This study performs the first multiclass sea ice classification using global navigation satellite system (GNSS) reflectometry (GNSS-R) data from the Fengyun-3E (FY-3E) satellite. A principal component analysis (PCA) based denoising scheme for GNSS-R delay Doppler maps (DDMs) is proposed. The newly proposed processing methods are applied to FY-3E satellite tracks to retain the most significant features of the data. After removing noise from the DDMs using PCA, DDM classification features are calculated using methods established in the literature. An extremely randomized trees (ET) machine learning (ML) classifier was used to classify the sea ice using the denoised classification observables. The classification results are validated using labels derived from National Snow and Ice Data Center (NSIDC) sea ice concentration data. The output classification accuracy was 87.14% with a 0.7834 Kappa coefficient. Per class F1 scores were 89.72%, 85.53%, and 81.34% for first-year ice (FYI), multi-year ice (MYI), and thin ice (TI), respectively. The proposed method produced improvements across all performance metrics compared to classification without PCA based observables and denoising.

Research on estimating sea ice concentration (SIC) from synthetic aperture radar (SAR) data using convolutional neural networks (CNNs) has been widely reported. However, the presence of speckle noise in dual-polarization SAR signals and confusion at ice-water boundaries complicates accurate density estimation, often leading to significant underestimations of SIC. Recently, diffusion models (DMs) have shown significant success in various remote sensing tasks, demonstrating their potential to address these challenges. However, applying DMs directly to SIC estimation leads to singularity issues, hindering the accuracy of results. Additionally, directly incorporating conditional images can cause denoising models to overlook the differences between conditional and noise information. We introduce a conditional denoising diffusion probabilistic model (DiffSIC) that can fundamentally resolve the singularity problem by reweighting the loss function. We designed a U-shaped architecture that integrates conditional information, time steps, and noise information for SIC estimation. Extensive experiments conducted on the AI4Arctic dataset indicate that the proposed DiffSIC framework achieves a coefficient of determination (R2) of 90.959% and a root mean square error (RMSE) of 8.632%, demonstrating the effectiveness and potential of diffusion models in the task of SIC estimation.

This study introduces a method for filling the data gaps in the International Soil Moisture Network (ISMN) by soil moisture (SM) estimated using data from the Cyclone Global Navigation Satellite System (CYGNSS). The estimation process leverages the random forest (RF) algorithm, incorporating CYGNSS-derived products along with soil and surface parameters as input features. This research was conducted based on the daily SM data from the ISMN for the entire years of 2019 and 2020, which served as training and test datasets. Comparison experiments were performed to highlight the limitations of existing methods and SM products for gap filling in ISMN SM data. Subsequently, the optimal retrieval model was deployed to estimate SM for the duration of the study, thereby filling the gaps within the ISMN dataset. The SM results after gap filling showed strong consistency with measured SM, achieving an R-squared ($R^{2}$) of 0.7930 and a root-mean-square error (RMSE) of 0.0492 cm3/cm3. These results indicate that CYGNSS-based SM inversion is a promising approach to enhance the completeness of the ISMN dataset.

Recent advances in Cyclone Global Navigation Satellite System (CYGNSS) data have significantly improved the extraction of monthly surface water fraction (SWF), with neural networks being widely used for large-scale water body mapping based on GNSS-R signals. However, inherent noise in CYGNSS signals, such as multipath effects and interference, presents substantial challenges to the accuracy of SWF estimation. Diffusion models, an emerging class of generative deep learning techniques, have shown remarkable capabilities in capturing complex data distributions. By leveraging an iterative process of noise addition and removal, these models demonstrate significant advantages in processing low signal-to-noise ratio data, offering a novel methodology for precise SWF estimation from CYGNSS data. This study introduces DiffWater, a framework designed to address the unique characteristics of CYGNSS data and systematically explore the applicability of conditional diffusion models for remote sensing tasks. Utilizing a composite reference dataset, which includes the Global Surface Water (GSW) dataset and the Global Surface Water Dynamics (GLAD) dataset as training targets, DiffWater enhances the objectives of conditional diffusion models by integrating advanced conditional feature extractors and implementing multi-level fusion of conditional and temporal features, thereby achieving significant improvements in SWF estimation performance. Comprehensive experimental evaluations on the reference dataset demonstrate that DiffWater achieved the best performance, with a root mean square error (RMSE) of 4.987% and a correlation coefficient (R) of 0.946. Compared to state-of-the-art SWF estimation methods, the proposed approach demonstrated significant improvements in both quantitative and qualitative results.

