{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T10:47:24Z","timestamp":1761130044818,"version":"build-2065373602"},"reference-count":85,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,2,18]],"date-time":"2023-02-18T00:00:00Z","timestamp":1676678400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001807","name":"Sao Paulo Research Foundation\u2014FAPESP","doi-asserted-by":"publisher","award":["2021\/15001-9","2017\/50205-9"],"award-info":[{"award-number":["2021\/15001-9","2017\/50205-9"]}],"id":[{"id":"10.13039\/501100001807","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Regenerative agricultural practices are a suitable path to feed the global population. Integrated Crop\u2013livestock systems (ICLSs) are key approaches once the area provides animal and crop production resources. In Brazil, the expectation is to increase the area of ICLS fields by 5 million hectares in the next five years. However, few methods have been tested regarding spatial and temporal scales to map and monitor ICLS fields, and none of these methods use SAR data. Therefore, in this work, we explored the potential of three machine and deep learning algorithms (random forest, long short-term memory, and transformer) to perform early-season (with three-time windows) mapping of ICLS fields. To explore the scalability of the proposed methods, we tested them in two regions with different latitudes, cloud cover rates, field sizes, landscapes, and crop types. Finally, the potential of SAR (Sentinel-1) and optical (Sentinel-2) data was tested. As a result, we found that all proposed algorithms and sensors could correctly map both study sites. For Study Site 1(SS1), we obtained an overall accuracy of 98% using the random forest classifier. For Study Site 2, we obtained an overall accuracy of 99% using the long short-term memory net and the random forest. Further, the early-season experiments were successful for both study sites (with an accuracy higher than 90% for all time windows), and no significant difference in accuracy was found among them. Thus, this study found that it is possible to map ICLSs in the early-season and in different latitudes by using diverse algorithms and sensors.<\/jats:p>","DOI":"10.3390\/rs15041130","type":"journal-article","created":{"date-parts":[[2023,2,20]],"date-time":"2023-02-20T01:36:37Z","timestamp":1676856997000},"page":"1130","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["SAR and Optical Data Applied to Early-Season Mapping of Integrated Crop\u2013Livestock Systems Using Deep and Machine Learning Algorithms"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0122-8819","authenticated-orcid":false,"given":"Ana P. S. G. D. D.","family":"Toro","sequence":"first","affiliation":[{"name":"School of Agricultural Engineering, University of Campinas, Campinas 13083-875, SP, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6662-3396","authenticated-orcid":false,"given":"Inacio T.","family":"Bueno","sequence":"additional","affiliation":[{"name":"School of Agricultural Engineering, University of Campinas, Campinas 13083-875, SP, Brazil"},{"name":"Interdisciplinary Center of Energy Planning, University of Campinas, Campinas 13083-896, SP, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5219-3551","authenticated-orcid":false,"given":"Jo\u00e3o P. S.","family":"Werner","sequence":"additional","affiliation":[{"name":"School of Agricultural Engineering, University of Campinas, Campinas 13083-875, SP, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8114-9971","authenticated-orcid":false,"given":"Jo\u00e3o F. G.","family":"Antunes","sequence":"additional","affiliation":[{"name":"Embrapa Digital Agriculture, Brazilian Agricultural Research Corporation, Campinas 13083-886, SP, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4344-1263","authenticated-orcid":false,"given":"Rubens A. C.","family":"Lamparelli","sequence":"additional","affiliation":[{"name":"School of Agricultural Engineering, University of Campinas, Campinas 13083-875, SP, Brazil"},{"name":"Interdisciplinary Center of Energy Planning, University of Campinas, Campinas 13083-896, SP, Brazil"}]},{"given":"Alexandre C.","family":"Coutinho","sequence":"additional","affiliation":[{"name":"Embrapa Digital Agriculture, Brazilian Agricultural Research Corporation, Campinas 13083-886, SP, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7190-2931","authenticated-orcid":false,"given":"J\u00falio