CYGNSS toprak nemi verilerinin SMAP uydusu ve ISMN istasyonları ile karşılaştırmalı analizi

Year 2024, Volume: 9 Issue: 2, 227 - 237, 29.08.2024

Muhammed Raşit Çevikalp , Mustafa Serkan Işık , Mehmet Furkan Çelik , Nebiye Musaoğlu

https://doi.org/10.29128/geomatik.1424069

Abstract

Küresel Navigasyon Uydu Sistemi Reflektometrisi (GNSS-R) toprak nem izleme için elverişli bir uzaktan algılama yöntemidir. CYclone Global Navigation Satellite System (CYGNSS) görevi başlangıçta tropikal kasırga ve siklon etkinliklerinin belirlenmesi için tasarlanmış olsa da, toprak neminin yüksek zamansal çözünürlükte izlenebilmesi için değerli veriler sağlamaktadır. Bu çalışmada, Kıtasal Amerika Birleşik Devletleri (CONUS) bölgesinde Uluslararası Toprak Nemi Ağı’na (ISMN) ait yer istasyonlarına ait veriler kullanılarak CYGNSS gözlemlerinden elde edilen toprak nemi değerlerinin doğruluğu ve güvenirliği test edilmiştir. CYGNSS misyonunun toprak nemini belirlemedeki performansı, NASA tarafından geliştirilmiş aktif ve pasif radar ölçme sistemi kullanan Soil Moisture Active Passive (SMAP) uydusunun performansı ile karşılaştırılarak verilerin tutarlılıkları incelenmiştir. Ayrıca CYGNSS uydusunun farklı iklim koşulları ve toprak yapısındaki sinyal karakteristiğini incelemek amacıyla, ISMN istasyonlarında elde edilen istatistiksel sonuçlar iklim çeşitliliklerine ve toprak dokusunun değişkenliklerine göre sınıflandırılmıştır. Yapılan karşılaştırmalar sonucunda, CYGNSS verileri ile yer istasyonu verileri arasındaki korelasyon R=0.45 olarak bulunurken SMAP verileriyle korelasyonu R=0.67 olarak hesaplanmıştır. Böylece, uydu tabanlı GNSS-R misyonlarının başlangıcı olan CYGNSS misyonunun küresel ölçekte toprak neminin etkin bir şekilde elde edilebilmesi için değerli bir veri sağladığı görülmüştür.

Keywords

Uzaktan algılama, GNSS-R, CYGNSS, Toprak nemi, CONUS

Ethical Statement

Herhangi bir çıkar çatışması bulunmamaktadır.

Thanks

Bu çalışmada kullanılan yer istasyonları verileri Uluslararası Toprak Nemi Ağı'ndan (ISMN) temin edilmiş olup verilere erişim https://www.geo.tuwien.ac.at/insitu/data_viewer adresi ile sağlanmıştır. CYGNSS Seviye 3 Toprak Nemi Versiyon 1.0 verileri UCAR/CU tarafından NASA Physical Oceanography Distributed Active Archive Center (PODAAC) veri merkezinde ücretsiz olarak servis edilmektedir (https://podaac.jpl.nasa.gov).

