Detecting small objects in complex remote sensing environments presents significant challenges, including insufficient extraction of local spatial information, rigid feature fusion, and limited global feature representation. In addition, improving model performance requires a delicate balance between improving accuracy and managing computational complexity. To address these challenges, we propose the SMA-YOLO algorithm. First, we introduce the Non-Semantic Sparse Attention (NSSA) mechanism in the backbone network, which efficiently extracts non-semantic features related to the task, thus improving the model’s sensitivity to small objects. In the model’s throat, we design a Bidirectional Multi-Branch Auxiliary Feature Pyramid Network (BIMA-FPN), which integrates high-level semantic information with low-level spatial details, improving small object detection while expanding multi-scale receptive fields. Finally, we incorporate a Channel-Space Feature Fusion Adaptive Head (CSFA-Head), which fully handles multi-scale features and adaptively handles consistency problems of different scales, further improving the robustness of the model in complex scenarios. Experimental results on the VisDrone2019 dataset show that SMA-YOLO achieves a 13% improvement in mAP compared to the baseline model, demonstrating exceptional adaptability in small object detection tasks for remote sensing imagery. These results provide valuable insights and new approaches to further advance research in this area.

In response to the challenges of remote sensing imagery, such as unmanned aerial vehicle (UAV) aerial imagery, including differences in target dimensions, the dominance of small targets, and dense clutter and occlusion in complex environments, this paper optimizes the YOLOv8n model and proposes an innovative small-object-detection model called DDSC-YOLO. First, a DualC2f structure is introduced to improve the feature-extraction capabilities of the model. This structure uses dual-convolutions and group convolution techniques to effectively address the issues of cross-channel communication and preserving information in the original input feature mappings. Next, a new attention mechanism, DCNv3LKA, was developed. This mechanism uses adaptive and fine-grained information-extraction methods to simulate receptive fields similar to self-attention, allowing adaptation to a wide range of target size variations. To address the problem of false and missed detection of small targets in aerial photography, we designed a Semantics and Detail Infusion Feature Pyramid Network (SDI-FPN) and added a dedicated detection scale specifically for small targets, effectively mitigating the loss of contextual information in the model. In addition, the coordinate adaptive spatial feature fusion (CASFF) mechanism is used to optimize the original detection head, effectively overcoming multi-scale information conflicts while significantly improving small target localization accuracy and long-range dependency perception. Testing on the VisDrone2019 dataset shows that the DDSC-YOLO model improves the mAP0.5 by 9.3% over YOLOv8n, and its performance on the SSDD and RSOD datasets also confirms its superior generalization capabilities. These results confirm the effectiveness and significant progress of our novel approach to small target detection.

Environmental trace gas monitoring instrument (EMI) onboard GaoFen-5 was launched in May 2018 and has successfully operated on-orbit for more than a year. EMI contains four grating spectrometers, covering wavelengths in the range 240-710 nm with a spectral resolution of 0.3-0.5 nm, and enables one-day global coverage. For EMI on-orbit calibration, two onboard solar diffusers (SD), one surface reflectance aluminum diffuser, and one quartz volume diffuser (QVD) are used to measure solar spectra. The solar spectra are used to perform accurate spectral and radiometric calibrations. EMI on-orbit spectral calibration contains wavelength calibration and instrument spectral response function (ISRF), both of which are the key quantities in trace gas retrievals based on differential optical absorption spectroscopy (DOAS) analysis. The wavelength calibration is performed using the Fraunhofer lines in the solar spectrum, and the ISRF parameters are obtained by fitting high-resolution and EMI-measured solar spectra. Based on the known solar irradiation and characteristic of SD, SD radiance can be calculated and is used for EMI on-orbit radiometric calibration. The radiometric calibration also determines the absolute Earth reflectance spectra that are used as inputs for atmospheric retrieval algorithms. For EMI on-orbit radiometric monitoring, aluminum diffuser is used as reference SD to monitor QVD degradation. An internal white light source is used to detect pixel performance and monitor radiometric throughput.

The environmental trace gas monitoring instrument (EMI) is a space-borne imaging spectrometer onboard GaoFen-5, which was launched in May 2018, covering wavelengths in the range of 240–710 nm to measure NO₂, O₃, HCHO, and SO₂. An advanced EMI-2 instrument with a higher spatial resolution and sufficient signal-to-noise is currently planned for launch on the GaoFen-5(02) satellite in 2021. The EMI-2 instrument bidirectional scattering distribution function (BSDF) is obtained from the absolute irradiance and radiance calibration on-ground. Based on EMI-2 earth and sun optical paths, the key factors of BSDF parameters are introduced. An NIST-calibrated 1000 W FEL quartz tungsten halogen lamp and a 2D turntable are adopted for the absolute irradiance calibration. A large aperture integrating sphere system is used for the absolute radiance calibration. Based on absolute irradiance and radiance calibration functions, the BSDF parameters are obtained, with accuracy of 4.9% for UV1, 4.3% for UV2, 4.1% for VIS1, and 4.2% for VIS2. The on-ground measurement results show that the reflectance spectrum can be calculated from BSDF parameters. On-orbit application of the EMI-2 instrument BSDF are also discussed.

Global measurements of total ozone are necessary to evaluate ozone hole recovery above Antarctica. The Environmental Trace Gases Monitoring Instrument (EMI) onboard GaoFen 5, launched in May 2018, was developed to measure and monitor the global total ozone column (TOC) and distributions of other trace gases. In this study, some of the first global TOC results of the EMI using the differential optical absorption spectroscopy (DOAS) method and validation with ground-based TOC measurements and data derived from Ozone Monitoring Instrument (OMI) and TROPOspheric Monitoring Instrument (TROPOMI) observations are presented. Results show that monthly average EMI TOC data had a similar spatial distribution and a high correlation coefficient (R ≥ 0.99) with both OMI and TROPOMI TOC. Comparisons with ground-based measurements from the World Ozone and Ultraviolet Radiation Data Centre also revealed strong correlations (R > 0.9). Continuous zenith sky measurements from zenith scattered light differential optical absorption spectroscopy instruments in Antarctica were also used for validation (R = 0.9). The EMI-derived observations were able to account for the rapid change in TOC associated with the sudden stratospheric warming event in October 2019; monthly average TOC in October 2019 was 45% higher compared to October 2018. These results indicate that EMI TOC derived using the DOAS method is reliable and has the potential to be used for global TOC monitoring.

A violent volcanic eruption attracting considerable attention occurred on 15 January 2022 near the South Pacific island nation of Tonga. To investigate its environmental impact, we retrieved the sulfur dioxide (SO₂) and bromine monoxide (BrO) vertical column densities of environmental trace gas monitoring instrument 2 (EMI-2) based on the differential optical absorption spectroscopy algorithm. The results showed westward and southeastward transport of principal parts of SO₂ and BrO plumes, respectively, from the Hunga Tonga–Hunga Ha’apai (HTHH) eruption. On 15 January, most of the released SO₂ entered the stratosphere (above 20 km) directly and spread rapidly westward (approximately 30 m/s). In contrast, the principal portion of the BrO spread southeastward slowly (approximately 10 m/s) within the 8–15 km altitude layer on 16 January. Our research results also suggest that during the HTHH eruption, BrO was released from the magmatic melt later than SO₂. The total SO₂ emissions from this eruption were approximately 0.24 Tg. The majority of SO₂ and BrO plumes were transported within the Southern Hemisphere. This study is an important extension to the empirical database of volcanological and magmatic degassing research.