C. D. M.","family":"Esquerdo","sequence":"additional","affiliation":[{"name":"School of Agricultural Engineering, University of Campinas, Campinas 13083-875, SP, Brazil"},{"name":"Embrapa Digital Agriculture, Brazilian Agricultural Research Corporation, Campinas 13083-886, SP, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5374-3591","authenticated-orcid":false,"given":"Paulo S. G.","family":"Magalh\u00e3es","sequence":"additional","affiliation":[{"name":"Interdisciplinary Center of Energy Planning, University of Campinas, Campinas 13083-896, SP, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5017-8320","authenticated-orcid":false,"given":"Gleyce K. D. A.","family":"Figueiredo","sequence":"additional","affiliation":[{"name":"School of Agricultural Engineering, University of Campinas, Campinas 13083-875, SP, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,18]]},"reference":[{"key":"ref_1","unstructured":"Toensmeier, E. (2016). The Carbon Farming Solution: A Global Toolkit of Perennial Crops and Regenerative Agriculture Practices for Climate Change Mitigation and Food Security, Chelsea Green Publishing."},{"key":"ref_2","unstructured":"Cordeiro, L., Kluthcouski, J., Silva, J., Rojas, D., Omote, H., Moro, E., Silva, P., Tiritan, C., and Longen, A. (2020). Integra\u00e7\u00e3o Lavoura-Pecu\u00e1ria em Solos Arenosos: Estudo de Caso da Fazenda Campina no Oeste Paulista, Embrapa Cerrados-Doc. (INFOTECA-E)."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1177\/0030727021998063","article-title":"Regenerative agriculture: An agronomic perspective","volume":"50","author":"Giller","year":"2021","journal-title":"Outlook Agric."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1196","DOI":"10.1038\/s41477-019-0564-z","article-title":"The FAO contribution to monitoring SDGs for food and agriculture","volume":"5","author":"Gennari","year":"2019","journal-title":"Nat. Plants"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Dos Reis, A.A., Werner, J.P., Silva, B.C., Figueiredo, G.K., Antunes, J.F., Esquerdo, J.C., Coutinho, A.C., Lamparelli, R.A., Rocha, J.V., and Magalh\u00e3es, P.S. (2020). Monitoring pasture aboveground biomass and canopy height in an integrated crop\u2013livestock system using textural information from PlanetScope imagery. Remote Sens., 12.","DOI":"10.3390\/rs12162534"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"111402","DOI":"10.1016\/j.rse.2019.111402","article-title":"Remote sensing for agricultural applications: A meta-review","volume":"236","author":"Weiss","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Kuchler, P.C., Sim\u00f5es, M., Ferraz, R., Arvor, D., de Almeida Machado, P.L.O., Rosa, M., Gaetano, R., and B\u00e9gu\u00e9, A. (2022). Monitoring Complex Integrated Crop\u2013Livestock Systems at Regional Scale in Brazil: A Big Earth Observation Data Approach. Remote Sens., 14.","DOI":"10.3390\/rs14071648"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Almeida, H.S., Dos Reis, A.A., Werner, J.P., Antunes, J.F., Zhong, L., Figueiredo, G.K., Esquerdo, J.C., Coutinho, A.C., Lamparelli, R.A., and Magalh\u00e3es, P.S. (2021, January 11\u201316). Deep Neural Networks for Mapping Integrated Crop-Livestock Systems Using Planetscope Time Series. Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium.","DOI":"10.1109\/IGARSS47720.2021.9554500"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Manabe, V.D., Melo, M.R., and Rocha, J.V. (2018). Framework for mapping integrated crop-livestock systems in Mato Grosso, Brazil. Remote Sens., 10.","DOI":"10.3390\/rs10091322"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"105618","DOI":"10.1016\/j.compag.2020.105618","article-title":"Accessing the temporal and spectral features in crop type mapping using multi-temporal Sentinel-2 imagery: A case study of Yi\u2019an County, Heilongjiang province, China","volume":"176","author":"Zhang","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zhao, H., Duan, S., Liu, J., Sun, L., and Reymondin, L. (2021). Evaluation of Five Deep Learning Models for Crop Type Mapping Using Sentinel-2 Time Series Images with Missing Information. Remote Sens., 13.","DOI":"10.3390\/rs13142790"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Sarvia, F., Xausa, E., Petris, S.D., Cantamessa, G., and Borgogno-Mondino, E. (2021). A Possible Role of Copernicus Sentinel-2 Data to Support Common Agricultural Policy Controls