Global Navigation Satellite System Reflectometry (GNSS-R) plays a crucial role in sea surface wind speed measurement, and convolutional neural networks (CNNs) have been a widely used method for wind speed retrieval from GNSS-R data. However, CNNs have limitations in global feature extraction due to the fixed convolutional kernels. Moreover, current studies on wind speed retrieval from GNSS-R data exhibit significant overfitting at low wind speed and underestimation at high wind speed due to the extremely imbalanced data distribution. To address these issues, a hybrid CNN-Transformer Network (CTN) with a weighted mean square error (MSE) loss is proposed in this study. Specifically, the designed CTN incorporates CNN and transformer encoder blocks to capture local and global features, with a dedicated feature fusion module to integrate these features. In addition, a novel weighted MSE loss function is designed to tackle the issue of imbalanced data distribution and enhance the estimation accuracy at high wind speeds. The proposed method is validated on Cyclone GNSS (CYGNSS) data and demonstrates improved performance compared with other machine learning algorithms. It achieves a root mean square difference (RMSD) of 1.417 m/s compared to the European Center for Medium-Range Weather Forecasts (ECMWF) reanalysis data, and 1.620 m/s compared to the buoy data from National Data Buoy Center (NDBC). Notably, the proposed model trained with the weighted MSE loss function shows an improvement of 20.3% in RMSD for samples with wind speeds exceeding 15 m/s, highlighting the effectiveness of the designed loss function in handling high wind speed data.

Masked Image Modeling (MIM) has made significant advancements across various fields in recent years. Previous research in the hyperspectral (HS) domain often utilizes conventional Transformers to model spectral sequences, overlooking the impact of local details on HS image classification. Furthermore, training models using raw image features as reconstruction targets entails significant challenges. In this study, we specifically focus on the reconstruction targets and feature modeling capabilities of the Vision Transformer (ViT) to address the limitations of MIM methods in the HS domain. As a proposed solution, we introduce a novel and effective method called LFSMIM, which incorporates two key strategies: (1) filtering out high-frequency components from the reconstruction target to mitigate the network’s sensitivity to noise, and (2) enhancing the local and global modeling capabilities of the ViT to effectively capture weakened texture details and exploit global spectral features. LFSMIM demonstrated superior performance in overall accuracy compared to other methods on the Indian Pines, Pavia University, and Houston 2013 datasets, achieving accuracies of 95.522%, 98.820%, and 98.160% respectively. The code will be made available at https://github.com/yuweikong/LFSMIM.

Arctic sea ice has long been a focal point of scientific research globally, with sea ice thickness (SIT) recognized as a critical parameter for predicting local marine environments, climate dynamics, and ensuring the safety of maritime transport. This study focuses on the retrieval of SIT, utilizing an established two-layer (sea ice-seawater) Global Navigation Satellite System-Reflectometry (GNSS-R) model and is extended to new data from Fengyun-3E (FY-3E) satellite. The research introduces an innovative empirical approach aimed at reducing reliance on ancillary data, allowing for stand-alone SIT retrieval solely based on GNSS-R data. This work underscores the potential for developing a practical semi-empirical model and parameterization scheme for SIT estimation through GNSS-R data. Furthermore, FY-3E has the capability to process signals from both the BeiDou Navigation Satellite System (BDS) and the Global Positioning System (GPS). Compared to the reference SIT, for the training set the root mean square error (RMSE) and correlation coefficient (r) between GPS-R SIT and reference are 0.1347 m and 0.8087 respectively, and for the test set, they are 0.1442 m and 0.7821. Based on BDS-R data, for the training set the RMSE andrare 0.1325 m and 0.8152, and for the test set, they are 0.1289 m and 0.8063, respectively. Experimental results indicate that BDS-based outcomes slightly outperform those obtained using GPS in the context of SIT retrieval.

