Spatially and Temporally Complete Satellite Soil Moisture Data Based on a Data Assimilation Method
AbstractMultiple 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. View Full-Text
Share & Cite This Article
Xiao, Z.; Jiang, L.; Zhu, Z.; Wang, J.; Du, J. Spatially and Temporally Complete Satellite Soil Moisture Data Based on a Data Assimilation Method. Remote Sens. 2016, 8, 49.
Xiao Z, Jiang L, Zhu Z, Wang J, Du J. Spatially and Temporally Complete Satellite Soil Moisture Data Based on a Data Assimilation Method. Remote Sensing. 2016; 8(1):49.Chicago/Turabian Style
Xiao, Zhiqiang; Jiang, Lingmei; Zhu, Zhongli; Wang, Jindi; Du, Jinyang. 2016. "Spatially and Temporally Complete Satellite Soil Moisture Data Based on a Data Assimilation Method." Remote Sens. 8, no. 1: 49.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.