References

• Adeyemi, O., Grove, I., Peets, S., Domun, Y., & Norton, T. (2018). Dynamic neural network modelling of soil moisture content for predictive irrigation scheduling. Sensors, 18(10), 3408. https://doi.org/10.3390/s18103408
• Altuntaş, C., & Tunalıoğlu, N. (2022). Deniz seviyesi değişimlerinin belirlenmesinde GNSS-IR yönteminin kullanımı ve doğruluk analizi üzerine bir araştırma. Geomatik, 7(3), 187-196. https://doi.org/10.29128/geomatik.946594
• Arroyo, A. A., Camps, A., Aguasca, A., Forte, G. F., Monerris, A., Rüdiger, C., ... & Onrubia, R. (2014). Dual-polarization GNSS-R interference pattern technique for soil moisture mapping. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(5), 1533-1544. https://doi.org/10.1109/JSTARS.2014.2320792
• Asgarimehr, M., Wickert, J., & Reich, S. (2019). Evaluating impact of rain attenuation on space-borne GNSS reflectometry wind speeds. Remote Sensing, 11(9), 1048. https://doi.org/10.3390/rs11091048
• Bell, J., Palecki, M., Baker, B., Collins, W., Lawrimore, J., Leeper, R., Hall, M., Kochendorfer, J., Meyers, T., Wilson, T., & Diamond, H. (2013). U.S. Climate Reference Network Soil Moisture and Temperature Observations. Journal of Hydrometeorology, 14, 977-988. https://doi.org/10.1175/JHM-D-12-0146.1
• Bünyan Ünel, F., Kuşak, L., Yakar, M., & Doğan, H. (2023). Coğrafi bilgi sistemleri ve analitik hiyerarşi prosesi kullanarak Mersin ilinde otomatik meteoroloji gözlem istasyonu yer seçimi. Geomatik, 8(2), 107-123. https://doi.org/10.29128/geomatik.1136951
• Caldwell, T. G., Bongiovanni, T., Cosh, M. H., Jackson, T. J., Colliander, A., Abolt, C. J., Casteel, R., Larson, T., Scanlon, B. R., & Young, M. H. (2019). The Texas Soil Observation Network:A Comprehensive Soil Moisture Dataset for Remote Sensing and Land Surface Model Validation. Vadose Zone Journal, 18(1), 1-20. https://doi.org/10.2136/vzj2019.04.0034
• Cardellach, E., Rius, A., Martin-Neira, M., Fabra, F., Nogues-Correig, O., Ribo, S., Kainulainen, J., Camps, A., & D’Addio, S. (2014). Consolidating the Precision of Interferometric GNSS-R Ocean Altimetry Using Airborne Experimental Data. IEEE Transactions on Geoscience and Remote Sensing, 52(8), 4992-5004. https://doi.org/10.1109/TGRS.2013.2286257
• Celik, M. F., Isik, M. S., Yuzugullu, O., Fajraoui, N., & Erten, E. (2022). Soil Moisture Prediction from Remote Sensing Images Coupled with Climate, Soil Texture and Topography via Deep Learning. Remote Sensing, 14(21), 5584. https://doi.org/10.3390/rs14215584
• Chew, C. C., & Small, E. E. (2018). Soil Moisture Sensing Using Spaceborne GNSS Reflections: Comparison of CYGNSS Reflectivity to SMAP Soil Moisture. Geophysical Research Letters, 45(9), 4049-4057. https://doi.org/10.1029/2018gl077905
• Chew, C., & Small, E. (2020a). Description of the UCAR/CU Soil Moisture Product. Remote Sensing, 12(10). https://doi.org/10.3390/rs12101558
• Chew, C., & Small, E. (2020b). UCAR-CU CYGNSS Level 3 Soil Moisture Version 1.0. NASA Physical Oceanography Distributed Active Archive Center. https://doi.org/10.5067/CYGNU-L3SM1
• Clarizia, M. P., Gommenginger, C., Gleason, S., Galdi, C., & Unwin, M. (2008). Global Navigation Satellite System-Reflectometry (GNSS-R) from the UK-DMC Satellite for Remote Sensing of the Ocean Surface. IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium, 1, I-276-I-279. https://doi.org/10.1109/IGARSS.2008.4778847
• Clarizia, M. P., Pierdicca, N., Costantini, F., & Floury, N. (2019). Analysis of CYGNSS Data for Soil Moisture Retrieval. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(7), 2227-2235. https://doi.org/10.1109/jstars.2019.2895510
• Clarizia, M. P., & Ruf, C. S. (2016). Wind Speed Retrieval Algorithm for the Cyclone Global Navigation Satellite System (CYGNSS) Mission. IEEE Transactions on Geoscience and Remote Sensing, 54(8), 4419-4432. https://doi.org/10.1109/tgrs.2016.2541343
