From late 2019 to early 2020, forest fires in southeastern Australia caused huge economic losses and huge environmental pollution. Monitoring forest fires has become increasingly important. A new method of fire detection using the difference between global navigation satellite system (GNSS)-derived precipitable water vapor and radiosonde-derived precipitable water vapor (ΔPWV) is proposed. To study the feasibility of the new method, the relationship is studied between particulate matter 10 (PM10) (2.5 to 10 microns particulate matter) and ΔPWV based on Global Positioning System (GPS) data, radiosonde data, and PM10 data from 1 June 2019 to 1 June 2020 in southeastern Australia. The results show that before the forest fire, ΔPWV and PM10 were smaller and less fluctuating. When the forest fire happened, ΔPWV and PM10 were increasing. Then after the forest fire, PM10 became small with relatively smooth fluctuations, but ΔPWV was larger and more fluctuating. Correlation between the 15-day moving standard deviation (STD) time series of ΔPWV and PM10 after the fire was significantly higher than that before the fire. This study shows that ΔPWV is effective in monitoring forest fires based on GNSS technique before and during forest fires in climates with more uniform precipitation, and using ΔPWV to detect forest fires based on GNSS needs to be further investigated in climates with more precipitation and severe climate change.

The classical harmonic analysis (CHA) method only can be used to obtain the harmonic constants (amplitude and phase) of ocean tide loading displacement (OTLD). In fact, there are significant seasonal variations in the harmonic constants of OTLD. A moving harmonic analysis (MHA) method is proposed, which can effectively capture the seasonal variation of OTLD parameters. Based on 5 years of kinematic coordinate time series in direction U of six Global Positioning System (GPS) stations in Hong Kong, the MHA method is used to explore the seasonal variation of the OTLD parameters of the 6 principal tidal constituents (M2, S2, N2, K1, O1, Q1). The influence of mass loading on the seasonal variation of OTLD parameters is analyzed. The results show that there are obviously seasonal variations in OTLD parameters of the 6 principal tidal constituents in Hong Kong. The OTLD’s amplitude’s changes of the 6 principal tidal constituents are around 4–25.1% and the oscillation ranges of OTLD’s phase parameters vary from 8.8° to 20.4°. Among the seasonal variations of OTLD parameters, the annual signal, the semi-annual signal, and the ter-annual signal are the most significant. By analyzing the influence of atmospheric loading on the seasonal variation of OTLD parameters, it is found that atmospheric loading has certain contribution to the seasonal variation of OTLD parameters. Hydrological loading and non-tidal ocean loading have little influence on the seasonal variation of OTLD parameters.

With the development of remote sensing technology, the semantic segmentation and recognition of various things in the ocean have become more and more frequent. Due to the wide variety of marine things and the large differences in morphology, it has brought greater difficulties to the recognition of marine remote sensing images. In order to obtain better segmentation results of ocean remote sensing images, this paper proposes an cross attention mechanism(Horizontal and Vertical) of exponential operation combined with multi-scale convolution algorithm. Among them, the cross attention mechanism and expanded distribution weight coefficient mentioned in this paper are first proposed. First, Input the marine remote sensing image features into an cross attention mechanism algorithm of exponential operation to obtain feature weight coefficients and joint weight coefficients in multiple directions; Then, the features with weight coefficients are input into the multi-access convolutional layer and the multi-scale dilated convolutional layer respectively for deep feature mining; Then the above steps are repeated twice, and finally the semantic segmentation of marine remote sensing images is achieved by fusing multiple deep-level features afterwards. Experiments were conducted on three public marine remote sensing data sets, and the results proved the effectiveness of our proposed cross attention mechanism of extended operation algorithm. The F values of the MAMC model on Beach, Island and Sea ice data sets have reached 99.4%, 91.25%, 87.08% respectively. Compared with other models, the effect is significantly improved, and proved the powerful performance of the algorithm in the semantic segmentation of marine remote sensing images.

On 15 January 2022, a violent eruption and tsunami of the Hunga Tonga-Hunga Ha’apai (HTHH) volcano in Tonga, South Pacific, caused widespread international concern. In order to detect the anomalous environmental response caused by the HTHH volcanic eruption based on GNSS ionospheric data, GNSS tropospheric data and GNSS coordinate time series, a new method combining the zenith non-hydrostatic delay difference method and the extreme-point symmetric mode decomposition (ESMD) method, was proposed to detect tropospheric anomalies. The moving interquartile range method and the ESMD method were introduced to detect ionospheric anomalous and coordinate time series anomalies, respectively. The results showed that 9–10 h before the eruption of the Tonga volcano and 11–12 h after the eruption of the Tonga volcano, obvious total electron content (TEC) anomalies occurred in the volcanic eruption center and its northeast and southeast, with the maximum abnormal value of 15 TECU. Significant tropospheric anomalies were observed on the day of the HTHH volcano eruption as well as 1–3 days and 16–17 days after the eruption, and the abnormal intensity was more than 10 times that of normal. The coordinate time series in direction E showed very significant anomalies at approximately 2:45 p.m. on 14 January, at approximately 4:30 a.m.–5:40 a.m. on 15 January, and at approximately 3:45 a.m. on 16 January, with anomalies reaching a maximum of 7–8 times daily. The abnormality in the direction north (N) is not obvious. Very prominent anomalies can be observed in the direction up (U) at approximately 4:30 a.m.–5:40 a.m., with the intensity of the anomalies exceeding the normal by more than 10 times. In this study, GNSS was successfully used to detect the anomalous environmental response during this HTHH volcano eruption.

Water transparency is one of the important parameters to describe the optical properties of seawater. It's a visual reflection of the degree of turbidity of seawater and the degree of absorption and scattering of light by seawater. Firstly, this paper verified and analyzed the suitability of the water transparency retrieval method proposed by Lee in 2015 (denoted as Zsd_lee_2015) for Jiaozhou Bay and Qingdao offshore waters by using the in-situ data. Secondly, the water transparency estimation results retrieved from GOCI (Geostationary Ocean Color Imager) using four atmospheric correction algorithms were compared and analyzed with in-situ data in study area. The four atmospheric correction algorithms are the standard atmospheric correction algorithms of KOSC GOCI GDPS1.3 and GDPS2.0, the standard near-infrared atmospheric correction algorithm of NASA, and MUMM (Management Unit of the North Sea Mathematical Models). At last, GOCI data on Mar 09, 2018 were used to analyze the differences of hourly changes of transparency caused by different atmospheric correction algorithms in detail. The results show that the Zsd_lee_2015 model is suitable for the study area (R ² >0.8, MAPE = 16.4%). The difference of transparency value caused by atmospheric correction algorithm cannot be ignored completely. Moreover, there are great differences in the moment when the maximum value of transparency presented based on different atmospheric correction algorithms, while the moment when the minimum value of transparency presented is basically the same, mostly occurring at 00:16. The results cast doubt on the previous conclusions about the hourly variations of biochemical parameters using GOCI because the hourly variations obtained under different atmospheric correction algorithms are not completely consistent.