Haizhen Zhang

Satellite-derived aerosol optical depth (AOD) products from MODIS and VIIRS sensors are vital for monitoring global aerosol distributions. However, inconsistencies in quality control algorithms and spatial resolution introduce errors that complicate validation processes and reduce the accuracy of satellite-to-ground comparisons. This study proposes the “optimal” spatial matching method to minimize these errors and enable a more accurate evaluation of retrieval algorithm performance. Using AERONET ground observations from 2012 to 2021, MODIS and VIIRS AOD products were systematically validated with three spatial matching methods—“direct”, “average”, and “optimal”. Results demonstrate that the “optimal” method consistently outperformed the other methods by selecting pixel values. The study highlights significant quality control disparities across AOD products and demonstrates that high-resolution products, with purer pixels, achieve superior accuracy under the “optimal” method. These insights provide valuable guidance for optimizing dataset applications and refining aerosol retrieval algorithms.

The FengYun (FY-3E) satellite was successfully launched on July 5, 2021. It is a polar-orbiting meteorological satellite and the world's first civilian morning-and-night orbit meteorological satellite. It carries a Medium-Resolution Spectral Imager-LL payload that delivers high-resolution OLR products. OLR can reflect more atmospheric change information, is more sensitive to temperature change, and can better reflect the entire surface atmospheric system, which is conducive to the in-depth understanding of the seismic sphere coupling module. OLR is widely used in seismic infrared anomaly extraction. The additive tectonic stress from the celestial tide-generating force (ATSCTF) seismic anomaly extraction algorithm has been continuously improved. However, the threshold of seismic thermal anomaly is mostly based on experience. The results of the quartile algorithm are objective, and it has some advantages in recognizing abnormal values. Whether the FY-3E can be used for seismic risk monitoring remains to be investigated and how. In this study, the 2023 Turkey double earthquakes are taken as an example, the seismic anomalies of FY-3E OLR data are extracted based on the ATSCTF algorithm, and the anomaly threshold is determined via the quartile method. The results show that the ATSCTF algorithm based on the quartile threshold method is suitable for FY-3E data, and FY-3E OLR data have a certain response to pre-earthquake radiation anomalies, which can be used for seismic infrared anomaly tracking. The comprehensive use of ascending and descending data can better serve the extraction of seismic anomalies. High-resolution temporal remote sensing data are more conducive to the extraction of seismic anomalies via ATSCTF. The research and practice of seismic risk monitoring based on the ATSCTF algorithm can be further strengthened.

The extraction of buildings from synthetic aperture radar (SAR) images poses a challenging task in the realm of remote sensing (RS). In recent years, convolutional neural networks (CNNs) have rapidly advanced and found application in the field of RS. Researchers have investigated the potential of CNNs for the semantic segmentation of SAR images, bringing excellent improvements. However, the semantic segmentation of buildings in SAR images still encounters challenges due to the high similarity between features of ground objects and buildings in SAR images, as well as the variability in building structures. In this paper, we propose the local-global multiscale fusion network (LGMFNet), based on a dual encoder-decoder structure, for the semantic segmentation of buildings in SAR images. The proposed LGMFNet introduces an auxiliary encoder with a transformer structure to address the limitation of using the main encoder with a CNN structure for global modeling. To embed global dependencies hierarchically into the CNN, we designed the global-local semantic aggregation module (GLSM). The GLSM serves as a bridge between the dual encoders to achieve semantic guidance and coupling from the local to the global level. Furthermore, to bridge the semantic gap between different scales, we designed the multi-scale feature fusion network (MSFN) as the decoder. MSFN achieves the interactive fusion of semantic information between various scales by constructing the multi-scale feature fusion module (MSFM). Experimental results demonstrate that the proposed LGMFNet achieves the mIoU of 91.17% on the BIGSARDATA 2023 AISAR competition dataset, outperforming the second-best method by a margin of 0.78%. This evidences the superiority of LGMFNet in comparison to other state-of-the-art methods.