{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T23:07:48Z","timestamp":1775862468275,"version":"3.50.1"},"reference-count":92,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2022,6,22]],"date-time":"2022-06-22T00:00:00Z","timestamp":1655856000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Helmholtz Association of German Research Centers under the Helmholtz Climate Initiative (\u201cHI-CAM: Helmholtz-Initiative Climate Adaptation and Mitigation\u201d) project and by the Federal Ministry of Food and Agriculture through the \u201cEinsatz von Fernerkundungstechnologien f\u00fcr die Digitalisierung im Pflanzenbau (AgriSens DEMMIN 4.0)\u201d project","award":["28DE114C18"],"award-info":[{"award-number":["28DE114C18"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Nationwide and consistent information on agricultural land use forms an important basis for sustainable land management maintaining food security, (agro)biodiversity, and soil fertility, especially as German agriculture has shown high vulnerability to climate change. Sentinel-1 and Sentinel-2 satellite data of the Copernicus program offer time series with temporal, spatial, radiometric, and spectral characteristics that have great potential for mapping and monitoring agricultural crops. This paper presents an approach which synergistically uses these multispectral and Synthetic Aperture Radar (SAR) time series for the classification of 17 crop classes at 10 m spatial resolution for Germany in the year 2018. Input data for the Random Forest (RF) classification are monthly statistics of Sentinel-1 and Sentinel-2 time series. This approach reduces the amount of input data and pre-processing steps while retaining phenological information, which is crucial for crop type discrimination. For training and validation, Land Parcel Identification System (LPIS) data were available covering 15 of the 16 German Federal States. An overall map accuracy of 75.5% was achieved, with class-specific F1-scores above 80% for winter wheat, maize, sugar beet, and rapeseed. By combining optical and SAR data, overall accuracies could be increased by 6% and 9%, respectively, compared to single sensor approaches. While no increase in overall accuracy could be achieved by stratifying the classification in natural landscape regions, the class-wise accuracies for all but the cereal classes could be improved, on average, by 7%. In comparison to census data, the crop areas could be approximated well with, on average, only 1% of deviation in class-specific acreages. Using this streamlined approach, similar accuracies for the most widespread crop types as well as for smaller permanent crop classes were reached as in other Germany-wide crop type studies, indicating its potential for repeated nationwide crop type mapping.<\/jats:p>","DOI":"10.3390\/rs14132981","type":"journal-article","created":{"date-parts":[[2022,6,22]],"date-time":"2022-06-22T23:11:19Z","timestamp":1655939479000},"page":"2981","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":56,"title":["Mapping Crop Types of Germany by Combining Temporal Statistical Metrics of Sentinel-1 and Sentinel-2 Time Series with LPIS Data"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7302-6813","authenticated-orcid":false,"given":"Sarah","family":"Asam","sequence":"first","affiliation":[{"name":"German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), 82234 Wessling, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ursula","family":"Gessner","sequence":"additional","affiliation":[{"name":"German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), 82234 Wessling, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Roger","family":"Almengor Gonz\u00e1lez","sequence":"additional","affiliation":[{"name":"German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), 82234 Wessling, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Martina","family":"Wenzl","sequence":"additional","affiliation":[{"name":"German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), 82234 Wessling, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0369-5819","authenticated-orcid":false,"given":"Jennifer","family":"Kriese","sequence":"additional","affiliation":[{"name":"German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), 82234 Wessling, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Claudia","family":"Kuenzer","sequence":"additional","affiliation":[{"name":"German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), 82234 Wessling, Germany"},{"name":"Institute of Geography and Geology, University of Wuerzburg, 97074 Wuerzburg, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,22]]},"reference":[{"key":"ref_1","unstructured":"Statistisches Bundesamt (Destatis) (2022, March 21). Genesis-Online, Available online: https:\/\/www-genesis.destatis.de\/genesis\/online."