As the largest freshwater lake in China, Poyang Lake is an internationally important wetland and the largest migratory bird habitat in Asia. Many sub-lakes distributed in the lake basin are seasonal lakes, which have a significant impact on hydro-ecological processes and are susceptible to various changes. In this study, using multi-source remote sensing data, a continuous time-series construction method of water coverage suitable in Poyang Lake was developed. That method combined the downscaling of the MNDWI (modified normalized difference water index) with the ISODATA (iterative self-organizing data analysis technique algorithm), and its accuracy can be up to 97% in the months when Landsat 8 is available or 87% when it is unavailable. Based on that method, the increasing variation in water coverage was observed in the sub-lakes of Poyang Lake during 2013–2020 to be within a range of 200–690 km2 normally. The center of the sub-lakes always remained inundated (>80% inundation frequency), while the surrounding areas were probably kept dry for seven months (except for June to September). The dominant influencing factors of water coverage variations were different in different hydrological periods (wet season and dry–wet season: discharge; dry season: temperature and wind speed; wet–dry season: temperature and precipitation). In addition, “returning farmland to lakes” affected the increase in the water area in the sub-lakes. This study is helpful for the management of water resources and the protection of migratory birds in the Poyang Lake region.
Soil moisture (SM) is an important parameter in all environments because it affects the relationship between the land surface and atmospheric processes. Therefore, finding products that can accurately measure SM is critical to improving drought management. The objective of this study was to investigate the accuracy of satellite data from SM produced by the China Meteorological Administration Land Data Assimilation System (CLDAS), focusing on the Huaihe and Heihe River basins in China, as both are prone to drought. To verify the accuracy of the daily surface data SM, measurements were obtained from 34 meteorological stations between January and December 2016. In addition, CLDAS measurement data were collected at a depth of 10 cm and 40 cm and compared with observed soil moisture measurements (OBS SM). The results show that the agreement of CLDAS SM with OBS was R > 0.66 at 10 cm and R > 0.47 at 40 cm in the Huaihe River Basin. R > 0.63 at 10 cm and R > 0.44 at 40 cm was observed in Heihe River Basin.
This study calibrated a refined split-window algorithm for land surface temperature (LST) retrieval based on Fengyun-2D (FY-2D) meteorological satellite. First, FY-2D land surface emissivity (LSE) was predicted from Moderate-resolution Imaging Spectroradiometer (MODIS) LSE based on sensors spectral similarities. The retrieved FY-2D LST data were validated in an arid region where the traditional split-window algorithm generally performed unsatisfactorily. Validation results show R 2 (coefficient of determination) and RMSE (root mean square error) values range 0.53-0.67 and 2.86-6.21 K, respectively, against ground observed LST. Better LST retrievals were observed over vegetated regions with an RMSE value of ~2.8 K. Spatially, the FY-2D LST was highly correlated (R 2 = 0.83) with and showed marginal differences (±2 K) from MODIS LST for ~40% of the whole area.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.