Evaluating the performance of the EUMETSAT H SAF H35 fractional snow-covered area product over the Tibetan Plateau

Abstract

Background and aims This study evaluates the performance of the H35 fractional snow-covered area (fSCA) product over the Tibetan Plateau (TP) from May 2019 to December 2021. The H35 product, derived from AVHRR satellite data, provides daily fSCA estimates at a resolution of 0.01°. The aim of this work is to assess the accuracy and reliability of this product in capturing snow cover dynamics over a significant period. Methods Validation of the H35 product uses a high-resolution, cloud-free snow cover dataset derived from long-term MODIS data, ensuring temporal consistency and high accuracy. Statistical metrics, including probability of detection (POD), false alarm ratio (FAR), and accuracy (ACC), were employed to assess the product's performance. Results The results reveal seasonal variations in performance, with POD values reaching a peak of 0.91 during the winter months. The FAR shows an inverse trend, while the overall ACC values remain consistently high, indicating reliable performance across the study period. Conclusions This study contributes to the understanding of snow cover dynamics over the TP and highlights the significance of validating satellite-derived products for hydrological studies. The consistently high accuracy of the H35 product underscores its potential for use in monitoring snow cover in the region.

Keywords

• Optical Remote Sensing of Snow
• EUMETSAT
• H SAF
• Fractional Snow Cover
• H35

EUMETSAT H SAF H35 fraksiyonel karla kaplı alan ürününün Tibet Platosu üzerinde performansının değerlendirilmesi

Öz

Giriş ve Hedefler Tibet Platosu (TP) üzerindeki kar örtüsü dinamiklerini anlamak, hidrolojik çalışmalar ve iklim modellemeleri için kritik öneme sahiptir. Bu çalışma, H35 fraksiyonel karla kaplı alan (fSCA) ürününün Mayıs 2019 ile Aralık 2021 arasındaki performansını değerlendirmeyi amaçlamaktadır. H35 ürünü, AVHRR uydu verilerinden türetilmiş olup, 0,01° çözünürlükte günlük fSCA tahminleri sunmaktadır. Yöntemler Değerlendirme, uzun vadeli MODIS verilerinden türetilen yüksek çözünürlüklü ve bulutsuz bir kar örtüsü veri seti kullanılarak gerçekleştirilmiştir. İstatistiksel analizde algılama olasılığı (POD), yanlış alarm oranı (FAR) ve doğruluk (ACC) gibi metrikler kullanılmıştır. Bulgular Sonuçlar H35 ürününün performansında mevsimsel değişimlere işaret etmektedir. Kış aylarında POD değerleri 0,91’e ulaşırken, FAR düşüş eğilimi göstermiştir. Genel doğruluk (ACC) değerleri ise sürekli olarak yüksek seviyede kalmıştır, bu da ürünün güvenilirliğini ortaya koymaktadır. Sonuçlar H35 ürününün doğrulama sonuçları, TP üzerindeki kar örtüsü dinamiklerinin anlaşılmasına katkı sağlamaktadır. Ayrıca, uydu verilerinden türetilen ürünlerin doğruluk analizinin, özellikle hidrolojik çalışmalar bağlamında, önemini vurgulamaktadır.

Anahtar Kelimeler

• Karın Optik Uzaktan Algılanması
• EUMETSAT
• H SAF
• Fraksiyonel Kar Örtüsü
• H35.

Kaynakça

1. Breen, C., Vuyovich, C., Odden, J., Hall, D., Prugh, L., 2023. Evaluating MODIS snow products using an extensive wildlife camera network. Remote Sensing of Environment, 295, 113648.
2. Brown, R.D., Mote, P.W., 2009. The response of northern hemisphere snow cover to a changing climate. Journal of Climate, 22, 2124-2145.
3. Brubaker, K.L., Pinker, R.T., Deviatova, E., 2005. Evaluation and comparison of MODIS and IMS snow-cover estimates for the continental United States Using Station Data. Journal of Hydrometeorology, 6, 1002-1017.
4. Chen, X., Yang, Y., Yin, C., 2021. Contribution of changes in snow cover extent to shortwave radiation perturbations at the top of the atmosphere over the northern hemisphere during 2000–2019. Remote Sensing Letters, 13, 4938.
5. de Rosnay, P., Fairbairn, D., 2021. H SAF web page. Available at: https://confluence.ecmwf.int/display/LDAS/H+SAF, Accessed on 1 August, 2023.
6. Dietz, A.J., Kuenzer, C., Gessner, U., Dech, S., 2012. Remote sensing of snow – a review of available methods. International Journal of Remote Sensing, 33, 4094-4134.
7. Doswell III, C.A., Davies-Jones, R., Keller, D.L., 1990. On summary measures of skill in rare event forecasting based on contingency tables. Weather and Forecasting, 5, 576-585.
8. Fawcett, T., 2006. An introduction to ROC analysis. Pattern recognition letters, 27, 861-874.

