{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T14:51:15Z","timestamp":1781189475357,"version":"3.54.1"},"reference-count":88,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2022,8,25]],"date-time":"2022-08-25T00:00:00Z","timestamp":1661385600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000780","name":"European Union","doi-asserted-by":"publisher","award":["101037619"],"award-info":[{"award-number":["101037619"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>One of the precepts of food security is the proper functioning of the global food markets. This calls for open and timely intelligence on crop production on an agroclimatically meaningful territorial scale. We propose an operationally suitable method for large-scale in-season crop yield estimations from a satellite image time series (SITS) for statistical production. As an object-based method, it is spatially scalable from parcel to regional scale, making it useful for prediction tasks in which the reference data are available only at a coarser level, such as counties. We show that deep learning-based temporal convolutional network (TCN) outperforms the classical machine learning method random forests and produces more accurate results overall than published national crop forecasts. Our novel contribution is to show that mean-aggregated regional predictions with histogram-based features calculated from farm-level observations perform better than other tested approaches. In addition, TCN is robust to the presence of cloudy pixels, suggesting TCN can learn cloud masking from the data. The temporal compositing of information do not improve prediction performance. This indicates that with end-to-end learning less preprocessing in SITS tasks seems viable.<\/jats:p>","DOI":"10.3390\/rs14174193","type":"journal-article","created":{"date-parts":[[2022,8,26]],"date-time":"2022-08-26T02:04:32Z","timestamp":1661479472000},"page":"4193","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Scalable Crop Yield Prediction with Sentinel-2 Time Series and Temporal Convolutional Network"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1528-7246","authenticated-orcid":false,"given":"Maria","family":"Yli-Heikkil\u00e4","sequence":"first","affiliation":[{"name":"Natural Resources Institute Finland, Latokartanonkaari 9, FI-00790 Helsinki, Finland"},{"name":"Department of Geosciences and Geography, University of Helsinki, FI-00014 Helsinki, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9625-7235","authenticated-orcid":false,"given":"Samantha","family":"Wittke","sequence":"additional","affiliation":[{"name":"Finnish Geospatial Research Institute, National Land Survey of Finland, Vuorimiehentie 5, FI-02150 Espoo, Finland"},{"name":"Department of Built Environment, Aalto University, FI-00076 Espoo, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0463-0750","authenticated-orcid":false,"given":"Markku","family":"Luotamo","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Helsinki, FI-00014 Helsinki, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0985-4443","authenticated-orcid":false,"given":"Eetu","family":"Puttonen","sequence":"additional","affiliation":[{"name":"Finnish Geospatial Research Institute, National Land Survey of Finland, Vuorimiehentie 5, FI-02150 Espoo, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-8268-7933","authenticated-orcid":false,"given":"Mi","family":"Sulkava","sequence":"additional","affiliation":[{"name":"Natural Resources Institute Finland, Latokartanonkaari 9, FI-00790 Helsinki, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5996-9268","authenticated-orcid":false,"given":"Petri","family":"Pellikka","sequence":"additional","affiliation":[{"name":"Department of Geosciences and Geography, University of Helsinki, FI-00014 Helsinki, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3899-8860","authenticated-orcid":false,"given":"Janne","family":"Heiskanen","sequence":"additional","affiliation":[{"name":"Department of Geosciences and Geography, University of Helsinki, FI-00014 Helsinki, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7950-1355","authenticated-orcid":false,"given":"Arto","family":"Klami","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Helsinki, FI-00014 Helsinki, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,25]]},"reference":[{"key":"ref_1","unstructured":"United Nations (2021, February 04). Transforming Our World: The 2030 Agenda for Sustainable Development 2015. Available online: https:\/\/sustainabledevelopment.un.org\/post2015\/transformingourworld\/publication."},{"key":"ref_2","unstructured":"FAO, IFAD, UNICEF, WFP, and WHO (2021, February 04). The State of Food Security and Nutrition in the World 2021. Transforming Food Systems for Food Security, Improved Nutrition and Affordable Healthy Diets for All, Available online: https:\/\/www.fao.org\/documents\/card\/en\/c\/cb4474en."