A machine learning-based accuracy enhancement on EUMETSAT H-SAF H35 effective snow-covered area product

2022-04-01
Kuter, Semih
Bolat, Kenan
Akyürek, Sevda Zuhal
Snow is a major element of the cryosphere with significant impact on the Earth's water cycle and global energy budget. Acquiring consistent and long time series data on the spatial extent of snow cover doubtlessly plays a key role in our understanding and modeling of the current and future environmental dynamics. Remote sensing offers a powerful tool for continuous retrieval of snow cover information by utilizing snow's contrasting reflectance characteristics at optical wavelengths. The pre-operational H35 covers the Northern Hemisphere, and it is the successor of the operational Pan-European H12 daily fractional snow-covered area (fSCA) product at ~1 km. Both products are developed through the Satellite Application Facility on Support to Operational Hydrology and Water Management (H-SAF) project of EUMETSAT by exploiting AVHRR channels. This study is focused on developing an alternative fully data-driven H35 product with improved accuracy using a machine learning (ML)-based approach. Multivariate adaptive regression splines (MARS) algorithm is trained by using AVHRR reflectance data as well as the well-known snow and vegetation indices (i.e., NDSI and NDVI) to generate the new version of H35 fSCA product. The reference fSCA maps required for the training of MARS models are obtained from the higher resolution Sentinel 2 multispectral imagery. The MARS-based fSCA models are validated against an initial test dataset composed of 15 Sentinel 2 scenes over European Alps, Tatra Mountain Range, and Turkey. The final MARS-H35 product is then rigorously assessed over the whole Northern Hemisphere within a temporal domain spanning from Nov 2018 to Nov 2019. The quantitative testing process involves the use of reference data in both continuous and dichotomous scales: i) Sentinel 2 derived reference fSCA maps, ii) ERA5-Land snow depth data, iii) MODIS MOD10A1 NDSI snow cover data, and finally iv) in-situ snow depth data. Additionally, qualitative assessment is also performed by visually comparing MARS-H35/MODIS false-color and MARS-H35/Sentinel 2-derived reference fSCA image pairs over various geographic regions. The overall results indicate that: i) the proposed MARS-H35 fSCA product overperforms the original H35, and ii) it has higher capability in detecting the fine variations in the extent of snow cover, especially across the fringes of the slopes in complex mountainous terrains.
Remote Sensing of Environment

Suggestions

Special issue on remote sensing of snow and its applications
Arslan, Ali Nadir; Akyürek, Sevda Zuhal (2019-06-01)
Snow cover is an essential climate variable directly affecting the Earth's energy balance. Snow cover has a number of important physical properties that exert an influence on global and regional energy, water, and carbon cycles. Remote sensing provides a good understanding of snow cover and enable snow cover information to be assimilated into hydrological, land surface, meteorological, and climate models for predicting snowmelt runoff, snow water resources, and to warn about snow-related natural hazards. Th...
A multi-band metamaterial absorber design for solar cell applicatıins
Mulla, Batuhan; Sabah, Cumali; Sustainable Environment and Energy Systems (2016-8)
Solar energy is one of the most abundant energy in nature. Harvesting this energy in a more efficient way can be realized by metamaterials. Metamaterials which are manmade artificial materials can provide great absorption characteristics as well as reduced material costs with their compact structures. In this thesis, unique metamaterial absorber designs for thermo-photovoltaic and for photovoltaic applications are proposed and numerically analyzed in terms of their absorption capacity, polarization an...
Modeling of enhanced coalbed methane recovery from Amasra coalbed in Zonguldak coal basin
Sınayuç, Çağlar; Gümrah, Fevzi; Department of Petroleum and Natural Gas Engineering (2007)
The increased level of greenhouse gases due to human activity is the main factor for climate change. CO2 is the main constitute among these gases. Subsurface storage of CO2 in geological systems such as coal reservoirs is considered as one of the promising perspectives. Coal can be safely and effectively utilized to both store CO2 and recover CH4. By injecting CO2 into the coal beds, methane is released with CO2 adsorption in the coal matrix and this process is known as enhanced coal bed methane recovery (E...
A Multi-level Inverter System Design with Multi-winding Transformer
Sankurt, Turev; Sezenoglu, Ceyhun; BALIKÇI, ABDULKADİR (2011-09-10)
Because of environmental factors and sustainability concerns, renewable energy sources are the state-of-art today. In energy conversion it is important, the output of the system to be suitable with standard applications, for both economical and compatibility issues. To fit the system output voltage and frequency with the international standards, battery packs and transformers are widely used. For modularity the major problem in this area is large physical dimensions. In this study a high frequency multi-win...
Fractional Snow Cover Mapping by Artificial Neural Networks and Support Vector Machines
ÇİFTÇİ, BORA BERKAY; KUTER, SEMİH; Akyürek, Sevda Zuhal; WEBER, GERHARD WİEHELM (Copernicus GmbH; 2017-10-15)
Snow is an important land cover whose distribution over space and time plays a significant role in various environmental processes. Hence, snow cover mapping with high accuracy is necessary to have a real understanding for present and future climate, water cycle, and ecological changes. This study aims to investigate and compare the design and use of artificial neural networks (ANNs) and support vector machines (SVMs) algorithms for fractional snow cover (FSC) mapping from satellite data. ANN and SVM models...
Citation Formats
S. Kuter, K. Bolat, and S. Z. Akyürek, “A machine learning-based accuracy enhancement on EUMETSAT H-SAF H35 effective snow-covered area product,” Remote Sensing of Environment, vol. 272, pp. 0–0, 2022, Accessed: 00, 2022. [Online]. Available: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85124673656&origin=inward.