Soil moisture sensing by L-band radiometry for prairie grassland

Yuei An Liou, Y. C. Tzeng, K. S. Chen

研究成果: 會議貢獻類型會議論文同行評審

1 引文 斯高帕斯(Scopus)

摘要

A Land Surface Process/Radiobrightness (LSP/R) model is integrated with a Dynamic Learning Neural Network (DLNN) to examine the impact of L-band on radiometric sensing of soil moisture for prairie grassland. The LSP/R model predictions of brightness temperature, and surface parameters are used as the training and evaluating data for the DLNN. Both horizontally- and vertically-polarized brightnesses at 1.4, 19, and 37 GHz for an incidence angle of 53 degrees make up the input nodes of the DLNN. The corresponding output nodes compose of land surface parameters, canopy temperature and water content, and soil temperature and moisture (uppermost 5 mm). Under no noise conditions, the root mean square (rms) errors between the retrieved surface parameters and the reference are smaller than 2% for a four-channel case with 19 and 37 GHz brightnesses as the inputs of the DLNN. The rms errors are reduced to within 0.5% if additional 1.4 GHz brightnesses are used (a six-channel case). The results demonstrate that 1.4 GHz is a better frequency in probing soil parameters than 19 and 37 GHz. In addition, the proposed inversion approach on the radiometric sensing of the land surface parameters is promising.

原文???core.languages.en_GB???
頁面1846-1848
頁數3
出版狀態已出版 - 1998
事件Proceedings of the 1998 IEEE International Geoscience and Remote Sensing Symposium, IGARSS. Part 1 (of 5) - Seattle, WA, USA
持續時間: 6 7月 199810 7月 1998

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???event.eventtypes.event.conference???Proceedings of the 1998 IEEE International Geoscience and Remote Sensing Symposium, IGARSS. Part 1 (of 5)
城市Seattle, WA, USA
期間6/07/9810/07/98

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