干旱区生态环境知识资源中心(Arid): High-Resolution Monitoring of the Snow Cover on the Moroccan Atlas through the Spatio-Temporal Fusion of Landsat and Sentinel-2 Images

Arid

DOI	10.3390/rs14225814
	High-Resolution Monitoring of the Snow Cover on the Moroccan Atlas through the Spatio-Temporal Fusion of Landsat and Sentinel-2 Images
	Bousbaa, Mostafa; Htitiou, Abdelaziz; Boudhar, Abdelghani; Eljabiri, Youssra; Elyoussfi, Haytam; Bouamri, Hafsa; Ouatiki, Hamza; Chehbouni, Abdelghani
通讯作者	Bousbaa, M
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2022
卷号	14 期号:22
英文摘要	Mapping seasonal snow cover dynamics provides essential information to predict snowmelt during spring and early summer. Such information is vital for water supply management and regulation by national stakeholders. Recent advances in remote sensing have made it possible to reliably estimate and quantify the spatial and temporal variability of snow cover at different scales. However, because of technological constraints, there is a compromise between the temporal, spectral, and spatial resolutions of available satellites. In addition, atmospheric conditions and cloud contamination may increase the number of missing satellite observations. Therefore, data from a single satellite is insufficient to accurately capture snow dynamics, especially in semi-arid areas where snowfall is extremely variable in both time and space. Considering these limitations, the combined use of the next generation of multispectral sensor data from the Landsat-8 (L8) and Sentinel-2 (S2), with a spatial resolution ranging from 10 to 30 m, provides unprecedented opportunities to enhance snow cover mapping. Hence, the purpose of this study is to examine the effectiveness of the combined use of optical sensors through image fusion techniques for capturing snow dynamics and producing detailed and dense normalized difference snow index (NDSI) time series within a semi-arid context. Three different models include the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), the flexible spatio-temporal data fusion model (FSDAF), and the pre-classification flexible spatio-temporal data fusion model (pre-classification FSDAF) were tested and compared to merge L8 and S2 data. The results showed that the pre-classification FSDAF model generates the most accurate precise fused NDSI images and retains spatial detail compared to the other models, with the root mean square error (RMSE = 0.12) and the correlation coefficient (R = 0.96). Our results reveal that, the pre-classification FSDAF model provides a high-resolution merged snow time series and can compensate the lack of ground-based snow cover data.
英文关键词	snow cover image fusion Sentinel-2 Landsat-8 normalized difference snow index (NDSI) Atlas Mountain
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000887638800001
WOS关键词	REFLECTANCE FUSION ; MODIS ; SATELLITE ; RUNOFF ; CLASSIFICATION ; VARIABILITY ; CATCHMENT ; FRAMEWORK ; PYRENEES ; PRODUCTS
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/394233
推荐引用方式 GB/T 7714	Bousbaa, Mostafa,Htitiou, Abdelaziz,Boudhar, Abdelghani,et al. High-Resolution Monitoring of the Snow Cover on the Moroccan Atlas through the Spatio-Temporal Fusion of Landsat and Sentinel-2 Images[J],2022,14(22).
APA	Bousbaa, Mostafa.,Htitiou, Abdelaziz.,Boudhar, Abdelghani.,Eljabiri, Youssra.,Elyoussfi, Haytam.,...&Chehbouni, Abdelghani.(2022).High-Resolution Monitoring of the Snow Cover on the Moroccan Atlas through the Spatio-Temporal Fusion of Landsat and Sentinel-2 Images.REMOTE SENSING,14(22).
MLA	Bousbaa, Mostafa,et al."High-Resolution Monitoring of the Snow Cover on the Moroccan Atlas through the Spatio-Temporal Fusion of Landsat and Sentinel-2 Images".REMOTE SENSING 14.22(2022).