Multi-satellite Fusion Research Articles

Enhancing Surface Water Monitoring through Multi-Satellite Data-Fusion of Landsat-8/9, Sentinel-2, and Sentinel-1 SAR

Long revisit intervals and cloud susceptibility have restricted the applicability of earth observation satellites in surface water studies. Integrating multiple satellites offers potential for more frequent observations, yet combining different satellite sources, particularly optical and SAR satellites, presents complexities. This research explores the data-fusion potential and limitations of Landsat-8/9 Operational Land Imager (OLI), Sentinel-2 Multispectral Instrument (MSI), and Sentinel-1 Synthetic Aperture (SAR) satellites to enhance surface water monitoring. By focusing on segmented surface water images, we demonstrate that combining optical and SAR data is generally effective and straightforward using a simple statistical thresholding algorithm. Kappa coefficients(κ) ranging from 0.80 to 0.95 indicate very strong harmony for integration across reservoirs, lakes, and river environments. In vegetative environments, integration with S1SAR shows weak harmony, with κ values ranging from 0.27 to 0.45, indicating the need for further studies. Global revisit interval maps reveal significant improvement in median revisit intervals from 15.87 to 22.81 days using L8/9 alone, to 4.51 to 7.77 days after incorporating S2, and further to 3.48 to 4.62 days after adding S1SAR. Even during wet season months, multi-satellite fusion maintained the median revisit intervals to less than a week. Maximizing all available open-source earth observation satellites is integral for advancing studies requiring more frequent surface water observations, such as flood, inundation, and hydrological modeling.

In-Situ GNSS-R and Radiometer Fusion Soil Moisture Retrieval Model Based on LSTM

Global navigation satellite system reflectometry (GNSS-R) is a remote sensing technology of soil moisture measurement using signals of opportunity from GNSS, which has the advantages of low cost, all-weather detection, and multi-platform application. An in situ GNSS-R and radiometer fusion soil moisture retrieval model based on LSTM (long–short term memory) is proposed to improve accuracy and robustness as to the impacts of vegetation cover and soil surface roughness. The Oceanpal GNSS-R data obtained from the experimental campaign at the Valencia Anchor Station are used as the main input data, and the TB (brightness temperature) and TR (soil roughness and vegetation integrated attenuation coefficient) outputs of the ELBARA-II radiometer are used as auxiliary input data, while field measurements with a Delta-T ML2x ThetaProbe soil moisture sensor were used for reference and validation. The results show that the LSTM model can be used to retrieve soil moisture, and that it performs better in the data fusion scenario with GNSS-R and radiometer. The STD of the multi-satellite fusion model is 0.013. Among the single-satellite models, PRN13, 20, and 32 gave the best retrieval results with STD = 0.011, 0.012, and 0.007, respectively.

Vehicle Information Acquisition and Communication System Based on Multi-Satellite Fusion, CAN Bus and GPS

This paper proposes a GPS and GPRS-based multifunctional vehicle terminal design scheme, which uses an embedded platform based on S3C2440 (bit 23 microprocessor of ARM920T core) and operating system, expands GPS navigation module and GPRS module, video surveillance The module integrates the CAN bus interface to realize vehicle navigation, wireless Internet access, CAN bus communication, and video monitoring functions, and fully meets people's requirements for car safety, convenience and comfort. The GPRS navigation module selects the JP13 receiver, and completes the related hardware circuit design. The navigation system adopts navigation software design based on the eSuperMap platform, which can realize electronic map display and operation, satellite positioning information display, path analysis, navigation and other functions.

GNSS Spoofing Suppression Based on Multi-Satellite and Multi-Channel Array Processing

The endless spoofing interference affects the credibility of the navigation system seriously. In order to suppress the forward spoofing which is more threatening to military signals in GNSS, this paper proposes a spoofing suppression algorithm based on angle of arrival estimation and multi-satellite fusion. On the basis of successfully suppressing the spoofing signal, the algorithm improves the estimation accuracy of the angle of arrival of the forwarded spoofing and reduces the attenuation of the carrier to noise ratio of the real satellite signal. Finally, the effectiveness of the algorithm is verified by simulation, which has guiding significance for the anti-spoofing research of GNSS.

