A high-spatial-resolution hyperspectral image (HR-HSI) can be obtained by fusing a hyperspectral image and a multispectral image (HSI-MSI) since it takes the advantage of combing the low-spatial-resolution hyperspectral image (LR-HSI) and the high-spatial-resolution multispectral image (HR-MSI). Currently, most HSI-MSI fusion methods decompose HSIs in both spatial and spectral domains, which destroys the correlation of the two domains and results in poor fusion results. To further explore the correlation of spatial and spectral information, an unsupervised blind spectral-spatial cross-super-resolution network (CSRNet) is proposed for HSI-MSI fusion. The network transforms the fusion into two super-resolution reconstructions for a HR-HSI to avoid the decomposition of HSIs and achieves interaction with the two reconstructions to provide strong constraints for each other. The network uses the following procedure. First, the HSI-MSI fusion is transformed into a spectral-spatial cross-super-resolution model, which consists two branches: one for spatial and the other for spectral super-resolution reconstructions. As the basic blocks of the two branches, spatial super-resolution (SpaSR) and the spectral super-resolution (SpeSR) blocks in the new network are designed via iterations unfolding from half quadratic splitting (HQS). Then their corresponding spatial constraint (SpaC) and spectral constraint (SpeC) modules are established for the mutual constraints and interactions between the two branches. The SpaSR/SpeSR and SpaC/SpeC modules are alternately connected to form the above spatial/spectral super-resolution branches for reconstructing a HR-HSI. Both visual and quantitative results of experiments based on both simulated and real observation datasets showed that the proposed method outperformed seven commonly used methods, suggesting that the new method is effective for preserving the correlation of spatial and spectral information in HR-HSIs.

Precipitable water vapor (PWV), as an important representative parameter of atmospheric water vapor contents, can be obtained by means of Global Navigation Satellite Systems (GNSS) using both ground-based and space-borne observation techniques. However, the PWV prediction models currently accessible tend to be simplistic combinations or individual models. In this study, we develop a WOA-CNN-LSTM-Attention model to predict PWV, which takes the sixteen GNSS PWV values near the HKKP station as characteristic parameters and the spatial relationship between the point of interest and its neighboring GNSS stations into consideration. An optimal model via the whale optimization algorithm (WOA) is investigated by using a wavelet analysis to separate noises, through combining convolutional neural network (CNN), long short-term memory neural network (LSTM) and attention mechanism. Results show that considerable improvement in the prediction accuracy has been achieved through a comparison between CNN-LSTM-Attention and the conventional LSTM and CNN-LSTM models. In terms of long-term predictability, CNN-LSTM-Attention is proven to be a superior model when 8 features are incorporated. The model’s root mean square error (RMSE) is 2.30 mm which is reduced by 20.42 % than in the case of 0 feature is used. As a further analysis, we also examine the prediction performance of various models for hourly PWV using 7, 15, 30, 60 and 90 days of data as different lengths of training. The results show that CNN-LSTM-Attention has a better prediction effect when the training length is 30 days, the RMSE is 0.74 mm and the Nash-Sutcliffe efficiency coefficient (NSE) is 0.98.

