It is imperative to quantitatively analyze the long-term temporal and spatial characteristics of the urban heat island (UHI) effect on cities for applications, such as urban expansion and environmental protection. Owing to the high spatial resolution and availability of long time-series data, remote sensing images from Landsat satellites are widely used for land surface temperature (LST) retrieval. However, limited by the satellite revisit cycle and image quality, the use of multisource Landsat images in a long-term study of the UHI effect is inevitable. Nonetheless, owing to the differences among multisource sensors, such as Landsat-7 and Landsat-8, there may be apparent deviations in the LST results retrieved from different sensor data, which are obtained from the same area and under similar circumstances. Consequently, it is necessary to build a relationship between the LST results generated from multisource Landsat sensors for future research on the UHI effect. In this study, Shenzhen city was studied to explore the fitting relationship between the corresponding LST products from Landsat-7 and Landsat-8 images obtained from adjacent dates with similar climatic conditions. Furthermore, factors affecting the fitting models, such as land cover types, seasonal and inter-annual differences, were analyzed. The constructed fitting model had a strong relationship with land cover types but a relatively weak relationship with seasonal and inter-annual differences; this indicates that a pseudo Landsat-8-based LST product can be generated from a Landsat-7-based LST product using a model fitted by a Landsat-7/8 pair obtained from adjacent years (or different seasons). Finally, by considering the consistency between LST products from multisource Landsat images, the spatiotemporal variations in the UHI effect in Shenzhen can be accurately explored using long time-series data.

The origin of the detached dust layers observed by the Mars Climate Sounder (MCS) aboard the Mars Reconnaissance Orbiter (MRO) is still debated. Spiga et al. [2013] revealed that deep mesoscale convective “rocket dust storms” are likely to play an important role in forming these dust layers. To investigate how the detached dust layers are generated by this mesoscale phenomenon, and subsequently evolve at larger scales, a parameterization of rocket dust storms to represent the mesoscale dust convection is designed and included into the LMD Martian Global Climate Model (GCM). The new parameterization allows dust particles in the GCM to be transported to higher altitudes than in traditional GCMs. Combined with the horizontal transport by large‐scale winds, the dust particles spread out and form detached dust layers. During the Martian dusty seasons, the LMD GCM with the new parameterization is able to form detached dust layers. The formation, evolution and decay of the simulated dust layers are largely in agreement with the MCS observations. This suggests that mesoscale rocket dust storms are among the key factors to explain the observed detached dust layers on Mars. However, the detached dust layers remain absent in the GCM during the clear seasons, even with the new parameterization. This implies that other relevant atmospheric processes, operating when no dust storms are occurring, are needed to explain the Martian detached dust layers. More observations of local dust storms could improve the ad hoc aspects of this parameterization, such as the trigger and timing of dust injection.

With more detailed spatial information being represented in very-high-resolution (VHR) remote sensing images, stringent requirements are imposed on accurate image classification. Due to the diverse land objects with intraclass variation and interclass similarity, efficient and fine classification of VHR images especially in complex scenes are challenging. Even for some popular deep learning (DL) frameworks, geometric details of land objects may be lost in deep feature levels, so it is difficult to maintain the highly detailed spatial information (e.g., edges, small objects) only relying on the last high-level layer. Moreover, many of the newly developed DL methods require massive well-labeled samples, which inevitably deteriorates the model generalization ability under the few-shot learning. Therefore, in this article, a lightweight shallow-to-deep feature fusion network (SDF ² N) is proposed for VHR image classification, where the traditional machine learning (ML) and DL schemes are integrated to learn rich and representative information to improve the classification accuracy. In particular, the shallow spectral–spatial features are first extracted and then a novel triple-stage fusion (TSF) module is designed to learn the saliency and discriminative information at different levels for classification. The TSF module includes three feature fusion stages, that is, low-level spectral–spatial feature fusion, middle-level multiscale feature fusion, and high-level multilayer feature fusion. The proposed SDF ² N takes the advantage of the shallow-to-deep features, which can extract representative and complementary information from crossing layers. It is important to note that even with limited training samples, the SDF ² N still can achieve satisfying classification performance. Experimental results obtained on three real VHR remote sensing datasets including two multispectral and one airborne hyperspectral images covering complex urban scenarios confirm the effectiveness of the proposed approach compared with the state-of-the-art methods.