in Agriculture. Agronomy, 11.","DOI":"10.3390\/agronomy11010110"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.isprsjprs.2019.06.007","article-title":"Estimating leaf area index and aboveground biomass of grazing pastures using Sentinel-1, Sentinel-2 and Landsat images","volume":"154","author":"Wang","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"647","DOI":"10.1016\/j.rse.2018.06.036","article-title":"Estimating smallholder crops production at village level from Sentinel-2 time series in Mali\u2019s cotton belt","volume":"216","author":"Lambert","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2973","DOI":"10.3390\/rs5062973","article-title":"Removal of optically thick clouds from multi-spectral satellite images using multi-frequency SAR data","volume":"5","author":"Eckardt","year":"2013","journal-title":"Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Alonso, J., Vaidyanathan, K., and Pietrantuono, R. (2020, January 12\u201315). SAR Handbook Chapter: Measurements-based aging analysis. Proceedings of the 2020 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW), Coimbra, Portugal.","DOI":"10.1109\/ISSREW51248.2020.00093"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"506","DOI":"10.1016\/S2095-3119(18)62016-7","article-title":"Research advances of SAR remote sensing for agriculture applications: A review","volume":"18","author":"Liu","year":"2019","journal-title":"J. Integr. Agric."},{"key":"ref_18","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_19","doi-asserted-by":"crossref","unstructured":"Solano-Correa, Y.T., Bovolo, F., Bruzzone, L., and Fern\u00e1ndez-Prieto, D. (2017, January 27\u201329). Spatio-temporal evolution of crop fields in Sentinel-2 Satellite Image Time Series. Proceedings of the 2017 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp), Brugge, Belgium.","DOI":"10.1109\/Multi-Temp.2017.8035236"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Tian, H., Wang, Y., Chen, T., Zhang, L., and Qin, Y. (2021). Early-Season Mapping of Winter Crops Using Sentinel-2 Optical Imagery. Remote Sens., 13.","DOI":"10.3390\/rs13193822"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"105962","DOI":"10.1016\/j.compag.2020.105962","article-title":"An automated early-season method to map winter wheat using time-series Sentinel-2 data: A case study of Shandong, China","volume":"182","author":"Zhang","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_22","unstructured":"Charvat, K., Horakova, S., Druml, S., Mayer, W., Safar, V., Kubickova, H., and Catucci, A. D5. 6 White Paper on Earth Observation Data in Agriculture, Deliverable of the EO4Agri Project, Grant Agreement 821940 (2020)."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/j.isprsjprs.2016.05.014","article-title":"Automated mapping of soybean and corn using phenology","volume":"119","author":"Zhong","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"6789","DOI":"10.1109\/JSTARS.2022.3198475","article-title":"Early-Season Crop Mapping on an Agricultural Area in Italy Using X-Band Dual-Polarization SAR Satellite Data and Convolutional Neural Networks","volume":"15","author":"Fontanelli","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1897","DOI":"10.1016\/S2095-3119(19)62812-1","article-title":"Early-season crop type mapping using 30-m reference time series","volume":"19","author":"Hao","year":"2020","journal-title":"J. Integr. Agric."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"6472","DOI":"10.3390\/rs6076472","article-title":"Integration of optical and Synthetic Aperture Radar imagery for improving crop mapping in Northwestern Benin, West Africa","volume":"6","author":"Forkuor","year":"2014","journal-title":"Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Campos-Taberner, M., Garc\u00eda-Haro, F.J., Mart\u00ednez, B., S\u00e1nchez-Ru\u00edz, S., and Gilabert, M.A. (2019). A copernicus sentinel-1 and sentinel-2 classification framework for the 2020+ European common agricultural policy: A case study in Val\u00e8ncia (Spain). Agronomy, 9.","DOI":"10.3390\/agronomy9090556"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.isprsjprs.2019.04.015","article-title":"Deep learning in remote sensing applications: A meta-analysis and review","volume":"152","author":"Ma","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_29","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., and Kaiser, \u0141. (2017, January 4\u20139). Polosukhin, Attention is all you need. Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_30","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_31","unstructured":"Garnot, V.S.F., Landrieu, L., Giordano, S., and Chehata, N. (2020, January 13\u201319). Satellite image time series classification with pixel-set encoders and temporal self-attention. Proceedings of the Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA."},{"key":"ref_32","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_33","unstructured":"Obadic, I., Roscher, R., Oliveira, D.A.B., and Zhu, X.X. (2022). Exploring Self-Attention for Crop-type Classification Explainability. arXiv."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1204","DOI":"10.1017\/S1751731114001189","article-title":"Farming system design for innovative crop-livestock integration in Europe","volume":"8","author":"Moraine","year":"2014","journal-title":"Animal"},{"key":"ref_35","unstructured":"Balbino, L.C., Barcellos, A.O., and Stone, L.F. (2011). Marco Referencial: Integra\u00e7\u00e3o Lavoura-Pecu\u00e1ria-Floresta, Embrapa."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"841","DOI":"10.1016\/j.landusepol.2019.01.006","article-title":"Perceptions of integrated crop-livestock systems for sustainable intensification in the Brazilian Amazon","volume":"82","author":"Cortner","year":"2019","journal-title":"Land Use Policy"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"711","DOI":"10.1127\/0941-2948\/2013\/0507","article-title":"K\u00f6ppen\u2019s climate classification map for Brazil","volume":"22","author":"Alvares","year":"2013","journal-title":"Meteorol. Z."},{"key":"ref_38","unstructured":"Skorupa, L.A.C.V., and Manzatto, C.N.P.M.A. (2019). Sistemas de Integra\u00e7\u00e3o Lavoura-Pecu\u00e1ria-Floresta no Brasil: Estrat\u00e9gias Regionais de Transfer\u00eancia de Tecnologia, Avalia\u00e7\u00e3o da Ado\u00e7\u00e3o e de Impactos, Embrapa."},{"key":"ref_39","unstructured":"(2022, January 30). CONAB-Calendario Agr\u00edcola Kernel Description, Available online: https:\/\/www.conab.gov.br\/institucional\/publicacoes\/outras-publicacoes\/item\/7694-calendario-agricola-plantio-e-colheita."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"525","DOI":"10.1080\/22797254.2018.1457937","article-title":"Atmospheric correction of Landsat-8\/OLI and Sentinel-2\/MSI data using iCOR algorithm: Validation for coastal and inland waters","volume":"51","author":"Sterckx","year":"2018","journal-title":"Eur. J. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1353691","DOI":"10.1155\/2017\/1353691","article-title":"Significant remote sensing vegetation indices: A review of developments and applications","volume":"2017","author":"Xue","year":"2017","journal-title":"J. Sens."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Li, T., Wang, Y., Liu, C., and Tu, S. (2021). Research on identification of multiple cropping index of farmland and regional optimization scheme in China based on NDVI data. Land, 10.","DOI":"10.3390\/land10080861"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Holtgrave, A.K., R\u00f6der, N., Ackermann, A., Erasmi, S., and Kleinschmit, B. (2020). Comparing Sentinel-1 and-2 data and indices for agricultural land use monitoring. Remote Sens., 12.","DOI":"10.3390\/rs12182919"},{"key":"ref_44","first-page":"309","article-title":"Monitoring vegetation systems in the Great Plains with ERTS","volume":"351","author":"Rouse","year":"1974","journal-title":"NASA Spec. Publ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1016\/S0176-1617(11)81633-0","article-title":"Spectral reflectance changes associated with autumn senescence of Aesculus hippocastanum Land Acer platanoides L. leaves. Spectral features and relation to chlorophyll estimation","volume":"143","author":"Gitelson","year":"1994","journal-title":"J. Plant Physiol."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1109\/TGRS.1995.8746027","article-title":"A feedback based modification of the NDVI to minimize canopy back-ground and atmospheric noise","volume":"33","author":"Liu","year":"1995","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"2579","DOI":"10.1080\/01431169608949094","article-title":"Airborne multi-spectral monitoring of agricultural crop status: Effect of time of year, crop type and crop condition parameter","volume":"17","author":"Cloutis","year":"1996","journal-title":"Remote Sens."