigation Satellite Systems (GNSS) data processing is the ZTD, this study investigated the feasibility of directly using ZTD to predict heavy precipitation. Based on the finding that, prior to a heavy precipitation events, ZTD was likely to start with a duration of continuous rise followed by a sharp drop, a new heavy precipitation detection model containing seven predictors derived from ZTD was established. The seven predictors reflect not only the ascending and descending trends but also long-term and short-term variations in the ZTD time series. Three criteria, representing different situations for the formation of heavy precipitation, were also constructed using the predictors to detect heavy precipitation. The optimal set of thresholds for the seven predictors for each summer month were determined based on hourly ZTD and precipitation records at a pair of co-located GNSS/weather station−HKSC-KP in Hong Kong over the period 2010−2017. The model was evaluated using the predictions in 2018 and 2019 to compare against the corresponding precipitation records. Results showed that the new model correctly predicted 98.8% of the heavy precipitation events, with a mean lead time of 4.37 h. Compared with the existing models, the new model also reduced the false alarms by 32.3%. Similar results were also obtained from the other three pairs of co-located stations. These results suggest that it is rational and effective to use the new ZTD-based model for improving the performance of heavy precipitation detection.

Abstract The ionospheric total electron content (TEC) is an important ionospheric parameter, and it is widely utilized in research such as space weather prediction and precise positioning. However, it is still challenging to develop an ionospheric TEC prediction model with high accuracy. In this study, a new ionospheric TEC model over China was developed using the bidirectional long short‐term memory (bi‐LSTM) method and observations from 257 ground‐based global navigation satellite system (GNSS) stations in the Crustal Movement Observation Network of China from January 2018 to December 2021. The root mean square errors of the bi‐LSTM‐based model’s 1 and 2 hr ahead predictions on the test data set (from June 2021 to December 2021) are 1.12 and 1.68 TECU, respectively, which are 75/50/32% and 72/48/22% smaller than those of the IRI‐2016, artificial neural network and LSTM‐based models, correspondingly. The bi‐LSTM‐based model shows the best performance, which is most likely due to the fact that the sequence information in both forward and backward directions is taken into consideration in the new model. In addition, the diurnal variation, seasonal variation of the ionospheric TEC, and variations under geomagnetic storm conditions are successfully captured by the bi‐LSTM‐based model. Moreover, the TEC maps resulting from the bi‐LSTM model agree well with those obtained from the final ionospheric product from the Chinese Academy of Sciences. Hence, the new model can be a good choice for the investigation of the spatiotemporal variation trend in the ionosphere and GNSS navigation.

Abstract The adverse effect of the ionospheric scintillation on Global Navigation Satellite System (GNSS) requires scintillation monitoring on a global scale. Ionospheric Scintillation Monitoring Receivers (ISMR) are usually adopted to monitor scintillation, while they are not suitable for global monitoring due to the 50 Hz data collecting rate, which restricts the distribution. This paper proposes a new method to extract the phase scintillation index from each GNSS carrier with 1s‐sampling‐interval, mainly based on the cycle slip detection, the geodetic detrending and the wavelet transform, in which the optimal symmetry parameter and the time‐bandwidth product are determined with trial calculation. Taken the σϕ index provided by ISMR as the reference, 1‐year observations are utilized to evaluate the scintillation monitoring performance of the extracted index regarding the correlation of the magnitude in each observation arc, the detected daily scintillation occurrence rate, the diurnal variation pattern of the ionospheric scintillation, the correlation between the scintillation occurrence rate and the space weather parameter, and the complementary cumulative distribution of the magnitudes. Compared to the performance of Rate of Total electron content Index, a higher consistency can be achieved between the extracted index and the σϕ index, indicating the rationality of applying the proposed method in monitoring scintillations. The extracted scintillation index can be expected to introduce geodetic receivers operating at 1s‐sampling‐interval into the field of ionospheric scintillation monitoring on a global scale.

Precipitable water vapor (PWV) is an important meteorological factor for predicting extreme weather events such as tropical cyclones, which can be obtained from zenith tropospheric delay (ZTD) by using a conversion. A time difference of ZTD arrival (TDOZA) model was proposed to monitor the movement of tropical cyclones, and the fifth-generation reanalysis dataset of the European Centre for Medium-range Weather Forecasting (ERA5)-derived ZTD (ERA5-ZTD) was used to estimate the movement of tropical cyclones based on the model. The global navigation satellite system-derived ZTD and radiosonde data-derived PWV (RS-PWV) were used to test the accuracy of the ERA5-ZTD and analyze the correlation between ZTD and PWV, respectively. The statistics showed that the mean Bias, RMS and STD of the ERA5-ZTD were 6.4 mm, 17.1 mm and 16.5 mm, respectively, and the mean correlation coefficient of the ERA5-ZTD and RS-PWV was 0.951, which indicates that the ZTD can be used to predict weather events instead of PWV. Then, spatiao-temporal characteristics of ZTD during the four tropical cyclone (i.e., Merbok, ROKE, Neast and Hato) periods in 2017 were analyzed, and the result showed that the moving directions of ZTD and the tropical cyclones were consistent. Thus, the ZTD time series over the ERA5 grids around the tropical cyclones’ paths were used to estimate the velocity of the tropical cyclones based on the TDOZA model, when the tropical cyclones are approaching or leaving. Compared with the result from the China Meteorological Administration, the mean absolute and relative deviations of the TDOZA model-derived velocity were 2.55 km/h and 10.0%, respectively. These results suggest that ZTD can be used as a new supplementary meteorological parameter for monitoring tropical cyclone events.

