Abstract
Multiple soil moisture products have been generated from data acquired by satellite. However, these satellite soil moisture products are not spatially or temporally complete, primarily due to track changes, radio-frequency interference, dense vegetation, and frozen soil. These deficiencies limit the application of soil moisture in land surface process simulation, climatic modeling, and global change research. To fill the gaps and generate spatially and temporally complete soil moisture data, a data assimilation algorithm is proposed in this study. A soil moisture model is used to simulate soil moisture over time, and the shuffled complex evolution optimization method, developed at the University of Arizona, is used to estimate the control variables of the soil moisture model from good-quality satellite soil moisture data covering one year, so that the temporal behavior of the modeled soil moisture reaches the best agreement with the good-quality satellite soil moisture data. Soil moisture time series were then reconstructed by the soil moisture model according to the optimal values of the control variables. To analyze its performance, the data assimilation algorithm was applied to a daily soil moisture product derived from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), the Microwave Radiometer Imager (MWRI), and the Advanced Microwave Scanning Radiometer 2 (AMSR2). Preliminary analysis using soil moisture data simulated by the Global Land Data Assimilation System (GLDAS) Noah model and soil moisture measurements at a multi-scale Soil Moisture and Temperature Monitoring Network on the central Tibetan Plateau (CTP-SMTMN) was performed to validate this method. The results show that the data assimilation algorithm can efficiently reconstruct spatially and temporally complete soil moisture time series. The reconstructed soil moisture data are consistent with the spatial precipitation distribution and have strong positive correlations with the values simulated by the GLDAS Noah model over large areas of the region. Compared to the soil moisture measurements at the medium and large networks, the reconstructed soil moisture data have almost the same accuracy as the soil moisture product derived from AMSR-E/MWRI/AMSR2 for ascending and descending orbits.
Highlights
Soil moisture is one of the key parameters for the environment and climate system
This research has developed a data assimilation algorithm to fill the gaps in the satellite soil moisture products and to generate spatially and temporally complete soil moisture data
Soil moisture time series were reconstructed by the soil moisture model using the optimal values of the control variables
Summary
Soil moisture is one of the key parameters for the environment and climate system. It influences hydrological and agricultural processes and impacts the climate system through atmospheric feedback loops. The presence of data gaps limits the application of soil moisture in land surface process simulation, climatic modeling, and global change research. Wang et al [16] introduced a penalized least-squares method based on three-dimensional discrete cosine transforms for the purpose of filling data gaps in a global soil moisture product derived from satellite images. The simulated soil moisture data provide spatially and temporally consistent time series, but their accuracy is hindered by model deficiencies, and uncertainties in both model parameters and atmospheric forcing variables [14,29]. The meteorological data used to force the soil moisture model and the satellite soil moisture product used in this study are briefly described .
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