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Ray, D.K., West, P.C., Clark, M., Gerber, J.S., Prishchepov, A.V., and Chatterjee, S. (2019). Climate change has likely already affected global food production. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0217148"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"102960","DOI":"10.1016\/j.jcs.2020.102960","article-title":"How changes in climate and agricultural practices influenced wheat production in Western Europe","volume":"93","author":"Oury","year":"2020","journal-title":"J. Cereal Sci."},{"key":"ref_4","unstructured":"Eckstein, D., K\u00fcnzel, V., Sch\u00e4fer, L., and Winges, M. (2019). Global Climate Risk Index 2020, Germanwatch, e.V."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1007\/s10584-018-2234-y","article-title":"Unprecedented risk of spring frost damage in Switzerland and Germany in 2017","volume":"149","author":"Vitasse","year":"2018","journal-title":"Clim. Chang."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1007\/s10584-009-9652-9","article-title":"A Ricardian analysis of the impact of climate change on agriculture in Germany","volume":"97","author":"Lippert","year":"2009","journal-title":"Clim. Chang."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1016\/j.rse.2018.11.007","article-title":"Near real-time agriculture monitoring at national scale at parcel resolution: Performance assessment of the Sen2-Agri automated system in various cropping systems around the world","volume":"221","author":"Defourny","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Ibrahim, E.S., Rufin, P., Nill, L., Kamali, B., Nendel, C., and Hostert, P. (2021). Mapping Crop Types and Cropping Systems in Nigeria with Sentinel-2 Imagery. Remote Sens., 13.","DOI":"10.3390\/rs13173523"},{"key":"ref_9","first-page":"122","article-title":"How much does multi-temporal Sentinel-2 data improve crop type classification?","volume":"72","author":"Vuolo","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Immitzer, M., Vuolo, F., and Atzberger, C. (2016). First Experience with Sentinel-2 Data for Crop and Tree Species Classifications in Central Europe. Remote Sens., 8.","DOI":"10.3390\/rs8030166"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"83","DOI":"10.5194\/isprs-archives-XLIII-B3-2020-83-2020","article-title":"Exploring Sentinel-2 for Land Cover and Crop Mapping in Portugal","volume":"43","author":"Hernandez","year":"2020","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1016\/j.rse.2017.10.005","article-title":"Sentinel-2 cropland mapping using pixel-based and object-based time-weighted dynamic time warping analysis","volume":"204","author":"Belgiu","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Mazzia, V., Khaliq, A., and Chiaberge, M. (2019). Improvement in Land Cover and Crop Classification based on Temporal Features Learning from Sentinel-2 Data Using Recurrent-Convolutional Neural Network (R-CNN). Appl. Sci., 10.","DOI":"10.3390\/app10010238"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Paris, C., Weikmann, G., and Bruzzone, L. (2020, January 21). Monitoring of Agricultural Areas by using Sentinel 2 Image Time Series and Deep Learning Techniques. Proceedings of the SPIE 11533, Image and Signal Processing for Remote Sensing XXVI, Online.","DOI":"10.1117\/12.2574745"},{"key":"ref_15","first-page":"1337","article-title":"Application of Temporal Convolutional Neural Network for the Classification of Crops on Sentinel-2 Time Series","volume":"43","author":"Peressutti","year":"2020","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1109\/JSTARS.2020.3038152","article-title":"Semantically Enriched Crop Type Classification and Linked Earth Observation Data to Support the Common Agricultural Policy Monitoring","volume":"14","author":"Rousi","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Sitokonstantinou, V., Papoutsis, I., Kontoes, C., Arnal, A., Andr\u00e9s, A.P., and Zurbano, J.A. (2018). Scalable Parcel-Based Crop Identification Scheme Using Sentinel-2 Data Time-Series for the Monitoring of the Common Agricultural Policy. Remote Sens., 10.","DOI":"10.3390\/rs10060911"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Turkoglu, M.O., D\u2019Aronco, S., Perich, G., Liebisch, F., Streit, C., Schindler, K., and Wegner, J.D. (2021). Crop mapping from image time series: Deep learning with multi-scale label hierarchies. Remote Sens. Environ., 264.","DOI":"10.1016\/j.rse.2021.112603"},{"key":"ref_19","first-page":"102264","article-title":"Phenology-based sample generation for supervised crop type classification","volume":"95","author":"Belgiu","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Martini, M., Mazzia, V., Khaliq, A., and