CubeSat constellations provide enhanced crop phenology and digital agricultural insights using daily leaf area index retrievals

Satellite remote sensing has great potential to deliver on the promise of a data-driven agricultural revolution, with emerging space-based platforms providing spatiotemporal insights into precision-level attributes such as crop water use, vegetation health and condition and crop response to management practices. Using a harmonized collection of high-resolution Planet CubeSat, Sentinel-2, Landsat-8 and additional coarser resolution imagery from MODIS and VIIRS, we exploit a multi-satellite data fusion and machine learning approach to deliver a radiometrically calibrated and gap-filled time-series of daily leaf area index (LAI) at an unprecedented spatial resolution of 3 m. The insights available from such high-resolution CubeSat-based LAI data are demonstrated through tracking the growth cycle of a maize crop and identifying observable within-field spatial and temporal variations across key phenological stages. Daily LAI retrievals peaked at the tasseling stage, demonstrating their value for fertilizer and irrigation scheduling. An evaluation of satellite-based retrievals against field-measured LAI data collected from both rain-fed and irrigated fields shows high correlation and captures the spatiotemporal development of intra- and inter-field variations. Novel agricultural insights related to individual vegetative and reproductive growth stages were obtained, showcasing the capacity for new high-resolution CubeSat platforms to deliver actionable intelligence for precision agricultural and related applications.

Improved GNSS-Based Bistatic SAR Using Multi-Satellites Fusion: Analysis and Experimental Demonstration.

The Global Navigation Satellite System (GNSS)-based Bistatic Synthetic Aperture Radar (SAR) is getting more and more attention in remote sensing for its all-weather and real-time global observation capability. Its low range resolution results from the narrow signal bandwidth limits in its development. The configuration difference caused by the illumination angle and movement direction of the different satellites makes it possible to improve resolution by multi-satellite fusion. However, this also introduces new problems with the resolution-enhancing efficiency and increased computation brought about by the fusion. In this paper, we aim at effectively improving the resolution of the multi-satellite fusion system. To this purpose, firstly, the Point Spread Function (PSF) of the multi-satellite fusion system is analyzed, and focusing on the relationship between the fusion resolution and the geometric configuration and the number of satellites. Numerical simulation results show that, compared with multi-satellite fusion, dual-satellite fusion is a combination with higher resolution enhancement efficiency. Secondly, a method for dual-satellite fusion imaging based on optimized satellite selection is proposed. With the greedy algorithm, the selection is divided into two steps: in the first step, according to geometry configuration, the single-satellite with the optimal 2-D resolution is selected as the reference satellite; in the second step, the angles between the azimuthal vector of the reference satellite and the azimuthal vector of the other satellites were calculated by the traversal method, the satellite corresponding to the intersection angle which is closest to 90° is selected as the auxiliary satellite. The fused image was obtained by non-coherent addition of the images generated by the reference satellite and the auxiliary satellite, respectively. Finally, the GPS L1 real orbit multi-target simulation and experimental validation were conducted, respectively. The simulation results show that the 2-D resolution of the images produced by our proposed method is globally optimal 15 times and suboptimal 8 times out of 24 data sets. The experimental results show that the 2-D resolution of our proposed method is optimal in the scene, and the area of the resolution unit is reduced by 70.1% compared to the single-satellite’s images. In the experiment, there are three navigation satellites for imaging, the time taken to the proposed method was 66.6% that of the traversal method. Simulations and experiments fully demonstrate the feasibility of the method.