In satellite laser altimetry, it is a challenging task to accurately extract peak positions from full waveforms due to the overlapped or weak peaks within the large laser footprints, which substantially affects the subsequent applications. In this paper, to improve the laser ranging resolution and accuracy, we propose a novel approach by combining deconvolution with Gaussian decomposition. The approach is applied in two main phases: 1) the deconvolution is first used to remove the system contribution (the transmit pulse spreading over several nanoseconds, system noise); and 2) Gaussian decomposition is then adopted to extract the peak parameters of each object. Experiments using simulated and ICESat waveforms were conducted to validate and evaluate the proposed approach by comparing it to the benchmark Gaussian decomposition technique. The results indicated that: 1) the combined approach can significantly improve the peak detection rate: the four combined methods found at least 15.8% more echoes in simulated forested areas; and 2) for ICESat waveforms, the quantitative evaluation and visual assessment of the Blind -Gaussian combination obtained more echoes (on average, approximately 2.5 components) than the other combinations (on average, approximately 1.5 and 1.2 components), and the derived relative object heights were very close to the results obtained from airborne LiDAR data. These results confirmed that the Blind -Gaussian combination is more accurate for the range retrieval of vegetated and urbanized landscapes.

Solid waste detection is of great significance for environmental protection. In recent years, object detection methods based on deep learning have progressed rapidly. However, it is often extremely difficult to collect sufficient data to train a model with a good performance. In this article, a data augmentation strategy was introduced to generate sufficient synthetic high-quality images for solid waste detection. First, a modified pix2pix model was proposed, in which a local-global discriminator was designed to improve the detailed and global information of the generated images, which are commonly fuzzy with the original pix2pix model. Second, a copy-paste operator was utilized, which simply pastes the bounding box of the generated objects into different images to enhance the diversity of the samples. In this manner, the expanded dataset can be utilized to train different object detection models, for which FPN and Yolo-v4 were introduced as the validation models in this article. The experimental results show that the proposed strategy outperforms the traditional pix2pix method and the generated synthetic images can effectively improve the performance of object detection methods.

As the accuracy of satellite laser altimetry is susceptible to real-time atmospheric conditions, within footprint topography fluctuation, detector noise, and so on, we proposed a method by comprehensively analyzing the laser ranging error and evaluation labels to extract high-accuracy elevation control points that is suitable for satellite imagery-based topographic mapping applications. Using the ICESat laser altimetry data, a global high-accuracy laser altimetry dataset, including more than 60 million control points, based on the laser altimetry ranging error model and waveform quality analysis is proposed in this article. For land areas, except for areas of water, snow/ice, and polar ice sheets, the dataset can provide the elevation control points for worldwide satellite topographic mapping using high spatial resolution imageries or other science research that depend on accurate Earth’s elevation information. We further used airborne LiDAR data from six study areas around the world to carefully validate the datasets’ accuracy. The results showed that this dataset can meet the accuracy requirement of global mapping using high spatial resolution satellite imageries in terrains with a slope below 25°. Compared to the raw dataset, the proportion of footprint elevations that conform to the accuracy standard (0.5 m @ slope < 2°, 1.5 m @ 2° $\le $ slope < 6°, and 3 m @ $6^{\circ } \le $ slope $< 25^{\circ }$ ) is increased from 68.24%, 59.97%, and 26.52% to 87.58%, 90.04%, and 83.91%, respectively. This method can assure that its extracted results’ accuracy is either very close to or better than that obtained by the methods proposed in relevant studies, with a much larger number of laser footprints (LFs) that have been reserved.