Plant microbiota are of great importance for host nutrition and health. As a C4 plant species with a high carbon fixation capacity, sugarcane also associates with beneficial microbes, though mechanisms underlying sugarcane root-associated community development remain unclear. Here, we identify microbes that are specifically enriched around sugarcane roots and report results of functional testing of potentially beneficial microbes propagating with sugarcane plants. First, we analyzed recruitment of microbes through analysis of 16S rDNA enrichment in greenhouse cultured sugarcane seedlings growing in field soil. Then, plant-associated microbes were isolated and assayed for beneficial activity, first in greenhouse experiments, followed by field trials for selected microbial strains. The promising beneficial microbe SRB-109, which quickly colonized both roots and shoots of sugarcane plants, significantly promoted sugarcane growth in field trials, nitrogen and potassium acquisition increasing by 35.68 and 28.35%, respectively. Taken together, this report demonstrates successful identification and utilization of beneficial plant-associated microbes in sugarcane production. Further development might facilitate incorporation of such growth-promoting microbial applications in large-scale sugarcane production, which may not only increase yields but also reduce fertilizer costs and runoff.

To address the problems of wind and solar generation volatility and lose of wind and photovoltaic resources, on the basis of the complementary property of wind-solar-water, the topology structure of the wind-solar-water synergy power generation system is constructed. Taking the minimum grid fluctuation index, the minimum wind-photovoltaic-hydro discard rate and the greatest economic effectiveness of the power station as the goal functions and considering various constraints of the wind, photovoltaic, and hydrostation units, a triobjective optimization running model of the wind-photovoltaic-water synergy system is established. Meanwhile, this essay suggests an IMOSSA on the basis of tent chaotic sequence and random wandering strategy to settle the described triobjective optimization issue. Taking Hubei Pankou as an example for simulation analysis, after choosing the best scheme, IMOSSA compared with MOSSA, MOGWO, and NSGA-II, the volatility of sunny days is reduced by 12.39%, 19.5%, and 36.71%, respectively; the wind-photovoltaic abandonment rate is reduced by 11.17%, 22.5%, and 38.03%, respectively, while in the rainy days the volatility is reduced by 8.09%, 18.34%, and 47.03%, respectively; the wind-photovoltaic abandonment rate is reduced by 14.84%, 16.86%, and 40%, respectively. Therefore, it is possible to demonstrate the validity of the proposed three-objective model and the efficiency of the IMOSSA in solving the issue. The efficiency of the optimization operation approach suggested in this research is confirmed by the case study, providing a new idea for the large-scale consumption of new energy in high-proportion hydropower grids.

The segmentation of crop disease zones is an important task of image processing since the knowledge of the growth status of crops is critical for agricultural management. Nowadays, images taken by unmanned aerial vehicles (UAVs) have been widely used in the segmentation of crop diseases, and almost all current studies use the study paradigm of full supervision, which needs a large amount of manually labelled data. In this study, a weakly supervised method for disease segmentation of UAV images is proposed. In this method, auxiliary branch block (ABB) and feature reuse module (FRM) were developed. The method was tested using UAV images of maize northern leaf blight (NLB) based on image-level labels only, i.e., only the information as to whether NBL occurs is given. The quality (intersection over union (IoU) values) of the pseudo-labels in the validation dataset achieved 43% and the F1 score reached 58%. In addition, the new method took 0.08 s to generate one pseudo-label, which is highly efficient in generating pseudo-labels. When pseudo-labels from the train dataset were used in the training of segmentation models, the IoU values of disease in the test dataset reached 50%. These accuracies outperformed the benchmarks of the ACoL (45.5%), RCA (36.5%), and MDC (34.0%) models. The segmented NLB zones from the proposed method were more complete and the boundaries were more clear. The effectiveness of ABB and FRM was also explored. This study is the first time supervised segmentation of UAV images of maize NLB using only image-level data was applied, and the above test results confirm the effectiveness of the proposed method.

Water vapor is a dominant greenhouse gas. It significantly impacts the atmosphere by trapping heat and infrared radiation. The greenhouse effect is essential for life on Earth but can also be harmful. Although the amount of water vapor in the atmosphere is not much during the water cycle, it is the most active element in rapid changes in both spatial and temporal domains. GNSS tomography’s ability to model the high-resolution 3D distribution of water vapor is a promising means of measuring and monitoring the spatial-temporal variation of water vapor. This study developed and tested a new GNSS tomographic model using adaptive voxel parameterization. It uses a 3D traversal algorithm to dynamically determine the position of voxels at each tomographic sampling epoch. It means that the new algorithm can exclude the voxels that no GNSS signals pass through, reducing the influence of such voxels in the construction of the tomographic model. This study provides a new approach to investigating the inversion of atmospheric water vapor. The experiment used one-month data from the Hong Kong network in September 2020, and the results were compared with the general system. The local radiosonde data is a reference for verification of the two approaches. The mean root-mean-square error (RMSE) and IQR of the water vapor profiles derived from AAR are decreased by 55% and 48% with respect to the GFR results, respectively. The results show that the accuracy of the new method outperforms the general approach in the result statistics. The successful implementation of the research has significant potential to drive the development of GNSS tomography in the study of weather and climate change.

