Remote sensing image classification is a common application of remote sensing images. In order to improve the performance of Remote sensing image classification, multiple classifier combinations are used to classify the Landsat-8 Operational Land Imager (Landsat-8 OLI) images. Some techniques and classifier combination algorithms are investigated. The classifier ensemble consisting of five member classifiers is constructed. The results of every member classifier are evaluated. The voting strategy is experimented to combine the classification results of the member classifier. The results show that all the classifiers have different performances and the multiple classifier combination provides better performance than a single classifier, and achieves higher overall accuracy of classification. The experiment shows that the multiple classifier combination using producer’s accuracy as voting-weight (MCC_mod2 and MCC_mod3) present higher classification accuracy than the algorithm using overall accuracy as voting-weight (MCC_mod1).And the multiple classifier combinations using different voting-weights affected the classification result in different land-cover types. The multiple classifier combination algorithm presented in this article using voting-weight based on the accuracy of multiple classifier may have stability problems, which need to be addressed in future studies.

As we all know, waste pollution is one of the most serious environmental issues in the world. Efficient detection of solid waste (SW) in aerial images can improve subsequent waste classification and automatic sorting on the ground. However, traditional methods have some problems, such as poor generalization and limited detection performance. This article presents an anchor-based object detector for solid waste in aerial images (SWDet). Specifically, we construct asymmetric deep aggregation (ADA) network with structurally reparameterized asymmetric blocks to extract waste features with inconspicuous appearance. Besides, considering the waste with blurred boundaries caused by the resolution of aerial images, this article constructs efficient attention fusion pyramid network (EAFPN) to obtain contextual information and multiscale geospatial information via attention fusion. And the model can capture the scattering features of irregular shape waste. In addition, we construct the dataset for solid waste aerial detection (SWAD) by collecting aerial images of SW in Henan Province, China, to validate the effectiveness of our method. Experimental results show that SWDet outperforms most of existing methods for SW detection in aerial images.

In recent years, significant progress has been obtained in object detection using Convolutional Neural Networks (CNNs). However, owing to the particularity of Remote Sensing Images (RSIs), common object detection methods are not well suited for RSIs. Aiming at the difficulties in RSIs, this paper proposes an object detection method based on the Dense Feature Fusion Path Aggregation Network (DFF-PANet). Firstly, for better improving the detection performance of small and medium-sized instances, we propose Feature Reuse Module (FRM), which can integrate semantic and location information contained in feature maps; this module can reuse feature maps in the backbone to enhance the detection capability of small and medium-sized instances. After that, we design the DFF-PANet, which can help feature information extracted from the backbone to be fused more efficiently, and thus cope with the problem of external interference factors. We performed experiments on the Dataset of Object deTection in Aerial images (DOTA) dataset and the HRSC2016 dataset; the accuracy reached 71.5% mAP, which exceeds most object detectors of one-stage and two-stages at present. Meanwhile, the size of our model is only 9.2 M, which satisfies the requirement of being lightweight. The experimental results demonstrate that our method not only has better detection accuracy but also maintains high efficiency in RSIs.

Nitrogen oxides (NOx) are known to be irritant gases, which present considerable risks to human health. TROPOMI NO₂ vertical column density (VCD) is commonly employed to estimate NOx emissions through the integration of complex models. However, satellite data often suffer from incompleteness, hindering the ability to achieve long-term and comprehensive estimates. In this study, we propose a reconstruction method to achieve comprehensive coverage of NO₂ VCD in China by leveraging the relationship between satellite data and meteorological variables. In addition, the CNN-BiLSTM-ATT model was developed to estimate China’s monthly NOx emissions from 2021 to 2023 in combination with other ancillary data, such as ERA5 meteorological data, topographic data, and nighttime light data, achieving a correlation coefficient (R) of 0.83 and a root mean squared error (RMSE) of 9.05 tons (T). The factors influencing NO₂ VCD were assessed using SHAP values, and the spatiotemporal characteristics and density distribution of NOx emissions were analyzed. Additionally, annual emission trends were evaluated. This study offers valuable insights for air quality management and policymaking, contributing to efforts focused on mitigating the adverse health and environmental impacts of NOx emissions.

Spectral variables such as spectral characteristic parameters (SCPs) commonly change with intraday phenology. Empirical retrieval methods, which are generally used in leaf area index (LAI) retrieval due to their simplicity and computational efficiency, typically relate the biophysical parameter of interest to the spectral variable during the whole observation period. Whilst information regarding diurnal changes in spectral variables is necessary and useful in applied contexts. We analysed the diurnal change characteristics of canopy spectral reflectance and SCPs of winter wheat in the jointing stage based on field data collected at fixed sampling points with different vegetation canopies, and validated the effectiveness of data splitting strategy with field data collected in random sample pattern. The key results are as follows: (i) Canopy spectral reflectance of winter wheat in the jointing stage exhibited clear intraday variability, typically presenting a double-peak characteristic occurring from 11:35 to 12:34, where the reflectance changed substantively during this period. (ii) The SCPs of winter wheat in the jointing stage exhibited different diurnal patterns. Specifically, the blue edge position presented ‘blue shifts’, the yellow edge position generally exhibited steady fluctuations, and the red edge position followed divergent trends between the two sampling points due to differences in the vegetation canopy. Amplitude and area parameters exhibited a double-peak characteristic but there were slight differences between them. (iii) By dividing the whole observation period into sub-periods, the coefficient of variation (CV) of each spectral characteristic parameter can be greatly reduced, whilst the coefficient of determination (R²) of LAI retrieval can be greatly increased. Optimal spectral parameters and sub-periods for LAI retrieval were confirmed based on the diurnal variation of SCPs. To optimize LAI retrieval the suggested spectral parameters are blue edge amplitude, red edge amplitude, and red edge area, and the sub-periods are 09:50–11:35, 11:35–12:34, 12:34–13:50, and 13:50–15:00, respectively. The 11:35–12:34 sub-period should be carefully considered due to possible midday depression of photosynthesis.

Features of SBUV earth image are assessed based on all-time simulation A optimal spectral range is confirmed for recognizing available earth limb imag...