9. H-SAF_H12_PUM, 2018. Product User Manual (PUM) for product H12 – SN-OBS-3 Effective snow cover by VIS/IR radiometry. Available at: https://hsaf.meteoam.it/Products/Detail?prod=H12, Accessed on 30 August, 2023.
10. H-SAF_H35_ATBD, 2020. Algorithm Theoretical Baseline Document (ATBD) for product H35 – FSC-H Effective snow cover by VIS/IR radiometry. Available at: https://hsaf.meteoam.it/Products/Detail?prod=H35, Accessed on 30 August, 2023.
11. H-SAF_H35_PUM, 2020. Product User Manual (PUM) for product H35 – SN-OBS-1P Effective snow cover by VIS/IR radiometry. Available at: https://hsaf.meteoam.it/Products/Detail?prod=H35, Accessed on 30 August, 2023.
12. Hall, D.K., Riggs, G.A., Salomonson, V.V., 1995. Development of Methods for Mapping Global Snow Cover Using Moderate Resolution Imaging Spectroradiometer Data. Remote Sensing of Environment, 54, 127-140.
13. Huang, Y., Liu, H., Yu, B., Wu, J., Kang, E.L., Xu, M., Wang, S., Klein, A., Chen, Y., 2018. Improving MODIS snow products with a HMRF-based spatio-temporal modeling technique in the Upper Rio Grande Basin. Remote Sensing of Environment, 204, 568-582.
14. Immerzeel, W.W., van Beek, L.P.H., Bierkens, M.F.P., 2010. Climate change will affect the asian water towers. Science, 328, 1382-1385.
15. Kuter, S., Akyurek, Z., Weber, G.W., 2018. Retrieval of fractional snow covered area from MODIS data by multivariate adaptive regression splines. Remote Sensing of Environment, 205, 236-252.
16. Kuter, S., Karaman, Ç.H., Akpınar, M.B., Akyürek, Z., 2022. Validation of EUMETSAT H-SAF space-born snow water equivalent product (H13) for the 2020-2021 snow year over Turkey. Anadolu Orman Araştırmaları Dergisi, 8, 16-21.
17. Kuter, S., Karaman, Ç.H., Akpınar, M.B., Akyürek, Z., 2024. From Anatolian Plateau to American Plains: A transcontinental assessment of the EUMETSAT H SAF’s new generation snow water equivalent product over Türkiye and the conterminous U.S. Anadolu Orman Araştırmaları Dergisi, 9, 33-40.
18. Liu, J., Milne, R.I., Zhu, G.-F., Spicer, R.A., Wambulwa, M.C., Wu, Z.-Y., Boufford, D.E., Luo, Y.-H., Provan, J., Yi, T.-S., Cai, J., Wang, H., Gao, L.-M., Li, D.-Z., 2022. Name and scale matter: Clarifying the geography of Tibetan Plateau and adjacent mountain regions. Global and Planetary Change, 215, 103893.
19. Matsunobu, L.M., Pedro, H.T.C., Coimbra, C.F.M., 2021. Cloud detection using convolutional neural networks on remote sensing images. Solar Energy, 230, 1020-1032.
20. Metsämäki, S., Pulliainen, J., Salminen, M., Luojus, K., Wiesmann, A., Solberg, R., Böttcher, K., Hiltunen, M., Ripper, E., 2015. Introduction to GlobSnow Snow Extent products with considerations for accuracy assessment. Remote Sensing of Environment, 156, 96-108.
21. Metsämäki, S.J., Anttila, S.T., Markus, H.J., Vepsäläinen, J.M., 2005. A feasible method for fractional snow cover mapping in boreal zone based on a reflectance model. Remote Sensing of Environment, 95, 77-95.
22. Painter, T.H., Dozier, J., Roberts, D.A., Davis, R.E., Green, R.O., 2003. Retrieval of subpixel snow-covered area and grain size from imaging spectrometer data. Remote Sensing of Environment, 85, 64-77.
23. Painter, T.H., Rittger, K., McKenzie, C., Slaughter, P., Davis, R.E., Dozier, J., 2009. Retrieval of subpixel snow covered area, grain size, and albedo from MODIS. Remote Sensing of Environment, 113, 868-879.
24. Pan, X., Guo, X., Li, X., Niu, X., Yang, X., Feng, M., Che, T., Jin, R., Ran, Y., Guo, J., Hu, X., Wu, A., 2021. National Tibetan Plateau Data Center: promoting earth system science on the third pole. Bulletin of the American Meteorological Society, 102, E2062-E2078.
25. Piazzi, G., Tanis, C.M., Kuter, S., Simsek, B., Puca, S., Toniazzo, A., Takala, M., Akyürek, Z., Gabellani, S., Arslan, A.N., 2019. Cross-Country Assessment of H-SAF Snow Products by Sentinel-2 Imagery Validated against In-Situ Observations and Webcam Photography. Geosciences, 9, 129.
26. Pulliainen, J., Hallikainen, M., 2001. Retrieval of Regional Snow Water Equivalent from Space-Borne Passive Microwave Observations. Remote Sensing of Environment, 75, 76-85.
