Large forest fires can cause significant damage to forest ecosystems and threaten human life and property. Heilongjiang Province is a major forested area in China with the highest number and concentration of lightning-caused forest fires in the country. This study examined the spatial and temporal distribution patterns of forest fires in Heilongjiang Province, as well as the ability of satellite remote sensing to detect these fires using VIIRS 375 m fire point data, ground history forest fire point data, and land cover dataset. The study also investigated the occurrence patterns of lightning-caused forest fires and the factors affecting satellite identification of these fires through case studies. Results show that April has the highest annual number of forest fires, with 77.6% of forest fires being caused by lightning. However, less than 30% of forest fires can be effectively detected by satellites, and lightning-caused forest fires account for less than 15% of all fires. There is a significant negative correlation between the two. Lightning-caused forest fires are concentrated in the Daxing’an Mountains between May and July, and are difficult to monitor by satellites due to cloud cover and lack of satellite transit. Overall, the trend observed in the number of forest fire pixels that are monitored by satellite remote sensing systems is generally indicative of the trends in the actual number of forest fires. However, lightning-caused forest fires are the primary cause of forest fires in Heilongjiang Province, and satellite remote sensing is relatively weak in monitoring these fires due to weather conditions and the timing of satellite transit.

Due to the advantage of geostationary satellites, Himawari-8/AHI can provide near-real-time air quality monitoring over China with a high temporal resolution. Satellite-based aerosol optical depth (AOD) retrieval over land is a challenge because of the large surface contribution to the top of atmosphere (TOA) signal and the uncertainty of aerosol modes. Here, by combining satellite TOA reflectance, sun-sensor geometries, meteorological factors and vegetation information, we propose a data-driven AOD detection algorithm based on a deep neural network (DNN) model for Himawari-8/AHI. It is trained by sample data of 2018 and 2019 and is applied to derive hourly AODs over China in 2020. By comparison with ground-based AERONET measurements, R² for DNN-estimated AOD is up to 0.8702, which is much higher than that for the AHI AOD product with R² = 0.4869. The hourly AOD results indicate that the DNN model has a good potential in improving the performance of AOD retrieval in the early morning and in the late afternoon, and the spatial distribution is reliable for capturing the variation of aerosol pollution on the regional scale. By analyzing different DNN modeling strategies, it is found that seasonal modeling can hardly increase the accuracy of AOD retrieval to a certain extent, and R² increases from 0.7394 to 0.8168 when meteorological features, especially air pressure, are involved in the model training.

Methane is a potent greenhouse gas, and its accurate detection is critical for addressing global climate change. Although remote sensing has been a crucial technique for understanding the spatial distribution and temporal dynamics of methane emissions, it is still urgently needed that automate the identification of methane emission plume and effectively deconvolve the signal from background noise. In this study, we propose the Methane Plume Segmentation UNet (MPSUNet) to achieve precise segmentation of methane plumes from remote sensing imagery. MPSUNet incorporates the Pyramid Squeeze Attention (PSA) module to enhance feature representation and employs a joint loss function combining Dice Loss and Focal Loss to address challenges such as class imbalance and noisy data. A novel dataset, MPDataset, was constructed using EMIT methane enhancement and RGB radiance data, providing 4172 high-quality samples for model training and evaluation. Our results show that MPSUNet achieves a mean intersection over union (MIoU) of 78.20%, mean precision of 80.78%, recall of 71.11%, and mean pixel accuracy (MPA) of 85.41% on the complete four-channel MPDataset. Compared with seven classical segmentation models, the most improvents of MPSUNet in MIoU, MPrecision, Recall and MPA reach up to 5.33%, 12.28%, 18.04% and 8.94%, respectively. Notably, the integration of RGB channels enhances the segmentation of small and intricate plume structures. Cross-dataset evaluation using Sentinel-2 data further validates the model’s robustness, achieving an MIoU of 77.65% and an MPA of 83.61%. Generally, the proposed MPSUNet model marks a substantial performance in methane detection, which provides a robust technical framework for global-scale methane emission monitoring as emphasized by global climate agreements.

