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Snow cover is a sensitive indicator of global climate change, and optical images are an important means for monitoring its spatiotemporal changes. Due to the high reflectivity, rapid change, and intense spatial heterogeneity of mountainous snow cover, Sentinel-2 (S2) and Landsat 8 (L8) satellite imagery with both high spatial resolution and spectral resolution have become major data sources. However, optical sensors are more susceptible to cloud cover, and the two satellite images have significant spectral differences, making it challenging to obtain snow cover beneath clouds and cloud shadows (CCSs). Based on our previously published approach for snow reconstruction on S2 images using the Google Earth Engine (GEE), this study introduces two main innovations to reconstruct snow cover: (1) combining S2 and L8 images and choosing different CCS detection methods, and (2) improving the cloud shadow detection algorithm by considering land cover types, thus further improving the mountainous-snow-monitoring ability. The Babao River Basin of the Qilian Mountains in China is chosen as the study area; 399 scenes of S2 and 35 scenes of L8 are selected to analyze the spatiotemporal variations of snow cover from September 2019 to August 2022 in GEE. The results indicate that the snow reconstruction accuracies of both images are relatively high, and the overall accuracies for S2 and L8 are 80.74% and 88.81%, respectively. According to the time-series analysis of three hydrological years, it is found that there is a marked difference in the spatial distribution of snow cover in different hydrological years within the basin, with fluctuations observed overall.

We studied the use of self-attention mechanism networks (SAN) and convolutional neural networks (CNNs) for forest tree species classification using unmanned aerial vehicle (UAV) remote sensing imagery in Dongtai Forest Farm, Jiangsu Province, China. We trained and validated representative CNN models, such as ResNet and ConvNeXt, as well as the SAN model, which incorporates Transformer models such as Swin Transformer and Vision Transformer (ViT). Our goal was to compare and evaluate the performance and accuracy of these networks when used in parallel. Due to various factors, such as noise, motion blur, and atmospheric scattering, the quality of low-altitude aerial images may be compromised, resulting in indistinct tree crown edges and deficient texture. To address these issues, we adopted Real-ESRGAN technology for image super-resolution reconstruction. Our results showed that the image dataset after reconstruction improved classification accuracy for both the CNN and Transformer models. The final classification accuracies, validated by ResNet, ConvNeXt, ViT, and Swin Transformer, were 96.71%, 98.70%, 97.88%, and 98.59%, respectively, with corresponding improvements of 1.39%, 1.53%, 0.47%, and 1.18%. Our study highlights the potential benefits of Transformer and CNN for forest tree species classification and the importance of addressing the image quality degradation issues in low-altitude aerial images.

The ISTTWN algorithm overcame the defect of separating the production process of skeleton points and skeleton lines in tree branch point cloud skeleton extraction and improved the accuracy of the extracted initial skeletons, but the skeletons need further optimization. In the existing skeleton optimization, it is difficult to see the stump adjustment, and most of the bifurcation optimization and skeleton smoothness adopt fitting. Based on the characteristics of the initial skeletons extracted by the ISTTWN algorithm, this research optimizes the skeleton from four aspects. An algorithm for the stump adjustment for reconstructing the stump based on the layer and hierarchical relationship and an algorithm for the bifurcation optimization based on the local branch point cloud and cosine correlation are proposed, and an existing pruning method and a skeleton smoothing method are used. The results show that the skeleton optimization method proposed or used in this research has a high computational efficiency in general and can ultimately retain the necessary skeleton lines. In a visual analysis, the optimized skeleton is obviously much more natural and more in line with the actual topology of trees. In the quantitative analysis, the completeness, accuracy and effectiveness reached 97.82%, 95.72% and 89.47%, respectively. In this study, in addition to the existing tree parameters extracted by the skeleton or generalized cylinder model, the generated skeleton is used to extract the branch attributes. The R2 of the deflection angle of the branch tip, distance from branch tip and branch length are about 0.897, 0.986 and 0.988, respectively, which illustrates that their models are very good. This research can further expand the application of the skeleton.