Spatiotemporally seamless precipitable water vapor (PWV) maps with high accuracy, spatiotemporal resolution, and continuity are of significance for climatical research. The current frameworks for the PWV maps have predominantly concentrated on fusing PWV derived from ERA5 reanalysis (ERA-PWV) and Moderate Resolution Imaging Spectroradiometer (MODIS) near-infrared data (MOD-NIR-PWV), falling short in accuracy. In this study, PWV derived from global navigation satellite systems (GNSS-PWV) is introduced to produce spatiotemporally PWV maps with improved accuracy. The fusion framework involves two main steps: 1) spatial fusion for producing an initial PWV map with spatiotemporal continuity through spherical cap harmonic (SCH) analysis and 2) temporal fusion for producing a refined PWV map with high accuracy through residual correction. GNSS-PWV over 188 stations,$0.25^{\circ } \times 0.25^{\circ }$ERA-PWV, and$0.05^{\circ } \times 0.05^{\circ }$MOD-NIR-PWV over China from 2013 to 2018 are used to produce the daily$0.05^{\circ } \times 0.05^{\circ }$PWV maps. The performance is evaluated using out-of-sample data, containing 18 GNSS-PWV and 90 ERA-PWV, and independent reference data, containing radiosonde-derived PWV over 72 stations. When compared to the out-of-sample GNSS-PWV and ERA-PWV, the PWV maps exhibit the mean biases of −1.04 and −0.55 mm and the rms of 1.75 and 0.97 mm, respectively. These are equivalent to 8.7% and 32.1% reductions in bias and 25.5% and 49.5% reductions in RMSE relative to MOD-NIR-PWV. When radiosonde-derived PWV is used as the reference, the PWV maps have the mean bias and the RMSE of −0.53 and 2.21 mm, respectively, which outperforms ERA-PWV (−0.75 and 2.69 mm). These results indicate the effectiveness of the novel fusion framework in producing seamless PWV maps.

Water vapor (WV), as an essential climate variable, its content and migration process have significant implications for determining the intensity, time, and extent of various weather extremes. Hence, it is essential to conduct continuous, timely, and accurate monitoring of WV, especially its migration. Nowadays, the ground-based Global Navigation Satellite Systems (GNSS) tropospheric sounding technique has been effectively applied to sensing WV content. To advance its application, a new method for monitoring WV migration using GNSS-derived zenith total delay and tropospheric gradients is developed. The utility of the method was evaluated in the context of New Zealand to monitor the rapid migration of WV resulting from Cyclone Cody and the eruption of the Hunga Tonga-Hunga Ha’apai volcano over the period 15-16 January 2022. By using products obtained from a dense GNSS network with 40 stations, the determined isochrones clearly depict the migration direction and speed of WV. Results indicated that the WV moved southwestward and southeastward over the North and South Islands of New Zealand, respectively. It took about fifteen hours for the WV to pass over the study region, with a mean speed of 79.79 km/h.

This study utilizes L1B level data from reflected global navigation satellite system (GNSS) signals from the Cyclone GNSS (CYGNSS) mission to estimate sea surface significant wave height (SWH). The normalized bistatic radar cross Section (NBRCS), the leading edge slope (LES), the signal-to-noise ratio (SNR), and the delay-Doppler map average (DDMA) are used as the key variables for the SWH retrieval. Eight other parameters, including instrument gain and scatter area, are also utilized as auxiliary variables to enhance the SWH retrieval performance. A variety of multivariable regression models are investigated to clarify the relationship between the SWH and the variables by using the following five methods: stepwise linear regression, Gaussian support vector machine, artificial neural network, sparrow search algorithm–extreme learning machine, and bagging tree (BT). Results show that, among the five regression models developed, the BT model performs the best with the root mean square error (RMSE) of 0.48 m and the correlation coefficient (CC) of 0.82 when testing one million sets of data randomly selected, while the RMSE and CC of BT model are 0.44 m and 0.73 in the 4500 National Data Buoy Center (NDBC) buoy testing dataset. Meanwhile, the BT model also has the best generalization ability, which means that it performs well in practical applications. In addition, the impacts of different input variables, the size of the training dataset, and the sea surface wind speed are also investigated. These findings are anticipated to serve as helpful guides for creating future SWH retrieval algorithms that are more advanced.