27. Pulliainen, J., Luojus, K., Derksen, C., Mudryk, L., Lemmetyinen, J., Salminen, M., Ikonen, J., Takala, M., Cohen, J., Smolander, T., Norberg, J., 2020. Patterns and trends of Northern Hemisphere snow mass from 1980 to 2018. Nature, 581, 294-298.
28. Rittger, K., Painter, T.H., Dozier, J., 2013. Assessment of methods for mapping snow cover from MODIS. Advances in Water Resources, 51, 367-380.
29. Romanov, P., Tarpley, D., Gutman, G., Carroll, T., 2003. Mapping and monitoring of the snow cover fraction over North America. Journal of Geophysical Research: Atmospheres, 108.
30. Saberi, N., Kelly, R., Flemming, M., Li, Q., 2020. Review of snow water equivalent retrieval methods using spaceborne passive microwave radiometry. International Journal of Remote Sensing, 41, 996-1018.
31. Stillinger, T., Rittger, K., Raleigh, M.S., Michell, A., Davis, R.E., Bair, E.H., 2023. Landsat, MODIS, and VIIRS snow cover mapping algorithm performance as validated by airborne lidar datasets. The Cryosphere, 17, 567-590.
32. Takala, M., Luojus, K., Pulliainen, J., Derksen, C., Lemmetyinen, J., Kärnä, J.-P., Koskinen, J., Bojkov, B., 2011. Estimating northern hemisphere snow water equivalent for climate research through assimilation of space-borne radiometer data and ground-based measurements. Remote Sensing of Environment, 115, 3517-3529.
33. Tekeli, A.E., Akyürek, Z., Şorman, A.A., Şensoy, A., Şorman, Ü., 2005. Using MODIS snow cover maps in modeling snowmelt runoff process in the eastern part of Turkey. Remote Sensing of Environment, 97, 216-230.
34. Vikhamar, D., Solberg, R., Seidel, K., 2004. Reflectance modeling of snow-covered forests in hilly terrain. Photogrammetric Engineering & Remote Sensing, 70, 1069-1079.
35. Wang, Y., Huang, X., Liang, H., Sun, Y., Feng, Q., Liang, T., 2018. Tracking snow variations in the northern hemisphere using multi-source remote sensing data (2000–2015). Remote Sens-Basel, 10, 136.
36. Wolfe, R., 2013. MODIS Land Digital Elevation Model and Land/Water Mask in the Sinusoidal Grid Version 6.0. Available at: https://landweb.modaps.eosdis.nasa.gov/data/userguide/DEM.pdf, Accessed on 30 August 2024.
37. Wu, G., Liu, Y., He, B., Bao, Q., Duan, A., Jin, F.-F., 2012. Thermal controls on the asian summer monsoon. Scientific Reports, 2, 404.
38. Xiao, X., He, T., Liang, S., Liu, X., Ma, Y., Liang, S., Chen, X., 2022. Estimating fractional snow cover in vegetated environments using MODIS surface reflectance data. International Journal of Applied Earth Observation and Geoinformation, 114, 103030.
39. Xu, J., Grumbine, R.E., Shrestha, A., Eriksson, M., Yang, X., Wang, Y.U.N., Wilkes, A., 2009. The melting Himalayas: cascading effects of climate change on water, biodiversity, and livelihoods. Conservation Biology, 23, 520-530.
40. Yan, H., Jianghui, X., 2022. Daily cloud-free snow cover products for Tibetan Plateau from 2002 to 2021. A Big Earth Data Platform for Three Poles Available at: https://dx.doi.org/10.11888/Cryos.tpdc.272204, Accessed on 1 January 2024.
41. Yao, T., Thompson, L., Yang, W., Yu, W., Gao, Y., Guo, X., Yang, X., Duan, K., Zhao, H., Xu, B., Pu, J., Lu, A., Xiang, Y., Kattel, D.B., Joswiak, D., 2012. Different glacier status with atmospheric circulations in Tibetan Plateau and surroundings. Nature Climate Change, 2, 663-667.
42. Yin, A., Harrison, T.M., 2000. Geologic evolution of the Himalayan-Tibetan Orogen. Annual Review of Earth and Planetary Sciences, 28, 211-280.
43. Yu, X., Lary, D.J., 2021. Cloud detection using an ensemble of pixel-based machine learning models incorporating unsupervised classification. Remote Sens-Basel, 13.
44. Zhang, G., Yao, T., Xie, H., Yang, K., Zhu, L., Shum, C.K., Bolch, T., Yi, S., Allen, S., Jiang, L., Chen, W., Ke, C., 2020. Response of Tibetan Plateau lakes to climate change: Trends, patterns, and mechanisms. Earth-Science Reviews, 208, 103269.
45. Zhu, Z., Woodcock, C.E., 2014. Automated cloud, cloud shadow, and snow detection in multitemporal Landsat data: An algorithm designed specifically for monitoring land cover change. Remote Sensing of Environment, 152, 217-234.