A method for GNSS-IR soil moisture inversion based on GPS multi-satellite and triple-frequency data fusion

A method for GNSS-IR soil moisture inversion based on GPS multi-satellite and triple-frequency data fusion

Analysis of BeiDou/Galileo combined pseudo-range positioning performance in urban environment

The observation conditions have a significant influence on the positioning performance. GNSS signals are easily blocked in urban environments, which leads to discontinuity and instability of position solution. With the construction of BeiDou and Galileo satellite navigation system in the final stage, multi-satellite fusion will be the future development trend of GNSSs. This paper introduces the mathematical fusion model of Galileo and BeiDou satellite navigation system, typical urban coverage conditions are simulated, and a detailed in the aspects of number of visible satellites, positioning error and PDOP values are analysed. The results show that the positioning accuracy of the integrated navigation system in the horizontal is within 3 m, the elevation is within 5 m, and the PDOP value are less than 1.5 in the open sky condition. In any coverage conditions, Galileo stand-alone system cannot guarantee the positioning continuity in the whole day, and positioning performance is the worst in the north coverage condition, unable to obtain the position solutions for more than 40% time in the whole day. In the north coverage condition, BeiDou has the best positioning performance, with a minimum of 4 visible satellites and 99% of PDOP values are less than 10.

Rolling estimation model of soil moisture based on multi-satellite fusion

Rolling estimation model of soil moisture based on multi-satellite fusion

Research on the soil moisture sliding estimation method using the LS-SVM based on multi-satellite fusion

ABSTRACTGlobal Navigation Satellite System Interferometric Reflectometry (GNSS-IR) is a new remote-sensing technique; it can be used to estimate near-surface soil moisture from signal-to-noise ratio (SNR) data. Considering the advantages of multi-satellite convergence and the time and space scale of soil moisture, based on multi-satellite fusion, non-linear sliding estimation method of soil moisture is proposed. Firstly, the direct and reflection signals of Global Navigation Satellite System (GNSS) satellites are separated by means of a low-order polynomial fitting, and then, by establishing the sinusoidal fitting model of reflection signals, the relative delay phase of the SNR interferogram is obtained. Finally, a linear regression model is used to analyse and select the phase of the SNR interferogram, and a sliding estimation method of soil moisture using the least squares Support Vector Machine based on multi-satellites fusion is established. Based on the monitoring data provided by the Plate Boundary Observations network, the feasibility and effectiveness of using single and multiple GNSS satellites to slide estimate soil moisture are compared and analysed. Theoretical analysis and experimentation show that GNSS-IR can provide full play to the advantages of artificial intelligence and effectively integrate the performance of each satellite. The fitting process is stable, and the model needs less modelling data; the sliding method can achieve a long time estimation, and the estimation error is relatively stable. Based on the satellites which the correlation coefficient (r) between the phase of the SNR interferogram and the soil moisture is greater than 0.50, the fusion satellites model is established; the results are better than that of using single satellites; the r between estimated results and reference values of soil moisture is greater than or equal to 0.94, and the root mean square error and mean absolute error are less than 0.060 and 0.050, respectively. Therefore, the estimation of soil moisture can be treated as a non-linear event, and multi- satellite fusion estimation is feasible and effective.

The quality analysis of GNSS satellite positioning data

Focused on the Beidou satellite system (BDS), dual satellite system (GPS/GLONASS) and three satellite system (BDS/GPS/GLONASS), the parameters influencing the quality of static and real-time dynamic positioning date of the GNSS systems, such as the multipath effect, ionospheric delay, data utilization rate and signal-to-noise ratio, number of satellites and accuracy factor PDOP value, have been comparatively analyzed in this paper. The results of tests have shown that compared to the single satellite system BDS the average multipath errors (mp1 and mp2) of multi satellite system fusion decreased by 18 and 8% respectively, due to the increase in the number of visible satellites in multi-satellite fusion, it is possible to compensate each other to a certain extent, thus weakening the influence of the multipath effect on the positioning results. When the cut-off angle is greater than 40°, the single system cannot meet the need of navigation and positioning, however having more visible satellites than the single system it is possible for multi satellite system fusion to maintain lower and more stable PDOP value for high precision positioning services.