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EUMETSAT H-SAF H10 Kar Algılama Ürününün Yer Verisi ve Sentinel 2 Görüntüleri Kullanılarak 2018-2019 Türkiye Kar Sezonu için Doğrulaması

Year 2021, , 52 - 58, 15.06.2021
https://doi.org/10.53516/ajfr.944309

Abstract

Kar örtüsünün özellikleriyle ilgili bilgiler, özellikle hidrolojik amaçlar için yürütülen çeşitli bilimsel çalışmalar ve operasyonel uygulamalar için kritik öneme sahiptir. Uzaktan algılama mevsimsel kar örtüsünün geniş alanlarda ve sürekli olarak izlenebilmesi için oldukça uygun bir kaynaktır. Bu çalışmanın temel amacı Avrupa Meteorolojik Uydulardan Yararlanma Teşkilatı'nın (EUMETSAT - European Organization for the Exploitation of Meteorological Satellites) Operasyonel Hidroloji ve Su Yönetimine Destek (H-SAF - Satellite Application Facility on Support to Operational Hydrology and Water Management) projesi kapsamında üretilmekte olan düşük çözünürlüklü günlük operasyonel H10 (SN-OBS-1) kar algılama ürününün yer istasyon verisi ve daha yüksek çözünürlüğe sahip Sentinel 2 uydu görüntüleri kullanılarak 2018-2019 Türkiye kar sezonu için doğrulamasının yapılmasıdır. Çalışmada 101 yer istasyon verisinden elde edilen kar derinliği ölçümleri ile 106 Sentinel 2 görüntüsünden üretilen ikili kar haritaları referans veri olarak kullanılmıştır. Yer verisiyle yapılan doğrulama sonuçlarına göre H-SAF H10 kar ürününün algılama olasılığı 0.60 olurken, referans uydu görüntüleri ürünün 2018-2019 kar sezonu için algılama olasılığının 0.89 olduğunu ortaya koymaktadır. Her iki referans veriye göre ürünün yanlış algılama oranının ise oldukça düşük olduğu görülmektedir (≤ 0,11).

