Satellite-based remote sensing technologies are utilized extensively to investigate urban thermal environment under rapid urban expansion. Current Moderate Resolution Imaging Spectroradiometer (MODIS) data are, however, unable to adequately represent the spatially detailed information because of its relatively coarser spatial resolution, while Landsat data cannot explore the temporally continued analysis due to the lower temporal resolution. Combining MODIS and Landsat data, “Landsat-like” data were generated by using the Flexible Spatiotemporal Data Fusion method (FSDAF) to measure land surface temperature (LST) variations, and Landsat-like data including Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built Index (NDBI) were generated to analyze LST dynamic driving forces. Results show that (1) the estimated “Landsat-like” data are capable of measuring the LST variations; (2) with the urban expansion from 2013 to 2016, LST increases ranging from 1.80 °C to 3.92 °C were detected in areas where the impervious surface area (ISA) increased, while LST decreases ranging from −3.52 °C to −0.70 °C were detected in areas where ISA decreased; (3) LST has a significant negative correlation with the NDVI and a strong positive correlation with NDBI in summer. Our findings can provide information useful for mitigating undesirable thermal conditions and for long-term urban thermal environmental management.

The landscape visual effect of a city, which is generated by its long-term development, is an important index in city planning. In this study, we build a quantitative evaluation and remote sensing estimation scheme of landscape visual effect. The study contains two main steps. First, utilizing the Elo rating system and in situ sampled panoramic pictures, the quantitative assessment of the city landscape visual effect was carried out. Then, the landscape visual effect estimation model was built and applied to Landsat remote sensing image to generate the spatial distribution of landscape visual effect in Zhengzhou city, 2017. At last, the effect of different combination of land use and elevation to the landscape visual effect was discussed. The results showed the following: (1) the Elo rating system is an effective method to quantitatively evaluate the city landscape visual effect; (2) the landscape visual effect remote sensing estimation model had a good performance, with the mean absolute percentage error (MAPE) and root mean square error (RMSE) of the model are less than 0.05 and 80, respectively; (3) the landscape visual effect score of Zhengzhou city, 2017, was high in the southwest and low in the northeast; (4) different land use situation and average surface elevation had a complex influence on the landscape visual effect.