摘要
为了有效解决大尺度区域土壤水分时、空间变化监测问题,在总结了被动微波遥感反演土壤湿度规律的基础上,基于先进的AMSR-E星载被动微波遥感数据,提出了利用双谱模型计算土壤表面发射率的计算方法.首先需要由双站散射系数计算反射率和发射率,然后应用人工神经网络反演土壤湿度,实现了在随机粗糙面状况下基于被动微波遥感的土壤表面水分反演,并在实验区进行了成功的应用.结果表明:其成果对于利用星载被动微波遥感反演土壤湿度具有一定的推广意义.
In order to effectively solve the problem of monitoring time-space changes of soil moisture in large-scale regions, according to the conclusion of soil moisture retrievingby passive microwave remote sensing, a methodology of the soil surface emissivity was proposed by model BSM based on AMSR-E microwave remote sensing data, proof of the relationship between surface soil moisture and surface emissivity. A suitable artificial neural network models was established, and the emissivity for bare soil surface is calculated based on the theoretical backscattering model BSM. The results show that it＇s feasible to invert soil moisture by artificial neural network （ANN） based on BSM model and the technology is of promoting significance.
出处
《中国矿业大学学报》
EI
CAS
CSCD
北大核心
2007年第2期262-265,共4页
Journal of China University of Mining & Technology
基金
博士后研究基金项目(B2536)
关键词
微波遥感
发射率
土壤湿度
BSM模型
人工神经网络
microwave remote sensing
emissivity
soil moisture
BSM model
artificial neural network