References

  • Akyurek, Z., Arslan, A.N., Bolat, K., Gabellani, S., Puca, S., Simsek, B., Kuter, S., Takala, M. ve Toniazzo, A., 2020. EUMETSAT HSAF Snow Cover Products: 10 Years On. 9th EARSeL workshop on Land Ice and Snow (3 - 5 February 2020), Bern, Switzerland, syf.
  • Barnett, T.P., Adam, J.C. ve Lettenmaier, D.P., 2005. Potential impacts of a warming climate on water availability in snow-dominated regions. Nature, 438(7066): 303-309.
  • Bormann, K.J., Brown, R.D., Derksen, C. ve Painter, T.H., 2018. Estimating snow-cover trends from space. Nature Climate Change, 8(11): 924-928.
  • Doswell III, C.A., Davies-Jones, R. ve Keller, D.L., 1990. On summary measures of skill in rare event forecasting based on contingency tables. Weather and Forecasting, 5(4): 576-585.
  • Drusch, M., Del Bello, U., Carlier, S., Colin, O., Fernandez, V., Gascon, F., Hoersch, B., Isola, C., Laberinti, P., Martimort, P., Meygret, A., Spoto, F., Sy, O., Marchese, F. ve Bargellini, P., 2012. Sentinel-2: ESA's Optical High-Resolution Mission for GMES Operational Services. Remote Sensing of Environment, 120: 25-36.
  • EUMETSAT, 2018. Product User Manual (PUM) for product H10 –SN-OBS-1. EUMETSAT Satellite Application Facility on Support to Operational Hydrology and Water Management.
  • Frei, A., Tedesco, M., Lee, S., Foster, J., Hall, D.K., Kelly, R. ve Robinson, D.A., 2012. A review of global satellite-derived snow products. Advances in Space Research, 50(8): 1007-1029.
  • Hall, D., Foster, J., Verbyla, D., Klein, A. ve Benson, C., 1998. Assessment of snow-cover mapping accuracy in a variety of vegetation-cover densities in central Alaska. Remote Sensing of Environment, 66(2): 129-137.
  • Hall, D.K., Riggs, G.A. ve 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.
  • Hüsler, F., Jonas, T., Wunderle, S. ve Albrecht, S., 2012. Validation of a modified snow cover retrieval algorithm from historical 1-km AVHRR data over the European Alps. Remote Sensing of Environment, 121: 497-515.
  • Kuter, S., 2021. Completing the machine learning saga in fractional snow cover estimation from MODIS Terra reflectance data: Random forests versus support vector regression. Remote Sensing of Environment, 255: 112294.
  • Kuter, S., Akyurek, Z. ve 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.
  • López-Moreno, J.I., Fassnacht, S.R., Heath, J.T., Musselman, K.N., Revuelto, J., Latron, J., Morán-Tejeda, E. ve Jonas, T., 2013. Small scale spatial variability of snow density and depth over complex alpine terrain: Implications for estimating snow water equivalent. Advances in Water Resources, 55: 40-52.
  • Louis, J., Debaecker, V., Pflug, B., Main-Korn, M., Bieniarz, J., Mueller-Wilm, U., Cadau, E. ve Gascon, F., 2016. Sentinel-2 Sen2Cor: L2A Processor for Users. Living Planet Symposium, Prague, Czech Republic, syf.
  • Metsämäki, S., Vepsäläinen, J., Pulliainen, J. ve Sucksdorff, Y., 2002. Improved linear interpolation method for the estimation of snow-covered area from optical data. Remote Sensing of Environment, 82(1): 64-78.
  • Metsämäki, S.J., Anttila, S.T., Markus, H.J. ve 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(1): 77-95.
  • Mueller-Wilm, U., 2019. Sen2Cor Configuration and User Manual - v2.8. http://step.esa.int/thirdparties/sen2cor/2.8.0/docs/S2-PDGS-MPC-L2A-SUM-V2.8.pdf. [Erişim tarihi: 22 Feb 2019]. Piazzi, G., Tanis, C.M., Kuter, S., Simsek, B., Puca, S., Toniazzo, A., Takala, M., Akyürek, Z., Gabellani, S. ve 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(3): 129.
  • Pulliainen, J., Luojus, K., Derksen, C., Mudryk, L., Lemmetyinen, J., Salminen, M., Ikonen, J., Takala, M., Cohen, J., Smolander, T. ve Norberg, J., 2020. Patterns and trends of Northern Hemisphere snow mass from 1980 to 2018. Nature, 581(7808): 294-298.
  • Salomonson, V.V. ve Appel, I., 2004. Estimating fractional snow cover from MODIS using the normalized difference snow index. Remote Sensing of Environment, 89: 351-360.
  • Sturm, M., Goldstein, M.A. ve Parr, C., 2017. Water and life from snow: A trillion dollar science question. Water Resources Research, 53(5): 3534-3544.
  • Sürer, S. ve Akyürek, Z., 2012. Evaluating the utility of the EUMETSAT HSAF snow recognition product over mountainous areas of eastern Turkey. Hydrological Sciences Journal, 57(8): 1684-1694.
  • Tuttu, U. ve Kuter, S., 2020. 2017-2018 Türkiye Kar Sezonu İçin MODIS Etkili Kar Örtüsü Ürününün Sentinel 2 Görüntüleriyle Doğrulaması. Bartın Orman Fakültesi Dergisi, 22(2): 556-570.
  • Vikhamar, D. ve Solberg, R., 2003. Snow-cover mapping in forests by constrained linear spectral unmixing of MODIS data. Remote Sensing of Environment, 88(3): 309-323.
  • Viviroli, D., Archer, D.R., Buytaert, W., Fowler, H.J., Greenwood, G.B., Hamlet, A.F., Huang, Y., Koboltschnig, G., Litaor, M.I., López-Moreno, J.I., Lorentz, S., Schädler, B., Schreier, H., Schwaiger, K., Vuille, M. ve Woods, R., 2011. Climate change and mountain water resources: overview and recommendations for research, management and policy. Hydrology and Earth System Sciences, 15(2): 471-504.
  • Wang, Y., Huang, X., Liang, H., Sun, Y., Feng, Q. ve Liang, T., 2018. Tracking Snow Variations in the Northern Hemisphere Using Multi-Source Remote Sensing Data (2000–2015). 10(1): 136.
  • WMO-No.8, 2008. Guide to Meteorological Instruments and Methods of Observation. https://library.wmo.int/pmb_ged/wmo_8_en-2012.pdf [Erişim tarihi: 22 Feb 2019].

