{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T11:45:56Z","timestamp":1776685556700,"version":"3.51.2"},"reference-count":62,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2021,7,27]],"date-time":"2021-07-27T00:00:00Z","timestamp":1627344000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This study explores the potential of Sentinel-1 Synthetic Aperture Radar (SAR) to identify phenological phases of wheat, sugar beet, and canola. Breakpoint and extreme value analyses were applied to a dense time series of interferometric (InSAR) and polarimetric (PolSAR) features recorded during the growing season of 2017 at the JECAM site DEMMIN (Germany). The analyses of breakpoints and extrema allowed for the distinction of vegetative and reproductive stages for wheat and canola. Certain phenological stages, measured in situ using the BBCH-scale, such as leaf development and rosette growth of sugar beet or stem elongation and ripening of wheat, were detectable by a combination of InSAR coherence, polarimetric Alpha and Entropy, and backscatter (VV\/VH). Except for some fringe cases, the temporal difference between in situ observations and breakpoints or extrema ranged from zero to five days. Backscatter produced the signature that generated the most breakpoints and extrema. However, certain micro stadia, such as leaf development of BBCH 10 of sugar beet or flowering BBCH 69 of wheat, were only identifiable by the InSAR coherence and Alpha. Hence, it is concluded that combining PolSAR and InSAR features increases the number of detectable phenological events in the phenological cycles of crops.<\/jats:p>","DOI":"10.3390\/rs13152951","type":"journal-article","created":{"date-parts":[[2021,7,27]],"date-time":"2021-07-27T22:35:02Z","timestamp":1627425302000},"page":"2951","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["The Impact of Phenological Developments on Interferometric and Polarimetric Crop Signatures Derived from Sentinel-1: Examples from the DEMMIN Study Site (Germany)"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8731-7534","authenticated-orcid":false,"given":"Johannes","family":"L\u00f6w","sequence":"first","affiliation":[{"name":"Department of Geoecology, Institute of Geosciences and Geography, University of Halle-Wittenberg, 06120 Halle (Saale), Germany"},{"name":"Department of Remote Sensing, Institute of Geography and Geology, University of W\u00fcrzburg, 97074 W\u00fcrzburg, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6626-3052","authenticated-orcid":false,"given":"Tobias","family":"Ullmann","sequence":"additional","affiliation":[{"name":"Department of Physical Geography, Institute of Geography and Geology, University of W\u00fcrzburg, 97074 W\u00fcrzburg, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0807-7059","authenticated-orcid":false,"given":"Christopher","family":"Conrad","sequence":"additional","affiliation":[{"name":"Department of Geoecology, Institute of Geosciences and Geography, University of Halle-Wittenberg, 06120 Halle (Saale), Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Lieth, H. (1974). Phenology and Seasonality Modeling, Springer.","DOI":"10.1007\/978-3-642-51863-8"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.rse.2016.11.004","article-title":"Toward mapping crop progress at field scales through fusion of Landsat and MODIS imagery","volume":"188","author":"Gao","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1007\/s12571-013-0262-z","article-title":"Postharvest agriculture in changing climates: Its importance to African smallholder farmers","volume":"5","author":"Stathers","year":"2013","journal-title":"Food Secur."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/j.rse.2012.12.017","article-title":"MODIS-based corn grain yield estimation model incorporating crop phenology information","volume":"131","author":"Sakamoto","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2249","DOI":"10.1109\/JSTARS.2016.2639043","article-title":"Radar Remote Sensing of Agriciltural Canopies","volume":"10","author":"McNairn","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Ulaby, F., and Moore, R. (1973, January 22\u201324). Radar sensing of soil moisture. Proceedings of the 1973 Antennas and Propagation Society International Symposium, Boulder, CO, USA.","DOI":"10.1109\/APS.1973.1147125"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1109\/TAP.1975.1140999","article-title":"Radar response to vegetation","volume":"23","author":"Ulaby","year":"1975","journal-title":"IEEE Trans. Antennas Propag."