As a city undergoes large-scale construction and expansion, there is an urgent need to monitor the stability of the ground and infrastructure. The time-series InSAR technique is an effective tool for measuring surface displacements. However, interpreting these displacements in a built environment, where observed displacements consist of mixed signals, poses a challenge. This study uses principal component analysis (PCA) and the k-means clustering method for exploring deformation series within an unsupervised learning context. The PCA method extracts the dominant components in deformation series, whereas the clustering method identifies similar deformation series. This method was tested on Kunming City (KMC) using C-band Sentinel-1, X-band TerraSAR-X, and L-band ALOS-2 PALSAR-2 data acquired between 2017 to 2022. The experiment demonstrated that the suggested unsupervised learning approach can group PS points with similar kinematic characteristics. Five types of deformation kinematic characteristics were discovered in the three SAR datasets: upward, slight upward, stability, slight downward, and downward. According to the results, less than 20% of points exhibit significant motion trends, whereas 50% show small velocity values but still demonstrate movement trends. The remaining 30% are relatively stable. Similar clustering results were obtained from the three datasets using unsupervised methods, highlighting the effectiveness of identifying spatial–temporal patterns over the study area. Moreover, It was found that clustering based on kinematic characteristics enhances the interpretation of InSAR deformation, particularly for points with small deformation velocities. Finally, the significance of PCA decomposition in interpreting InSAR deformation was discussed, as it can better represent series with noise, enabling their accurate identification.

ASTER (Advanced Spaceborne Thermal Emission and Reflection) satellite imagery is useful in assisting lithologic mapping and, however, its effectiveness is yet to be evaluated for lithologic complex such as tectonic mélange. The Mugagangri Group (MG), the signature unit of the Bangong-Nujiang suture zone (BNSZ), Tibet and consisting of ophiolitic mélanges, was previously mapped as a single unit due to its poorly-described internal structures and an informative map with refined lithologic subdivision is needed for future petrologic and tectonic studies. In this paper, based on a combination of field work and ASTER data analysis, the MG is mapped as five subunits according to our newly-proposed lithologic subdivision scheme. In particular, we apply a data-processing sequence to first analyze the TIR band ratios to reveal approximate distribution of carbonates and silicate-dominated lithologies and then the VNIR/SWIR band ratios and false color images to differentiate the lithologic units and delineate their boundaries. The generalized procedures of ASTER data processing and lithologic mapping are applicable for future studies in not only the BNSZ but also other Tibetan ranges. Moreover, the mapping result is consistent with that the MG represents an accretionary complex accreted to the south Qiangtang margin as a result of northward-subduction of the Bangong-Nujiang oceanic crust.

Reliable satellite-based estimation of rice grain total amino acid content (TAA) remains challenging because TAA is an indirect grain-quality trait with weak expression in broad-band canopy spectra, while multiband feature construction generates a high-dimensional predictor space relative to the limited number of field samples. This study therefore examined whether TAA-related multispectral information could be effectively enhanced, retained, and predicted under this small-sample, high-dimensional setting. Sentinel-2 Level-1C imagery and 31 composite rice grain samples collected in the Daying River Valley, Yingjiang County, China, in 2019 were used to develop a framework based on fractional-order derivative (FOD) transformation, multiband spectral-index construction, feature selection, and machine learning prediction. FOD orders of 1.2–1.8 generally strengthened spectral associations with TAA, and three-band indices showed stronger associations than two-band indices, with TBI1 (B12, B3, B7) reaching the highest correlation at order 1.6 (r = 0.641). Ablation analysis further showed that predictive performance improved progressively as FOD-transformed and multiband features were incorporated beyond the original Sentinel-2 bands and conventional vegetation indices. Elastic Net + SVM achieved the highest stratified out-of-fold performance (R² = 0.886, RMSE = 0.264%, RPD = 2.96), whereas Boruta + Stacking performed best under both four- and five-block spatial cross-validation (R² = 0.7489 and 0.7750, respectively). These results indicate that FOD-based multiband feature construction can recover useful information associated with rice grain TAA from broad-band Sentinel-2 observations, while the reduction in performance under spatial separation highlights the need for the cautious interpretation of within-study cross-validation accuracy. The resulting maps should therefore be regarded as model-assisted estimates of within-region TAA variation that can support candidate quality-zone identification and targeted field sampling under the sampled 2019 Yingjiang conditions. Validation across additional years, cultivars, and regions is required before broader operational application.