Validation of the EUMETSAT H-SAF H10 Snow Detection Product for the 2018-2019 Snow Season in Turkey using in-Situ Data and Sentinel 2 Imagery

Year 2021, , 52 - 58, 15.06.2021
https://doi.org/10.53516/ajfr.944309

Abstract

Information on the characteristics of the snow cover is critical for various scientific studies and operational applications, especially for hydrological purposes. Remote sensing is a very convenient source for continuous monitoring of seasonal snow cover over large areas. The main purpose of this study is to validate the EUMETSAT H-SAF H10 coarse resolution snow detection product for the 2018-2019 Turkey snow season using ground station data and higher resolution Sentinel 2 satellite imagery. In the study, snow depth measurements obtained from 101 ground stations and binary snow maps produced from 106 Sentinel 2 images were used as reference dataset. Validation with in-situ data shows that the probability of detection of the H-SAF H10 snow product is 0.60, whereas the reference satellite images reveal that the probability of detection for the 2018-2019 snow season is 0.89. According to both reference datasets, it is seen that the false detection ratio of the product is quite low (≤ 0.11).

References

  • Akyurek, Z., Arslan, A.N., Bolat, K., Gabellani, S., Puca, S., Simsek, B., Kuter, S., Takala, M. ve Toniazzo, A., 2020. EUMETSAT HSAF Snow Cover Products: 10 Years On. 9th EARSeL workshop on Land Ice and Snow (3 - 5 February 2020), Bern, Switzerland, syf.
  • Barnett, T.P., Adam, J.C. ve Lettenmaier, D.P., 2005. Potential impacts of a warming climate on water availability in snow-dominated regions. Nature, 438(7066): 303-309.
  • Bormann, K.J., Brown, R.D., Derksen, C. ve Painter, T.H., 2018. Estimating snow-cover trends from space. Nature Climate Change, 8(11): 924-928.
  • Doswell III, C.A., Davies-Jones, R. ve Keller, D.L., 1990. On summary measures of skill in rare event forecasting based on contingency tables. Weather and Forecasting, 5(4): 576-585.
  • Drusch, M., Del Bello, U., Carlier, S., Colin, O., Fernandez, V., Gascon, F., Hoersch, B., Isola, C., Laberinti, P., Martimort, P., Meygret, A., Spoto, F., Sy, O., Marchese, F. ve Bargellini, P., 2012. Sentinel-2: ESA's Optical High-Resolution Mission for GMES Operational Services. Remote Sensing of Environment, 120: 25-36.
  • EUMETSAT, 2018. Product User Manual (PUM) for product H10 –SN-OBS-1. EUMETSAT Satellite Application Facility on Support to Operational Hydrology and Water Management.
  • Frei, A., Tedesco, M., Lee, S., Foster, J., Hall, D.K., Kelly, R. ve Robinson, D.A., 2012. A review of global satellite-derived snow products. Advances in Space Research, 50(8): 1007-1029.
  • Hall, D., Foster, J., Verbyla, D., Klein, A. ve Benson, C., 1998. Assessment of snow-cover mapping accuracy in a variety of vegetation-cover densities in central Alaska. Remote Sensing of Environment, 66(2): 129-137.
  • Hall, D.K., Riggs, G.A. ve 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.
  • Hüsler, F., Jonas, T., Wunderle, S. ve Albrecht, S., 2012. Validation of a modified snow cover retrieval algorithm from historical 1-km AVHRR data over the European Alps. Remote Sensing of Environment, 121: 497-515.
  • Kuter, S., 2021. Completing the machine learning saga in fractional snow cover estimation from MODIS Terra reflectance data: Random forests versus support vector regression. Remote Sensing of Environment, 255: 112294.