Chiaberge, M. (2021). Domain-Adversarial Training of Self-Attention-Based Networks for Land Cover Classification Using Multi-Temporal Sentinel-2 Satellite Imagery. Remote Sens., 13.","DOI":"10.3390\/rs13132564"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Gallo, I., La Grassa, R., Landro, N., and Boschetti, M. (2021). Sentinel 2 Time Series Analysis with 3D Feature Pyramid Network and Time Domain Class Activation Intervals for Crop Mapping. ISPRS Int. J. Geoinf., 10.","DOI":"10.3390\/ijgi10070483"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"111673","DOI":"10.1016\/j.rse.2020.111673","article-title":"Introducing APiC for regionalised land cover mapping on the national scale using Sentinel-2A imagery","volume":"240","author":"Preidl","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1016\/j.isprsjprs.2020.06.006","article-title":"Self-attention for raw optical Satellite Time Series Classification","volume":"169","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_24","first-page":"040316","article-title":"Multi-temporal Crop Type and Field Boundary Classification with Google Earth Engine","volume":"2020","author":"Marszalek","year":"2020","journal-title":"Preprints"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.agsy.2019.01.005","article-title":"Scalable pixel-based crop classification combining Sentinel-2 and Landsat-8 data time series: Case study of the Duero river basin","volume":"171","author":"Piedelobo","year":"2019","journal-title":"Agric. Syst."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.rse.2018.10.031","article-title":"Intra-annual reflectance composites from Sentinel-2 and Landsat for national-scale crop and land cover mapping","volume":"220","author":"Griffiths","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_27","first-page":"53","article-title":"A Progressive Crop-Type Classification Using Multitemporal Remote Sensing Data and Phenological Information","volume":"86","author":"Heupel","year":"2018","journal-title":"PFG\u2013J. Photogramm. Remote Sens. Geoinf. Sci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"105864","DOI":"10.1016\/j.compag.2020.105864","article-title":"Agricultural crop discrimination in a heterogeneous low-mountain range region based on multi-temporal and multi-sensor satellite data","volume":"179","author":"Kyere","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"6553","DOI":"10.1080\/01431161.2019.1569791","article-title":"Crop type classification using a combination of optical and radar remote sensing data: A review","volume":"40","author":"Orynbaikyzy","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Arias, M., Campo-Besc\u00f3s, M.\u00c1., and \u00c1lvarez-Mozos, J. (2020). Crop Classification Based on Temporal Signatures of Sentinel-1 Observations over Navarre Province, Spain. Remote Sens., 12.","DOI":"10.3390\/rs12020278"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"112708","DOI":"10.1016\/j.rse.2021.112708","article-title":"From parcel to continental scale\u2013A first European crop type map based on Sentinel-1 and LUCAS Copernicus in-situ observations","volume":"266","author":"Verhegghen","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Beriaux, E., Jago, A., Lucau-Danila, C., Planchon, V., and Defourny, P. (2021). Sentinel-1 Time Series for Crop Identification in the Framework of the Future CAP Monitoring. Remote Sens., 13.","DOI":"10.3390\/rs13142785"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"4070","DOI":"10.1109\/JSTARS.2020.3008096","article-title":"Time-Series of Sentinel-1 Interferometric Coherence and Backscatter for Crop-Type Mapping","volume":"13","author":"Jacob","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"803","DOI":"10.5194\/isprs-archives-XLII-4-W18-803-2019","article-title":"Application of Sentinel-1 Multi-Temporal Data for Crop Monitoring and Mapping","volume":"42","author":"Nasirzadehdizaji","year":"2019","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Planque, C., Lucas, R., Punalekar, S., Chognard, S., Hurford, C., Owers, C., Horton, C., Guest, P., King, S., and Williams, S. (2021). National Crop Mapping Using Sentinel-1 Time Series: A Knowledge-Based Descriptive Algorithm. Remote Sens., 13.","DOI":"10.3390\/rs13050846"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Teimouri, N., Dyrmann, M., and J\u00f8rgensen, R.N. (2019). A Novel Spatio-Temporal FCN-LSTM Network for Recognizing Various Crop Types Using Multi-Temporal Radar Images. Remote Sens., 11.","DOI":"10.3390\/rs11080990"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Denize, J., Hubert-Moy, L., and Pottier, E. (2019). Polarimetric SAR Time-Series for Identification of Winter Land Use. Sensors, 