多角度c波段SAR数据在土壤水分反演中的潜力

IEEE 1999 International Geoscience and Remote Sensing Symposium. IGARSS'99 (Cat. No.99CH36293) Pub Date : 1999-06-28 DOI:10.1109/IGARSS.1999.774552

G. Satalino, G. Pasquariello, F. Mattia, T. Le Toan, M. Davidson, M. Borgeaud

{"title":"多角度c波段SAR数据在土壤水分反演中的潜力","authors":"G. Satalino, G. Pasquariello, F. Mattia, T. Le Toan, M. Davidson, M. Borgeaud","doi":"10.1109/IGARSS.1999.774552","DOIUrl":null,"url":null,"abstract":"A retrieval algorithm to estimate soil moisture content from C-band SAR data is presented. The algorithm consists of a neural network trained with simulated data obtained by exploiting the LEM model. The algorithm performances are assessed as a function of current and future SAR configurations by using both experimental and simulated data.","PeriodicalId":169541,"journal":{"name":"IEEE 1999 International Geoscience and Remote Sensing Symposium. IGARSS'99 (Cat. No.99CH36293)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1999-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"The potential of multi angle C-band SAR data for soil moisture retrieval\",\"authors\":\"G. Satalino, G. Pasquariello, F. Mattia, T. Le Toan, M. Davidson, M. Borgeaud\",\"doi\":\"10.1109/IGARSS.1999.774552\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A retrieval algorithm to estimate soil moisture content from C-band SAR data is presented. The algorithm consists of a neural network trained with simulated data obtained by exploiting the LEM model. The algorithm performances are assessed as a function of current and future SAR configurations by using both experimental and simulated data.\",\"PeriodicalId\":169541,\"journal\":{\"name\":\"IEEE 1999 International Geoscience and Remote Sensing Symposium. IGARSS'99 (Cat. No.99CH36293)\",\"volume\":\"16 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1999-06-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE 1999 International Geoscience and Remote Sensing Symposium. IGARSS'99 (Cat. No.99CH36293)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IGARSS.1999.774552\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE 1999 International Geoscience and Remote Sensing Symposium. IGARSS'99 (Cat. No.99CH36293)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IGARSS.1999.774552","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 1

摘要

提出了一种基于c波段SAR数据估算土壤含水量的检索算法。该算法由利用LEM模型获得的模拟数据训练的神经网络组成。通过使用实验和模拟数据，评估了算法性能作为当前和未来SAR配置的函数。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

The potential of multi angle C-band SAR data for soil moisture retrieval

A retrieval algorithm to estimate soil moisture content from C-band SAR data is presented. The algorithm consists of a neural network trained with simulated data obtained by exploiting the LEM model. The algorithm performances are assessed as a function of current and future SAR configurations by using both experimental and simulated data.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

IEEE 1999 International Geoscience and Remote Sensing Symposium. IGARSS'99 (Cat. No.99CH36293)

自引率

0.00%

发文量