Abstract
Moderate-resolution imaging spectroradiometer (MODIS) snow-cover products have relatively low accuracy over the Tibetan Plateau because of its complex terrain and shallow, fragmented snow cover. In this study, fractional snow-cover (FSC) mapping algorithms were developed using a linear regression model (LR), a linear spectral mixture analysis model (LSMA) and a back-propagation artificial neural network model (BP-ANN) based on MODIS data (version 006) and unmanned aerial vehicle (UAV) data. The accuracies of the three models were validated against Landsat 8 Operational Land Imager (OLI) snow-cover maps (Landsat 8 FSC) and compared with the MODIS global FSC product (MOD10A1 FSC, version 005) for the purpose of finding the optimal algorithm for FSC extraction for the Tibetan Plateau. The results showed that (1) the overall retrieval results of the LR and BP-ANN models based on MODIS and UAV data were relatively similar to the OLI snow-cover maps; the accuracy and stability were greatly improved, with even some reduction in errors; compared to the Landsat 8 FSC, the correlation coefficients (r) were 0.8222 and 0.8445 respectively and the root-mean-square errors (RMSEs) were 0.2304 and 0.2201, respectively. (2) The accuracy and stability of the fully constrained LSMA model using the pixel purity index (PPI) endmember extraction method based only on MODIS data suffered the worst performance of the three models; r was only 0.7921 and the RMSE was as large as 0.3485. There were some serious omission phenomena in the study area, specifically for the largest mean absolute error (MAE = 0.2755) and positive mean error (PME = 0.3411). (3) The accuracy of the MOD10A1 FSC product was much lower than that of the LR and BP-ANN models, although its accuracy slightly better that of the LSMA based on comprehensive evaluation of six accuracy indices. (4) The optimal model was the BP-ANN model with combined inputs of surface reflectivity data (R1–R7), elevation (DEM) and temperature (LST), which can easily incorporate auxiliary information (DEM and LST) on the basis of (R1–R7) during the relationship training period and can effectively improve the accuracy of snow area monitoring—it is the ideal algorithm for retrieving FSC for the Tibetan Plateau.
Highlights
Snow cover is among the most active natural factors on land surfaces and its characteristics are important input parameters for global energy balance, climate, hydrological and ecological models [1]
A linear regression analysis between the moderate-resolution imaging spectroradiometer (MODIS) normalized difference snow index (NDSI) and Unmanned Aerial Vehicle (UAV) fractional snow-cover (FSC) was performed based on the sampled data and the results are presented as a scatter diagram (Figure 3)
There was a significant correlation between NDSI and FSC, the data points were distributed around a straight line, which demonstrates the good goodness-of-fit of the linear regression model (LR) model (R2 = 0.6864)
Summary
Snow cover is among the most active natural factors on land surfaces and its characteristics (e.g., distribution, area and depth) are important input parameters for global energy balance, climate, hydrological and ecological models [1]. The Tibetan Plateau is an important seasonal snow-capped area in China and it is of great significance to accurately estimate the snow in the area. Remote sensing technology is the only means for large-scale real-time snow-cover monitoring of the area [2]. The accuracy of the most commonly used global MODIS product, the binary MODIS snow-cover map [6], is unsatisfactory because of mixed pixel problems over the Tibetan Plateau [7]. Fractional snow-cover (FSC) mapping algorithms have been the optimal means for solving the mixed pixel problem
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