The downward surface shortwave radiation (DSSR) received by an inclined surface can be estimated accurately based on the mountain radiation transfer model by using the digital elevation model (DEM) and high-resolution optical remote sensing images. However, it is still challenging to obtain the high-resolution daily average DSSR affected by the atmosphere and local topography in mountain areas. In this study, the spatial downscaling and temporal extrapolation methods were explored separately to estimate the high-resolution daily average DSSR under clear-sky conditions based on Himawari-8, Sentinel-2 satellite radiation products and DEM data. The upper and middle reaches of the Heihe River Basin (UM-HRB) and the Laohugou area of Qilian Mountain (LGH) were used as the study areas because there are many ground observation stations in the UM-HRB that are convenient for DSSR spatial downscaling studies and the high-resolution instantaneous DSSR datasets published for the LHG are helpful for DSSR temporal extrapolation studies. The verification results show that both methods of spatial downscaling and temporal extrapolation can effectively estimate the daily average DSSR. A total of 3002 measurements from six observation sites showed that the 50 m downscaled results of the Himawari-8 10-min 5 km radiation products had quite a high correlation with the ground-based measurements from the UM-HRB. The coefficient of determination (R2) exceeded 0.96. The mean bias error (MBE) and the root-mean-squared error (RMSE) were about 41.57 W/m2 (or 8.22%) and 49.25 W/m2 (or 9.73%), respectively. The fifty-two measurements from two stations in the LHG indicated that the temporal extrapolated results of the Sentinel-2 10 m instantaneous DSSR datasets published previously performed well, giving R2, MBE, and RMSE values of 0.65, 41.06 W/m2 (or 7.89%) and 88.90 W/m2 (or 17.07%), respectively. By comparing the estimation results of the two methods in the LHG, it was found that although the temporal extrapolation method of instantaneous high-resolution radiation products can more finely describe the spatial heterogeneity of solar radiation in complex terrain areas, the overall accuracy is lower than that achieved with the spatial downscaling approach.

Downward surface shortwave radiation (DSSR) is the main energy source for most glacial melting, and Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat Thematic Mapper (TM) data have been used extensively in the inversion of input parameters for estimating DSSR. However, for valley glaciers under complex climatic conditions, the values of MODIS atmospheric products, especially aerosol products, are often invalid, and TM images are always saturated with snow. Furthermore, an estimation model based on optical satellite images must simultaneously consider terrain and atmospheric effects and the transient nature of ice/snow albedo. Based on a high-resolution (12 m) digital elevation model (DEM), the newly launched Sentinel-2 satellites, rather than MODIS and TM, were used to provide input data for our published mountain radiation scheme in a valley glacier. Considering Laohugou Glacier No. 12 as the study area, 62 typical Sentinel-2 scenes were selected and spatiotemporal DSSR variations on the glacier surface were obtained with a 10 m spatial resolution during a mass-balance year from September 2017 to August 2018. Ground-based measurements on 52 clear-sky days were used for validation and the mean bias error (MBE = −16.0 W/m2) and root-mean-square difference (RMSD = 73.6 W/m2) were relatively low. The results confirm that DSSR is affected mainly by the solar zenith angle and atmospheric attenuation in flat areas of valley glaciers, while in areas with complex terrain, the DSSR received by the glacier surface is affected primarily by the terrain and ice/snow albedo, which exhibits very high spatial heterogeneity.

In rugged terrain, the accuracy of surface reflectance estimations is compromised by atmospheric and topographic effects. We propose a new method to simultaneously eliminate atmospheric and terrain effects in Landsat Thematic Mapper (TM) images based on a 30 m digital elevation model (DEM) and Moderate Resolution Imaging Spectroradiometer (MODIS) atmospheric products. Moreover, we define a normalized factor of a Bidirectional Reflectance Distribution Function (BRDF) to convert the sloping pixel reflectance into a flat pixel reflectance by using the Ross Thick-Li Sparse BRDF model (Ambrals algorithm) and MODIS BRDF/albedo kernel coefficient products. Sole atmospheric correction and topographic normalization were performed for TM images in the upper stream of the Heihe River Basin. The results show that using MODIS atmospheric products can effectively remove atmospheric effects compared with the Fast Line-of-Sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) model and the Landsat Climate Data Record (CDR). Moreover, superior topographic effect removal can be achieved by considering the surface BRDF when compared with the surface Lambertian assumption of topographic normalization.