19.","DOI":"10.3390\/s19245574"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.isprsjprs.2021.03.004","article-title":"Mapping crop types in complex farming areas using SAR imagery with dynamic time warping","volume":"175","author":"Gella","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Valcarce-Di\u00f1eiro, R., Arias-P\u00e9rez, B., Lopez-Sanchez, J.M., and S\u00e1nchez, N. (2019). Multi-Temporal Dual- and Quad-Polarimetric Synthetic Aperture Radar Data for Crop-Type Mapping. Remote Sens., 11.","DOI":"10.3390\/rs11131518"},{"key":"ref_40","first-page":"102683","article-title":"Multi-temporal phenological indices derived from time series Sentinel-1 images to country-wide crop classification","volume":"107","author":"Rybicki","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Reu\u00df, F., Greimeister-Pfeil, I., Vreugdenhil, M., and Wagner, W. (2021). Comparison of Long Short-Term Memory Networks and Random Forest for Sentinel-1 Time Series Based Large Scale Crop Classification. Remote Sens., 13.","DOI":"10.3390\/rs13245000"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1016\/j.rse.2017.06.022","article-title":"A new method for crop classification combining time series of radar images and crop phenology information","volume":"198","author":"Bargiel","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"H\u00fctt, C., Waldhoff, G., and Bareth, G. (2020). Fusion of Sentinel-1 with Official Topographic and Cadastral Geodata for Crop-Type Enriched LULC Mapping Using FOSS and Open Data. ISPRS Int. J. Geoinf, 9.","DOI":"10.3390\/ijgi9020120"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"6383","DOI":"10.1080\/01431161.2018.1460503","article-title":"Crop-type mapping from a sequence of Sentinel 1 images","volume":"39","author":"Kenduiywo","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Sun, L., Chen, J., Guo, S., Deng, X., and Han, Y. (2020). Integration of Time Series Sentinel-1 and Sentinel-2 Imagery for Crop Type Mapping over Oasis Agricultural Areas. Remote Sens., 12.","DOI":"10.3390\/rs12010158"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Kpienbaareh, D., Sun, X., Wang, J., Luginaah, I., Bezner Kerr, R., Lupafya, E., and Dakishoni, L. (2021). Crop Type and Land Cover Mapping in Northern Malawi Using the Integration of Sentinel-1, Sentinel-2, and PlanetScope Satellite Data. Remote Sens., 13.","DOI":"10.3390\/rs13040700"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"8810279","DOI":"10.1155\/2021\/8810279","article-title":"Machine Learning-Based Classification for Crop-Type Mapping Using the Fusion of High-Resolution Satellite Imagery in a Semiarid Area","volume":"2021","author":"Moumni","year":"2021","journal-title":"Scientifica"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Chakhar, A., Hern\u00e1ndez-L\u00f3pez, D., Ballesteros, R., and Moreno, M.A. (2021). Improving the Accuracy of Multiple Algorithms for Crop Classification by Integrating Sentinel-1 Observations with Sentinel-2 Data. Remote Sens., 13.","DOI":"10.3390\/rs13020243"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Inglada, J., Vincent, A., Arias, M., and Marais-Sicre, C. (2016). Improved Early Crop Type Identification By Joint Use of High Temporal Resolution SAR And Optical Image Time Series. Remote Sens., 8.","DOI":"10.3390\/rs8050362"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"2500","DOI":"10.1109\/JSTARS.2016.2560141","article-title":"Parcel-Based Crop Classification in Ukraine Using Landsat-8 Data and Sentinel-1A Data","volume":"9","author":"Kussul","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Van Tricht, K., Gobin, A., Gilliams, S., and Piccard, I. (2018). Synergistic Use of Radar Sentinel-1 and Optical Sentinel-2 Imagery for Crop Mapping: A Case Study for Belgium. Remote Sens., 10.","DOI":"10.20944\/preprints201808.0066.v1"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"778","DOI":"10.1109\/LGRS.2017.2681128","article-title":"Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data","volume":"14","author":"Kussul","year":"2017","journal-title":"IEEE Geosci. Remote. Sens. Lett."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"627","DOI":"10.1080\/22797254.2018.1454265","article-title":"Crop inventory at regional scale in Ukraine: Developing in season and end of season crop maps with multi-temporal optical and SAR satellite imagery","volume":"51","author":"Kussul","year":"2018","journal-title":"Eur. J. Remote. Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"431","DOI":"10.14358\/PERS.86.7.431","article-title":"Improved Crop Classification with Rotation Knowledge using Sentinel-1 and -2 Time Series","volume":"86","author":"Giordano","year":"2020","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Ofori-Ampofo, S., Pelletier, C., and Lang, S. (2021). Crop Type Mapping from Optical and Radar Time Series Using Attention-Based Deep Learning. Remote Sens., 13.","DOI":"10.3390\/rs13224668"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"112831","DOI":"10.1016\/j.rse.2021.112795","article-title":"Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany","volume":"269","author":"Schwieder","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Orynbaikyzy, A., Gessner, U., Mack, B., and Conrad, C. (2020). Crop Type Classification Using Fusion of Sentinel-1 and Sentinel-2 Data: Assessing the Impact of Feature Selection, Optical Data Availability, and Parcel Sizes on the Accuracies. Remote Sens., 12.","DOI":"10.3390\/rs12172779"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Orynbaikyzy, A., Gessner, U., and Conrad, C. (2022). Spatial Transferability of Random Forest Models for Crop Type Classification Using Sentinel-1 and Sentinel-2. Remote Sens., 14.","DOI":"10.3390\/rs14061493"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"4130","DOI":"10.1080\/01431161.2017.1317933","article-title":"Accurate crop-type classification using multi-temporal optical and multi-polarization SAR data in an object-based image analysis framework","volume":"38","author":"Salehi","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Inglada, J., Vincent, A., Arias, M., Tardy, B., Morin, D., and Rodes, I. (2017). Operational High Resolution Land Cover Map Production at the Country Scale Using Satellite Image Time Series. Remote Sens., 9.","DOI":"10.3390\/rs9010095"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.compag.2012.07.015","article-title":"Crop type mapping using spectral\u2013temporal profiles and phenological information","volume":"89","author":"Foerster","year":"2012","journal-title":"Comput. Electron. Agric."},{"key":"ref_62","unstructured":"Kerner, H., Sahajpal, R., Skakun, S., Becker-Reshef, I., Barker, B., Hosseini, M., Puricelli, E., and Gray, P. (2020). Resilient in-season crop type classification in multispectral satellite observations using growth stage normalization. arXiv."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Skakun, S., Vermote, E., Franch, B., Roger, J.-C., Kussul, N., Ju, J., and Masek, J. (2019). Winter Wheat Yield Assessment from Landsat 8 and Sentinel-2 Data: Incorporating Surface Reflectance, Through Phenological Fitting, into Regression Yield Models. Remote Sens., 11.","DOI":"10.3390\/rs11151768"},{"key":"ref_64","unstructured":"DESTATIS (2022, April 21). Landwirtschaftliche Betriebe, Ausgew\u00e4hlte Merkmale im Zeitvergleich, Available online: https:\/\/www.destatis.de\/DE\/Themen\/Branchen-Unternehmen\/Landwirtschaft-Forstwirtschaft-Fischerei\/Landwirtschaftliche-Betriebe\/Tabellen\/ausgewaehlte-merkmale-zv.html."},{"key":"ref_65","unstructured":"BMEL (2022, April 21). Daten und Fakten-Land-, Forst- und Ern\u00e4hrungswirtschaft mit Fischerei und Wein- und Gartenbau, Available online: https:\/\/www.bmel.de\/SharedDocs\/Downloads\/DE\/Broschueren\/Daten-und-Fakten-Landwirtschaft.pdf."},{"key":"ref_66","unstructured":"German Environment Agency (2022, April 21). Environment and Agriculture 2018, Available online: https:\/\/www.umweltbundesamt.de\/sites\/default\/files\/medien\/421\/publikationen\/180608_uba_fl_umwelt_und_landwirtschaft_engl_bf_neu.pdf."},{"key":"ref_67","unstructured":"ESA (2022, April 21). Sentinel-2 User Handbook, Available online: https:\/\/sentinel.esa.int\/documents\/247904\/685211\/Sentinel-2_User_Handbook."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"de Los Reyes, R., Langheinrich, M., Schwind, P., Richter, R., Pflug, B., Bachmann, M., Muller, R., Carmona, E., Zekoll, V., and Reinartz, P. (2020). PACO: Python-Based Atmospheric COrrection. Sensors, 20.","DOI":"10.3390\/s20051428"},{"key":"ref_69","unstructured":"Veci, L., Prats-Iraola, P., Scheiber, R., Collard, F., Fomferra, N., and Engdahl, M. (2012, January 14\u201318). The sentinel-1 toolbox. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Qu\u00e9bec, QC, Canada."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Dobrini\u0107, D., Ga\u0161parovi\u0107, M., and Medak, D. (2021). Sentinel-1 and 2 Time-Series for Vegetation Mapping Using Random Forest Classification: A Case Study of Northern Croatia. Remote Sens., 13.","DOI":"10.3390\/rs13122321"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Ye, Y., Yang, C., Zhu, B., Zhou, L., He, Y., and Jia, H. (2021). Improving Co-Registration for Sentinel-1 SAR and Sentinel-2 Optical Images. Remote Sens., 13.","DOI":"10.3390\/rs13050928"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.neucom.2020.01.107","article-title":"Multimodal image registration using histogram of oriented gradient distance and data-driven grey wolf optimizer","volume":"392","author":"Yan","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_73","unstructured":"EUROSTAT (2022, April 21). Land Use and Coverage Area frame Survey-LUCAS, Available online: https:\/\/ec.europa.eu\/eurostat\/de\/web\/lucas."