For better modeling the vertical distribution and variations of water vapor, a 10-year time-series of a newly derived water vapor parameter (termed IRPWV), defined as the ratio of water vapor density (WVD) to the total precipitable water vapor (TPWV), was statistically analyzed. This research showed that the vertical distribution of IRPWV presents a periodic pattern and is highly correlated with the relative magnitude of its corresponding TPWV when compared with the other TPWVs in the same time range. Six TPWV ranges were first chosen to determine the relative magnitude and then used to classify the IRPWV vertical distributions of TPWV. For the periodic variations in each of the six classified IRPWV vertical distribution time-series, a temporal IRPWV model was developed accordingly with six sets of coefficients. The new models were validated by comparing their predictions against the reference values from sounding data at 12 radiosonde stations in China, and their performance was also evaluated against that of the commonly used exponential model. Results showed that, first, the proportions of the height range that had reduced annual root mean square error (RMSE) of WVD in all height ranges within all TPWV ranges were over 75% at the 12 stations. Then, the annual RMSEs of the WVD for all the stations were reduced by at least 11%, 20%, 43%, 48%, 40%, 38%, 32%, 35%, 32%, and 28% in each of the 10 selected height ranges, respectively.

One of the main challenges of Global Navigation Satellite System (GNSS) tomography is in solving ill-conditioned system equations. Vertical constraint models are typically used in the solution procedure and play an important role in the quality of the GNSS tomography, in addition to helping resolve ill-posed problems in system equations. In this study, based on a water vapor (WV) parameter, namely IRPWV, a new vertical constraint model with six sets of coefficients for six different WV states was developed and tested throughout 2019 in the Hong Kong region with four tomographic schemes, which were carried out with the model and the traditional vertical constraint model using three different types of water vapor scale height parameters. Experimental results were numerically compared against their corresponding radiosonde-derived WV values. Compared with the tests that used the traditional model, our results showed that, first, for the daily relative error of WV density (WVD) less than 30%, the new model can lead to at least 10% and 49% improvement on average at the lower layers (below 3 km, except for the ground surface) and the upper layers (about 5–10 km), respectively. Second, the skill score of the monthly root-mean-square error (RMSE) of layered WVD above 10 accounted for about 83%, 87%, and 64%. Third, for the annual biases of layered WVD, the new model significantly decreased by 1.1–1.5 g/m3 at layers 2–3 (about 1 km), where all schemes showed the maximal bias value. Finally, for the annual RMSE of layered WVD, the new model at the lower (about 0.6–3 km) and upper layers improved by 13–42% and 5–47%, respectively. Overall, the new model performed better on GNSS tomography and significantly improved the accuracy of GNSS tomographic results, compared to the traditional model.

The leaf area index (LAI) is critical for the respiration, transpiration, and photosynthesis of crops. Color indices (CIs) and vegetation indices (VIs) extracted from unmanned aerial vehicle (UAV) imagery have been widely applied to the monitoring of the crop LAI. However, when the coverage of the crop canopy is large and only spectral data are used to monitor the LAI of the crop, the LAI tends to be underestimated. The canopy height model (CHM) data obtained from UAV-based point clouds can represent the height and canopy structure of the plant. However, few studies have been conducted on the use of the CHM data in the LAI modelling. Thus, in this study, the feasibility of combining the CHM data and CIs and VIs, respectively, to establish LAI fitting models for winter wheat in four growth stages was investigated, and the impact of image resolution on the extraction of remote sensing variables (the CHM data, CIs, and VIs) and on the accuracy of the LAI models was evaluated. Experiments for acquiring remote sensing images of wheat canopies during the four growth stages from the RGB and multispectral sensors carried by a UAV were carried out. The partial least squares regression (PLSR), random forest regression (RFR), and support vector machine regression (SVR) were used to develop the LAI fitting models. Results showed that the accuracy of the wheat LAI models can be improved in the entire growth stages by the use of the additional CHM data with the increment of 0.020–0.268 in R2 for three regression methods. In addition, the improvement from the Cis-based models was more noticeable than the Vis-based ones. Furthermore, the higher the spatial resolution of the CHM data, the better the improvement made by the use of the additional CHM data. This result provides valuable insights and references for UAV-based LAI monitoring.