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1126\/science.1239402","article-title":"Climate change impacts on global food security","volume":"341","author":"Wheeler","year":"2013","journal-title":"Science"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1038\/s43016-022-00504-z","article-title":"Crop harvests for direct food use insufficient to meet the UN\u2019s food security goal","volume":"3","author":"Ray","year":"2022","journal-title":"Nat. Food"},{"key":"ref_5","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."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.rse.2019.02.015","article-title":"Current status of Landsat program, science, and applications","volume":"225","author":"Wulder","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.rse.2011.08.028","article-title":"The European Earth monitoring (GMES) programme: Status and perspectives","volume":"120","author":"Aschbacher","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"126153","DOI":"10.1016\/j.eja.2020.126153","article-title":"A systematic review of local to regional yield forecasting approaches and frequently used data resources","volume":"120","author":"Schauberger","year":"2020","journal-title":"Eur. J. Agron."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"105709","DOI":"10.1016\/j.compag.2020.105709","article-title":"Crop yield prediction using machine learning: A systematic literature review","volume":"177","author":"Kassahun","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_10","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_11","doi-asserted-by":"crossref","unstructured":"Voormansik, K., Zalite, K., S\u00fcnter, I., Tamm, T., Koppel, K., Verro, T., Brauns, A., Jakovels, D., and Praks, J. (2020). Separability of mowing and ploughing events on short temporal baseline Sentinel-1 coherence time series. Remote Sens., 12.","DOI":"10.3390\/rs12223784"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"112419","DOI":"10.1016\/j.rse.2021.112419","article-title":"Recurrent-based regression of Sentinel time series for continuous vegetation monitoring","volume":"263","author":"Garioud","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"De Vroey, M., Radoux, J., and Defourny, P. (2021). Grassland mowing detection using Sentinel-1 time series: Potential and limitations. Remote Sens., 13.","DOI":"10.3390\/rs13030348"},{"key":"ref_14","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_15","doi-asserted-by":"crossref","unstructured":"You, J., Li, X., Low, M., Lobell, D.B., and Ermon, S. (2017, January 4\u20139). Deep Gaussian Process for Crop Yield Prediction Based on Remote Sensing Data. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, San Francisco, CA, USA. Available online: https:\/\/cs.stanford.edu\/~ermon\/papers\/cropyield_AAAI17.pdf.","DOI":"10.1609\/aaai.v31i1.11172"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Sun, J., Di, L., Sun, Z., Shen, Y., and Lai, Z. (2019). County-level soybean yield prediction using deep CNN-LSTM model. Sensors, 19.","DOI":"10.3390\/s19204363"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Luotamo, M., Yli-Heikkil\u00e4, M., and Klami, A. (2022). Density estimates as representations of agricultural fields for remote sensing-based monitoring of tillage and vegetation cover. Appl. Sci., 12.","DOI":"10.3390\/app12020679"},{"key":"ref_18","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_19","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"Random forest in remote sensing: A review of applications and future directions","volume":"114","author":"Belgiu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.rse.2016.10.010","article-title":"Assessing the robustness of Random Forests to map land cover with high resolution satellite image time series over large areas","volume":"187","author":"Pelletier","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"4831","DOI":"10.1080\/01431161.2017.1323282","article-title":"Forecasting wheat yield from weather data and MODIS NDVI using Random Forests for Punjab province, Pakistan","volume":"38","author":"Saeed","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_22","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_23","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_24","doi-asserted-by":"crossref","first-page":"3426","DOI":"10.1080\/01431161.2020.1871102","article-title":"New spectral indicator Potato Productivity Index based on Sentinel-2 data to improve potato yield prediction: A machine learning approach","volume":"42","author":"Salvador","year":"2021","journal-title":"Int. J. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MGRS.2017.2762307","article-title":"Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources","volume":"5","author":"Zhu","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.aca.2019.01.002","article-title":"DeepSpectra: An end-to-end deep learning approach for quantitative spectral analysis","volume":"1058","author":"Zhang","year":"2019","journal-title":"Anal. Chim. Acta"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"12158","DOI":"10.1109\/ACCESS.2021.3051403","article-title":"An end-to-end deep model with discriminative facial features for facial expression recognition","volume":"9","author":"Liu","year":"2021","journal-title":"IEEE Access"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1982","DOI":"10.7150\/thno.52508","article-title":"Fully end-to-end deep-learning-based diagnosis of pancreatic tumors","volume":"11","author":"Si","year":"2021","journal-title":"Theranostics"},{"key":"ref_30","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_31","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1016\/j.rse.2018.11.032","article-title":"Deep learning based multi-temporal crop classification","volume":"221","author":"Zhong","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Pelletier, C., Webb, G.I., and Petitjean, F. (2019). Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series. Remote Sens., 11.","DOI":"10.3390\/rs11050523"},{"key":"ref_33","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_34","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_35","doi-asserted-by":"crossref","unstructured":"Lemaire, V., Malinowski, S., Bagnall, A., Guyet, T., Tavenard, R., and Ifrim, G. (2020). Lightweight Temporal Self-attention for Classifying Satellite Images Time Series. Proceedings of the Advanced Analytics and Learning on Temporal Data, Springer International Publishing.","DOI":"10.1007\/978-3-030-65742-0"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Lea, C., Flynn, M.D., Vidal, R., Reiter, A., and Hager, G.D. (2017, January 21\u201326). Temporal Convolutional Networks for Action Segmentation and Detection. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.113"},{"key":"ref_37","unstructured":"Bai, S., Kolter, J.Z., and Koltun, V. (2018). An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling. arXiv."},{"key":"ref_38","first-page":"1337","article-title":"Application of Temporal Convolutional Neural Network for the Classification of Crops on SENTINEL-2 Time Series","volume":"43B2","author":"Peressutti","year":"2020","journal-title":"ISPRS-Int. Arch. Photogramm. Remote. Sens. Spat. Inf. Sci."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Gong, L., Yu, M., Jiang, S., Cutsuridis, V., and Pearson, S. (2021). Deep Learning Based Prediction on Greenhouse Crop Yield Combined TCN and RNN. Sensors, 21.","DOI":"10.3390\/s21134537"},{"key":"ref_40","first-page":"1","article-title":"Channel Attention-Based Temporal Convolutional Network for Satellite Image Time Series Classification","volume":"19","author":"Tang","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Main-Knorn, M., Pflug, B., Louis, J., Debaecker, V., M\u00fcller-Wilm, U., and Gascon, F. (2017, January 11\u201313). Sen2Cor for Sentinel-2. Proceedings of the Image and Signal Processing for Remote Sensing XXIII. International Society for Optics and Photonics, Warsaw, Poland.","DOI":"10.1117\/12.2278218"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.rse.2011.10.028","article-title":"Object-based cloud and cloud shadow detection in Landsat imagery","volume":"118","author":"Zhu","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"100010","DOI":"10.1016\/j.srs.2020.100010","article-title":"Comparison of cloud detection algorithms for Sentinel-2 imagery","volume":"2","author":"Tarrio","year":"2020","journal-title":"Sci. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1007\/s40747-019-00128-0","article-title":"Cloud detection methodologies: Variants and development\u2014A review","volume":"6","author":"Mahajan","year":"2020","journal-title":"Complex Intell. Syst."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1109\/36.297976","article-title":"Detection and removal of cloud contamination from AVHRR images","volume":"32","author":"Cihlar","year":"1994","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.neucom.2014.09.102","article-title":"A cloud image detection method based on SVM vector machine","volume":"169","author":"Li","year":"2015","journal-title":"Neurocomputing"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.rse.2019.03.039","article-title":"A cloud detection algorithm for satellite imagery based on deep learning","volume":"229","author":"Jeppesen","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"4972","DOI":"10.1109\/TGRS.2020.3015272","article-title":"Multiscale cloud detection in remote sensing images using a dual convolutional neural network","volume":"59","author":"Luotamo","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.agrformet.2013.01.007","article-title":"Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics","volume":"173","author":"Bolton","year":"2013","journal-title":"Agric. For. Meteorol."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.agrformet.2015.02.021","article-title":"Towards regional grain yield forecasting with 1km-resolution EO biophysical products: Strengths and limitations at pan-European level","volume":"206","author":"Duveiller","year":"2015","journal-title":"Agric. For. Meteorol."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.eja.2018.09.006","article-title":"Improving regional winter wheat yield estimation through assimilation of phenology and leaf area index from remote sensing data","volume":"101","author":"Chen","year":"2018","journal-title":"Eur. J. Agron."