Due to the limitations of hyperspectral optical imaging, hyperspectral images have a dilemma between spectral and spatial resolutions. Hyperspectral and multispectral image (HSI-MSI) fusion, which combines a low-spatial-resolution hyperspectral image (LR-HSI) and a high-spatial-resolution multispectral image (HR-MSI), can generate a high-spatial-resolution hyperspectral image (HR-HSI). In existing methods for hyperspectral and multispectral fusion, correlation between spectral and spatial domains in HSIs is mostly neglected. To address this issue, an unsupervised encoder-decoder network under spatial and spectral guidance for hyperspectral and multispectral image fusion (uEDSSG) was proposed in this study. To learn more accurate abundances of a LR-HSI and a HR-MSI, multi-hierarchical encoders under spatial and spectral guidance were designed to extract multi-hierarchical fused features from the LR-HSI and HR-MSI with the guidance of the HR-MSI and LR-HSI, respectively. In the new method, deep coupling of the point spread function (PSF) or spectral response function (SRF) and edge of the HSIs was designed to maintain the spatial and spectral details of the HR-HSI; a spatial-spectral constraint was constructed to establish the relationship of the HSIs. Both visual and quantitative evaluation results of experiments based on both synthetic and real datasets showed that the proposed method outperformed seven common methods. The results suggest that the new method by maintaining the correlation between spectral and spatial domains can improve the result of HSI-MSI fusion.

In recent years, global navigation satellite systems-reflectometry (GNSS-R) technology has been increasingly considered for applications in sea surface monitoring. This paper presents a new method to retrieve the density of sea surface green algae by using the reflected signals of geostationary Earth orbit (GEO) satellites collected by shipborne receiver. Because GEO satellites are stationary relative to a fixed receiver on the earth’s surface, the reflected GEO satellite (GEO-R) signals are not affected by Doppler frequency or elevation angle, which can greatly simplify the modeling of the reflected power and realize continuous green algae monitoring in the same area. Specifically, the influence of green algae on GEO-R power through varying reflection coefficient and roughness was analyzed. Then, an empirical model was established to retrieve the green algae density by using the GEO-R power. Finally, the experimental data collected in the Qingdao Jiaozhou bay were used to verify the developed models, and the results show that the inversion accuracy of the green algae density model is better than 4%.

Due to the continuous intensification of human activities in the ocean, the frequent outbreaks of red tide have caused great harm to the marine environment and ecology. Thus, the rapid detection and monitoring of red tide become particularly important. At present, the main monitoring methods depend on artificial and buoy data, as well as optical satellite remote sensing. However, these methods may not be able to effectively deal with the characteristics of red tide bloom, such as suddenness and unpredictability. The global navigation satellite system-reflectometry (GNSS-R) is an emerging technology that makes use of navigation signals as a remote sensing opportunity to obtain Earth surface information. GNSS-R has already been proved to be capable of retrieving sea surface parameters (e.g., dielectric constant and sea surface roughness) closely related to the outbreak of a red tide. In this article, we proposed a new method to estimate red tide density, which utilizes an all-new model associating GNSS-R observations with sea surface red tide density. This method can remove the weather influence and greatly decrease the revisit period, which is much longer for optical red tide remote sensing methods. The Landsat-8 near-infrared data and TechDemoSat-1 (TDS-1) GNSS-R data of a red tide outbreak in the sea off the Tsingtao coast in China are used to build and test the proposed method. The results demonstrate that the correlation coefficient is 0.73, and the root mean square error of retrieved red tide density is 2.84%, which shows that the GNSS-R technology shows great potential to perform the rapid and preliminary red tide monitoring and judgment.

In recent years, tropospheric products obtained from ground-based global navigation satellite system (GNSS) measurements, especially the zenith total delay (ZTD) and precipitable water vapor (PWV) estimates, have advanced their usages in meteorological applications such as the detection of precipitation events. Generally, a cumulative anomaly (CA) time series of any atmospheric variable, which represents the long-term departure of the variable from its “normal” cycle, is widely used for quantitatively estimating the variable’s variations in response to a weather event. In this study, a new cumulative anomaly-based model (NCAM) containing 14 variables, including not only PWV and ZTD values but also their respective six types of derivatives, for detecting heavy precipitation was developed. The 6-h CA time series of the variables were calculated based on the data of hourly precipitation records and time series of ZTD and PWV collected at the co-located HKSC–King’s Park (KP) stations over the eight-year period 2010–2017. The model was evaluated using the 14 variables’ CA time series to detect heavy precipitation events happened in the summer months over the period 2018–2019, and precipitation records in the same period were used as the reference. Results demonstrated that 99.1% of heavy precipitation was correctly detected by the NCAM with a lead time of 2.87 h, and the false alarm ratio (FAR) score resulting from the model was reduced to 22.4%. In addition, two case studies were also conducted to verify the effectiveness of the NCAM. These results all provide a promising direction for the application of using the CA time series of GNSS tropospheric products to the detection of heavy precipitation events.