},{"key":"ref_8","unstructured":"Weise, K., and Davidson, M.W.J. (2004, January 20\u201324). Dualband\u2014TerraSAR simulation\/campaign results for L-band configuration. Proceedings of the IGARSS 2004 IEEE International Geoscience and Remote Sensing Symposium, Anchorage, AK, USA."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"833","DOI":"10.5194\/hess-13-833-2009","article-title":"EAGLE 2006\u2014Multi-purpose, multi-angle and multi-sensor in-situ and airborne campaigns over grassland and forest","volume":"13","author":"Su","year":"2009","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_10","first-page":"544","article-title":"Farmland monitoring with SAR interferometry","volume":"1","author":"Werner","year":"1995","journal-title":"Geosci. Remote Sens. Symp."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1724","DOI":"10.1016\/j.rse.2009.04.005","article-title":"Potential of SAR sensors TerraSAR-X, ASAR\/ENVISAT and PALSAR\/ALOS for monitoring sugarcane crops on Reunion Island","volume":"113","author":"Baghdadi","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3077","DOI":"10.1080\/01431161.2015.1055608","article-title":"Retrieval of phenological stages of onion fields during the first year of growth by means of C-band polarimetric SAR measurements","volume":"36","author":"Mascolo","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"498","DOI":"10.1109\/36.485127","article-title":"A review of target decomposition theorems in radar polarimetry","volume":"34","author":"Cloude","year":"1996","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","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_15","doi-asserted-by":"crossref","unstructured":"Ndikumana, E., Ho Tong Minh, D., Baghdadi, N., Courault, D., and Hossard, L. (2018). Deep Recurrent Neural Network for Agricultural Classification using multitemporal SAR Sentinel-1 for Camargue, France. Remote Sens., 10.","DOI":"10.1117\/12.2325160"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"111814","DOI":"10.1016\/j.rse.2020.111814","article-title":"Sentinel-1 time series data for monitoring the phenology of winter wheat","volume":"246","author":"Schlund","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.rse.2011.05.028","article-title":"GMES Sentinel-1 mission","volume":"120","author":"Torres","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"217","DOI":"10.2298\/JAS0502217R","article-title":"Importance of phenological observations and predictions in agriculture","volume":"50","author":"Ruml","year":"2005","journal-title":"J. Agric. Sci. Belgrade"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Wali, E., Tasumi, M., and Moriyama, M. (2020). Combination of linear regression lines to understand the response of sentinel-1 dual polarization SAR data with crop phenology-case study in Miyazaki, Japan. Remote Sens., 12.","DOI":"10.3390\/rs12010189"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Song, Y., and Wang, J. (2019). Mapping winter wheat planting area and monitoring its phenology using Sentinel-1 backscatter time series. Remote Sens., 11.","DOI":"10.3390\/rs11040449"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"111660","DOI":"10.1016\/j.rse.2020.111660","article-title":"Detecting flowering phenology in oil seed rape parcels with Sentinel-1 and -2 time series","volume":"239","author":"Taymans","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Khabbazan, S., Vermunt, P., Steele-Dunne, S., Arntz, L.R., Marinetti, C., van der Valk, D., Iannini, L., Molijn, R., Westerdijk, K., and van der Sande, C. (2019). Crop monitoring using Sentinel-1 data: A case study from The Netherlands. Remote Sens., 11.","DOI":"10.3390\/rs11161887"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Meroni, M., d\u2019Andrimont, R., Vrieling, A., Fasbender, D., Lemoine, G., Rembold, F., Seguini, L., and Verhegghen, A. (2021). Comparing land surface phenology of major European crops as derived from SAR and multispectral data of Sentinel-1 and -2. Remote Sens. Environ., 253.","DOI":"10.1016\/j.rse.2020.112232"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Shang, J., Liu, J., Poncos, V., Geng, X., Qian, B., Chen, Q., Dong, T., Macdonald, D., Martin, T., and Kovacs, J. (2020). Detection of crop seeding and harvest through analysis of time-series Sentinel-1 interferometric SAR data. Remote Sens., 12.","DOI":"10.3390\/rs12101551"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.isprsjprs.2015.01.007","article-title":"The Kennaugh element framework for multi-scale, multi-polarized, multi-temporal and multi-frequency SAR image preparation","volume":"102","author":"Schmitt","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.rse.2012.02.022","article-title":"Near real-time disturbance detection using satellite image time series","volume":"123","author":"Verbesselt","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2970","DOI":"10.1016\/j.rse.2010.08.003","article-title":"Phenological change detection while accounting for abrupt and gradual trends in satellite image time series","volume":"114","author":"Verbesselt","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_28","unstructured":"Meier, U. (2001). Growth Stages of Mono-and Dicotyledonous Plants. BBCH Monograph, Federal Biological Research Centre for Agriculture and Forestry. [2nd ed.]