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"9662","DOI":"10.1029\/2018GL079291","article-title":"Benefits of seasonal climate prediction and satellite data for forecasting US maize yield","volume":"45","author":"Peng","year":"2018","journal-title":"Geophys. Res. Lett."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Liu, J., Shang, J., Qian, B., Huffman, T., Zhang, Y., Dong, T., Jing, Q., and Martin, T. (2019). Crop Yield Estimation Using Time-Series MODIS Data and the Effects of Cropland Masks in Ontario, Canada. Remote Sens., 11.","DOI":"10.3390\/rs11202419"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.fcr.2019.02.005","article-title":"Toward building a transparent statistical model for improving crop yield prediction: Modeling rainfed corn in the U.S","volume":"234","author":"Li","year":"2019","journal-title":"Field Crop. Res."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"112408","DOI":"10.1016\/j.rse.2021.112408","article-title":"Corn yield prediction and uncertainty analysis based on remotely sensed variables using a Bayesian neural network approach","volume":"259","author":"Ma","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Cao, J., Wang, H., Li, J., Tian, Q., and Niyogi, D. (2022). Improving the Forecasting of Winter Wheat Yields in Northern China with Machine Learning\u2014Dynamical Hybrid Subseasonal-to-Seasonal Ensemble Prediction. Remote Sens., 14.","DOI":"10.1002\/essoar.10510222.1"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"106704","DOI":"10.1016\/j.compag.2022.106704","article-title":"Assessing machine leaning algorithms on crop yield forecasts using functional covariates derived from remotely sensed data","volume":"194","author":"Sartore","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"108377","DOI":"10.1016\/j.fcr.2021.108377","article-title":"Machine learning for regional crop yield forecasting in Europe","volume":"276","author":"Paudel","year":"2022","journal-title":"Field Crop. Res."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1093\/jae\/ejv011","article-title":"From guesstimates to GPStimates: Land area measurement and implications for agricultural analysis","volume":"24","author":"Carletto","year":"2015","journal-title":"J. Afr. Econ."},{"key":"ref_60","unstructured":"Braimoh, A.K., Durieux, M., Trant, M., Riungu, C., Gaye, D., Balakrishnan, T.K., and Umali-Deininger, D. (2018). Capacity Needs Assessment for Improving Agricultural Statistics in Kenya, World Bank. Available online: http:\/\/documents.worldbank.org\/curated\/en\/801111542740476532\/pdf\/Capacity-Needs-Assessment-for-Improving-Agricultural-Statistics-in-Kenya.pdf."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Burke, M., Driscoll, A., Lobell, D.B., and Ermon, S. (2021). Using satellite imagery to understand and promote sustainable development. Science, 371.","DOI":"10.1126\/science.abe8628"},{"key":"ref_62","unstructured":"Meybeck, A., Lankoski, J., Redfern, S., Azzu, N., and Gitz, V. (2012). Crop production in a northern climate. Proceedings of the FAO\/OECD Workshop: Building Resilience for Adaptation to Climate Change in the Agriculture Sector, FAO. Available online: https:\/\/www.fao.org\/3\/i3084e\/i3084e15.pdf."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1931","DOI":"10.1111\/ppl.13418","article-title":"Influence of Arctic light conditions on crop production and quality","volume":"172","author":"Dalmannsdottir","year":"2021","journal-title":"Physiol. Plant."},{"key":"ref_64","unstructured":"Natural Resources Institute Finland (2021, December 27). Crop production statistics: Use of arable land area by Year and Species. Available online: https:\/\/stat.luke.fi\/en\/crop-production-statistics."},{"key":"ref_65","unstructured":"Natural Resources Institute Finland (2021, December 27). Statistics on Utilised Agricultural Area; Sowing Dates. Available online: https:\/\/stat.luke.fi\/sites\/default\/files\/kevatkylvot_2000-2021_0.xls."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1477","DOI":"10.1007\/s00382-021-05970-y","article-title":"High-resolution analysis of observed thermal growing season variability over northern Europe","volume":"58","author":"Aalto","year":"2022","journal-title":"Clim. Dyn."},{"key":"ref_67","unstructured":"Lilja, H., Uusitalo, R., Yli-Halla, M., Nevalainen, R., V\u00e4\u00e4n\u00e4nen, R., Tamminen, P., and Tuhtar, J. (2021, November 22). Suomen maannostietokanta: K\u00e4ytt\u00f6opas versio 1.1 (Finnish Soil Database: Manual, version 1.1). Available online: http:\/\/www.luke.fi\/economydoctor."},{"key":"ref_68","unstructured":"Baruth, B., Bassu, S., Bussay, A., Ceglar, A., Cerrani, I., Chemin, Y., De Palma, P., Fumagalli, D., Lecerf, R., and Manfron, G. (2022, February 04). JRC MARS Bulletin\u2014Crop monitoring in Europe. Available online: https:\/\/publications.jrc.ec.europa.eu\/repository\/handle\/JRC120745."},{"key":"ref_69","unstructured":"Natural Resources Institute Finland (2021, April 01). Crop Production Statistics: Advance Estimates of Annual Harvests. Available online: https:\/\/stat.luke.fi\/en\/crop-production-statistics."