• Cook, D. R. (2016). Soil Temperature and Moisture Profile (STAMP) System Handbook. https://doi.org/10.2172/1332724
• Dorigo, W. A., Wagner, W., Hohensinn, R., Hahn, S., Paulik, C., Xaver, A., Gruber, A., Drusch, M., Mecklenburg, S., van Oevelen, P., & others. (2011). The International Soil Moisture Network: a data hosting facility for global in situ soil moisture measurements. Hydrology and Earth System Sciences, 15(5), 1675-1698.
• Dorigo, W., Himmelbauer, I., Aberer, D., Schremmer, L., Petrakovic, I., Zappa, L., Preimesberger, W., Xaver, A., Annor, F., Ardö, J., Baldocchi, D., Bitelli, M., Blöschl, G., Bogena, H., Brocca, L., Calvet, J.-C., Camarero, J. J., Capello, G., Choi, M., … Sabia, R. (2021). The International Soil Moisture Network: serving Earth system science for over a decade. Hydrology and Earth System Sciences, 25(11), 5749-5804. https://doi.org/10.5194/hess-25-5749-2021
• Entekhabi, D., Njoku, E. G., O’Neill, P. E., Kellogg, K. H., Crow, W. T., Edelstein, W. N., Entin, J. K., Goodman, S. D., Jackson, T. J., Johnson, J., Kimball, J., Piepmeier, J. R., Koster, R. D., Martin, N., McDonald, K. C., Moghaddam, M., Moran, S., Reichle, R., Shi, J. C., … Zyl, J. Van. (2010). The Soil Moisture Active Passive (SMAP) Mission. Proceedings of the IEEE, 98(5), 704-716. https://doi.org/10.1109/JPROC.2010.2043918
• Eroglu, O., Kurum, M., Boyd, D., & Gurbuz, A. C. (2019). High Spatio-Temporal Resolution CYGNSS Soil Moisture Estimates Using Artificial Neural Networks. Remote Sensing, 11(19), 2272. https://doi.org/10.3390/rs11192272
• Filipović, N., Brdar, S., Mimić, G., Marko, O., & Crnojević, V. (2022). Regional soil moisture prediction system based on Long Short-Term Memory network. Biosystems Engineering, 213, 30-38. https://doi.org/https://doi.org/10.1016/j.biosystemseng.2021.11.019
• Gleason, S. A. M. U. M. (2005). Sensing Ocean, Ice and Land Reflected Signals from Space: Results from the UK-DMC GPS Reflectometry Experiment. Proceedings of the 18th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS 2005), 1679-1685.
• Jin, S., & Komjathy, A. (2010). GNSS reflectometry and remote sensing: New objectives and results. Advances in Space Research, 46(2), 111-117. https://doi.org/https://doi.org/10.1016/j.asr.2010.01.014
• Kerr, Y. H., Waldteufel, P., Richaume, P., Wigneron, J. P., Ferrazzoli, P., Mahmoodi, A., Bitar, A. Al, Cabot, F., Gruhier, C., Juglea, S. E., Leroux, D., Mialon, A., & Delwart, S. (2012). The SMOS Soil Moisture Retrieval Algorithm. IEEE Transactions on Geoscience and Remote Sensing, 50(5), 1384-1403. https://doi.org/10.1109/TGRS.2012.2184548
• Larson, K., Small, E., Gutmann, E., Bilich, A., Braun, J., Zavorotny, V., & Larson, C. (2008). Use of GPS receivers as a soil moisture network for water cycle studies. Geophysical Research Letters - GEOPHYS RES LETT, 35(24). https://doi.org/10.1029/2008GL036013
• Leavesley. (2010). A Modelling Framework for Improved Agricultural Water-Supply Forecasting. Li, Q., Zhu, Y., Shangguan, W., Wang, X., Li, L., & Yu, F. (2022). An attention-aware LSTM model for soil moisture and soil temperature prediction. Geoderma, 409, 115651. https://doi.org/10.1016/j.geoderma.2021.115651
• Ma, C., Li, X., Wei, L., & Wang, W. (2017). Multi-Scale Validation of SMAP Soil Moisture Products over Cold and Arid Regions in Northwestern China Using Distributed Ground Observation Data. Remote Sensing, 9(4), 327. https://doi.org/10.3390/rs9040327
• Moghaddam, M., Entekhabi, D., Goykhman, Y., Li, K., Liu, M., Mahajan, A., Nayyar, A., Shuman, D., & Teneketzis, D. (2011). A Wireless Soil Moisture Smart Sensor Web Using Physics-Based Optimal Control: Concept and Initial Demonstrations. Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 3, 522-535. https://doi.org/10.1109/JSTARS.2010.2052918
