For Soil Moisture And Ocean Salinity (SMOS) data, significant errors exist near coastal areas because of the contamination by the nearby land. For analyzing the origin of the land contamination, in this letter, it is found, from the global map of SMOS data error, that although the SMOS data error due to land contamination near coasts varies in different regions, the characteristic that the error increases significantly in proximity to land is similar in global scope. The detailed analysis of SMOS brightness temperature (TB) error in the selected area reveals a decreasing trend with the increase of the distance to coast (DC). An SMOS TB measurement model according to geophysical parameters of the selected area and SMOS/microwave imaging radiometer aperture synthesis antenna array is established to analyze the origin of the TB error variation due to land contamination. The simulation results, which agree with the SMOS TB data analysis, show that within 40 km of DC, TB error is large and decreases sharply from ~60 to ~4 K with the increase of DC, since the mainlobe of the antenna array is departing from land to ocean; during 40-400 km of DC, TB error decreases smoothly from ~4 to ~0.15 K, since the lower near sidelobes are observing and leaving land; TB error slightly decreases to 0 from 400 to 800 km when the lower far sidelobes leave land. The study of the origin of land contamination on ocean TB will be helpful for improving the observation of salinity near coastal areas.

The Water Cycle Observation Mission (WCOM) is an Earth science mission focused on the observation of the water cycle global climate change intensity through three different payloads. WCOM’s main payload is an interferometric microwave imager (IMI). IMI is a tri-frequency, one-dimensional aperture synthesis microwave radiometer operating at the L-, S-, and C-bands to perform measurements of soil moisture and ocean salinity. Focusing on sea surface salinity (SSS), an end-to-end simulator of WCOM/IMI has been realized and tested on climatological data. Results indicate a general agreement between original and retrieved SSS, with a single measurement root mean square error of 0.26 psu and with an orbital measurement of 0.17 psu in open sea. In accordance with previous studies, good results are obtained in open sea, while strong contamination is observed in coastal areas.

Topology identification of biological neural networks has received great attention in recent years. But most existing researches ignored the importance of linear independence condition. Once network occurs inner synchronization, such a condition would inevitably break, which could lead to the failure of topology identification. However, synchronous firing between neurons is unavoidable in biological neural network. This phenomenon makes it difficult to identify the topology of neural networks. In this paper, we proposed a new method to identify Hindmarsh-Rose Neural Networks. By this method, the original neural network can implement inner desynchronization and the topology observers can successfully identify the unknown topology. It is worth mentioning that the our method can ensure that the linear independence condition naturally holds. This means that topology identification will not meet with failure. Finally, the effectiveness of the proposed method is successfully validated by numerical simulations.

For SMOS data, significant errors exist in coastal areas because of the contamination by the nearby land. In this paper, the analysis of SMOS brightness temperature (TB) data in coastal area of South China Sea (SCS) reveals a decreasing trend with the increase of the distance to coast. A SMOS TB measurement model according to geophysical parameters and SMOS/MIRAS antenna array is established. The simulation shows that TB is large within 40 km since land is much warmer than ocean and is observed by the mainlobe of the antenna array. Then the simulation result of land contamination is used for correction of SMOS measured TB data in coastal area of SCS. Then the corrected TB is applied for sea surface salinity (SSS) retrieval, which reduces SSS outliers and improves the accuracy of SSS in coastal area of SCS.