},{"key":"ref_48","unstructured":"Dey, S. (2022, June 03). Radar Vegetation Index Code for Dual Polarimetric Sentinel-1 Data in EO Browser. Available online: https:\/\/custom-scripts.sentinel-hub.com\/custom-scripts\/sentinel-1\/radar_vegetation_index_code_dual_polarimetric\/supplementary_material.pdf."},{"key":"ref_49","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_50","doi-asserted-by":"crossref","unstructured":"Achanta, R., and Susstrunk, S. (2017, January 21\u201326). Superpixels and polygons using simple non-iterative clustering. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.520"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google Earth Engine: Planetary-scale geospatial analysis for everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Ren, X., and Malik, J. (2003, January 13\u201316). Learning a classification model for segmentation. Proceedings of the Computer Vision, IEEE International Conference on IEEE Computer Society, Nice, France.","DOI":"10.1109\/ICCV.2003.1238308"},{"key":"ref_53","first-page":"389","article-title":"Multitemporal segmentation of sentinel-2 images in an agricultural intensification region in brazil","volume":"3","author":"Werner","year":"2022","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"2661","DOI":"10.3390\/rs4092661","article-title":"Tree species classification with random forest using very high spatial resolution 8-band WorldView-2 satellite data","volume":"4","author":"Immitzer","year":"2012","journal-title":"Remote Sens."},{"key":"ref_55","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_56","doi-asserted-by":"crossref","first-page":"424","DOI":"10.1016\/j.atmosres.2015.09.021","article-title":"Comparison of four machine learning algorithms for their applicability in satellite-based optical rainfall retrievals","volume":"169","author":"Meyer","year":"2016","journal-title":"Atmos. Res."},{"key":"ref_57","first-page":"683","article-title":"Crop classification on single date sentinel-2 imagery using random forest and suppor vector machine. Int. Arch. Photogramm","volume":"XLII-5","author":"Saini","year":"2018","journal-title":"Remote Sens. Spat. Inf. Sci."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"7221","DOI":"10.1080\/01431161.2019.1601285","article-title":"A random forest-based framework for crop mapping using temporal, spectral, textural and polarimetric observations","volume":"40","author":"Khosravi","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_59","unstructured":"Hochreiter, S., and Schmidhuber, J. (1996, January 2\u20135). LSTM can solve hard long time lag problems. Proceedings of the Advances in Neural Information Processing Systems 9 (NIPS 1996), Denver, CO, USA."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1601","DOI":"10.1109\/JSTARS.2016.2636877","article-title":"Hyperspectral image classification with rotation random forest via KPCA","volume":"10","author":"Xia","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Ahmed, M., Samee, M.R., and Mercer, R.E. (February, January 30). Improving tree-LSTM with tree attention. Proceedings of the 2019 IEEE 13th International Conference on Semantic Computing (ICSC), Newport Beach, CA, USA.","DOI":"10.1109\/ICOSC.2019.8665673"},{"key":"ref_62","unstructured":"G\u00e9ron, A. (2019). Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, O\u2019Reilly Media."},{"key":"ref_63","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Zhao, H., Chen, Z., Jiang, H., Jing, W., Sun, L., and Feng, M. (2019). Evaluation of three deep learning models for early crop classification using sentinel-1A imagery time series\u2014A case study in Zhanjiang, China. Remote Sens., 11.","DOI":"10.3390\/rs11222673"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Dai, Z., Yang, Z., Yang, Y., Carbonell, J., Le, Q.V., and Salakhutdinov, R. (2019). Transformer-xl: Attentive language models beyond a fixed-length context. arXiv.","DOI":"10.18653\/v1\/P19-1285"},{"key":"ref_66","unstructured":"Ru\u00dfwurm, M., Lef\u00e8vre, S., and K\u00f6rner, M. (2019, January 10\u201315). Breizhcrops: A satellite time series dataset for crop type identification. Proceedings of the International Conference on Machine Learning Time Series Workshop, Long Beach, CA, USA."