This study explores the use of CYGNSS satellite data for detecting cyanobacterial harmful algal blooms (HABs). CYGNSS's unique capabilities in measuring surface reflectivity are harnessed to identify the telltale signs of algal blooms, offering a significant improvement over the traditional observation method. Preliminary results demonstrate that machine learning algorithms applied to CYGNSS data provide effective and timely detection of HABs. This method is promising for enhancing global water quality monitoring and management.

Understanding soil moisture dynamics is crucial for ecosystem health. Microwave remote sensing-derived surface soil moisture (SSM) products often have coarse spatial resolutions, necessitating downscaling for finer spatial resolution data. This study employs a CNN-based method to fill gaps in SMAP data, generating seamless 9-km soil moisture data. We then integrate these data with kilometer-scale gridded soil moisture products and use a random forest algorithm to produce seamless 1-km soil moisture data. Comparative analysis with in-situ station data shows an RMSE value of 0.083 m³/m³. Subsequently, both the downscaled soil moisture data and existing Solar-Induced Fluorescence (SIF) products are resampled to 0.05°, and their relationship is analyzed. The analysis shows that over 37% of the area has a correlation coefficient greater than 0.5 between the two datasets. This indicates a dynamic correlation between SSM and SIF, suggesting potential interactions between soil moisture behavior and vegetation activity. Further exploration of these interactions could enhance our understanding of ecosystem dynamics.

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.

The use of deep learning (DL) based hyperspectral image (HSI) classification has been made remarkable progress in recent years. However, obtaining sufficient labeled samples for training DL models remains a challenge. Transfer learning is effective in addressing the problem of HSI classification with limited labeled samples. However, cross-domain HSI classification using transfer learning remain difficult, as differences in ground object categories between two datasets make it challenging to transfer and learn accurate. To address this issue, we propose a simple yet effective method for HSI classification using Model-Agnostic Meta-Learning (MAML) and Regularized Fine-tuning (MRFSL). Our method uses optimized 3-Dimension Convolutional Neural Networks (3D-CNNs) model, aided by MAML and cutout data augmentation to enable cross-domain transfer learning and carry out the HSI classification with limited target samples. Experiments conducted on three HSI datasets demonstrate that the MRFSL method achieves excellent results compared to existing methods. Specifically, the overall accuracy of our proposed MRFSL method reached 91.81%, 71.04%, and 88.35%, when only five labeled samples for each category were randomly extracted from the Salinas, Indian Pines, and University of Pavia datasets, respectively.

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.

Accurate and high-frequency sea level monitoring is of great importance in ocean environments and global climatic studies, but traditional techniques have their respective limitations. In the last decades, the application of Global Navigation Satellite System Multipath Reflectometry (GNSS-MR) in sea level monitoring has developed rapidly. Recently, more available GNSS signals are expected to bring new opportunities to improve its performance and achieve high spatial-temporal resolution. In this paper, a new algorithm is developed to optimize the method of multi-GNSS multipath reflectometry and improve the precision and sampling rate for GNSS-MR sea level monitoring. In order to make full use of the short-term multipath oscillation information, a sliding window is used to collect the SNR sequences. A weighted iterative least-square method is introduced to combine the selected SNR observations of GPS, GLONASS, Galileo, and BDS systems, and retrieve sea level with 10-minute intervals at BRST station for one year. A novel index called Local Kurtosis (LK) is proposed, which can be used to evaluate the quality of the Lomb-Scargle periodogram (LSP) and design the weight matrix in the least-square combinatorial process. Compared to using individual signals, the optimized combination algorithm decreased the root mean square error (RMSE) by 78%, from 0.610 m to 0.134 m, and increased the correlation coefficient R2from 0.851 to 0.992. In addition, the tidal constituents monitored by multi-GNSS-MR and tide gauge are highly consistent, demonstrating that the multi-GNSS-MR can accurately retrieve daily and subdaily tidal constituents of periods longer than 10 min.

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.

GNSS-Reflectometry as a tool for remote sensing plays a key role in various applications. Recently, soil moisture and vegetation sensing in land field have attracted widespread interest. Little attention has been paid to sensing of these parameters using the reflectivity polarization ratio. In this work, airborne polarimetric measurements were carried out flying over areas with different geographical characteristics. Three polarimetric observables were used to investigate the vegetation and soil moisture fluctuation. Results show a good correlation with the type of terrain.