  • Kuter, S., Akyurek, Z. ve 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.
  • López-Moreno, J.I., Fassnacht, S.R., Heath, J.T., Musselman, K.N., Revuelto, J., Latron, J., Morán-Tejeda, E. ve Jonas, T., 2013. Small scale spatial variability of snow density and depth over complex alpine terrain: Implications for estimating snow water equivalent. Advances in Water Resources, 55: 40-52.
  • Louis, J., Debaecker, V., Pflug, B., Main-Korn, M., Bieniarz, J., Mueller-Wilm, U., Cadau, E. ve Gascon, F., 2016. Sentinel-2 Sen2Cor: L2A Processor for Users. Living Planet Symposium, Prague, Czech Republic, syf.
  • Metsämäki, S., Vepsäläinen, J., Pulliainen, J. ve Sucksdorff, Y., 2002. Improved linear interpolation method for the estimation of snow-covered area from optical data. Remote Sensing of Environment, 82(1): 64-78.
  • Metsämäki, S.J., Anttila, S.T., Markus, H.J. ve 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(1): 77-95.
  • Mueller-Wilm, U., 2019. Sen2Cor Configuration and User Manual - v2.8. http://step.esa.int/thirdparties/sen2cor/2.8.0/docs/S2-PDGS-MPC-L2A-SUM-V2.8.pdf. [Erişim tarihi: 22 Feb 2019]. Piazzi, G., Tanis, C.M., Kuter, S., Simsek, B., Puca, S., Toniazzo, A., Takala, M., Akyürek, Z., Gabellani, S. ve 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(3): 129.
  • Pulliainen, J., Luojus, K., Derksen, C., Mudryk, L., Lemmetyinen, J., Salminen, M., Ikonen, J., Takala, M., Cohen, J., Smolander, T. ve Norberg, J., 2020. Patterns and trends of Northern Hemisphere snow mass from 1980 to 2018. Nature, 581(7808): 294-298.
  • Salomonson, V.V. ve Appel, I., 2004. Estimating fractional snow cover from MODIS using the normalized difference snow index. Remote Sensing of Environment, 89: 351-360.
  • Sturm, M., Goldstein, M.A. ve Parr, C., 2017. Water and life from snow: A trillion dollar science question. Water Resources Research, 53(5): 3534-3544.
  • Sürer, S. ve Akyürek, Z., 2012. Evaluating the utility of the EUMETSAT HSAF snow recognition product over mountainous areas of eastern Turkey. Hydrological Sciences Journal, 57(8): 1684-1694.
  • Tuttu, U. ve Kuter, S., 2020. 2017-2018 Türkiye Kar Sezonu İçin MODIS Etkili Kar Örtüsü Ürününün Sentinel 2 Görüntüleriyle Doğrulaması. Bartın Orman Fakültesi Dergisi, 22(2): 556-570.
  • Vikhamar, D. ve Solberg, R., 2003. Snow-cover mapping in forests by constrained linear spectral unmixing of MODIS data. Remote Sensing of Environment, 88(3): 309-323.
  • Viviroli, D., Archer, D.R., Buytaert, W., Fowler, H.J., Greenwood, G.B., Hamlet, A.F., Huang, Y., Koboltschnig, G., Litaor, M.I., López-Moreno, J.I., Lorentz, S., Schädler, B., Schreier, H., Schwaiger, K., Vuille, M. ve Woods, R., 2011. Climate change and mountain water resources: overview and recommendations for research, management and policy. Hydrology and Earth System Sciences, 15(2): 471-504.
  • Wang, Y., Huang, X., Liang, H., Sun, Y., Feng, Q. ve Liang, T., 2018. Tracking Snow Variations in the Northern Hemisphere Using Multi-Source Remote Sensing Data (2000–2015). 10(1): 136.
  • WMO-No.8, 2008. Guide to Meteorological Instruments and Methods of Observation. https://library.wmo.int/pmb_ged/wmo_8_en-2012.pdf [Erişim tarihi: 22 Feb 2019].
There are 26 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Articles
Authors