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Voita, E., Talbot, D., Moiseev, F., Sennrich, R., and Titov, I. (2019). Analyzing multi-head self-attention: Specialized heads do the heavy lifting, the rest can be pruned. arXiv.","DOI":"10.18653\/v1\/P19-1580"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Wang, J., Xu, M., Wang, H., and Zhang, J. (2006, January 16\u201320). Classification of imbalanced data by using the SMOTE algorithm and locally linear embedding. Proceedings of the 8th International Conference on Signal Processing, Guilin, China.","DOI":"10.1109\/ICOSP.2006.345752"},{"key":"ref_69","first-page":"525","article-title":"Tables of the power of the F-test","volume":"62","author":"Tiku","year":"1967","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"E928","DOI":"10.21037\/jtd.2016.08.16","article-title":"P value interpretations and considerations","volume":"8","author":"Thiese","year":"2016","journal-title":"J. Thorac. Dis."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1093\/biomet\/38.1-2.112","article-title":"Charts of the power function for analysis of variance tests, derived from the non-central F-distribution","volume":"38","author":"Pearson","year":"1951","journal-title":"Biometrika"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1016\/j.cosust.2013.06.002","article-title":"Challenges and opportunities in mapping land use intensity globally","volume":"5","author":"Kuemmerle","year":"2013","journal-title":"Curr. Opin. Environ. Sustain."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1016\/j.isprsjprs.2022.04.018","article-title":"TimeMatch: Unsupervised cross-region adaptation by temporal shift estimation","volume":"188","author":"Nyborg","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Sun, C., Bian, Y., Zhou, T., and Pan, J. (2019). Using of multi-source and multi-temporal remote sensing data improves crop-type mapping in the subtropical agriculture region. Sensors, 19.","DOI":"10.3390\/s19102401"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Misra, G., Cawkwell, F., and Wingler, A. (2020). Status of phenological research using Sentinel-2 data: A review. Remote Sens., 12.","DOI":"10.3390\/rs12172760"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Phiri, D., Simwanda, M., Salekin, S., Nyirenda, V.R., Murayama, Y., and Ranagalage, M. (2020). Sentinel-2 data for land cover\/use mapping: A review. Remote Sens., 12.","DOI":"10.3390\/rs12142291"},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Qu, Y., Zhao, W., Yuan, Z., and Chen, J. (2020). Crop mapping from sentinel-1 polarimetric time-series with a deep neural network. Remote Sens., 12.","DOI":"10.3390\/rs12152493"},{"key":"ref_78","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_79","doi-asserted-by":"crossref","unstructured":"Guo, Z., Qi, W., Huang, Y., Zhao, J., Yang, H., Koo, V.C., and Li, N. (2022). Identification of Crop Type Based on C-AENN Using Time Series Sentinel-1A SAR Data. Remote Sens., 14.","DOI":"10.3390\/rs14061379"},{"key":"ref_80","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_81","doi-asserted-by":"crossref","unstructured":"Asam, S., Gessner, U., Almengor Gonz\u00e1lez, R., Wenzl, M., Kriese, J., and Kuenzer, C. (2022). Mapping Crop Types of Germany by Combining Temporal Statistical Metrics of Sentinel-1 and Sentinel-2 Time Series with LPIS Data. Remote Sens., 14.","DOI":"10.3390\/rs14132981"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/j.isprsjprs.2021.02.018","article-title":"Sentinel SAR-optical fusion for crop type mapping using deep learning and Google Earth Engine","volume":"175","author":"Adrian","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_83","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_84","first-page":"102451","article-title":"Transferable deep learning model based on the phenological matching principle for mapping crop extent","volume":"102","author":"Ge","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1016\/j.agsy.2018.05.010","article-title":"A comparison of global agricultural monitoring systems and current gaps","volume":"168","author":"Fritz","year":"2019","journal-title":"Agric. Syst."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/4\/1130\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:36:13Z","timestamp":1760121373000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/4\/1130"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,18]]},"references-count":85,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["rs15041130"],"URL":"https:\/\/doi.org\/10.3390\/rs15041130","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2023,2,18]]}}}