This study attempts to model snow wetness and snow density of Himalayan snow cover using a combination of Hyperspectral image processing and Artificial Neural Network (ANN). Initially, a total of 300 spectral signature measurements, synchronized with snow wetness and snow density, were collected in the field. The spectral reflectance of snow was then modeled as a function of snow properties using ANN. Four snow wetness and three snow density models were developed. A strong correlation was observed in near‐infrared and shortwave‐infrared region. The correlation analysis of ANN modeled snow density and snow wetness showed a strong linear relationship with field‐based data values ranging from 0.87–0.90 and 0.88–0.91, respectively. Our results indicate that an Artificial Intelligence (AI) approach, using a combination of Hyperspectral image processing and ANN, can be efficiently used to predict snow properties (wetness and density) in the Himalayan region.

中文翻译：

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使用现场数据和高光谱影像对雪属性进行基于人工神经网络的建模

这项研究试图结合使用高光谱图像处理和人工神经网络（ANN）对喜马拉雅雪盖的雪湿度和雪密度进行建模。最初，在野外收集了总计300个与雪湿度和雪密度同步的光谱特征测量值。然后使用ANN将雪的光谱反射率建模为雪属性的函数。开发了四个雪湿度和三个雪密度模型。在近红外和短波红外区域观察到强相关性。ANN建模的雪密度和雪湿度的相关性分析显示，与基于实地的数据值分别在0.87–0.90和0.88–0.91之间具有很强的线性关系。我们的结果表明，人工智能（AI）方法