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首页> 外文期刊>Remote Sensing >Soil Heat Flux Modeling Using Artificial Neural Networks and Multispectral Airborne Remote Sensing Imagery
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Soil Heat Flux Modeling Using Artificial Neural Networks and Multispectral Airborne Remote Sensing Imagery

机译：利用人工神经网络和多光谱机载遥感影像对土壤热通量进行建模

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摘要

The estimation of spatially distributed crop water use or evapotranspiration (ET) can be achieved using the energy balance for land surface algorithm and multispectral imagery obtained from remote sensing sensors mounted on air- or space-borne platforms. In the energy balance model, net radiation (Rn) is well estimated using remote sensing; however, the estimation of soil heat flux (G) has had mixed results. Therefore, there is the need to improve the model to estimate soil heat flux and thus improve the efficiency of the energy balance method based on remote sensing inputs. In this study, modeling of airborne remote sensing-based soil heat flux was performed using Artificial Neural Networks (ANN). Soil heat flux was modeled using selected measured data from soybean and corn crop covers in Central Iowa, U.S.A. where measured values were obtained with soil heat flux plate sensors. Results in the modeling of G indicated that the combination Rn with air temperature (Tair) and crop height (hc) better reproduced measured values when three independent variables were considered. The combination of Rn with leaf area index (LAI) from remote sensing, and Rn with surface aerodynamic resistance (rah) yielded relative larger overall correlation coefficient values when two independent variables were included using ANN. In addition, air temperature (Tair) may be a key variable in the modeling of G as suggested by the ANN application (r of 0.83). Therefore, it is suggested that Rn, LAI, rah and hc and potentially Tair be considered in future modeling studies of G.
机译：可以使用陆面算法的能量平衡和从安装在机载或机载平台上的遥感传感器获得的多光谱图像，来实现对空间分布的作物用水或蒸散量（ET）的估算。在能量平衡模型中，使用遥感可以很好地估算净辐射（R n ）;然而，对土壤热通量（G）的估计却有不同的结果。因此，需要改进估计土壤热通量的模型，从而提高基于遥感输入的能量平衡方法的效率。在这项研究中，使用人工神经网络（ANN）对基于机载遥感的土壤热通量进行建模。使用美国爱荷华州中部的大豆和玉米作物覆盖区的选定测量数据对土壤热通量进行建模，其中使用土壤热通量板传感器获得测量值。 G的建模结果表明，结合R n 和气温（T air ）和作物高度（h c ）可以更好地重现当考虑三个自变量时的值。 R n 和遥感叶面积指数（LAI）的组合，以及R n 和表面空气动力学阻力（r ah ）的相对值当使用ANN包含两个自变量时，较大的整体相关系数值。另外，如ANN应用所建议的那样，空气温度（T air ）可能是G建模中的关键变量（r为0.83）。因此，建议考虑R n ，LAI，r ah 和h c 以及可能的T air 在G的未来建模研究中。

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