{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T02:29:19Z","timestamp":1775874559382,"version":"3.50.1"},"reference-count":41,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2023,10,17]],"date-time":"2023-10-17T00:00:00Z","timestamp":1697500800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"subsidy increased for the period 2020\u20132025"},{"name":"Wroclaw University of Environmental and Life Sciences"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Due to the expanding population and the constantly changing climate, food production is now considered a crucial concern. Although passive satellite remote sensing has already demonstrated its capabilities in accurate crop development monitoring, its limitations related to sunlight and cloud cover significantly restrict real-time temporal monitoring resolution. Considering synthetic aperture radar (SAR) technology, which is independent of the Sun and clouds, SAR remote sensing can be a perfect alternative to passive remote sensing methods. However, a variety of SAR sensors and delivered SAR indices present different performances in such context for different vegetation species. Therefore, this work focuses on comparing various SAR-derived indices from C-band and (Sentinel-1) and X-band (TerraSAR-X) data with the in situ information (phenp; pgy development, vegetation height and soil moisture) in the context of tracking the phenological development of corn, winter wheat, rye, canola, and potato. For this purpose, backscattering coefficients in VV and VH polarizations (\u03c3VV0, \u03c3VH0), interferometric coherence, and the dual pol radar vegetation index (DpRVI) were calculated. To reduce noise in time series data and evaluate which filtering method presents a higher usability in SAR phenology tracking, signal filtering, such as Savitzky\u2013Golay and moving average, with different parameters, were employed. The achieved results present that, for various plant species, different sensors (Sentinel-1 or TerraSAR-X) represent different performances. For instance, \u03c3VH0 of TerraSAR-X offered higher consistency with corn development (r = 0.81), while for canola \u03c3VH0 of Sentinel-1 offered higher performance (r = 0.88). Generally, \u03c3VV0, \u03c3VH0 performed better than DpRVI or interferometric coherence. Time series filtering makes it possible to increase an agreement between phenology development and SAR-delivered indices; however, the Savitzky\u2013Golay filtering method is more recommended. Besides phenological development, high correspondences can be found between vegetation height and some of SAR indices. Moreover, in some cases, moderate correlation was found between SAR indices and soil moisture.<\/jats:p>","DOI":"10.3390\/rs15204996","type":"journal-article","created":{"date-parts":[[2023,10,17]],"date-time":"2023-10-17T08:10:19Z","timestamp":1697530219000},"page":"4996","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Evaluation of C and X-Band Synthetic Aperture Radar Derivatives for Tracking Crop Phenological Development"],"prefix":"10.3390","volume":"15","author":[{"given":"Marta","family":"Pasternak","sequence":"first","affiliation":[{"name":"Institute of Geodesy and Geoinformatics, Wroclaw University of Environmental and Life Sciences, 50-375 Wroclaw, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2162-371X","authenticated-orcid":false,"given":"Kamila","family":"Paw\u0142uszek-Filipiak","sequence":"additional","affiliation":[{"name":"Institute of Geodesy and Geoinformatics, Wroclaw University of Environmental and Life Sciences, 50-375 Wroclaw, Poland"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e261","DOI":"10.1002\/fes3.261","article-title":"Impacts of land use, population, and climate change on global food security","volume":"10","author":"Molotoks","year":"2021","journal-title":"Food Energy Secur."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"111954","DOI":"10.1016\/j.rse.2020.111954","article-title":"Dual polarimetric radar vegetation index for crop growth monitoring using sentinel-1 SAR data","volume":"247","author":"Mandal","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"113046","DOI":"10.1016\/j.rse.2022.113046","article-title":"Spatial-aware SAR-optical time-series deep integration for crop phenology tracking","volume":"276","author":"Zhao","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_4","first-page":"217","article-title":"Importance of phenological observations and predictions in agriculture","volume":"50","author":"Ruml","year":"2005","journal-title":"J. Agric. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1016\/j.rse.2017.07.031","article-title":"Tracking crop phenological development using multi-temporal polarimetric Radarsat-2 data","volume":"210","author":"Canisius","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"8379391","DOI":"10.34133\/2021\/8379391","article-title":"Mapping crop phenology in near real-time using satellite remote sensing: Challenges and opportunities","volume":"2021","author":"Gao","year":"2021","journal-title":"J. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Pasternak, M., and Pawluszek-Filipiak, K. (2021). The Evaluation of Spectral Vegetation Indexes and Redundancy Reduction on the Accuracy of Crop Type Detection. Appl. Sci., 12.","DOI":"10.3390\/app12105067"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"438","DOI":"10.1016\/j.rse.2014.10.009","article-title":"Cloud cover throughout the agricultural growing season: Impacts on passive optical earth observations","volume":"156","author":"Whitcraft","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Eberhardt, I.D.R., Schultz, B., Rizzi, R., Sanches, I.D.A., Formaggio, A.R., Atzberger, C., and Jos\u00e9 Barreto Luiz, A. (2016). Cloud cover assessment for operational crop monitoring systems in tropical areas. Remote Sens., 8.","DOI":"10.3390\/rs8030219"},{"key":"ref_10","first-page":"102539","article-title":"The potential of active and passive remote sensing to detect frequent harvesting of alfalfa","volume":"104","author":"Zhou","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1080\/01431169108929656","article-title":"Change detection in SAR imagery","volume":"12","author":"White","year":"1991","journal-title":"Int. J. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Yaping, D., and Zhongxin, C. (2012, January 2\u20134). A review of crop identification and area monitoring based on SAR image. Proceedings of the 2012 First International Conference on Agro-Geoinformatics (Agro-Geoinformatics), Shanghai, China.","DOI":"10.1109\/Agro-Geoinformatics.2012.6311680"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"106118","DOI":"10.1016\/j.compag.2021.106118","article-title":"Sentinel-1 interferometric coherence and backscattering analysis for crop monitoring","volume":"185","author":"Nasirzadehdizaji","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.isprsjprs.2021.05.013","article-title":"Dual-polarimetric descriptors from Sentinel-1 GRD SAR data for crop growth assessment","volume":"178","author":"Bhogapurapu","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_15","first-page":"2519","article-title":"A time-series approach to estimate soil moisture using polarimetric radar data","volume":"47","author":"Kim","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","first-page":"564","article-title":"Radar vegetation index for estimating the vegetation water content of rice and soybean","volume":"9","author":"Kim","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_17","unstructured":"Kumar, D., Rao, S., and Sharma, J.R. (2013, January 19\u201321). Radar Vegetation Index as an alternative to NDVI for monitoring of soyabean and cotton. Proceedings of the XXXIII INCA International Congress (Indian Cartographer), Jodhpur, India."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Mandal, D., Bhattacharya, A., Kumar, V., Ratha, D., Dey, S., McNairn, H., and Rao, Y.S. (August, January 28). A novel radar vegetation index for compact polarimetric SAR data. Proceedings of the IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8898022"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"7102","DOI":"10.1109\/TGRS.2018.2848285","article-title":"Polarimetric radar vegetation index for biomass estimation in desert fringe ecosystems","volume":"56","author":"Chang","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"4372","DOI":"10.1080\/01431161.2023.2235639","article-title":"An optimum datasets analysis for monitoring crops using remotely sensed Sentinel-1A SAR data","volume":"44","author":"Salma","year":"2023","journal-title":"Int. J. Remote Sens."},{"key":"ref_21","first-page":"105","article-title":"Analysing the potential of polarimetric decomposition parameters of Sentinel\u20131 dual-pol SAR data for estimation of rice crop biophysical parameters","volume":"25","author":"DAVE","year":"2023","journal-title":"J. Agrometeorol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.isprsjprs.2023.07.023","article-title":"Vegetation descriptors from Sentinel-1 SAR data for crop growth monitoring","volume":"203","author":"Bao","year":"2023","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_23","first-page":"102505","article-title":"Potential of C-band Synthetic Aperture Radar Sentinel-1 time-series for the monitoring of phenological cycles in a deciduous forest","volume":"104","author":"Soudani","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"9410","DOI":"10.3390\/rs70709410","article-title":"Potential of C and X band SAR for shrub growth monitoring in sub-arctic environments","volume":"7","author":"Duguay","year":"2015","journal-title":"Remote Sens."