摘要
该文阐述了中纬度地区春季裸露耕地土壤水分监测的重要性,对比分析了目前几种主要的土壤水分反演方法。为提高裸露耕地土壤水分的监测精度,在分析AIEM土壤发射率模拟数据库的基础上,提出了一种基于L波段单角度双极化被动微波遥感数据的土壤湿度和土壤粗糙度的反演算法,并对新反演算法的创新性和可行性进行了基于仿真数据的论证。该反演算法以地表温度作为辅助数据,利用L波段47°双极化的微波亮温数据进行土壤湿度和粗糙度的反演。仿真数据的验证结果表明,在充分考虑土壤温度、土壤质地等辅助数据测量误差的条件下,算法对土壤湿度和土壤粗糙度的反演结果与AIEM模型输入值的均方根误差分别为0.0148和0.0461。
The importance of monitoring soil moisture of the cultivated land in the spring is discussed and the current several major inversion methods of soil moisture content is analyzed comparatively in this paper. In order to improve the soil moisture monitoring accuracy of the cultivated land,a new soil moisture content（SMC） inversion algorithm is proposed based on the analysis of soil emissivity database simulated by AIEM model. This new inversion algorithm and coefficient of the parameterized model developed by us can apply to most of the natural terrain. The soil moisture and roughness can be retrieved by using dual-polarized microwave brightness temperature data of 47 observing angle ancillary the soil temperature products retrieved from thermal infrared remote sensing data. Though initial verification,the new algorithm shows high inversion accuracy and the inversion accuracy of SMC and soil roughness are 0.0148 and 0.0461 respectively.
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2010年第11期24-29,共6页
Transactions of the Chinese Society of Agricultural Engineering
基金
国家自然科学基金重点项目(40730525)
中国科学院知识创新工程(KZCX2-YW-313)资助
关键词
土壤湿度
遥感
最佳角度算法
发射率
soil moisture
remote sensing
the optimum angle algorithm
emissivity