."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"727","DOI":"10.2135\/cropsci2006.05.0292","article-title":"Soybean sowing date: The vegetative, reproductive, and agronomic impacts","volume":"48","author":"Bastidas","year":"2008","journal-title":"Crop Sci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/S1369-5266(00)00137-0","article-title":"Transition from vegetative to reproductive phase","volume":"4","author":"Araki","year":"2001","journal-title":"Curr. Opin. Plant Biol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.crm.2015.06.003","article-title":"Climate Risk Management Flexible weather index-based insurance design","volume":"10","author":"Conradt","year":"2015","journal-title":"Clim. Risk Manag."},{"key":"ref_32","unstructured":"Borg, E., Lippert, K., Zabel, E., L\u00f6pmeier, F.-J., Fichtelmann, B., Jahncke, D., and Maass, H. (2009). DEMMIN Teststandort zur Kalibrierung und Validierung von Fernerkundungsmissionen, DLR Eigenverlag."},{"key":"ref_33","unstructured":"Spengler, D., Itzerott, S., Ahmadian, N., Borg, E., H\u00fcttich, C., Maass, H., Missling, K.-D., Schmullius, C., Truckenbrodt, S., and Conrad, C. (2018, January 17\u201320). The German JECAM site DEMMIN: Status and future perspectives. Proceedings of the Annual JECAM Meeting, Taichung City, Taiwan."},{"key":"ref_34","unstructured":"Team, S. (2013). Sentinel-1 User Handbook, ESA Communications."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1127\/zfg_suppl\/2019\/0524","article-title":"Data Processing, Feature Extraction, and Time-Series Analysis of Sentinel-1 Synthetic Aperture Radar (SAR) Imagery: Examples from Damghan and Bajestan Playa (Iran)","volume":"62","author":"Ullmann","year":"2019","journal-title":"Z. f\u00fcr Geomorphol. Suppl. Issues"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Ullmann, T., Sauerbrey, J., Hoffmeister, D., May, S.M., Baumhauer, R., and Bubenzer, O. (2019). Assessing spatiotemporal variations of sentinel-1 InSAR coherence at different time scales over the atacama desert (Chile) between 2015 and 2018. Remote Sens., 11.","DOI":"10.3390\/rs11242960"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3081","DOI":"10.1109\/TGRS.2011.2120616","article-title":"Flattening Gamma: Radiometric Terrain Correction for SAR Imagery","volume":"49","author":"Small","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Richards, J.A. (2009). Remote Sensing with Imaging Radar, Springer.","DOI":"10.1007\/978-3-642-02020-9"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Wang, C., Zhang, H., Tang, Y., and Liu, X. (2018). Analysis of Permafrost Region Coherence Variation in the Qinghai\u2013Tibet Plateau with a High-Resolution TerraSAR-X Image. Remote Sens., 10.","DOI":"10.3390\/rs10020298"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"596","DOI":"10.1016\/j.rse.2017.09.039","article-title":"On the potential of Polarimetric SAR Interferometry to characterize the biomass, moisture and structure of agricultural crops at L-, C- and X-Bands","volume":"204","author":"Pichierri","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_41","unstructured":"Scheuchl, B., Ullmann, T., and Koudogbo, F. (2009, January 2\u20135). Change Detection Using High Resolution TerraSAR-X Data Preliminary Results. Proceedings of the ISPRS Hannover Workshop 2009, Hannover, Germany."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"950","DOI":"10.1109\/36.175330","article-title":"Decorrelation in interferometric radar echoes","volume":"30","author":"Zebker","year":"1992","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_43","unstructured":"Esch, S. (2018). Determination of Soil Moisture and Vegetation Parameters from Spaceborne C-Band SAR on Agricultural Areas. [Ph.D. Thesis, Universit\u00e4t zu K\u00f6ln]."},{"key":"ref_44","unstructured":"Lee, J.-S., and Pottier, E. (2009). Polarimetric Radar Imaging: From Basics to Applications, CRC Press."