Abstract The inhomogeneities, the so‐called changepoints, inevitably occur in the precipitable water vapor (PWV) time series derived from Global Navigation Satellite Systems (GNSS‐PWV). Currently, the two predominant types of homogenization methods, that is, absolute and relative methods, are limited in the poor performance and dependency on reference time series. In this study, a new absolute approach named adaptive absolute homogenization test (AAHT) by combining Seasonal‐Trend decomposition based on LOESS (STL) and Penalized Maximal F‐test to account for the red noise (PMFred) is proposed. The performance of the new approach was validated using Monte Carlo simulations. Results showed the success rate and false alarm rate were better than 74.9% and 12.2%; the detection accuracy of the changepoint epochs and the offset magnitudes were 8.0 days and 0.153 mm. AAHT was also applied to homogenizing the real GNSS‐PWV time series over 91 International GNSS Service stations, from which 63 time series were identified as inhomogeneous containing 126 changepoints. Based on a comparison with the PWV time series from the fifth‐generation European center for medium‐range weather forecasts (ECMWF) reanalysis (ERA5), 43 changepoints were connected to climatic drivers, leaving 83 artificial changepoints at 48 stations. The offset magnitudes at these artificial changepoints were estimated and corrected in the inhomogeneous time series. The long‐term trends of those homogenized time series were compared with that of the PWV time series derived from ERA5 data sets (ERA‐PWV) and the correlation coefficient between the two sets of trends was 0.75, which was significantly larger than 0.23 before homogenization.

Abstract This study presents a comprehensive comparison of the impact of solar activity on forecasting the upper atmosphere through assimilation of radio occultation (RO)‐derived electron density (Ne) into a physics‐based model (TIE‐GCM) using an ensemble Kalman filter (KF). Globally abundant RO‐derived Ne offers one of the most promising means to test the effect of assimilation on the model forecasted state on a global scale. This study emphasizes the importance of understanding how the assimilation results vary with solar activity, which is one of the main drivers of thermosphere‐ionosphere dynamics. This study validates the forecast states with independent RO‐derived GRACE (Gravity Recovery and Climate Experiment mission) Ne data. The principal result of the study is that the agreement between forecast Ne and data is better during solar minimum than solar maximum. The results also show that the agreement between data and forecast is mostly better than that of the standalone TIE‐GCM driven with observed geophysical indices. The results emphasize that TIE‐GCM significantly underestimate Ne in altitudes below 250 km and the assimilation of Ne is not as effective in these lower altitudes as it is in higher altitudes. The results demonstrate that assimilation of Ne significantly impacts the neutral mass density estimates via the KF state vector—the impact is larger during solar maximum than solar minimum relative to a control case that does not assimilate Ne. The results are useful to explain the inherent model bias, to understand the limitations of the data, and to demonstrate the capability of the assimilation technique.

Reliably modelling and monitoring the climate requires robust data that can be used to feed meteorological models, and, most importantly, to independently validate those models [...]

Potential evapotranspiration (PET) is generally estimated using empirical models; thus, how to improve PET estimation accuracy has received widespread attention in recent years. Among all the models, although the temperature-driven Thornthwaite (TH) model is easy to operate, its estimation accuracy is rather limited. Although previous researchers proved that the accuracy of TH-PET can be greatly improved by using a limited number of variables to conduct calibration exercises, only preliminary experiments were conducted. In this study, to refine this innovation practice, we comprehensively investigated the factors that affect the calibration performances, including the selection of variables, seasonal effects, and spatial distribution of Global Navigation Satellite System (GNSS)/weather stations. By analyzing the factors and their effects, the following conclusions have been drawn: (1) an optimal variable selection scheme containing zenith total delay, temperature, pressure, and mean Julian Date was proposed; (2) the most salient improvements are in the winter and summer seasons, with improvement rates over 80%; (3) with the changes in horizontal (2.771–44.723 km) and height (1.239–344.665 m) differences among ten pairs of GNSS/weather stations, there are no obvious differences in the performances. These findings can offer an in-depth understanding of this practice and provide technical references to future applications.

The timely and accurate detection of wheat lodging at a large scale is necessary for loss assessments in agricultural insurance claims. Most existing deep-learning-based methods of wheat lodging detection use data from unmanned aerial vehicles, rendering monitoring wheat lodging at a large scale difficult. Meanwhile, the edge feature is not accurately extracted. In this study, a semantic segmentation network model called the pyramid transposed convolution network (PTCNet) was proposed for large-scale wheat lodging extraction and detection using GaoFen-2 satellite images with high spatial resolutions. Multi-scale high-level features were combined with low-level features to improve the segmentation’s accuracy and to enhance the extraction sensitivity of wheat lodging areas in the proposed model. In addition, four types of vegetation indices and three types of edge features were added into the network and compared to the increment in the segmentation’s accuracy. The F1 score and the intersection over union of wheat lodging extraction reached 85.31% and 74.38% by PTCNet, respectively, outperforming other compared benchmarks, i.e., SegNet, PSPNet, FPN, and DeepLabv3+ networks. PTCNet can achieve accurate and large-scale extraction of wheat lodging, which is significant in the fields of loss assessment and agricultural insurance claims.

