Assessment of MODIS-Based Fractional Snow Cover Products Over the Tibetan Plateau

Hao, Shirui; Jiang, Lingmei; Shi, Jiancheng; Wang, Gongxue; Liu, Xiaojing

doi:10.1109/jstars.2018.2879666

Cited by 40 publications

(33 citation statements)

References 91 publications

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“…Daily snow products produced by MODIS sensors were available since 2000. More than 90% efficiency of MODIS products is documented by scientists [29][30][31]. These studies endorse the application of MODIS data for determining snow cover area at high elevations, and in the mountainous area of UIB.…”

Section: Introductionmentioning

confidence: 64%

Simulating Current and Future River-Flows in the Karakoram and Himalayan Regions of Pakistan Using Snowmelt-Runoff Model and RCP Scenarios

Hayat

Akbar

Tahir

et al. 2019

Water

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Upper Indus Basin (UIB) supplies more than 70% flow to the downstream agricultural areas during summer due to the melting of snow and glacial ice. The estimation of the stream flow under future climatic projections is a pre-requisite to manage water resources properly. This study focused on the simulation of snowmelt-runoff using Snowmelt-Runoff Model (SRM) under the current and future Representative Concentration Pathways (RCP 2.6, 4.5 and 8.5) climate scenarios in the two main tributaries of the UIB namely the Astore and the Hunza River basins. Remote sensing data from Advanced Land Observation Satellite (ALOS) and Moderate Resolution Imaging Spectroradiometer (MODIS) along with in-situ hydro-climatic data was used as input to the SRM. Basin-wide and zone-wise approaches were used in the SRM. For the zone-wise approach, basin areas were sliced into five elevation zones and the mean temperature for the zones with no weather stations was estimated using a lapse rate value of −0.48 °C to −0.76 °C/100 m in both studied basins. Zonal snow cover was estimated for each zone by reclassifying the MODIS snow maps according to the zonal boundaries. SRM was calibrated over 2000–2001 and validated over the 2002–2004 data period. The results implied that the SRM simulated the river flow efficiently with Nash-Sutcliffe model efficiency coefficient of 0.90 (0.86) and 0.86 (0.86) for the basin-wide (zone-wise) approach in the Astore and Hunza River Basins, respectively, over the entire simulation period. Mean annual discharge was projected to increase by 11–58% and 14–90% in the Astore and Hunza River Basins, respectively, under all the RCP mid- and late-21st-century scenarios. Mean summer discharge was projected to increase between 10–60% under all the RCP scenarios of mid- and late-21st century in the Astore and Hunza basins. This study suggests that the water resources of Pakistan should be managed properly to lessen the damage to human lives, agriculture, and economy posed by expected future floods as indicated by the climatic projections.

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Section: Introductionmentioning

confidence: 64%

Simulating Current and Future River-Flows in the Karakoram and Himalayan Regions of Pakistan Using Snowmelt-Runoff Model and RCP Scenarios

Hayat

Akbar

Tahir

et al. 2019

Water

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“…According to statistics, the average snow depth of the ground observations is around 5 cm so it is difficult to monitor the snow cover accurately by using remote sensing because the snow is shallow in the Tibetan Plateau [53]. The strong solar radiation, wind-blown snow, and the rugged terrain are the main reasons causing the snow to be patchy [10,35]. This study concludes that the machine learning algorithm can obtain satisfactory FSC inversion accuracy over complex terrain and regions with fragmented snow distribution in the TP.…”

Section: Discussionmentioning

confidence: 99%

“…Three popular machine learning algorithms in snow cover mapping, including the random forest (RF), support vector machine (SVM) and back-propagation artificial neural network (BP-ANN) algorithms, were used to train the FSC inversion model in our study area. The BP-ANN algorithm is a commonly used machine learning algorithm that is composed of an input layer, a hidden layer and an output layer [34,35]. In this study, the parameters labeled error.criterium, Stao and method were set to "LMS", "NA" and "ADAPTgdwm", respectively.…”

Section: Machine Learningmentioning

confidence: 99%

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

Liu

Huang

et al. 2020

Remote Sensing

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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.

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“…The optimal endmembers were automatically selected from the current multispectral image using rigorous criteria [50]. To evaluate the Landsat-8 OLI fractional snow cover based on MESMA, a previous study used Chinese Gaofen-2 imagery at the spatial resolution of 3.2 m over the Tibetan Plateau and took into account three kinds of land cover types: forest, grass, and bare soil [51]. The evaluation results of that study showed that the overall accuracy of the Landsat-8 OLI fractional snow cover is about 0.95, and the root-mean-square error (RMSE) is lower than 0.1.…”

Section: Landsat-8 Operational Land Imager (Oli) Datamentioning

confidence: 99%

Snow-Covered Area Retrieval from Himawari–8 AHI Imagery of the Tibetan Plateau

et al. 2019

Self Cite

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Daily snow-covered area retrieval using the imagery in solar reflective bands often encounters extensive data gaps caused by cloud obscuration. With the inception of geostationary satellites carrying advanced multispectral imagers of high temporal resolution, such as Japan’s geostationary weather satellite Himawari–8, considerable progress can now be made towards spatially-complete estimation of daily snow-covered area. We developed a dynamic snow index (normalized difference snow index for vegetation-free background and normalized difference forest–snow index for vegetation background) fractional snow cover estimation method using Himawari–8 Advanced Himawari Imager (AHI) observations of the Tibetan Plateau. This method estimates fractional snow cover with the pixel-by-pixel linear relationship of snow index observations acquired under snow-free and snow-covered conditions. To achieve reliable snow-covered area mapping with minimal cloud contamination, the daily fractional snow cover can be represented as the composite of the high temporal resolution fractional snow cover estimates during daytime. The comparison against reference fractional snow cover data from Landsat–8 Operational Land Imager (OLI) showed that the root–mean–square error (RMSE) of the Himawari–8 AHI fractional snow cover ranged from 0.07 to 0.16, and that the coefficient of determination (R2) reached 0.81–0.96. Results from the 2015/2016 and 2016/2017 winters indicated that the daily composite of Himawari–8 observations obtained a 14% cloud percentage over the Tibetan Plateau, which was less than the cloud percentage (27%) from the combination of Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra and Aqua.

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Assessment of MODIS-Based Fractional Snow Cover Products Over the Tibetan Plateau

Cited by 40 publications

References 91 publications

Simulating Current and Future River-Flows in the Karakoram and Himalayan Regions of Pakistan Using Snowmelt-Runoff Model and RCP Scenarios

Simulating Current and Future River-Flows in the Karakoram and Himalayan Regions of Pakistan Using Snowmelt-Runoff Model and RCP Scenarios

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

Snow-Covered Area Retrieval from Himawari–8 AHI Imagery of the Tibetan Plateau

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