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Harfenmeister, K., Itzerott, S., Weltzien, C., and Spengler, D. (2021). Agricultural Monitoring Using Polarimetric Decomposition Parameters of Sentinel-1 Data. Remote Sens., 13.","DOI":"10.3390\/rs13040575"},{"key":"ref_46","first-page":"170","article-title":"Retrieving canopy height and density of paddy rice from Radarsat-2 images with a canopy scattering model","volume":"28","author":"Zhang","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"829","DOI":"10.1080\/01621459.1979.10481038","article-title":"Robust Locally Weighted Regression and Smoothing Scatterplots Robust Locally Weighted Regression and Smoothing Scatterplots","volume":"74","author":"Cleveland","year":"1979","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Cai, Z., J\u00f6nsson, P., Jin, H., and Eklundh, L. (2017). Performance of smoothing methods for reconstructing NDVI time-series and estimating vegetation phenology from MODIS data. Remote Sens., 9.","DOI":"10.3390\/rs9121271"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"478","DOI":"10.3390\/rs2020478","article-title":"Assessing plant diversity in a dry tropical forest: Comparing the utility of landsat and ikonos satellite images","volume":"2","author":"Nagendra","year":"2010","journal-title":"Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1686","DOI":"10.21105\/joss.01686","article-title":"Welcome to the Tidyverse","volume":"4","author":"Wickham","year":"2019","journal-title":"J. Open Source Softw."},{"key":"ref_51","unstructured":"R Core Team (2021, June 10). A Language and Environment for Statistical Computing. Available online: https:\/\/www.R-project.org\/."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1002\/jae.659","article-title":"Computation and analysis of multiple structural change models","volume":"18","author":"Bai","year":"2003","journal-title":"J. Appl. Econom."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/S0167-9473(03)00030-6","article-title":"Testing and dating of structural changes in practice","volume":"44","author":"Zeileis","year":"2003","journal-title":"Comput. Stat. Data Anal."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1142\/S1793536909000047","article-title":"Ensemble Empirical Mode Decomposition: A Noise-Assisted Data Analysis Method","volume":"1","author":"Wu","year":"2009","journal-title":"Adv. Data Sci. Adapt. Anal."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1016\/j.rse.2017.07.015","article-title":"Understanding the temporal behavior of crops using Sentinel-1 and Sentinel-2-like data for agricultural applications","volume":"199","author":"Veloso","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Vreugdenhil, M., Wagner, W., Bauer-Marschallinger, B., Pfeil, I., Teubner, I., R\u00fcdiger, C., and Strauss, P. (2018). Sensitivity of Sentinel-1 backscatter to vegetation dynamics: An Austrian case study. Remote Sens., 10.","DOI":"10.3390\/rs10091396"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1551","DOI":"10.1109\/TGRS.2003.813531","article-title":"Multitemporal C-Band Radar Measurements on Wheat Fields","volume":"41","author":"Mattia","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1016\/j.catena.2016.11.016","article-title":"Catena Coupling of phenological information and simulated vegetation index time series: Limitations and potentials for the assessment and monitoring of soil erosion risk","volume":"150","author":"Gerstmann","year":"2017","journal-title":"Catena"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.fcr.2018.03.018","article-title":"Field Crops Research The critical period for yield and quality determination in canola (Brassica napus L.)","volume":"222","author":"Kirkegaard","year":"2018","journal-title":"Field Crop. Res."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.eja.2018.03.005","article-title":"Source-sink manipulations indicate seed yield in canola is limited by source availability","volume":"96","author":"Zhang","year":"2018","journal-title":"Eur. J. Agron."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.rse.2018.10.012","article-title":"Estimating canola phenology using synthetic aperture radar","volume":"219","author":"McNairn","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_62","first-page":"102285","article-title":"All models of satellite-derived phenology are wrong, but some are useful: A case study from northern Australia","volume":"97","author":"Younes","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/15\/2951\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:35:40Z","timestamp":1760164540000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/15\/2951"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,27]]},"references-count":62,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2021,8]]}},"alternative-id":["rs13152951"],"URL":"https:\/\/doi.org\/10.3390\/rs13152951","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,27]]}}}