The main city of Kunming is located on the north bank of Dianchi Lake. The complex geological environment, large-scale construction, and expansion of the city in recent years have caused uneven land surface subsidence and threatened public safety. In this study, Sentinel-1 ascending and descending orbit datasets were collected for the period of February 2018 to May 2021. The characteristics of surface displacement in the Kunming downtown area were monitored using the time-series interferometric synthetic aperture radar (InSAR) technique, and attribution analysis was performed. It was found that areas with more severe surface settlement were concentrated in the International Exhibition Center area and the large, newly built communities near Dianchi Lake and the Xiaobanqiao Region. The multifactor attribution analysis results demonstrated that the subsidence areas are concentrated in urban villages and engineered, construction-intensive areas in the lakeside sedimentary layer area, with the maximum displacement rate reaching −23.12 mm/a in the line-of-sight direction of the Sentinel-1 ascending dataset. The reliability of the InSAR results was cross-validated with ascending and descending results. This study provides a scientific reference for urban development planning and potential geological disaster detection in Kunming.

The Open-Source Digital Elevation Model (DEM) is fundamental data of the geoscientific community. However, the variation of its accuracy with land cover type and topography has not been thoroughly studied. This study evaluates the accuracy of five globally covered and open-accessed DEM products (TanDEM-X90 m, SRTEM, NASADEM, ASTER GDEM, and AW3D30) in the mountain area using ICESat/GLAS data as the GCPs. The robust evaluation indicators were utilized to compare the five DEMs’ accuracy and explore the relationship between these errors and slope, aspect, landcover types, and vegetation coverage, thereby revealing the consistency differences in DEM quality under different geographical feature conditions. The Taguchi method is introduced to quantify the impact of these surface characteristics on DEM errors. The results show that the slope is the main factor affecting the accuracy of DEM products, accounting for about 90%, 81%, 85%, 83%, and 65% for TanDEM-X90, SRTM, NASADEM, ASTER GDEM, and AW3D30, respectively. TanDEM-X90 has the highest accuracy in very flat areas (slope < 2°), NASADEM and SRTM have the greatest accuracy in flat areas (2 ≤ slope < 5°), while AW3D30 accuracy is the best in other cases and shows the best consistency on slopes. This study makes a new attempt to quantify the factors affecting the accuracy of DEM, and the results can guide the selection of open-source DEMs in related geoscience research.

The quantitative retrieval of the chlorophyll-a concentration is an important remote sensing method that is used to monitor the nutritional status of water bodies. The high spatial resolution of the Sentinel-2 MSI and its subdivision in the red-edge band highlight the characteristics of water chlorophyll-a, which is an important detection tool for assessing water quality parameters in plateau lakes. In this study, the Nine Plateau Lakes in the Yunnan-Kweichow Plateau of China were selected as the study area. Using Sentinel-2 MSI transit images and in situ measured chlorophyll-a concentration as the data source, the chlorophyll-a concentrations of plateau lakes (CCAPLs) were investigated, and the surface temperatures of plateau lakes (STPLs) were retrieved to verify the hypothesis that the lake surface temperature could increase the chlorophyll-a concentration. By comparing feature importance using a random forest (RF), the Sentinel-2 MSI surface reflectance and in situ data were linearly fitted using four retrieval spectral indices with high feature importance, and the accuracy of the estimated concentration of chlorophyll-a was evaluated by monitoring station data in the same period. Then, Landsat-8 TIRS Band 10 data were used to retrieve the STPL with a single-channel temperature retrieval algorithm and to verify the correlation between the STPL and the CCAPL. The results showed that the retrievals of the CCAPL and the STPL were consistent with the actual situation. The root-mean-square error (RMSE) of the fifteenth normalized difference chlorophyll-a index (NDCI15) was 0.0249. When the CCAPL was greater than 0.05 mg/L and the STPL was within 28–34 °C, there was a positive linear correlation between the CCAPL and the STPL. These results will provide support for the remote sensing monitoring of eutrophication in plateau lakes and will contribute to the scientific and effective management of plateau lakes.