Abstract
Spatio-temporally continuous and high-quality soil moisture (SM) is very important for assessing changes in the water cycle and climate, especially over the Tibetan plateau (TP). Data fusion is an important method to improve the quality of SM product. However, limited observation overlaps between different satellite SM products, caused by inherent gaps, make it difficult to fuse them to create a continuous and high-quality product. In this study, an SM spatio-temporal continuity and quality simultaneously improving algorithm is proposed. The first step of the approach is obtaining spatio-temporally continuous reference data, including land surface temperature (LST), normalized difference vegetation index (NDVI), Albedo, and digital elevation model (DEM). The second step is training the general regression neural network (GRNN) model with all available essential climate variables (ECV) and Fengyun (FY) SM. The last step is predicting the spatio-temporally continuous and high-quality SM using the trained GRNN derived by the spatio-temporal continuity reference data. An implementation of the algorithm on the TP showed that, compared with the original ECV and FY SM, both the continuity and quality of the fused SM product were largely improved in terms of coverage (72.5%), correlation ( R = 0.809), root mean square error (0.081 cm3 cm−3) and bias (0.050 cm3 cm−3). The algorithm showed a good performance in obtaining spatio-temporal variation fusion weights over the TP. This spatio-temporally continuous and high-quality SM of the TP will help advance our understanding of global and regional changes in water cycle and climate.
Highlights
S OIL moisture (SM) plays a key role in water-energy exchanges between the land surface and the atmosphere, especially for the earth’s third pole of the Tibetan plateau (TP) [1], [2]
The general regression neural network (GRNN) was used to extract synergistic information from the original essential climate variables (ECV) and FY soil moisture (SM) based on the land surface variables
The fused SM was compared with the reconstructed ECV (ECV_Rec) and FY (FY_Rec) SM, which are spatio-temporally continuous, to show the improved quality
Summary
S OIL moisture (SM) plays a key role in water-energy exchanges between the land surface and the atmosphere, especially for the earth’s third pole of the Tibetan plateau (TP) [1], [2]. The SM is widely used in applications such as weather and climate forecasting, droughts and wildfires monitoring, and floods and landslides early warning [3]–[8]. Various active-based, passivebased, and merged SM products have been published, such as the Advanced Scatterometer (ASCAT) [9], advance microwave scanning radiometer 2 (AMSR2), soil moisture active passive (SMAP) [2], European space agency (ESA) essential climate variables (ECV) [9], and Fengyun-3B (FY-3B) [10]. There are two main disadvantages in applying these SM products. Second is low quality, including the low variation capturing ability (in correlation coefficient, R) and low accuracy [in root mean square error (RMSE)], which are very important to land surface model assimilations and flood forecasting, respectively [13]. The R and RMSE of different SM products can range from 0.194 to 0.704 and from 0.077 mm d−1 to 0.296 mm d−1 for Maqu sites over TP, respectively [14]
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