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Fluctuations in the surface water are indicators of climatic and biological environmental variations. The water index method is the predominant approach for water extraction owing to its simplicity of operation and high efficiency. Recognizing the limitations of individual water indices in extracting water over dense time-sequences, this study introduces a combined water index (CWI) frequency method to improve the water extraction results. The research findings indicate the following: 1) CWI demonstrates superior extraction accuracy for various types of water when compared with other water indices, underscoring its higher precision and broader applicability. 2) By integrating CWI with the water frequency method, we propose an effective approach for dynamically monitoring water. This method accurately reflects changes in water under different conditions within dense time-sequence images. 3) Our results highlight the method's ability to precisely monitor dynamic water changes, efficiently extract various water types from Sentinel-2 data, and its potential for large-scale surface water mapping applications.

Land surface temperature (LST) is one of the most crucial variables of surface energy processes. However, the trade-off between spatial and temporal resolutions of remote sensing data has greatly limited the availability of concurrently high-spatiotemporal resolution LST data for wide applications. Existing downscaling methods are easily affected by null values of LST data and effective time distribution of high-resolution LST data, resulting in large downscaling errors at sometimes. Within this context, this study proposes a novel spatiotemporal fusion model of LST based on diurnal variation information (BDSTFM) to predict LST data with a high temporal resolution and spatiotemporal continuity based on FY-4A and MODIS. Results indicated that the accuracy of the downscaling results was comparable to that of MODIS LST products. The BDSTFM model exhibited the following characteristics: use low-spatial resolution data to establish a diurnal temperature cycle (DTC) model for scale deduction, and retention of the temporal distribution characteristics of LST data; extend the observation time of high-spatial resolution data to improve the accuracy and stability of the model; add an invalid pixel reconstruction step that considers the LST spatiotemporal continuity, and can obtain a realistic and reliable 1-km seamless LST datasets at hourly intervals under clear skies. Compared with the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), 4-parameter DTC model, and Random Forest model, the BDSTFM model attained a higher downscaling accuracy.