For multi-temporal synthetic aperture radar (SAR) images, the change detection methods based on non-local theory can well suppress the adverse effects of coherent speckle noise over the change detection results. However, effectively retaining the edge information of the changed area is still a challenging task. To overcome this problem, this study proposes a change detection method based on progressive non-local theory. First, the progressive non-local theory is used to extract the spatial-temporal non-local information from multi-temporal SAR images. Compared with the traditional non-local theory, the progressive non-local theory proposed in this study has three distinctive characteristics: 1) the progressive non-local neighborhood from the matching window to the search window; 2) the progressive optimization of matching window weight from the isotropic Gaussian distribution to the irregular distribution; and 3) the progressive increase of noise level from the 2 sigma principle to the 4/3 sigma principle (the noise level corresponding to the 4/3 sigma principle is 1.5 times the noise level corresponding to the 2 sigma principle). The difference image is then obtained by using the spatial-temporal non-local information and the ratio operator. Finally, the change map is obtained by applying a threshold segmentation method to the difference image. Two data sets were used for the testing and it was shown that compared with other advanced methods, the method proposed in this study can better retain the edge information of the changed area and improve the Kappa coefficient and F1 score of the change map.

Compared with the synthetic aperture radar (SAR) image processing theory based on local neighborhood, the nonlocal theory is not limited to a local neighborhood of an image and has great potential in change detection of SAR images. In this study, an approach using ratio-based nonlocal information (RNLI) is proposed for change detection in multitemporal SAR images. First, the RNLI is extracted from a spatial–temporal nonlocal neighborhood where the similarity of two pixels in the nonlocal neighborhood is well characterized by the proposed ratio-based Gaussian kernel function. The parameters of RNLI: noise level and matching window size are adaptively determined to avoid the uncertainty of the change detection result caused by user experience. Second, the difference image is generated by using the RNLI and the ratio operator. Finally, the change map is obtained by segmenting the difference image with a threshold. Experiments conducted on two real datasets and two simulated datasets showed that the proposed method performed better than the other advanced change detection methods, which can better retain the edge information of the changed area while reducing the overall error of the change detection results.

In recent years, GNSS reflectometry (GNSS-R) research has mainly been focused on the Global Positioning System (GPS) while the use of Geostationary Earth Orbit (GEO) satellites has received little attention. This paper investigates the GEO satellite-based GNSS-R with a focus on the application of soil moisture retrieval. A new soil moisture estimation approach using GEO signals are proposed, which is termed GEO reflectometry (GEO-R). Two empirical models (linear and second-order) are developed for signal SNR ratio based GEO-R. Experimental datasets collected from different GEO systems were used to evaluate the proposed methods. The results demonstrate that the proposed GEO-R are able to monitor soil moisture reliably under bare soil condition, augmenting GNSS-R through significantly reduced processing complexity and increased temporal coverage.

Global Navigation Satellite System (GNSS) radio occultation (RO) has been widely used in the prediction of weather, climate, and space weather, particularly in the area of tropospheric analyses. However, one of the issues with GNSS RO measurements is that they are interfered with by the signals reflected from the earth's surface. Many RO events are subject to such interfered GNSS measurements, which are considerably difficult to extract from the GNSS RO measurements. To precisely identify interfered RO events, an improved machine learning approach-a gradient descent artificial neural network (ANN)-aided radio-holography method-is proposed in this paper. Since this method is more complex than most other machine learning methods, for improving its efficiency through the reduction in computational time for near-real-time applications, a scale factor and a regularization factor are also adjusted in the ANN approach. This approach was validated using Constellation Observing System for Meteorology, Ionosphere, and Climate/FC-3 atmPhs (level 1b) data during the period of day of year 172-202, 2015, and its detection results were compared with the flag data set provided by Radio Occultation Meteorology Satellite Application Facilities for the performance assessment and validation of the new approach. The results were also compared with those of the support vector machine method for improvement assessment. The comparison results showed that the proposed method can considerably improve both the success rate of GNSS RO reflection detection and the computational efficiency.

