MODIS Fractional Snow Cover Mapping Using Machine Learning Technology in a Mountainous Area

Abstract

To improve the poor accuracy of the MODIS (Moderate Resolution Imaging Spectroradiometer) daily fractional snow cover product over the complex terrain of the Tibetan Plateau (RMSE = 0.30), unmanned aerial vehicle and machine learning technologies are employed to map the fractional snow cover based on MODIS over this terrain. Three machine learning models, including random forest, support vector machine, and back-propagation artificial neural network models, are trained and compared in this study. The results indicate that compared with the MODIS daily fractional snow cover product, the introduction of a highly accurate snow map acquired by unmanned aerial vehicles as a reference into machine learning models can significantly improve the MODIS fractional snow cover mapping accuracy. The random forest model shows the best accuracy among the three machine learning models, with an RMSE (root-mean-square error) of 0.23, especially over forestland and shrubland, with RMSEs of 0.13 and 0.18, respectively. Although the accuracy of the support vector machine and back-propagation artificial neural network models are worse over forestland and shrubland, their average errors are still better than that of MOD10A1. Different fractional snow cover gradients also affect the accuracy of the machine learning algorithms. Nevertheless, the random forest model remains stable in different fractional snow cover gradients and is, therefore, the best machine learning algorithm for MODIS fractional snow cover mapping in Tibetan Plateau areas with complex terrain and severely fragmented snow cover.

Downscaling Snow Depth Mapping by Fusion of Microwave and Optical Remote-Sensing Data Based on Deep Learning

“…The MOD10A1 V006 product, which is an improved version of V005, includes four basic data layers: the NDSI_Snow_Cover, NDSI, albedo and data quality assessment layers [52]. The subpixel of fractional snow cover with 500 m resolution is not considered in this study.…”

Using the MODIS Sensor for Snow Cover Modeling and the Assessment of Drought Effects on Snow Cover in a Mountainous Area

“…The SVM, developed by Cortes and Vapnik [26], is based on the principle of construction risk minimization (SRM), which reduces the overestimation of the model [27]. The parameters of this model are γ, representing the adjustment constant, and σ2, representing the radial basis function (RBF) kernel width optimized by the least squares algorithm in this study [6].…”

“…Snow cover can be estimated using modeling, measurement stations, and remote sensing applications [5]. Various satellite data have been used to identify the extent of snow cover and, in recent years, the Moderate Resolution Imaging Spectroradiometer (MODIS) has been one of the most commonly used approaches for snow cover monitoring [6][7][8][9][10]. The disadvantage of this method is that the MODIS sensor was established in 2000, and before that, there were no images available for snow cover monitoring.…”

“…Statistical models and artificial intelligence-based methods have been widely used in hydrological modeling studies [6]. However, there are relatively few studies on the application of these methods for modeling snow parameters [7].…”

A Hybrid Data Balancing Method for Classification of Imbalanced Training Data within Google Earth Engine: Case Studies from Mountainous Regions

“…The RS community has been so far examined various datasets and methodologies to meet users' requirements for generating accurate MLC maps [11][12][13][14]. The advent of state-of-the-art Machine Learning (ML) techniques has particularly helped the RS community to improve the accuracy of MLC 2 of 21 classifications [4,15].…”