},{"key":"ref_70","unstructured":"European Space Agency (2015). Sentinel-2 User Handbook, European Space Agency. Available online: https:\/\/sentinel.esa.int\/documents\/247904\/685211\/Sentinel-2_User_Handbook."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Wittke, S., Fouilloux, A., Lehti, P., Varho, J., Karjalainen, M., Vaaja, M., and Puttonen, E. (2022, June 04). EODIE\u2014Earth Observation Data Information Extractor. Available online: http:\/\/dx.doi.org\/10.2139\/ssrn.4067133.","DOI":"10.2139\/ssrn.4067133"},{"key":"ref_72","first-page":"1","article-title":"Sentinel-2 MSI-Level 2A products algorithm theoretical basis document","volume":"49","author":"Richter","year":"2012","journal-title":"Eur. Space Agency Special Publ. ESA SP"},{"key":"ref_73","unstructured":"Louis, J. (2017). S2 MPC\u2014Level 2A Product Format Specification, European Space Agency. Available online: https:\/\/sentinel.esa.int\/documents\/247904\/685211\/Sentinel-2-MSI-L2A-Product-Format-Specifications.pdf."},{"key":"ref_74","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_75","unstructured":"Remy, P. (2022, February 04). Temporal Convolutional Networks for Keras. Available online: https:\/\/github.com\/philipperemy\/keras-tcn."},{"key":"ref_76","unstructured":"Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., and Devin, M. (2015). TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. arXiv."},{"key":"ref_77","unstructured":"Kingma, D.P., and Ba, J. (2015). Adam: A Method for Stochastic Optimization. arXiv."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Engen, M., Sand\u00f8, E., Sj\u00f8lander, B.L.O., Arenberg, S., Gupta, R., and Goodwin, M. (2021). Farm-Scale Crop Yield Prediction from Multi-Temporal Data Using Deep Hybrid Neural Networks. Agronomy, 11.","DOI":"10.3390\/agronomy11122576"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1016\/j.isprsjprs.2021.02.008","article-title":"Field-scale crop yield prediction using multi-temporal WorldView-3 and PlanetScope satellite data and deep learning","volume":"174","author":"Sagan","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/j.fcr.2006.07.002","article-title":"Grain number dominates grain weight in temperate cereal yield determination: Evidence based on 30 years of multi-location trials","volume":"100","author":"Kangas","year":"2007","journal-title":"Field Crop. Res."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1017\/S002185960700723X","article-title":"Within-field variation in grain yield, yield components and quality traits of two-row barley","volume":"145","author":"Rajala","year":"2007","journal-title":"J. Agric. Sci."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1016\/j.fcr.2009.08.007","article-title":"Spring wheat response to timing of water deficit through sink and grain filling capacity","volume":"114","author":"Rajala","year":"2009","journal-title":"Field Crop. Res."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1016\/j.rse.2013.10.027","article-title":"An assessment of pre- and within-season remotely sensed variables for forecasting corn and soybean yields in the United States","volume":"141","author":"Johnson","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"108666","DOI":"10.1016\/j.agrformet.2021.108666","article-title":"Integrating satellite-derived climatic and vegetation indices to predict smallholder maize yield using deep learning","volume":"311","author":"Zhang","year":"2021","journal-title":"Agric. For. Meteorol."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1016\/j.rse.2017.06.043","article-title":"The shared and unique values of optical, fluorescence, thermal and microwave satellite data for estimating large-scale crop yields","volume":"199","author":"Guan","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.agrformet.2019.03.010","article-title":"Integrating satellite and climate data to predict wheat yield in Australia using machine learning approaches","volume":"274","author":"Cai","year":"2019","journal-title":"Agric. For. Meteorol."},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Baetens, L., Desjardins, C., and Hagolle, O. (2019). Validation of Copernicus Sentinel-2 Cloud Masks Obtained from MAJA, Sen2Cor, and FMask Processors Using Reference Cloud Masks Generated with a Supervised Active Learning Procedure. Remote Sens., 11.","DOI":"10.3390\/rs11040433"},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Zekoll, V., Main-Knorn, M., Alonso, K., Louis, J., Frantz, D., Richter, R., and Pflug, B. (2021). Comparison of Masking Algorithms for Sentinel-2 Imagery. Remote Sens., 13.","DOI":"10.3390\/rs13010137"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/17\/4193\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:15:35Z","timestamp":1760141735000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/17\/4193"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,25]]},"references-count":88,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2022,9]]}},"alternative-id":["rs14174193"],"URL":"https:\/\/doi.org\/10.3390\/rs14174193","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,25]]}}}