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Microwave remote sensing of soil moisture with vegetation effect

机译:微波遥感土壤水分与植被效应

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The objectives of this study were: to examine the sensitivity of radar backscatter, to estimate soil moisture under a corn plot and to evaluate the effectiveness and sensitivity of a Radiative Transfer Model (RTM), adapted from the earlier work of Njoku and Kong, (1977) in predicting brightness temperature from a grass plot. Microwave radar measurements were collected from plots of different vegetation cover types, vegetation density, and moisture conditions during the Huntsville 1998 field experiment. A large amount of ground data on brightness temperatures, soil moisture, and vegetation characteristics (e.g., biomass, and water content) were collected. The experiments were conducted at Alabama A&M University's, Winfred Thomas Agricultural Research Station, located near Hazel Green, Alabama. Six plots, one 50 $MUL 60 m smooth bare plot, one 50 $MUL 60 m grass plot, and four 30 $MUL 50 m corn plots at full, 2/3, 1/2, and 1/3 densities were used. Radar backscatter data were obtained from a ground based truck mounted radar operating at L, C, and X bands (1.6, 4.75, and 10 GHz) with four linear polarization HH, HV, VV, and VH and two incidence angles (15 and 45 degrees). Soil moisture values were determined using Water Content Reflectometry (WCR). Three types of soil temperature sensors (Infrared Thermometer, Thermistor, and a 4-sensor averaging thermocouple probes) were used. A discrete backscatter approach model and RTM were evaluated. Comparisons between model prediction and experimental observation for HH polarization indicated good agreement for a corn full plot. The direct-reflected scattering coefficient is found to be the most dominant term for both polarization and the backscatter is also highly sensitive to soil moisture. The trends in time variation of brightness temperature are in agreement with the experimental results and the numerical results are within a few percent of the experimental results. The vegetation corrections as estimated by the Jackson and Schmugge method are very small. Detailed examination of the vegetation canopy contribution including the geometry of the canopy, the various absorption and scattering mechanisms are necessary.
机译:本研究的目的是:检查雷达反散射器的敏感性,以估计玉米图下的土壤水分,评价辐射转移模型(RTM)的有效性和灵敏度,改编自Njoku和Kong的早期工作( 1977)在预测来自草图的亮度温度。在亨茨维尔1998年野外实验期间,从不同植被覆盖类型,植被密度和湿气条件的图中收集微波雷达测量。收集亮度温度,土壤水分和植被特征(例如,生物质和水含量)的大量地面数据。实验在阿拉巴马A&M University的Winfred Thomas农业研究站进行,位于阿拉巴马州榛树附近。六个地块,一个50 $ MUL 60米平滑的裸图,一个50美元60米的草图,并使用了四个30美元50米的玉米图,使用了2/3,1 / 2和1/3密度。从在L,C和X条带(1.6,4.75和10GHz)的基于地面的卡车安装雷达中获得雷达反向散射数据,具有四个线性极化HH,HV,VV和VH和两个入射角(15和45程度)。使用水含量反射测量法(WCR)测定土壤湿度值。使用三种土壤温度传感器(红外温度计,热敏电阻和4传感器平均热电偶探针)。评估离散的反向散射方法模型和RTM。 HH极化模型预测与实验观察的比较表明玉米全图吻合良好。发现直接反射散射系数是两种极化最多的术语,反向散射对土壤水分也高度敏感。亮度温度的时间变化的趋势与实验结果一致,数值结果在实验结果的百分比范围内。杰克逊和施默格方法估计的植被校正非常小。详细检查植被冠层贡献,包括冠层的几何形状,各种吸收和散射机构是必要的。

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