With advantages of multispatial resolutions, a high retrieval accuracy, and a high temporal resolution, the satellite-derived land surface temperature (LST) products are very important LST sources. However, the greatest barrier to their wide application is the invalid values produced by large quantities of cloudy pixels, especially for regions frequently swathed in clouds. In this study, an effective method based on the land energy balance theory and similar pixels (SP) method was developed to reconstruct the LSTs over cloudy pixels for the widely used MODIS LST (MOD11A1). The southwest region of China was selected as the study area, where extreme drought has frequently occurred in recent years in the context of global climate change and which commonly exhibits cloudy and foggy weather. The validation results compared with in situ LSTs showed that the reconstructed LSTs have an average error < 1.00 K (0.57 K at night and −0.14 K during the day) and an RMSE < 3.20 K (1.90 K at night and 3.16 K in the daytime). The experiment testing the SP interpolation indicated that the spatial structure of the LST has a greater effect on the SP performance than the size of the data-missing area, which benefits the LST reconstruction in the area frequently covered by large clouds.

Inland lakes play a crucial role in monitoring global climate change and managing responses to extreme weather events, with lake elevation being critical for assessing their regulatory capacities. However, due to the limited temporal resolution of current altimetry satellites, obtaining high-frequency, high-precision elevation data for water bodies remains challenging. Consequently, most studies utilize elevation-area (E-A) models constructed from historical elevation and area records, integrated with area observations from high-temporal resolution optical satellites to infer precise water levels. Yet, the construction of the E-A model often assumes a uniform water level across the lake, thus overlooking potential segmentation during dry periods. To address this, our study implemented a zone-based approach, utilizing hydrological connectivity principles to ensure that elevation data within E-A models are confined to appropriate zonal regions. This method effectively minimized uncertainties by preventing errors from zonal discrepancies, significantly improving accuracy compared to traditional methods. It reduced root mean square errors (RMSE) by 0.71–1.73 m during the dry season, achieving RMSEs of 0.35, 0.64, and 0.37 m across three segments. Furthermore, this method ensures water level data are confined to specific zones, preventing the inconsistencies typically caused by averaging data across multiple stations or selecting data from varying elevations. This consistent domain definition reduces extrapolation errors during the model prediction and inversion. Moreover, the method compensates for time information losses often incurred by relying on multi-year percentile charts, thereby enabling more precise aquatic boundary delineation than traditional regional boundaries.

Analyzing the propagation dynamics from meteorological drought (MD) to hydrological drought (HD) is essential for sustainable water resource management, particularly under climate change. This study analyzed the multidimensional propagation characteristics and their driving factors from MD to HD in the Jialing River Basin from 1993 to 2020. The temporal characteristics of drought propagation were analyzed using monthly and daily drought indices, with a focus on variations in initiation lag times across seasons and drought grades. The attenuation and amplification effects during drought propagation were quantified using event propagation ratios, while examining the differential propagation patterns across different drought grades. Additionally, the Geographical Detector Model was employed to identify the main drivers of spatial heterogeneity in hydrological drought response rates. The main findings are as follows: (1) at the daily scale, the initiation stage had the shortest lag, while peak and termination stages showed longer lags. Seasonal and drought grade variations were observed in the initiation lag, with shorter lags in summer and autumn. (2) Drought propagation from MD to HD resulted in an attenuation of maximum intensity, while duration and severity were amplified. (3) Spatial heterogeneity in HD response rate was mainly influenced by evaporative conditions, vegetation cover, and topography.