Growing interest in Earth Orientation Parameters (EOP) resulted in various approaches to the EOP prediction algorithms, as well as in the exploitation of distinct input data, including the observed EOP values from various operational data centers and modeled effective angular momentum functions. Considering these developments and recently emerged new methodologies, the Second Earth Orientation Parameters Prediction Comparison Campaign (2nd EOP PCC) was pursued in 2021–2022. The campaign was led by Centrum Badań Kosmicznych Polskiej Akademii Nauk in cooperation with Deutsches GeoForschungsZentrum and under the auspices of the International Earth Rotation and Reference Systems Service. This paper provides the analysis and evaluation of the polar motion predictions submitted during the 2nd EOP PCC with the prediction horizons between 10 and 30 days. Our analysis shows that predictions are highly reliable with only a few occasional discrepancies identified in the submitted files. We demonstrate the accuracy of EOP predictions by (a) calculating the mean absolute error relative to polar motion observations from September 2021 through December 2022 and (b) assessing the stability of the predictions in time. The analysis shows unequal results for the x and y components of polar motion (PMx and PMy, respectively). Predictions of PMy are usually more accurate and have a smaller spread across all submitted files when compared to PMx. We present an analysis of similarity between the participants to indicate what methods and input data give comparable output. We also prepared the ranking of prediction methods for polar motion summarizing the achievements of the campaign.

Data fusion is an effective method to obtain high-precision and high-spatiotemporal-resolution precipitable water vapor (PWV) products, which play an important role in understanding climate change and meteorological monitoring. However, existing fusion methods have some shortcomings, such as ignoring the applicability of the model space or the high complexity of model operation. In this study, the high-precision and high-temporal-resolution Global Navigation Satellite System (GNSS) PWV was used to calibrate and optimize the ERA5 PWV product of the European Center for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) with high spatial resolution to improve its accuracy, and its applicability was verified at the spatiotemporal scale. First, this study obtained accurate GNSS PWV from meteorological data from stations and used it as the true value to analyze the distribution of the ERA5 PWV in mainland China. The results showed that the ERA5 PWV showed significant spatial and temporal differences. Then, a backpropagation neural network (BPNN) fusion correction model with additional constraints was established. The correction results showed that the bias of the ERA5 PWV mainly fluctuated near 0, the correlation between the ERA5 PWV and GNSS PWV was increased to 0.99, and the positive improvement rate of the root-mean-square error (RMSE) was 95%. In the temporal scale validation, the RMSE of the ERA5 PWV decreased from 2.05 mm to 1.67 mm, an improvement of 18.54%. In the spatial scale validation, the RMSE of the four seasons decreased by 0.26–80% (spring), 0.28–70.71% (summer), 0.28–45.23% (autumn), and 0.30–40.75% (winter). Especially in the summer and plateau mountainous areas where the ERA5 PWV performance was poor, the model showed suitable stability. Finally, the fusion model was used to generate a new PWV product, which improved the accuracy of ERA5 PWV on the basis of ensuring the spatial resolution.

The pure inertial navigation system (INS) contains three inherent periodic oscillations, namely, Schuler periodic oscillation (SPO, 84.4 min), Foucault periodic oscillation (FPO), and Earth periodic oscillation (EPO, 24 h), which can decrease the navigation accuracy, especially in long-term and low-velocity applications, such as ships and submarines. This article applies the inaction method (IM) to restrain SPO. Compared to external damping network technology (EDNT) and integrated navigation technology (INT), the IM is more independent, as it does not require auxiliary reference information. The IM is based on the so-called normal time–frequency transform (NTFT), and its uniqueness lies in recognizing and extracting instantaneous amplitude, frequency, and phase from a harmonic/quasi-harmonic signal directly and exactly. In this article, simulation data and experimental data are analyzed, and both show that the IM is effective. Moreover, the calculation results show that IM has wider applicability and higher stability than internal damping network technology (IDNT) and bandpass (BP) filtering. Compared to pure INS, the root mean square (rms) of the velocity error decreases significantly; the rmss of the latitude and longitude errors decrease as well. The IM can restrain SPO, and FPO is also restrained. However, the IM has no effect on the divergence of longitude error, and the edge effect is a drawback that needs to be addressed in the future.

With the continuous advancement of target detection technology in remote sensing, target detection technology in images captured by drones has performed well. However, object detection in drone imagery is still a challenge under rainy conditions. Rain is a common severe weather condition, and rain streaks often degrade the image quality of sensors. The main issue of rain streaks removal from a single image is to prevent over smoothing (or underclearing) phenomena. Aiming at the above problems, this paper proposes a deep learning (DL)-based rain streaks removal framework called GSDerainNet, which properly formulates the single image rain streaks removal problem; rain streaks removal is aptly described as a Gaussian Shannon (GS) filter-based image decomposition problem. The GS filter is a novel filter proposed by us, which consists of a parameterized Gaussian function and a scaled Shannon function. Two-dimensional GS filters exhibit high stability and effectiveness in dividing an image into low- and high-frequency parts. In our framework, an input image is first decomposed into a low-frequency part and a high-frequency part by using the GS filter. Rain streaks are located in the high-frequency part. We extract and separate the rain features of the high-frequency part through a deep convolutional neural network (CNN). The experimental results obtained on synthetic data and real data show that the proposed method can better suppress the morphological artifacts caused by filtering. Compared with state-of-the-art single image rain streaks removal methods, the proposed method retains finer image object structures while removing rain streaks.

Global navigation satellite system reflectometry (GNSS-R), a new remote sensing technology, has been used to monitor sea surface height (SSH) in recent years. In the process of SSH retrieval by GNSS-R, estimating the oscillation frequency of signal-to-noise ratio data is particularly critical. However, the sampling time of the commonly used Lomb-Scargle periodogram (LSP) is too low, and the wavelet transform (WT) is sensitive to noise and has a frequency shift in the non-L ¹ norm. To solve these problems, we propose a new method using the normal time-frequency transform (NTFT) to assist GNSS-R in retrieving SSH. NTFT corrects the frequency offset in the WT and has better noise resistance. We applied it to the SSH retrieval of 3 GPS sites (SC02, AT01, and BRST) and compared the results with those of LSP and WT. The results show that, except for the SC02 site, where the retrieval accuracy of NTFT is slightly lower than that of LSP, the accuracy and quantity of NTFT retrieval are better than those of LSP and WT at the other two sites. At the AT01 site, the accuracy of NTFT is 38.5% and 35.2% higher than that of LSP and WT, respectively, which verifies the accuracy of NTFT in determining the instantaneous frequency and anti-noise. We also performed NTFT analysis on SSH retrieved via GNSS-R to obtain the time-varying harmonic constants of the main tidal components. Compared with LSP and WT, the NTFT retrieval of high-frequency tidal components is significantly more accurate than that of low-frequency components.