Although the extensive research has been conducted on retrieving sea ice variables from synthetic aperture radar (SAR) and multimodal remote sensing data, cross-scene retrieval using regional training models remains a significant challenge. Previous studies have employed multi-task learning (MTL) but have not sufficiently explored the interplay between network architectures and multi-task performance. Moreover, self-supervised learning (SSL) has shown promise in improving tasks with limited training samples, though its potential in sea ice variable retrieval requires further study. To address the challenge of cross-scene retrieval of sea ice variables, we introduce a novel and effective method called multimodal fusion domain adaptation (MFDA), which combines three key strategies: 1) employ SSL methods for multimodal data to pretrain the model, improving its noise sensitivity and promoting a hierarchical understanding of multimodality; 2) propose a unified convolutional and Transformer-based data fusion architecture to enhance the integration of multimodal data and improve semantic understanding; and 3) incorporate a domain adaptation module between the multimodal encoder and the multi-task decoding predictor to facilitate the model’s understanding of the semantic gaps between different regional environments. The performance of the proposed MFDA has been extensively evaluated on the Ai4Arctic dataset. The experimental results demonstrate that MFDA achieves superior performance compared with other state-of-the-art (SOTA) sea ice classification approaches for the task of cross-scene sea ice retrieval. The code will be made available at:https://github.com/yuweikong/MFDA.

A new method, which integrates multi-variable consisting of Soil Moisture (SM) Active Passive (SMAP)-derived SM and vegetation optical depth, the water seasonality, geolocation, digital elevation model, slope, and biomass as inputs and adopts the technique of Bootstrap Aggregation of Regression Trees (BARTs) is proposed for retrieving monthly surface water fraction (SWF) at a spatial resolution of 0.025° from Cyclone Global Navigation Satellite System (CYGNSS) data. The model is trained using Surface Water Microwave Product Series (SWAMPS) data with a coarser resolution of 25 km and then applied to CYGNSS data with an enhanced resolution of 0.025° to generate high-resolution water maps. The resulting CYGNSS SWF (CSWF) maps are evaluated by comparing them with other water data sources, namely SWAMPS, Global Surface Water (GSW), and Global surface water dynamics (GLAD), as well as ground measurements. A quadruple collocation analysis indicates that the CSWF results exhibit the lowest error variance among the four SWF datasets. Furthermore, additional testing with water level measurements demonstrates a strong correlation with station data and clear seasonal patterns. Notably, the CSWF estimates significantly improve spatial coverage compared to both optical data (GSW and GLAD) with enhanced spatial resolution and the coarser SWAMPS data. This study underscores the effectiveness and efficiency of CSWF estimates, highlighting their potential as a valuable complement to existing microwave- and optical-based surface water products.

The use of the Cyclone Global Navigation Satellite System (CYGNSS) for soil moisture (SM) estimation is of interest. However, the advantage of the variable resolution of CYGNSS was not fully utilized, leading to the loss of detailed information. Geographically Weighted Regression (GWR) permits the co-existence of diverse spatial relationships across different geographic regions, with the regression coefficient varying spatially rather than being globally constant, thus enabling coefficient adjustments within specific spatial boundaries. Advanced GWR-based SM estimation offers a significant improvement over other competing estimation models. This study demonstrated that the CYGNSS with high temporal and spatial resolution has the potential for high-resolution independent SM retrieval.

Algal bloom has become a serious environmental problem caused by the overpropagation of planktons in many water-bodies, and effective remote sensing methods for monitoring it are urgently needed. Global Navigation Satellite System (GNSS)-Reectometry (GNSS-R) has been developed rapidly these years. The reflected GNSS signals received by GNSS-R satellites carry information (e.g., the surface roughness) about the specular points. When algal bloom emerges, the water surface will turn smoother, which could be detected by GNSS-R. In addition, meteorological factors also perform a key role in the formation of algal bloom. In this article, a new machine learning aided GNSS-R algal bloom detection method with the auxiliary of meteorological data is established. This work employs the Cyclone GNSS (CYGNSS) data and the fifth generation of European Reanalysis data with the application of Random Under-Sampling Boost (RUS-Boost) algorithm. During the evaluation stage, the test True Positive Rate of 80%, overall accuracy of 84.1% and the Area Under (Receiver Operating Characteristic) Curve of 0.9 were achieved when all the considered GNSS-R observables and meteorological factors are involved. Meanwhile, the contribution of each meteorological factor was also evaluated.

