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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.

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Aiming at the problem that the steady-state error and response speed cannot be taken into account in the traditional fixed-step incremental conductance method, combined with the current–voltage (I–U) characteristic curve of photovoltaic (PV) panel, this paper proposes a novel and improved variable-step incremental conductance method. The proposed tracking strategy of maximum power point (MPP) analyzes the I–U characteristic curve, and divides the I–U characteristic curve into four sections, each section has a different variable step size, thus realizing the variable step size control of global four-section. The traditional incremental conductance method and the improved incremental conductance method are simulated under the environment of Matlab/Simulink. The simulation results show that when the light intensity changes rapidly, the novel improved incremental conductance method not only has the advantages of no oscillation in steady state and fast response speed, but also improve the efficiency of photovoltaic power generation, which better realizes the fast and accurate tracking of MPP.