Geo-Kompsat-2A (GK-2A) is the third new-generation geostationary meteorological satellite that orbits Asia and monitors China and its surrounding areas, following the Himawari-8 and Fengyun-4A satellites. The nadir point positioning and satellite channel parameters of the GK-2A are better than those of the Himawari-8 and FY-4A, which are more conducive to fire monitoring in China. In this study, a new fire detection algorithm is proposed based on GK-2A satellite data. That is, considering the large solar zenith angle correction for reflectance and the proportion information of background pixels in the existing spatial threshold method, fires under the different underlying surface types and solar radiation states can be automatically identified. Moreover, the accuracy of the Himawari-8 fire monitoring algorithm and the present algorithm of GK-2A is compared and analyzed through the ground truth fire spot data. The results show that compared with the original fire monitoring algorithm with fixed parameter thresholds, the brightness temperature difference of this algorithm is reduced by 0.55 K, and the correction coefficient is reduced by 0.6 times, the fire can be found earlier, and the monitoring sensitivity is improved. According to the practical fire case, the present fire monitoring algorithm of GK-2A has better monitoring accuracy than the fire monitoring algorithm of Himawari-8. The present fire monitoring algorithm of GK-2A can meet the fire monitoring requirements under different sun angles, different cloud cover ratios and vegetation ratios with good versatility.

Phosvitin (Pv) is the principal phosphoprotein in chicken egg yolk and the most highly phosphorylated protein in nature. Pv is a good natural food antioxidant and emulsifier. However, the current extraction methods present disadvantages of complicated operation and are time-consuming. In this paper, Pv was extracted from the egg yolk by ultrasonic thermal-assisted extraction (UTAE). The effects of heating time, ultrasonic power and ultrasonic time on the extraction of Pv were investigated by a single factor. The purity of Pv, ratio of nitrogen to phosphorus (N/P), and activity were used as evaluation indexes. An efficient extraction of Pv was achieved when the sample was heated for 15 min at 80 °C and then processed for 10 min of ultrasonic treatment with an ultrasonic power of 600 W. Under optimal conditions, the purity and activity of Pv were 80% and 98%, respectively, whereas the ratio of N/P was 3.1. The obtained Pv was identified by sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS-PAGE), Fluorescence analyses, fourier-transform infrared (FT-IR), and liquid chromatography-nanoelectrospray ionization mass spectrometry (Nano LC-ESI-MS/MS) analysis. The results showed there is no significant difference in the properties of Pv obtained by UTAE and Pv standard. The developed extraction approach is a simple, industrial compatible method without the use of any organic solvents.

In this study, the channel data related to fire point identification of Advanced Geostationary Radiation Imager on Fengyun-4B (FY-4B/AGRI) and Advanced Meteorological Imager on GEO-KOMPSAT 2A (GK-2A/AMI) were cross-compared. A total of 267 sampling points in China (Guangdong, Guangxi, Guizhou, Yunnan, Hainan) were selected to carry out fine positioning correction on the data in different elevation intervals. Then, a fire monitoring algorithm based on FY-4B was proposed, in which the self-adaptive threshold adjustment of the underlying surface parameters and the reprocessing module of fire point identification were added. The algorithm can realize the high-precision and stable monitoring of fire. The continuous dynamic monitoring was carried out using the grassland fire Mongolia from April 18 to 20, 2022 as an example. The results showed that the parameters of FY-4B/AGRI and GK2A/AMI channels have high consistency. The root mean square error (RMSE) of reflection channel was 1.33%, and the maximum RMSE of brightness temperature channel was less than 1.3 Kelvin (K). Through the positioning analysis in different elevation intervals, the average longitude offset of FY-4B satellite data was -0.5°–0° and the average latitude offset was -0.9°–0.6°. Overall, these findings indicate that the high-frequency observations of FY-4B can be fully utilized to monitor forest and grassland fires, which can continuously track the dynamic evolution of fire and can distinguish the spatial distribution of different fire intensities in large-scale fire field.