The sensitivity of Cyclone Global Navigation Satellite System (CyGNSS) data to inland water bodies was well documented, however, its advantage over other sensors has seldom been reported. In this work, a semantic segmentation method is adopted for detecting inland water bodies using the CyGNSS data. The widely used LinkNet with the global attention mechanism (GAM) and atrous spatial pyramid pooling (ASPP), namely GA-LinkNet, is equipped to better extract water distributions. The performance comparison with an existing method and other deep networks proved the accuracy and effectiveness of this approach. Satisfactory agreement between the derived and referenced water masks was achieved, with the overall accuracy being 0.959 and 0.976, the mean intersection over union being 0.785 and 0.641, and the F1 scores being 0.879 and 0.781 for the Amazon and Congo regions, respectively. Furthermore, underestimation of water by the reference data was shown during evaluation, which proves the usefulness of the CyGNSS-derived water mask for improving the existing water mask products.

Global Navigation Satellite System-Reflectometry (GNSS-R) is a remote sensing technique and can be regarded as a bistatic radar system. GNSS-R uses GNSS signals as signal sources and obtains the Earth's surface environmental parameters, such as soil moisture (SM), by receiving the L-band microwave signal reflected from the Earth's surface. However, the surface vegetation could be one of the main factors influencing the accuracy of GNSS-R land applications since the plants, including branches and leaves, attenuate the GNSS signal. Also, the evaluation of signal attenuations caused by plant canopy is quite difficult. In this paper, we present a sensitivity study of received GPS signals (L1 and L2 bands) to the vegetation leaf area index (LAI) over different types of plants. The relationship of GPS signal Signal-to-noise ratio (SNR) attenuations (above-canopy and below-canopy) versus LAIs is established through field experiments. The results show that the SNR received at the L2 band is with a larger standard deviation (SD) than at the L1 band for each satellite. The sensitivity of L1 and L2 bands signal to LAI is revealed, which shows a larger sensitivity and a relatively good Person correlation coefficient (R) for lower vegetation biomass. In addition, the sensitivity of the L2 band signal to LAI is lower than the L1 band signal, and with a lower R. This study is significant for improving the quantitative representation of error estimations in GNSS-R SM retrieval.

The inland water bodies are critical components of ecosystems and hydrologic cycles. Thus, the water extent data are crucially important for hydrological and ecological studies. Due to its high temporal resolution, the Cyclone Global Navigation Satellite System (CYGNSS) has the potential for real-time inland water monitoring. In this letter, a high-resolution machine learning (ML) method for detecting inland water content using the CYGNSS data is implemented via the random undersampling boosted (RUSBoost) algorithm. The CYGNSS data of the year 2018 over the Congo and Amazon basins are gridded into$0.01^{\circ }\, \times \, 0.01^{\circ }$cells. The RUSBoost-based classifier is trained and tested with the CYGNSS data over the Congo basin. The data of the Amazon basin that is unknown to the classifier are then used for further evaluation. By only using the observables extracted from the CYGNSS data, the proposed technique is able to detect 95.4% and 93.3% of the water bodies over the Congo and Amazon basins, respectively. The performance of the RUSBoost-based classifier is also compared with an image processing-based inland water detection method. For the Congo and Amazon basins, the RUSBoost-based classifier has a 3.9% and 14.2% higher water detection accuracy, respectively.

This work presents a novel scheme to retrieve soil moisture (SM) from the Cyclone Global Navigation Satellite System (CyGNSS) data, which is accomplished by using a bagged regression trees (BRT) algorithm with the inputs being the CyGNSS-derived products, the corresponding geolocation, and associated climate type. This algorithm is validated with thein situhourly SM data acquired by China’s automatic SM observation stations throughout the year 2018. High consistency between the retrieved SM results and the measured SM is achieved, with a correlation coefficient of 0.86 and a root-mean-square error of 0.05 cm3/cm3. The results obtained in this work indicate that the proposed BRT-based method can effectively estimate SM from CyGNSS data in different scenarios of various station locations and climate types in a near real-time manner.

