Urban land cover (ULC) serves as fundamental environmental information for urban studies, while accurate and timely ULC mapping remains challenging due to cloud contamination in tropical and subtropical areas. Synthetic aperture radar (SAR) has excellent all-weather working capability to overcome the challenge, while optical SAR data fusion is often required due to the limited land surface information provided by SAR. However, the mechanism by which SAR can compensate optical images, given the occurrence of clouds, in order to improve the ULC mapping, remains unexplored. To address the issue, this study proposes a framework, through various sampling strategies and three typical supervised classification methods, to quantify the ULC classification accuracy using optical and SAR data with various cloud levels. The land cover confusions were investigated in detail to understand the role of SAR in distinguishing land cover under different types of cloud coverage. Several interesting experimental results were found. First, 50% cloud coverage over the optical images decreased the overall accuracy by 10–20%, while the incorporation of SAR images was able to improve the overall accuracy by approximately 4%, by increasing the recognition of cloud-covered ULC information, particularly the water bodies. Second, if all the training samples were not contaminated by clouds, the cloud coverage had a higher impact with a reduction of 35% in the overall accuracy, whereas the incorporation of SAR data contributed to an increase of approximately 5%. Third, the thickness of clouds also brought about different impacts on the results, with an approximately 10% higher reduction from thick clouds compared with that from thin clouds, indicating that certain spectral information might still be available in the areas covered by thin clouds. These findings provide useful references for the accurate monitoring of ULC over cloud-prone areas, such as tropical and subtropical cities, where cloud contamination is often unavoidable.

Coastal urbanization is a key driver of mangrove loss, yet its global impacts on mangroves have yet to be thoroughly understood. Here we present a fine-scale assessment of the hidden impacts of urbanization on mangroves mediated by climate, and the joint effects of urbanization and climate at the global scale. Surprisingly, both urbanization and climate had positive impacts on mangrove growth and carbon stock in some regions, which is different from the general belief of the adverse impacts from previous research. In total, 27.3% of global mangroves received positive impacts from urbanization regarding their extent and carbon stock, among which 59.5% are indirectly mediated by climate. Moreover, mangroves in subtropical/temperate climate zones experienced more indirect positive impacts from urbanization, which enhances local climate conditions for growth by altering temperature, rainfall and sea levels. These findings suggest the feasibility of facilitating mangrove conservation through effective urban planning to achieve coastal sustainability.

Lossy compression causes the degradation of the classification accuracy of remote sensing (RS) images due to the introduced distortion by compression. In this letter, a convolutional sparse coding (CSC)-based method is proposed to quantitatively measure such an effect. In detail, the filters used in CSC are learned by online convolutional dictionary learning (OCDL) to construct the dictionary. Thereafter, the sparse coefficient maps are obtained based on the alternating direction method of multipliers (ADMM) algorithm. In addition, multiple kernel learning (MKL) is used to estimate the corresponding classification accuracy. The experimental results demonstrate that our method performs better in predicting the classification accuracy of RS images compared with the other state-of-the-art algorithms.