As a frequently-occurred phenomenon in the high-latitude region, ionospheric scintillations affect the stable service of the positioning navigation and timing service of the Global Navigation Satellite System (GNSS), calling for an urgent need of monitoring the scintillations accurately. The monitoring of scintillations usually adopts a special type of receiver, called an ionospheric scintillation monitoring receiver (ISMR), which cannot cover the whole high-latitude region due to its loss distribution. Geodetic receivers are densely distributed, but set at a 30s-sampling-interval usually. It is a controversial issue, namely, the accuracy of the scintillation index extracted from 30s-sampling-interval observations. This paper evaluates the accuracy of two 30s-sampling-interval indices in monitoring scintillations from both the time and space aspects using observations collected in the whole year of 2020. The accuracy in the time aspect is assessed with the phase scintillation index from ISMR as the reference through the following three-pronged approaches, i.e., the accuracy of the daily scintillation occurrence rates in the year 2020, the correlation with space weather parameters, and the variation pattern of the scintillation occurrence rate with the local time and day of the year 2020. The accuracy in space is studied based on the scintillation grid model considering the following two aspects, i.e., the scintillation monitoring performance in a Swarm satellite observation arc, and the statistical scintillation occurrence rate in the whole research region throughout the year 2020. The results of this paper reveal the efficiency of the 30s-sampling-interval scintillation indices in monitoring scintillations and detecting the occurrence patterns in the high-latitude region. The outcome of this paper can provide a basic idea for introducing the widely distributed geodetic receivers to monitor and model the scintillations in the high-latitude region.

Global navigation satellite systems (GNSS) has been applied to the sounding of precipitable water vapor (PWV) due to its high accuracy and high spatiotemporal resolutions. PWV obtained from GNSS (GNSS-PWV) can be used to investigate extreme weather phenomena, such as the formation mechanism and prediction of rainfalls. In the study, a new, improved model for rainfall forecasting was developed based on GNSS data and rainfall data for the 9-year period from 2010 to 2018 at 66 stations located in the USA. The new model included three prediction factors—PWV value, PWV increase, maximum hourly PWV increase. The two key tasks involved for the development of the model were the determination of the thresholds for each prediction factor and the selection of the optimal strategy for using the three prediction factors together. For determining the thresholds, both critical success index (CSI) and true skill statistic (TSS) were tested, and results showed that TSS outperformed CSI for all rainfall events tested. Then, various strategies by combining the three prediction factors together were also tested, and results indicated that the best forecast result was from the case that any two of the prediction factors were over their own thresholds. Finally, the new model was evaluated using the GNSS data for the 2-year period from 2019 to 2020 at the above mentioned 66 stations, and the probability of detection (POD) and false-alarms rate (FAR) were adopted to measure the model performances. Over the 66 stations, the POD values ranged from 73% to 97% with the mean of 87%, and the FARs ranged from 26% to 77% with the mean of 53%. Moreover, it was also found that both POD and FAR values were related to the region of the station; e.g., the results at the stations that are located in humid regions were better than the ones located in dry regions. All these results suggest the feasibility and good performance of using GNSS-PWV for forecasting rainfall.

This paper focuses on sea surface wind speed estimation using L1B level v3.1 data of reflected GNSS signals from the Cyclone GNSS (CYGNSS) mission and European Centre for Medium-range Weather Forecast Reanalysis (ECMWF) wind speed data. Seven machine learning methods are applied for wind speed retrieval, i.e., Regression trees (Binary Tree (BT), Ensembles of Trees (ET), XGBoost (XGB), LightGBM (LGBM)), ANN (Artificial neural network), Stepwise Linear Regression (SLR), and Gaussian Support Vector Machine (GSVM), and a comparison of their performance is made. The wind speed is divided into two different ranges to study the suitability of the different algorithms. A total of 10 observation variables are considered as input parameters to study the importance of individual variables or combinations thereof. The results show that the LGBM model performs the best with an RMSE of 1.419 and a correlation coefficient of 0.849 in the low wind speed interval (0–15 m/s), while the ET model performs the best with an RMSE of 1.100 and a correlation coefficient of 0.767 in the high wind speed interval (15–30 m/s). The effects of the variables used in wind speed retrieval models are investigated using the XGBoost importance metric, showing that a number of variables play a very significant role in wind speed retrieval. It is expected that these results will provide a useful reference for the development of advanced wind speed retrieval algorithms in the future.

Abstract The ionosphere plays an important role in satellite navigation, radio communication, and space weather prediction. However, it is still a challenging mission to develop a model with high predictability that captures the horizontal‐vertical features of ionospheric electrodynamics. In this study, multiple observations during 2005–2019 from space‐borne global navigation satellite system (GNSS) radio occultation (RO) systems (COSMIC and FY‐3C) and the Digisonde Global Ionosphere Radio Observatory are utilized to develop a completely global ionospheric three‐dimensional electron density model based on an artificial neural network, namely ANN‐TDD. The correlation coefficients of the predicted profiles all exceed 0.96 for the training, validation and test datasets, and the minimum root‐mean‐square error of the predicted residuals is 7.8 × 104 el/cm3. Under quiet space weather, the predicted accuracy of the ANN‐TDD is 30%–60% higher than the IRI‐2016 at the Millstone Hill and Jicamarca incoherent scatter radars. However, the ANN‐TDD is less capable of predicting ionospheric dynamic evolution under severe geomagnetic storms compared to the IRI‐2016 with the STORM option activated. Additionally, the ANN‐TDD successfully reproduces the large‐scale horizontal‐vertical ionospheric electrodynamic features, including seasonal variation and hemispheric asymmetries. These features agree well with the structure revealed by the RO profiles derived from the FORMOSAT/COSMIC‐2 mission. Furthermore, the ANN‐TDD successfully captures the prominent regional ionospheric patterns, including the equatorial ionization anomaly, Weddell Sea anomaly and mid‐latitude summer nighttime anomaly. The new model is expected to play an important role in the application of GNSS navigation and in the explanation of the physical mechanisms involved.