In this study, we compare the data of the advanced geostationary radiation imager (AGRI) on board the FY-4B and the advanced meteorological imager (AMI) on board the GK-2A, in terms of overall data, different reflectivity/brightness temperature intervals, different regions, and different underlying surfaces. The results show that the AGRI and AMI data are generally consistent; the mean biases for reflectivity channels show a range of 0.50% to 1.69%, with channel VIR004 being exceptionally good, while brightness temperature (TB) differences in the IR channels ranging from 0.11 to 0.57 K, with channel IR120 being the most accurate. The reflectivity of the AGRI is higher than that of the AMI in terms of mean bias. The dispersion of the reflectivity difference between the AGRI and AMI is smaller at the short-wavelength channels than that at the longer-wavelength channels. The TB data observed by the AGRI are higher than those of AMI at conditions above 310 K. In the case of observing the same target, the difference in infrared brightness temperature due to the random noise signal is small. The differences between the two sensors can be considerably reduced by revising mean biases. In the following studies of quantitative product algorithms, the characteristics of sensor data need to be further analyzed in detail.

To develop a novel emulsifier with an antioxidant capacity, a phosvitin-gallic acid (Pv–GA) complex was prepared via a free-radical method. This emulsifier characterizes some key technologies. Changes in the molecular weight of the Pv–GA complex were analyzed by sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS–PAGE) and the matrix-assisted laser desorption/ionization time of light mass spectrometry (MALDI-TOF-MS). Fourier transform infrared spectroscopy (FTIR) indicated that C=O, C–N and N–H groups were also likely to be involved in the formation of the complex. A redshift was obtained in the fluorescence spectrogram, thereby proving that the covalent combination of Pv and GA was a free radical-forming complex. The results indicated that Pv and GA were successfully conjugated. Meanwhile, the secondary structure of Pv showed significant changes after conjugation with GA. The antioxidant activity and emulsifying properties of the Pv–GA complex were studied. The antioxidant activity of the Pv–GA complex proved to be much higher than that of the Pv, via assays of the scavenging activities of 2,2-Diphenyl-1-picrylhydrazyl (DPPH) and 2,2’-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) radicals and because of their ability to reduce power. The emulsification activity of the Pv–GA complex was also slightly higher than that of Pv. To function with the most demanding antioxidant and emulsification activities, the optimum conjugation condition was Pv (5 mg/mL) conjugated 1.5 mg/mL GA. Furthermore, the mechanism of Pv–GA conjugation was studied. This study indicated that GA could quench the inner fluorescence of Pv, and this quenching was static. There was a strong interaction between GA and Pv, which was not obviously affected by the temperature. Furthermore, several binding sites were close to 1, indicating that there was an independent class of binding sites on Pv for GA at different temperatures. The conjugation reaction was a spontaneous reaction, and the interaction forces of GA and Pv were hydrogen bonds and van der Waals force.

Microwave sensors possess the capacity to effectively penetrate through clouds and fog and are widely used in obtaining soil moisture, atmospheric water vapor, and surface temperature measurements. Long time-series datasets play a pivotal role in climate change studies. Unfortunately, the lifespan of operational satellites often falls short of the needs of these extensive datasets. Hence, comparing and cross-calibrating sensors with similar configurations is paramount. The Microwave Radiation Imager (MWRI) onboard Fengyun-3D (FY-3D) is the latest generation of satellite-based microwave remote sensing instruments in China, and its data quality and application prospects have attracted widespread attention. To comprehensively assess the data quality of MWRI, a comparison of the orbital brightness temperature (TB) data between FY-3D/MWRI and Global Change Observation Mission 1st-Water (GCOM-W1)/Advanced Microwave Scanning Radiometer 2 (AMSR2) is conducted, and then a calibration model is established. The results indicate a strong correlation between the two sensors, with a correlation coefficient exceeding 0.9 across all channels. The mean bias ranges from −1.5 K to 0.15 K. Notably, the bias of vertical polarization is more pronounced than that of horizontal polarization. The TB distribution patterns and temporal evolutions are highly consistent for both sensors, particularly under snow and ice. The small intercepts and close-to-1 slopes obtained during calibration further demonstrate the minor data differences between the two sensors. However, the calibration process effectively reduces the existing errors, and the calibrated FY-3D/MWRI TB data are closer to GCOM-W1/AMSR2, with a mean bias approximately equal to 0 K and a correlation coefficient exceeding 0.99. The excellent consistency of the TB data between the two sensors provides a vital data basis for retrieving surface parameters and establishing long time-series datasets.