Spatial downscaling is an important approach to obtain high-resolution land surface temperature (LST) for thermal environment research. However, existing downscaling methods are unable to sufficiently address both spatial heterogeneity and complex nonlinearity, especially in high-resolution scenes (<120 m), and accordingly limit the representation of regional details and accuracy of temperature inversion. In this study, by integrating normalized difference vegetation index (NDVI), normalized difference building index (NDBI), digital elevation model (DEM), and slope data, a high-resolution surface temperature downscaling method based on geographically neural network weighted regression (GNNWR) was developed to effectively handle the problem of surface temperature downscaling. The results show that the proposed GNNWR model achieved superior downscaling accuracy (maximum R² of 0.974 and minimum RMSE of 0.896 °C) compared to widely used methods in four test areas with large differences in topography, landforms, and seasons. We also achieved the best extracted and most detailed spatial textures. Our findings suggest that GNNWR is a practical method for surface temperature downscaling considering its high accuracy and model performance.

Conventional deep learning-based methods for single remote sensing image super-resolution (SRSISR) have made remarkable progress. However, the super-resolution (SR) outputs of these methods are yet to become sufficiently satisfactory in visual quality. Recent Diffusion model-based generative deep learning models are capable to enhance the visual quality of output images, but this capability is limited due to their sampling efficiency. In this paper, we propose FastDiffSR, an SRSISR method based on a conditional Diffusion model. Specifically, we devise a novel sampling strategy to reduce the number of sampling steps required by the Diffusion model while ensuring the sampling quality. Meanwhile, the residual image is adopted to reduce computational costs, demonstrating that integrating channel attention and spatial attention begets a further improvement in the visual quality of output images. Compared to the state-of-the-art (SOTA) CNN-based, GAN-based, and Transformer-based SR methods, our FastDiffSR improves the learned perceptual image patch similarity (LPIPS) by 0.1-0.2 and achieves better visual results in some real-world scenes. Compared with existing Diffusion-based SR methods, our FastDiffSR achieves significant improvements in pixel-level evaluation metric peak signal-noise ratio (PSNR) while having smaller model parameters, and obtaining better SR results on Vaihingen data with faster inference time by 2.8-28 times, showing excellent generalization ability and time efficiency. Our code will be open source at https://github.com/Meng-333/FastDiffSR.

The per capita carbon balance and carbon compensation zoning of Chinese counties from the perspective of major function-oriented zones is important for realizing the carbon peaking and carbon neutral target. In this study, the Kernel-K-means++ algorithm is used and a more comprehensive per capita carbon compensation zoning model is constructed. Based on this, combined with the major function-oriented zones, Chinese counties are divided into per capita carbon compensation-type zones. Further, spatial and temporal characteristics are detected, and suggestions for optimizing low-carbon development are put forward. The main results are as follows: (1) From 2000 to 2017, the per capita carbon emissions (PCO₂) of Chinese counties were large and showed a trend of stable expansion and a southeast–northwest pattern; (2) the per capita carbon emissions of key development zones accounted for the largest proportion of emissions; (3) there were 1410 payment zones, 170 balanced zones, and 242 compensated zones among China’s counties; and (4) 11 types of carbon compensation space optimization zones were finally formed, and low-carbon development directions and strategies were proposed for each type of area. Based on this, this study promotes regional carbon emissions management and reduction in China and provides a reference for other regions to reduce emissions.

Conventional methods based on bicubic degradation assumption for single remote sensing image super-resolution (SRSISR) have achieved remarkable progress. However, they would possibly fail in plenty of real-world scenarios due to the emergence of complex and diverse degradation factors. Therefore, devising the blind super-resolution (SR) method for remote sensing images towards the degradation of unknown blur kernel is of significant value. In this paper, we propose DiffBSR, an SRSISR framework based on the Diffusion model. Specifically, the proposed conditional Diffusion model contains a forward diffusion process and a reverse generation process. The reverse process employs a convolutional neural network (CNN)-based SR network and a degradation representation network to help the model generate high-resolution images with clear vision and accurate semantics. The former controls the generation direction by generating conditional guidance images corresponding to low resolution images. The latter can represent the degradation information as a feature vector to assist the super-resolution network in flexibly adapting to different degradations. Compared with the state-of-the-art CNN-based methods, our DiffBSR improves the learned perceptual image patch similarity (LPIPS) metric by 32%-76% and achieves higher segmentation accuracy in classification evaluation experiments. In addition, more visual results and higher scores in evaluation metrics are obtained on the Gaofen satellite data, demonstrating the potential of the proposed framework for applications with real-world remote sensing images. Our code will be available at https://github.com/Meng-333/DiffBSR.

Land surface temperature (LST) has a wide application in Earth Science-related fields, and spatial downscaling is an important method to retrieve high-resolution LST data. However, existing LST downscaling methods have difficulties in simultaneously constructing and expressing spatial non-stationarity, spatial autocorrelation, and complex non-linearity during the LST downscaling process, which limits the performance of the models. Moreover, there is a lack of research on high-resolution nighttime land surface temperature (NLST) reconstruction based on spatial downscaling, which does not meet the data needs for urban-scale nighttime urban heat island (UHI) studies. Therefore, this study combined Geographically Neural Network Weighted Regression (GNNWR) with Area-to-Point Kriging interpolation (ATPK) to propose a Geographically Neural Network Weighted Regression Kriging (GNNWRK) model for NLST downscaling. To verify the model’s generality and robustness, this study selected four study areas with different landform and climate type for NLST spatial downscaling experiments. The GNNWRK was compared with four benchmark downscaling methods, including TsHARP, Random Forest, Geographically Weighted Regression, and GNNWR. The results show that compared to these four benchmark methods, the GNNWRK method has higher accuracy in NLST downscaling, with a maximum Pearson’s Correlation Coefficient (Pcc) of 0.930 and a minimum Root Mean Square Error (RMSE) of 0.886 K. Moreover, the validation based on MODIS NLST data and ground-measured NLST data also indicates that the GNNWRK model can obtain more accurate, high-resolution NLST with richer and more detailed texture. This enhances the potential of NLST in studying the effects of urban nighttime heat islands at a finer scale.

Spatial downscaling is an effective way to improve the spatial resolution of precipitation products. However, the existing methods often fail to adequately consider the spatial heterogeneity and complex nonlinearity between precipitation and surface parameters, resulting in poor downscaling performance and inaccurate expression of regional details. In this study, we propose a precipitation downscaling model based on geographically neural network weighted regression (GNNWR), which integrates normalized difference vegetation index, digital elevation model, land surface temperature, and slope data to address spatial heterogeneity and complex nonlinearity. We explored the spatiotemporal trends of precipitation in the Sichuan region over the past two decades. The results show that the GNNWR model outperforms common methods in downscaling precipitation for the four distinct seasons, achieving a maximum R² of 0.972 and a minimum RMSE of 3.551 mm. Overall, precipitation in Sichuan Province exhibits a significant increasing trend from 2001 to 2019, with a spatial distribution pattern of low in the northwest and high in the southeast. The GNNWR downscaled results exhibit the strongest correlation with observed data and provide a more accurate representation of precipitation spatial patterns. Our findings suggest that GNNWR is a practical method for precipitation downscaling considering its high accuracy and model performance.