A novel type of polymethyl methacrylate (PMMA) fiber refractive index sensor based on surface plasmon resonance (SPR) is proposed in this work, which is applied to measure the refractive index of liquid. The performance is investigated by the SPR theory and the structural properties of the side-polished D-type fiber. In order to obtain the optimized parameters of SPR sensor, the effects of metal film material, the metal film thickness and the length of the sensing zone on spectral characteristics of the sensor are studied. The fiber SPR sensor is manufactured and applied to the preparation experiment. The change of refractive index of glycerol solution is detected and analyzed at a constant temperature of 20 ​°C. Experimental results indicate that the proposed fiber SPR sensor exhibits refractive index sensitivity up to 3328.1 nm/RIU in the range of 1.334–1.388, which can be utilized as the alternative material for bio-sensing studies.

The use of high spatial and temporal resolution data assimilation and forecasting around Australia’s capital cities and rural land provided an opportunity to improve moisture analysis and forecasting. To support this endeavour, RMIT University and Geoscience Australia worked with the Bureau of Meteorology (BoM) to provide real-time GNSS (global navigation satellite system) zenith total delay (ZTD) data over the Australian region, from which a high-resolution total water vapour field for SE Australia could be determined. The ZTD data could play an important role in high-resolution data assimilation by providing mesoscale moisture data coverage from existing GNSS surface stations over significant areas of the Australian continent. The data were used by the BoM’s high-resolution ACCESS-C3 capital city numerical weather prediction (NWP) systems, the ACCESS-G3 Global system and had been used by the ACCESS-R2-Regional NWP model. A description of the data collection and analysis system is provided. An example of the application of these local GNSS data for a heavy rainfall event over SE Australia/Victoria is shown using the 1.5-km resolution ACCESS-C3 model, which was being prepared for operational use. The results from the test were assessed qualitatively, synoptically and also examined quantitatively using the Fractions Skills Score which showed the reasonableness of the forecasts and demonstrated the potential for improving rainfall forecasts over south-eastern Australia by the inclusion of ZTD data in constructing the moisture field. These data have been accepted for operational use in NWP.

Global Navigation Satellite Systems (GNSS) tomography is a well-recognized modeling technique for reconstruction, which can be used to investigate the spatial structure of water vapor with a high spatiotemporal resolution. In this study, a refined near real-time tomographic model is developed based on multi-source data including GNSS observations, Global Forecast System (GFS) products and surface meteorological data. The refined tomographic model is studied using data from Hong Kong from 2 to 11 October 2021. The result is compared with the traditional model with physical constraints and is validated by the radiosonde data. It is shown that the root mean square error (RMSE) values of the proposed model and traditional model are 0.950 and 1.763 g/m3, respectively. The refined model can decrease the RMSE by about 46%, indicating a better performance than the traditional one. In addition, the accuracy of the refined tomographic model is assessed under both rainy and non-rainy conditions. The assessment shows that the RMSE in the rainy period is 0.817 g/m3, which outperforms the non-rainy period with the RMSE of 1.007 g/m3.

The use of digital technologies to detect, position, and quantify pests quickly and accurately is very important in precision agriculture. Imagery acquisition using air-borne drones in combination with the deep learning technique is a new and viable solution to replace human labor such as visual interpretation, which consumes a lot of time and effort. In this study, we developed a method for automatic detecting an important maize pest—Spodoptera frugiperda—by its gnawing holes on maize leaves based on convolution neural network. We validated the split-attention mechanism in the classical network structure ResNet50, which improves the accuracy and robustness, and verified the feasibility of two kinds of gnawing holes as the identification features of Spodoptera frugiperda invasion and the degree. In order to verify the robustness of this detection method against plant morphological changes, images at the jointing stage and heading stage were used for training and testing, respectively. The performance of the models trained with the jointing stage images has been achieved the validation accuracy of ResNeSt50, ResNet50, EfficientNet, and RegNet at 98.77%, 97.59%, 97.89%, and 98.07%, with a heading stage test accuracy of 89.39%, 81.88%, 86.21%, and 84.21%.