Global Navigation Satellite System-Reflectometry (GNSS-R) can retrieve Earth's surface parameters, such as soil moisture (SM) using the reflected signals transmitted from GNSS constellations. GNSS-R has advantages of non-contact, large coverage area, real-time, and continuity. The CYclone GNSS (CYGNSS) data used for SM retrieval have generated considerable interests. In this paper, estimating SM on a global scale is performed using machine learning (ML) regression. The the optimal XGBoost predicted model with root mean square error (RMSE) of 0.064 cm3/cm3is adopted. In addition, satisfactory daily SM estimation outcome with an overall correlation coefficient value of 0.86 is achieved at a global scale.

In this paper, a new scheme is proposed for simultaneously retrieving soil moisture (SM) and vegetation optical depth ($\tau$), solely from the Cyclone Global Navigation Satellite System (CyGNSS) data. This work is accomplished by employing two pre-trained neural networks as well as a brute-force searching. By adopting the proposed method, the posterior$\text{SM}/\tau$can be estimated merely using the CyGNSS data, free from other auxiliary data. Satisfactory agreements between the retrieved and referred$\text{SM}/\tau$illustrates the capability of CyGNSS as a new independent source for estimating pantropical SM and$\tau$.

In this paper, the sensitivity of Cyclone Global Navigation Satellite System (CYGNSS) data to precipitation is investigated. First, the soil moisture (SM) is estimated from the CYGNSS and Soil Moisture Active Passive (SMAP) data based on a three-layer model. Next, the correlation between the CYGNSS-derived global SM and the precipitation rate is analyzed. The CYGNSS data collected over the land surfaces within ±37° (latitude) during the whole year of 2018 are employed and the CPC Merged Analysis of Precipitation (CMAP) data are adopted. Experimental evaluation proves the sensitivity of CYGNSS-derived SM to precipitation, indicating possible applications of CYGNSS data for detecting rainfall events and estimating precipitation.

In this paper, an approach for estimating soil moisture (SM) from Cyclone Global Navigation Satellite System (CYGNSS) data is developed. Here, a three-layer model of air, vegetation cover, and soil is proposed. In application, the surface reflectivity along with its statistics are derived from the CYGNSS data and the ancillary vegetation opacity data are obtained from Soil Moisture Active Passive (SMAP). These variables are adopted for estimating SM using the devised linear regression function. Through comparing with the reference SM data obtained from SMAP, CYGNSS-derived SM demonstrates its satisfactory accuracy and plausible global coverage. The achieved results prove CYGNSS as an efficient complementary tool for global daily SM sensing.

In this letter, a scheme that uses convolutional neural networks (CNNs) is proposed for sea ice detection and sea ice concentration (SIC) prediction from TechDemoSat-1 Global Navigation Satellite System Reflectometry delay-Doppler maps (DDMs). Specifically, a classification-orientated CNN was designed for sea ice detection and a regression-based one for SIC estimation. Here, DDM images were used as input, and SIC data from Nimbus-7 Scanning Multi-Channel Microwave Radiometer and Defense Meteorological Satellite Program Special Sensor Microwave Imager-Special Sensor Microwave Imager/Sounder sensors were modified as targeted output. In the experimental phase, the CNN output resulted from inputting full-size DDM data (128-by-20 pixels) showed better accuracy than that of the existing NN-based method. Besides, both CNNs and NNs with further processed input data (40-by-20 pixels, and with a fixed position in each image) were evaluated and the performance of both networks was enhanced. It was found that when DDM data are adequately preprocessed, CNNs and NNs share similar accuracy; otherwise the former outperforms the latter. Further conclusion was thus drawn that CNNs were more tolerant to the data format changes than NNs.

A formulation of the relationship between sea-surface roughness and extension of the glistening zone (GZ) of a Global Navigation Satellite System Reflectometry (GNSS-R) system is presented. First, an analytical expression of the link between GZ area, viewing geometry, and surface mean square slope (MSS) is derived. Then, a strategy for retrieval of surface roughness from the delay-Doppler map (DDM) is illustrated, including details of data preprocessing, quality control, and GZ area estimation from the DDM. Next, an example for application of the proposed approach to spaceborne GNSS-R remote sensing is provided, using DDMs from the TechDemoSat-1 mission. The algorithm is first calibrated using collocated in situ roughness estimates using data sets from the National Data Buoy Center, its retrieval performance is then assessed, and some of the limitations of the suggested technique are discussed. Overall, good correlation is found between buoy-derived MSS and estimates obtained using the proposed strategy ($r=0.73$ ).