• Orth, R. (2021). Global soil moisture data derived through machine learning trained with in-situ measurements. Scientific Data, 8(1), 170. https://doi.org/10.1038/s41597-021-00964-1
• Poggio, L., de Sousa, L. M., Batjes, N. H., Heuvelink, G. B. M., Kempen, B., Ribeiro, E., & Rossiter, D. (2021). SoilGrids 2.0: producing soil information for the globe with quantified spatial uncertainty. Soil, 7(1), 217-240. https://doi.org/10.5194/soil-7-217-2021
• Rius, A., Nogués-Correig, O., Ribó, S., Cardellach, E., Oliveras, S., Valencia, E., Park, H., Tarongí, J. M., Camps, A., van der Marel, H., van Bree, R., Altena, B., & Martín-Neira, M. (2012). Altimetry with GNSS-R interferometry: first proof of concept experiment. GPS Solutions, 16(2), 231-241. https://doi.org/10.1007/s10291-011-0225-9
• Rodriguez-Alvarez, N., Bosch-Lluis, X., Camps, A., Vall-llossera, M., Valencia, E., Marchan-Hernandez, J. F., & Ramos-Perez, I. (2009). Soil Moisture Retrieval Using GNSS-R Techniques: Experimental Results Over a Bare Soil Field. IEEE Transactions on Geoscience and Remote Sensing, 47(11), 3616-3624. https://doi.org/10.1109/TGRS.2009.2030672
• Ruf, C., Gleason, S., Jelenak, Z., Katzberg, S., Ridley, A., Rose, R., Scherrer, J., & Zavorotny, V. (2012). The CYGNSS nanosatellite constellation hurricane mission. IEEE International Geoscience and Remote Sensing Symposium, 214-216. https://doi.org/10.1109/IGARSS.2012.6351600
• Santi, E., Paloscia, S., Pettinato, S., Fontanelli, G., Clarizia, M. P., Comite, D., Dente, L., Guerriero, L., Pierdicca, N., & Floury, N. (2020). Remote Sensing of Forest Biomass Using GNSS Reflectometry. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 2351-2368. https://doi.org/10.1109/jstars.2020.2982993
• Schaefer, G., Cosh, M., & Jackson, T. (2007). The USDA natural resources conservation service soil climate analysis network (SCAN). Journal of Atmospheric and Oceanic Technology, 24(12), 2073-2077. https://doi.org/10.1175/2007JTECHA930.1
• Senyurek, V., Lei, F., Boyd, D., Kurum, M., Gurbuz, A. C., & Moorhead, R. (2020). Machine Learning-Based CYGNSS Soil Moisture Estimates over ISMN sites in CONUS. Remote Sensing, 12(7), 1168. https://doi.org/10.3390/rs12071168
• Unwin, M. J., Pierdicca, N., Cardellach, E., Rautiainen, K., Foti, G., Blunt, P., Guerriero, L., Santi, E., & Tossaint, M. (2021). An Introduction to the HydroGNSS GNSS Reflectometry Remote Sensing Mission. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 6987-6999. https://doi.org/10.1109/JSTARS.2021.3089550
• Unwin, M., Jales, P., Tye, J., Gommenginger, C., Foti, G., & Rosello, J. (2016). Spaceborne GNSS-Reflectometry on TechDemoSat-1: Early Mission Operations and Exploitation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(10), 4525-4539. https://doi.org/10.1109/JSTARS.2016.2603846
• Wang, C., Yu, K., Qu, F., Bu, J., Han, S., & Zhang, K. (2022a). Spaceborne GNSS-R Wind Speed Retrieval Using Machine Learning Methods. Remote Sensing, 14(14). https://doi.org/10.3390/rs14143507
• Wang, H., Yuan, Q., Zhao, H., & Xu, H. (2022b). In-situ and triple-collocation based assessments of CYGNSS-R soil moisture compared with satellite and merged estimates quasi-globally. Journal of Hydrology, 615, 128716. https://doi.org/10.1016/j.jhydrol.2022.128716
• Yang, G., Bai, W., Wang, J., Hu, X., Zhang, P., Sun, Y., Xu, N., Zhai, X., Xiao, X., Xia, J., Huang, F., Yin, C., Du, Q., Wang, X., Cai, Y., Meng, X., Tan, G., Hu, P., & Liu, C. (2022). FY3E GNOS II GNSS Reflectometry: Mission Review and First Results. Remote Sensing, 14(4), 988. https://doi.org/10.3390/rs14040988
• Yu, K., Han, S., Bu, J., An, Y., Zhou, Z., Wang, C., Tabibi, S., & Cheong, J. W. (2022). Spaceborne GNSS Reflectometry. Remote Sensing, 14(7). https://doi.org/10.3390/rs14071605