Accurate prediction of food crop yield is of great significance for global food security and regional trade stability. Since remote sensing data collected from unmanned aerial vehicle (UAV) platforms have the features of flexibility and high resolution, these data can be used as samples to develop regional regression models for accurate prediction of crop yield at a field scale. The primary objective of this study was to construct regional prediction models for winter wheat yield based on multi-spectral UAV data and machine learning methods. Six machine learning methods including Gaussian process regression (GPR), support vector machine regression (SVR) and random forest regression (RFR) were used for the construction of the yield prediction models. Ten vegetation indices (VIs) extracted from canopy spectral images of winter wheat acquired from a multi-spectral UAV at five key growth stages in Xuzhou City, Jiangsu Province, China in 2021 were selected as the variables of the models. In addition, in situ measurements of wheat yield were obtained in a destructive sampling manner for prediction algorithm modeling and validation. Prediction results of single growth stages showed that the optimal model was GPR constructed from extremely strong correlated VIs (ESCVIs) at the filling stage (R2 = 0.87, RMSE = 49.22 g/m2, MAE = 42.74 g/m2). The results of multiple stages showed GPR achieved the highest accuracy (R2 = 0.88, RMSE = 49.18 g/m2, MAE = 42.57 g/m2) when the ESCVIs of the flowering and filling stages were used. Larger sampling plots were adopted to verify the accuracy of yield prediction; the results indicated that the GPR model has strong adaptability at different scales. These findings suggest that using machine learning methods and multi-spectral UAV data can accurately predict crop yield at the field scale and deliver a valuable application reference for farm-scale field crop management.

The convolutional neural network (CNN) method has been widely used in the classification of hyperspectral images (HSIs). However, the efficiency and accuracy of the HSI classification are inevitably degraded when small samples are available. This study proposes a multidimensional CNN model named MDAN, which is constructed with an attention mechanism, to achieve an ideal classification performance of CNN within the framework of few-shot learning. In this model, a three-dimensional (3D) convolutional layer is carried out for obtaining spatial–spectral features from the 3D volumetric data of HSI. Subsequently, the two-dimensional (2D) and one-dimensional (1D) convolutional layers further learn spatial and spectral features efficiently at an abstract level. Based on the most widely used convolutional block attention module (CBAM), this study investigates a convolutional block self-attention module (CBSM) to improve accuracy by changing the connection ways of attention blocks. The CBSM model is used with the 2D convolutional layer for better performance of HSI classification purposes. The MDAN model is applied for classification applications using HSI, and its performance is evaluated by comparing the results with the support vector machine (SVM), 2D CNN, 3D CNN, 3D–2D–1D CNN, and CBAM. The findings of this study indicate that classification results from the MADN model show overall classification accuracies of 97.34%, 96.43%, and 92.23% for Salinas, WHU-Hi-HanChuan, and Pavia University datasets, respectively, when only 1% HSI data were used for training. The training and testing times of the MDAN model are close to those of the 3D–2D–1D CNN, which has the highest efficiency among all comparative CNN models. The attention model CBSM is introduced into MDAN, which achieves an overall accuracy of about 1% higher than that of the CBAM model. The performance of the two proposed methods is superior to the other models in terms of both efficiency and accuracy. The results show that the combination of multidimensional CNNs and attention mechanisms has the best ability for small-sample problems in HSI classification.

The leaf area index (LAI) is of great significance for crop growth monitoring. Recently, unmanned aerial systems (UASs) have experienced rapid development and can provide critical data support for crop LAI monitoring. This study investigates the effects of combining spectral and texture features extracted from UAS multispectral imagery on maize LAI estimation. Multispectral images and in situ maize LAI were collected from test sites in Tongshan, Xuzhou, Jiangsu Province, China. The spectral and texture features of UAS multispectral remote sensing images are extracted using the vegetation indices (VIs) and the gray-level co-occurrence matrix (GLCM), respectively. Normalized texture indices (NDTIs), ratio texture indices (RTIs), and difference texture indices (DTIs) are calculated using two GLCM-based textures to express the influence of two different texture features on LAI monitoring at the same time. The remote sensing features are prescreened through correlation analysis. Different data dimensionality reduction or feature selection methods, including stepwise selection (ST), principal component analysis (PCA), and ST combined with PCA (ST_PCA), are coupled with support vector regression (SVR), random forest (RF), and multiple linear regression (MLR) to build the maize LAI estimation models. The results reveal that ST_PCA coupled with SVR has better performance, in terms of the VIs + DTIs (R2 = 0.876, RMSE = 0.239) and VIs + NDTIs (R2 = 0.877, RMSE = 0.236). This study introduces the potential of different texture indices for maize LAI monitoring and demonstrates the promising solution of using ST_PCA to realize the combining of spectral and texture features for improving the estimation accuracy of maize LAI.