},{"key":"ref_25","first-page":"405","article-title":"Corn Monitoring and Crop Yield Using Optical and Microwave Remote Sensing","volume":"10","author":"Ruiz","year":"2008","journal-title":"Geosci. Remote Sens."},{"key":"ref_26","unstructured":"McNairn, H., and Shang, J. (2021). Multitemporal Remote Sensing: Methods and Applications, Springer."},{"key":"ref_27","unstructured":"Solska, K. (2021, August 09). Prognosis of the Environmental Impact of the Local Area Development Plan for the Area Located in Jelcz-Laskowice, Jelcz-Laskowice Commune\u2014\u201cMPZP In\u017cynierska\u2014Aleja M\u0142odych\u201d. 2018. Available online: https:\/\/www.um.jelcz-laskowice.finn.pl\/res\/serwisy\/pliki\/18337686?version=1.0."},{"key":"ref_28","unstructured":"Kochanowska, J., Dziedzic, M., Gruszecki, J., Lis, J., Pasieczna, A., and Wo\u0142kowicz, S. (2004). Explanation of the Geoenvironmental Map of Poland 1: 50 000, Laskowice Sheet (765), PIG."},{"key":"ref_29","unstructured":"Wr\u00f3blewski, K., and Pasternak, A. (2005). Guide to the Land of Jelcz-Laskowice, Municipal and Communal Office of Jelcz-Laskowice."},{"key":"ref_30","first-page":"381","article-title":"Einkeitliche codierung der ph\u00e4nologischen stadien bei kultur-und schadpflanzen","volume":"41","author":"Bleiholder","year":"1989","journal-title":"Gesunde Pflanzen"},{"key":"ref_31","first-page":"41","article-title":"The BBCH scale for phonological growth stages","volume":"61","author":"Hack","year":"2001","journal-title":"Growth Stages Mono-Dicotyledonous Plants. Bbch Monogr."},{"key":"ref_32","unstructured":"(2015). Geotechnical testing\u2014Laboratory testing of soils\u2014Part 1: Determination of natural moisture content. Standard No. PN-EN ISO 17892-1:2015-02."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/j.isprsjprs.2020.03.009","article-title":"Evaluation of Sentinel-1 & 2 time series for predicting wheat and rapeseed phenological stages","volume":"163","author":"Mercier","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/0030-4018(77)90292-9","article-title":"Degree of polarization and the principal idempotents of the coherency matrix","volume":"23","author":"Barakat","year":"1977","journal-title":"Opt. Commun."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1021\/ac60214a047","article-title":"Smoothing and differentiation of data by simplified least squares procedures","volume":"36","author":"Savitzky","year":"1964","journal-title":"Anal. Chem."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1063\/1.4822961","article-title":"Savitzky-Golay smoothing filters","volume":"4","author":"Press","year":"1990","journal-title":"Comput. Phys."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1109\/MSP.2011.941097","article-title":"What is a Savitzky-Golay filter? [lecture notes]","volume":"28","author":"Schafer","year":"2011","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1016\/j.rse.2004.03.014","article-title":"A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky\u2013Golay filter","volume":"91","author":"Chen","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Harfenmeister, K., Spengler, D., and Weltzien, C. (2019). Analyzing Temporal and Spatial Characteristics of Crop Parameters Using Sentinel-1 Backscatter Data. Remote Sens., 11.","DOI":"10.3390\/rs11131569"},{"key":"ref_40","unstructured":"Ulaby, F.T., Moore, R.K., and Fung, A.K. (1986). Microwave Remote Sensing: Active and Passive, vol. III, Volume Scattering and Emission Theory, Advanced Systems and Applications, Artech House."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"El Hajj, M., Baghdadi, N., Bazzi, H., and Zribi, M. (2018). Penetration analysis of SAR signals in the C and L bands for wheat, maize, and grasslands. Remote Sens., 11.","DOI":"10.3390\/rs11010031"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/20\/4996\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:08:24Z","timestamp":1760130504000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/20\/4996"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,17]]},"references-count":41,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2023,10]]}},"alternative-id":["rs15204996"],"URL":"https:\/\/doi.org\/10.3390\/rs15204996","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,17]]}}}