Satellites provide global long-time series of spatio-temporal continuous CO₂ observations. However, it is difficult to be applied to the study of small-scale carbon cycle because of its low spatial resolution. In this paper, the Greenhouse Gases Observing SATellite (GOSAT) XCO₂ data are super-resolution reconstructed using bicubic interpolation, which improved the spatial resolution from 2.5° to 0.5°. CO₂ measurements from ten selected TCCON sites are used to compare with the reconstructed GOSAT. Further, the high accuracy Orbiting Carbon Observatory-2 (OCO-2) data analysed by the combination of geographical grid statistics and kriging is used to evaluate the reconstructed data. The results show that compared with the original GOSAT data, the reconstructed GOSAT data not only improves the spatial resolution but also has little loss of the average accuracy. The mean error of original data has significant seasonal fluctuations with a peak from February to March and a trough from June to July.

With the development of agricultural and rural modernization, the informatization of rural roads has been an inevitable requirement for promoting rural revitalization. To date, however, the vast majority of road extraction methods mainly focus on urban areas and rely on very high-resolution satellite or aerial images, whose costs are not yet affordable for large-scale rural areas. Therefore, a deep learning (DL)-based super-resolution mapping (SRM) method has been considered to relieve this dilemma by using freely available Sentinel-2 imagery. However, few DL-based SRM methods are suitable due to these methods only relying on the spectral features derived from remote sensing images, which is insufficient for the complex rural road extraction task. To solve this problem, this paper proposes a spatial relationship-informed super-resolution mapping network (SRSNet) for extracting roads in rural areas which aims to generate 2.5 m fine-scale rural road maps from 10 m Sentinel-2 images. Based on the common sense that rural roads often lead to rural settlements, the method adopts a feature enhancement module to enhance the capture of road features by incorporating the relative position relation between roads and rural settlements into the model. Experimental results show that the SRSNet can effectively extract road information, with significantly better results for elongated rural roads. The intersection over union (IoU) of the mapping results is 68.9%, which is 4.7% higher than that of the method without fusing settlement features. The extracted roads show more details in the areas with strong spatial relationships between the settlements and roads.