Abstract This contribution is the first of a two-part investigation into an unseasonal post-sunset equatorial F-region irregularity (EFI) event over the Southeast Asian region on the evening of 28 July 2014. Ground-based GPS scintillation data, space-based GPS radio occultation (RO) data, and ionosonde data show the existence of EFIs shortly after sunset over a region spanning 30° in longitude and 40° in latitude, centered on the geomagnetic equator. This post-sunset EFI event was observed during a time of the year when post-sunset equatorial plasma bubbles (EPBs) are very infrequent in the Southeast Asian longitude sector. GPS RO data shows that the EFI event over Southeast Asia coincided with the suppression of peak-season EPBs in the African and Pacific longitude sectors. Ionosonde data shows the presence of a strong pre-reversal enhancement (PRE) in the upward plasma drift over Southeast Asia prior to the detection of EFIs. Further, it is reported that this PRE was significantly stronger than on any other day of July 2014. An analysis of the geophysical conditions during this event reveals that this enhanced PRE was not caused by disturbed geomagnetic activity. Therefore, it is hypothesized that forcing from lower altitudes, perhaps tidal/planetary waves, was the potential cause of this strong PRE, and the subsequent EPB/EFI activity, on this day over the Southeast Asian sector.

This study investigated the relationship between variations in the atmospheric boundary layer height (ABLH) and typhoons over the Northwest Pacific using global navigation satellite system (GNSS) radio occultation (RO) data during the local summer typhoon season (July–October in the Northern Hemisphere) from 2007 to 2020. The minimum gradient of refractivity derived from COSMIC and COSMIC-2 was used to determine the ABLH. The RO profiles were co-located with the position of a typhoon track base within a 600 km space window and different time windows. ABLH climatology with a 2.5° × 2.5° horizontal resolution was developed, which can be used to obtain the interpolated mean ABLH at any target position. The mean ABLH at the central typhoon position in a specific year was compared with the results interpolated from the climatology of the same location (excluding the year in which the investigated typhoon occurred). In this paper, the results indicate that the ABLH is lower in the vicinity of typhoons relative to the undisturbed atmosphere by a significant amount, and that the reduction in ABLH ranges from 0.13 km to 0.39 km. It was also found that the ABLH was negatively correlated with wind speed, and that the mean correlation coefficient was −0.607. Moreover, similar results can be obtained via the RO water vapor partial pressure profile compared to the refractivity results.

Although numerous validations for the ionospheric peak parameters values (IPPVs) obtained from the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) have been conducted using ionosonde measurements as a reference, comprehensive evaluations of the quality of the COSMIC-2 data are still undesirable, especially under geomagnetic storm conditions. In this study, the IPPVs measured by ionosondes (Ramey, Boa Vista, Sao Luis, Jicamarca, Cachoeira Paulista, and Santa Maria) during the period 1 October 2019 to 31 August 2021, are used to evaluate the quality of COSMIC-2 data over low-latitude regions of the Americas. The results show that the NmF2 (hmF2) from COSMIC-2 agrees well with the ionosonde measurements, and the correlation coefficients for the two sets of data at the above six stations are 0.93 (0.84), 0.91 (0.85), 0.91 (0.88), 0.88 (0.79), 0.96 (0.83), and 0.96 (0.87), respectively. The data quality of COSMIC-2 derived NmF2 is largely dependent on geomagnetic latitude. It was also found that NmF2 derived from COSMIC-2 tends to be underestimated over the stations in Boa Vista and Cachoeira Paulista, which are close to the crests of the equatorial ionization anomaly (EIA), whilst that of the other stations is slightly overestimated. A comparison between COSMIC-measured and ionosonde-derived hmF2 indicates that the former is systematically higher than the latter. In addition, the differences in the two NmF2 datasets derived from COSMIC-2 and ionosonde measurements at night are generally smaller than those of daytime, when the EIA is well developed, and vice versa for hmF2, whose RMSE is slightly smaller during daytime (with the exception of Ramey). Furthermore, NmF2 obtained from COSMIC-2 is shown to perform best in summer at Ramey, Boa Vista, Sao Luis, and Santa Maria, best in winter at Jicamarca and Cachoeira Paulista. Finally, the COSMIC-2 electron densities capture the ionospheric dynamic enhancements under a moderate geomagnetic storm condition very well.

Abstract Many thermospheric mass density (TMD) variations have been recognized in observations and physical simulations; however, their impact on the low‐Earth‐orbit satellites has not been fully evaluated. The present study investigates the quantitative impact of periodic spatiotemporal TMD variations modulated by the empirical DTM2013 model. Also considered are two small‐scale variations, that is, the equatorial mass anomaly and the midnight density maximum, which are reproduced by the Thermosphere‐Ionosphere‐Electrodynamics General Circulation Model. This investigation is performed through a 1‐day orbit prediction (OP) simulation for a 400‐km circular orbit. The results show that the impact of TMD variations during solar maximum is 1 order of magnitude larger than that during solar minimum. The dominant impact has been found in the along‐track direction. Semiannual and semidiurnal variations in TMD exert the most significant impact on OP among the intra‐annual and intradiurnal variations, respectively. The zero mean periodic variations in TMD may not significantly affect the predicted orbit but increase the orbital uncertainty if their periods are shorter than the time span of OP. Additionally, the equatorial mass anomaly creates a mean orbit difference of 50 m (5 m) with a standard deviation of 30 m (3 m) in 1‐day OP during high (low) solar activity. The midnight density maximum exhibits a stronger impact in the order of 150±30 and 15±6 m during solar maximum and solar minimum, respectively. This study makes clear that careful selection of TMD variations is of great importance to balance the trade‐off between efficiency and accuracy in OP problems.