Anomalous GPS radio occultation (RO) events are characterised as those with L1 bending angle greater than their corresponding L2 bending angle. An investigation by EUMETSAT and the United Kingdom Meteorological Office revealed there are regions in the earth's atmosphere where at times up to 60% of Global Navigation Satellite System Receiver for Atmospheric Sounding RO events, at the Tangent Point height of 50 km, exhibited anomalous bending angle results. The exact source of these anomalous RO events has been unclear to the RO data user community, i.e., data processing artifact or atmospheric phenomenon. In this paper, the regions of increased occurrence of anomalous RO have been identified to be the mid-latitude ionospheric trough, ionospheric polar hole, and poleward edges of the equatorial anomaly. They are more frequent at nighttime and in the southern hemisphere winter months. This is when the plasma density in these regions is depleted. However, within these regions, there are ionospheric features of increased electron density gradients such as at the walls of the mid-latitude ionospheric trough. 3-D numerical ray tracing simulations of GPS RO are presented, showing that these increased electron density features in a weakly ionized ionosphere can produce the anomalous bending angle results.

Ray tracing is an important tool for the operation of GPS L-band frequency signal propagation; in particular GPS RO techniques where accurate and near real-time results are often required. Ray tracing techniques are commonly used for calculating the path of an electromagnetic signal in a medium specified by a position dependent refractive index, such as the Earth's atmosphere. In this study three dimensional numerical ray tracing techniques are used to simulate GPS L-band signals received by the LEO satellites and primarily focusing on regions of increased electron density gradients such as the equatorial anomaly. The down range refractive gradients on the GPS to LEO signal paths will be investigated. The ionosphere is a birefringent medium due to the presence of the Earth's magnetic field. The effects of the Earth's magnetic field on the transmitted signals and the paths of the ordinary and extraordinary signals are investigated.

The Australian Low Earth Orbit (LEO) microsatellite, FedSat (named to commemorate the centenary of the Australian Federation in 2001), was launched into orbit on December 14, 2002 from the Tanegashima Space Centre, Japan. A Global Positioning System (GPS) receiver was one of the instruments onboard. The received GPS signals can be used to investigate the ionospheric electron density and the atmosphere below FedSat's orbiting altitude, using radio occultation (RO) techniques. The RO technique developed involves a simplified form of the Abel transform using the slant total electron content (STEC) determined from radio signals that traverse below FedSat's orbiting altitude. Electron density profiles from the GPS RO data, recorded by the GPS receiver onboard the FedSat satellite, are determined for the first time. The technique combined with simultaneous occultation density profile extraction from different LEO satellites and satellite navigation systems has the potential to image near real-time 3-D structures of the ionospheric electron density.

GPS radio occultation, (RO) is an emerging and robust space-based earth observation system, with the potential for atmospheric profiling and meteorological applications. GPS RO requires GPS receivers onboard Low Earth Orbit (LEO) satellites to measure the radio signals from GPS satellites so that the atmospheric profiles of parameters such as temperature, pressure and water vapour can be obtained via a complicated atmospheric retrieval process. This research focuses on the ionospheric correction using the Radio Occultation Processing Package (ROPP) to investigate the effect from ionosphere for GPS radio occultation in the Australia region. The MSISE-90 model with the statistical optimization method produced the best results for altitudes greater than 40 km. The influence from the ionosphere can be removed using the generic Lc method which produced the best results for altitudes less than 40 km.