Semih Kuter 0000-0002-4760-3816

Kenan Bolat 0000-0002-6396-2173

Zuhal Akyürek 0000-0003-3744-2702

Publication Date June 15, 2021
Submission Date June 3, 2021
Published in Issue Year 2021

Cite

APA Kuter, S., Bolat, K., & Akyürek, Z. (2021). EUMETSAT H-SAF H10 Kar Algılama Ürününün Yer Verisi ve Sentinel 2 Görüntüleri Kullanılarak 2018-2019 Türkiye Kar Sezonu için Doğrulaması. Anadolu Orman Araştırmaları Dergisi, 7(1), 52-58. https://doi.org/10.53516/ajfr.944309
AMA Kuter S, Bolat K, Akyürek Z. EUMETSAT H-SAF H10 Kar Algılama Ürününün Yer Verisi ve Sentinel 2 Görüntüleri Kullanılarak 2018-2019 Türkiye Kar Sezonu için Doğrulaması. AOAD. June 2021;7(1):52-58. doi:10.53516/ajfr.944309
Chicago Kuter, Semih, Kenan Bolat, and Zuhal Akyürek. “EUMETSAT H-SAF H10 Kar Algılama Ürününün Yer Verisi Ve Sentinel 2 Görüntüleri Kullanılarak 2018-2019 Türkiye Kar Sezonu için Doğrulaması”. Anadolu Orman Araştırmaları Dergisi 7, no. 1 (June 2021): 52-58. https://doi.org/10.53516/ajfr.944309.
EndNote Kuter S, Bolat K, Akyürek Z (June 1, 2021) EUMETSAT H-SAF H10 Kar Algılama Ürününün Yer Verisi ve Sentinel 2 Görüntüleri Kullanılarak 2018-2019 Türkiye Kar Sezonu için Doğrulaması. Anadolu Orman Araştırmaları Dergisi 7 1 52–58.
IEEE S. Kuter, K. Bolat, and Z. Akyürek, “EUMETSAT H-SAF H10 Kar Algılama Ürününün Yer Verisi ve Sentinel 2 Görüntüleri Kullanılarak 2018-2019 Türkiye Kar Sezonu için Doğrulaması”, AOAD, vol. 7, no. 1, pp. 52–58, 2021, doi: 10.53516/ajfr.944309.
ISNAD Kuter, Semih et al. “EUMETSAT H-SAF H10 Kar Algılama Ürününün Yer Verisi Ve Sentinel 2 Görüntüleri Kullanılarak 2018-2019 Türkiye Kar Sezonu için Doğrulaması”. Anadolu Orman Araştırmaları Dergisi 7/1 (June 2021), 52-58. https://doi.org/10.53516/ajfr.944309.
JAMA Kuter S, Bolat K, Akyürek Z. EUMETSAT H-SAF H10 Kar Algılama Ürününün Yer Verisi ve Sentinel 2 Görüntüleri Kullanılarak 2018-2019 Türkiye Kar Sezonu için Doğrulaması. AOAD. 2021;7:52–58.
MLA Kuter, Semih et al. “EUMETSAT H-SAF H10 Kar Algılama Ürününün Yer Verisi Ve Sentinel 2 Görüntüleri Kullanılarak 2018-2019 Türkiye Kar Sezonu için Doğrulaması”. Anadolu Orman Araştırmaları Dergisi, vol. 7, no. 1, 2021, pp. 52-58, doi:10.53516/ajfr.944309.
Vancouver Kuter S, Bolat K, Akyürek Z. EUMETSAT H-SAF H10 Kar Algılama Ürününün Yer Verisi ve Sentinel 2 Görüntüleri Kullanılarak 2018-2019 Türkiye Kar Sezonu için Doğrulaması. AOAD. 2021;7(1):52-8.