},{"key":"ref_74","unstructured":"European Union (2022, April 21). Copernicus Land Monitoring Service 2018. Available online: https:\/\/land.copernicus.eu\/."},{"key":"ref_75","unstructured":"mundialis GmbH & Co. (2022, April 21). KG. Landcover Classification Map of Germany 2019 Based on Sentinel-2 Data. Available online: https:\/\/www.mundialis.de\/en\/deutschland-2019-landbedeckung-auf-basis-von-sentinel-2-daten\/."},{"key":"ref_76","unstructured":"OpenStreetMap-Contributors (2022, April 21). OpenStreetMap. Available online: https:\/\/planet.osm.org."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1038\/s41597-020-00580-5","article-title":"Outlining where humans live, the World Settlement Footprint 2015","volume":"7","author":"Marconcini","year":"2020","journal-title":"Sci. Data"},{"key":"ref_78","unstructured":"Meynen, E., Schmith\u00fcsen, J., Gellert, J., Neef, E., M\u00fcller-Miny, H., and Schultze, H.J. (1953\u20131962). Handbuch der Naturr\u00e4umlichen Gliederung Deutschlands, Bundesanstalt f\u00fcr Landeskunde und Raumforwschung."},{"key":"ref_79","unstructured":"DESTATIS (2022, April 21). Land- und Forstwirtschaft, Fischerei; Wachstum und Ernte-Feldfr\u00fcchte 2018, Available online: https:\/\/www.statistischebibliothek.de\/mir\/receive\/DEHeft_mods_00096325."},{"key":"ref_80","unstructured":"DESTATIS (2022, April 21). Land- und Forstwirtschaft, Fischerei; Wachstum und Ernte-Baumobst 2018, Available online: https:\/\/www.statistischebibliothek.de\/mir\/receive\/DEHeft_mods_00095516."},{"key":"ref_81","unstructured":"DESTATIS (2022, April 21). Land- und Forstwirtschaft, Fischerei; Wachstum und Ernte-Weinmost 2018, Available online: https:\/\/www.statistischebibliothek.de\/mir\/receive\/DEHeft_mods_00104653."},{"key":"ref_82","unstructured":"Bayerische Landesanstalt f\u00fcr Landwirtschaft (2022, April 21). Hopfen des Jahresheftes Agrarm\u00e4rkte 2020, Available online: https:\/\/www.stmelf.bayern.de\/mam\/cms07\/iem\/dateien\/16_hopfen__by__2020.pdf."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.rse.2014.02.015","article-title":"Good practices for estimating area and assessing accuracy of land change","volume":"148","author":"Olofsson","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_84","first-page":"595","article-title":"Combining Sentinel-1 and Sentinel-2 data for improved land use and land cover mapping of monsoon regions","volume":"73","author":"Steinhausen","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_86","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"620","DOI":"10.3390\/rs1040620","article-title":"On the Suitability of MODIS Time Series Metrics to Map Vegetation Types in Dry Savanna Ecosystems: A Case Study in the Kalahari of NE Namibia","volume":"1","author":"Gessner","year":"2009","journal-title":"Remote Sens."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.isprsjprs.2013.08.007","article-title":"Impact of feature selection on the accuracy and spatial uncertainty of per-field crop classification using Support Vector Machines","volume":"85","author":"Michel","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/0034-4257(91)90048-B","article-title":"A review of assessing the accuracy of classifications of remotely sensed data","volume":"37","author":"Congalton","year":"1991","journal-title":"Remote Sens. Environ."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1016\/j.ipm.2009.03.002","article-title":"A systematic analysis of performance measures for classification tasks","volume":"45","author":"Sokolova","year":"2009","journal-title":"Inf. Processing Manag."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"4923","DOI":"10.1080\/01431161.2014.930207","article-title":"Estimating area and map accuracy for stratified random sampling when the strata are different from the map classes","volume":"35","author":"Stehman","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/j.rse.2015.08.004","article-title":"Mapping rice paddy extent and intensification in the Vietnamese Mekong River Delta with dense time stacks of Landsat data","volume":"169","author":"Kontgis","year":"2015","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/13\/2981\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:37:30Z","timestamp":1760139450000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/13\/2981"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,22]]},"references-count":92,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2022,7]]}},"alternative-id":["rs14132981"],"URL":"https:\/\/doi.org\/10.3390\/rs14132981","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,22]]}}}