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

机译:L波段亮温与植被相关信息相结合的土壤湿度估算

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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, TB 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波段的被动辐射法已被广泛接受为监测土壤水分含量(SMC)的最有前途的技术之一。然而,在植被覆盖的情况下,植被对微波信号的散射和衰减使得SMC相关信号的判别变得复杂。为了提高SMC估计值,本研究提出了结合使用L波段亮度温度(T_B)和光学遥感数据来考虑植被的影响。归一化差异红外指数(NDII)和增强植被指数(EVI)被用作代表植被含水量和结构影响的指标。考虑到视角影响,将TB数据归一化为三个不同的角度(7°,21.5°和38.5°)。基于NDII和水平极化T_B归一化到7°的模型产生了最佳结果(R〜2 = 0.678。RMSE = 0.026 m〜3 / m〜3)。这表明将NDII纳入模型可以显着提高牧场覆盖的SMC估算准确性。

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