首页> 外文会议>IFIP WG International Conference on Computer and Computing Technologies in Agricultur >Soil Moisture Estimation by Combining L-Band Brightness Temperature and Vegetation Related Information
【24h】

Soil Moisture Estimation by Combining L-Band Brightness Temperature and Vegetation Related Information

机译:通过结合L波段亮度温度和植被相关信息来土壤湿度估算

获取原文

摘要

Passive radiometry at L-band has been widely accepted as one of the most promising techniques for monitoring soil moisture content (SMC). However, with vegetation cover, the scatter and attenuation of microwave signals by vegetation make the discrimination of SMC related signal complicated. To improve SMC estimate, this study proposed the combined use of L-band brightness temperature (T_B) and optical remote sensing data to take into account the effect of vegetation. The normalized difference infrared index (NDII) and enhanced vegetation index (EVI) were used as proxy for including the effect of vegetation water content and structure. Considering viewing angle effects, T_B data were normalized to three different angles (7°, 21.5°, and 38.5°). The model based on the combination of NDII and horizontally polarized T_B normalized to 7° produced the best result (R~2= 0.678, RMSE= 0.026 m~3/m~3). It suggests that involving NDII into the model could significantly improve pasture covered SMC estimation accuracy.
机译:L频带的无源辐射物已被广泛接受作为监测土壤含水量(SMC)的最有前途的技术之一。然而,通过植被覆盖,植被的微波信号的散射和衰减使得SMC相关信号复杂的判断。为了改善SMC估计,本研究提出了L波段亮度温度(T_B)和光学遥感数据的结合使用,以考虑植被的效果。归一化差异红外指数(NDII)和增强型植被指数(EVI)用作植被水含量和结构的效果。考虑到视角效应,将T_B数据标准化为三个不同的角度(7°,21.5°和38.5°)。该模型基于NDII和水平极化T_B的组合标准化为7°的最佳结果(R〜2 = 0.678,RMSE = 0.026 m〜3 / m〜3)。它表明,涉及NDII进入模型可能会显着提高牧场覆盖的SMC估计精度。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号