Fractional Snow-Cover Mapping Based on MODIS and UAV Data over the Tibetan Plateau

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.

The recent developments in cloud removal approaches of MODIS snow cover product

“…In general, optically based products have a high spatial resolution and are influenced by cloud cover, whereas microwave-based products have a low resolution and a good cloud-penetrating capacity. Therefore, the fusion of optical and microwave data has been the most representative multisource fusion method of cloud removal, e.g., MODIS and AMSR-E (Liang et al, 2008a;Gao et al, 2011b;Akyurek et al, 2010;Huang et al, 2014Huang et al, , 2016Deng et al, 2015;Bergeron et al, 2014), GOES and SSM/I (Romanov et al, 2000), and the visible/infrared spin-scan radiometer (VISSR) and microwave radiation imager (MWRI; Yang et al, 2014).…”

“…The cloudy pixels of the MODIS product are reclassified as land if SWE = 0 or as snow if SWE ranges from 1 to 240 (Wang et al, 2015). For more fusion rules, please refer to Liang et al (2008a). After re-coding the MODIS and AMSR-E products, some scholars have adopted a rule of the maximum value composite (Yu et al, 2012;Wang et al, 2018).…”

“…In addition, for the AMSR-E SWE, the gaps with daily changing locations are filled by the ATD method (Wang et al, 2015). The fusion accuracy is much higher than the MODIS daily and 8 d products (Liang et al, 2008a). However, the fusion decreases the spatial resolution and results in uncertainties because of the low spatial resolution of AMSR-E (Gao et al, 2010a, b).…”

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

“…Previous studies have shown that the linear regression algorithm employed by MOD10A1 exhibits large errors in the TP, and linear spectral mixture analysis can somehow improve the accuracy of FSC algorithms, but its accuracy is still unsatisfactory because complex terrain causes mixed pixel problems over the TP. Compared with the snow map retrieved from Landsat, the root mean square error (RMSE) of the MOD10A1 FSC is approximately 0.30, and the difference in the average FSC is approximately 6.71% in the TP [24,25]. However, machine learning algorithms boast a higher FSC mapping efficiency and accuracy than other algorithms in the TP, and the RMSE can be reduced to 0.22 [25].…”

Structural Health Monitoring System for Snow and Wind Load Measurement

“…Methods based on ultrasonic technology were used to measure depth of snow even on the ground, in Europe and Alaska [6,7]. However, snow cover monitoring of large areas is mainly based on Synthetic Aperture Radar SAR-type scanning [8,9], or satellite mapping [10,11]. This makes it possible to obtain coverage maps and forecasts for regions or even entire countries.…”