Oil wells play an important role in the extraction of oil and gas, and their future potential extends beyond oil and gas exploitation to include the development of geothermal resources for sustainable power generation. Identifying and detecting oil wells are of paramount importance given the crucial role of oil well distribution in energy planning. In recent years, significant progress has been made in detecting single oil well objects, with recognition accuracy exceeding 90%. However, there are still remaining challenges, particularly with regard to small-scale objects, varying viewing angles, and complex occlusions within the domain of oil well detection. In this work, we created our own dataset, which included 722 images containing 3749 oil well objects in Daqing, Huatugou, Changqing oil field areas in China, and California in the USA. Within this dataset, 2165 objects were unoccluded, 617 were moderately occluded, and 967 objects were severely occluded. To address the challenges in detecting oil wells in complex occlusion scenarios, we propose the YOLOv5s-seg CAM NWD network for object detection and instance segmentation. The experimental results show that our proposed model outperforms YOLOv5 with F1 improvements of 5.4%, 11.6%, and 23.1% observed for unoccluded, moderately occluded, and severely occluded scenarios, respectively.

The Multi-angle Imaging SpectroRadiometer (MISR) measurement with a large range of scattering angles provides valuable information about aerosol microphysical properties. The current MISR algorithm utilizes pre-defined aerosol mixtures in lookup tables (LUT) to infer aerosol types and microphysical parameters, which performs well globally but remains subject to considerable uncertainties in regional scales. To make efficient use of MISR measurement, we developed a physics-informed Deep Learning (PDL) method to retrieve aerosol optical/microphysical parameters over land in eastern China. By combining the physical constraint of radiative transfer simulation and modeling ability of DL methods, each aerosol parameter can be modeled with the whole used MISR measurements separately with high computational efficiency. PDL Aerosol Optical Depth (AOD) and fine AOD(FAOD) have high correlation coefficients (R>0.95) with Aerosol Robotic Network (AERONET) observations, with 89% and 81% values falling into expected error (EE) envelope of ± (0.05+20%AODAERONET) respectively. Despite only a slightly higher accuracy than recent MISR Version 23 products, PDL retrievals have solved the underestimation problem of AOD and FAOD at moderate-high values (>0.4). Besides better constraint of abnormal values in coarse AOD(CAOD), PDL algorithm significantly improves retrieval accuracy of MISR Single Scattering Albedo (SSA). With reliable and robust performance, PDL algorithm provides a flexible and efficient aerosol retrieval framework for emerging multi-angle polarimetric measurements.

The four wide-field-of-view (WFV) cameras aboard the GaoFen-1 (GF-1) satellite launched by China in April 2013 have been applied to the studies of the atmospheric environment. To highlight the advantages of GF-1 data in the atmospheric environment monitoring, an improved deep blue (DB) algorithm using only four bands (visible–near infrared) of GF-1/WFV was adopted to retrieve the aerosol optical depth (AOD) at ~500 m resolution in this paper. An optimal reflectivity technique (ORT) method was proposed to construct monthly land surface reflectance (LSR) dataset through converting from MODIS LSR product according to the WFV and MODIS spectral response functions to make the relationship more suitable for GF-1/WFV. There is a good spatial coincidence between our retrieved GF-1/WFV AOD results and MODIS/Terra or Himawari-8/AHI AOD products at 550 nm, but GF-1/WFV AOD with higher resolution can better characterized the details of regional pollution. Additionally, our retrieved GF-1/WFV AOD (2016–2019) results showed a good agreement with AERONET ground-based AOD measurements, especially, at low levels of AOD. Based on the same LSR dataset transmitted from 2016–2018 MODIS LSR products, R_ORT of 2016–2018 and 2019 GF-1/WFV AOD retrievals can reach up to 0.88 and 0.94, respectively, while both of RMSE_ORT are smaller than 0.13. It is indicated that using the ORT method to deal with LSR information can make GF-1/WFV AOD retrieval algorithm more suitable and flexible.