{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,14]],"date-time":"2026-06-14T05:10:10Z","timestamp":1781413810253,"version":"3.54.1"},"reference-count":56,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2021,7,23]],"date-time":"2021-07-23T00:00:00Z","timestamp":1626998400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000844","name":"European Space Agency","doi-asserted-by":"publisher","award":["4000121195\/17\/I-NB"],"award-info":[{"award-number":["4000121195\/17\/I-NB"]}],"id":[{"id":"10.13039\/501100000844","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003981","name":"Agenzia Spaziale Italiana","doi-asserted-by":"publisher","award":["2019-5-HH.O"],"award-info":[{"award-number":["2019-5-HH.O"]}],"id":[{"id":"10.13039\/501100003981","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Novel approaches and algorithms to estimate crop physiological processes from Earth Observation (EO) data are essential to develop more sustainable management practices in agricultural systems. Within this context, this paper presents the results of different research activities carried out within the ESA-MOST Dragon 4 programme. The paper encompasses two research avenues: (a) the retrieval of biophysical variables of crops and yield prediction; and (b) food security related to different crop management strategies. Concerning the retrieval of variables, results show that LAI, derived by radiative transfer model (RTM) inversion, when assimilated into a crop growth model (i.e., SAFY) provides a way to assess yields with a higher accuracy with respect to open loop model runs: 1.14 t\u00b7ha\u22121 vs 4.42 t\u00b7ha\u22121 RMSE for assimilation and open loop, respectively. Concerning food security, results show that different pathogens could be detected by remote sensing satellite data. A k coefficient higher than 0.84 was achieved for yellow rust, thus assuring a monitoring accuracy, and for the diseased samples k was higher than 0.87. Concerning permanent crops, neural network (NN) algorithms allow classification of the Pseudomonas syringae pathogen on kiwi orchards with an overall accuracy higher than 91%.<\/jats:p>","DOI":"10.3390\/rs13152889","type":"journal-article","created":{"date-parts":[[2021,7,23]],"date-time":"2021-07-23T10:31:44Z","timestamp":1627036304000},"page":"2889","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Sino\u2013EU Earth Observation Data to Support the Monitoring and Management of Agricultural Resources"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0587-8926","authenticated-orcid":false,"given":"Stefano","family":"Pignatti","sequence":"first","affiliation":[{"name":"Institute of Methodologies for Environmental Analysis (IMAA)-National Council of Research (CNR), C. da S. 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Appl. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1482","DOI":"10.3390\/rs70201482","article-title":"Meeting earth observation requirements for global agricultural monitoring: An evaluation of the revisit capabilities of current and planned moderate resolution optical earth observing missions","volume":"7","author":"Whitcraft","year":"2015","journal-title":"Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"5611","DOI":"10.3390\/rs70505611","article-title":"Mapping of agricultural crops from single high-resolution multispectral images\u2014Data-driven smoothing vs. parcel-based smoothing","volume":"7","author":"Ok","year":"2015","journal-title":"Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Zhou, T., Pan, J., Zhang, P., Wei, S., and Han, T. (2017). Mapping Winter Wheat with Multi-Temporal SAR and Optical Images in an Urban Agricultural Region. Sensors, 17.","DOI":"10.3390\/s17061210"},{"key":"ref_5","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_6","doi-asserted-by":"crossref","first-page":"111679","DOI":"10.1016\/j.rse.2020.111679","article-title":"A smart multiple spatial and temporal resolution system to support precision agriculture from satellite images: Proof of concept on Aglianico vineyard","volume":"240","author":"Brook","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Zhang, L., Zhang, Z., Luo, Y., Cao, J., and Tao, F. (2019). Combining Optical, Fluorescence, Thermal Satellite, and Environmental Data to Predict County-Level Maize Yield in China Using Machine Learning Approaches. Remote Sens., 12.","DOI":"10.3390\/rs12010021"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"107110","DOI":"10.1016\/j.agee.2020.107110","article-title":"Land use\/cover changes in the Oriental migratory locust area of China: Implications for ecological control and monitoring of locust area","volume":"303","author":"Zhao","year":"2020","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zhou, X., Zhang, J., Chen, D., Huang, Y., Kong, W., Yuan, L., Ye, H., and Huang, W. (2020). Assessment of Leaf Chlorophyll Content Models for Winter Wheat Using Landsat-8 Multispectral Remote Sensing Data. Remote Sens., 12.","DOI":"10.3390\/rs12162574"},{"key":"ref_10","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_11","doi-asserted-by":"crossref","unstructured":"Silvestro, P.C., Pignatti, S., Pascucci, S., Yang, H., Li, Z., Yang, G., Huang, W., and Casa, R. (2017). Estimating Wheat Yield in China at the Field and District Scale from the Assimilation of Satellite Data into the Aquacrop and Simple Algorithm for Yield (SAFY) Models. Remote Sens., 9.","DOI":"10.3390\/rs9050509"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.agrformet.2019.01.023","article-title":"Predicting wheat productivity: Integrating time series of vegetation indices into crop modeling via sequential assimilation","volume":"272","author":"Guo","year":"2019","journal-title":"Agric. For. Meteorol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"876","DOI":"10.1016\/j.envsoft.2007.10.003","article-title":"A simple algorithm for yield estimates: Evaluation for semi-arid irrigated winter wheat monitored with green leaf area index","volume":"23","author":"Duchemin","year":"2008","journal-title":"Environ. Model. Softw."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Upreti, D., Pignatti, S., Pascucci, S., Tolomio, M., Li, Z., Huang, W., and Casa, R. (2020). A Comparison of Moment-Independent and Variance-Based Global Sensitivity Analysis Approaches for Wheat Yield Estimation with the Aquacrop-OS Model. Agronomy, 10.","DOI":"10.3390\/agronomy10040607"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Upreti, D., Pignatti, S., Pascucci, S., Tolomio, M., Huang, W., and Casa, R. (2020). Bayesian Calibration of the Aquacrop-OS Model for Durum Wheat by Assimilation of Canopy Cover Retrieved from VEN\u00b5S Satellite Data. Remote Sens., 12.","DOI":"10.3390\/rs12162666"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"107711","DOI":"10.1016\/j.fcr.2019.107711","article-title":"A hierarchical interannual wheat yield and grain protein prediction model using spectral vegetative indices and meteorological data","volume":"248","author":"Li","year":"2020","journal-title":"Field Crop. Res."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"4410","DOI":"10.1109\/JSTARS.2020.3013340","article-title":"Automatic System for Crop Pest and Disease Dynamic Monitoring and Early Forecasting","volume":"13","author":"Dong","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Guo, A., Huang, W., Ye, H., Dong, Y., Ma, H., Ren, Y., and Ruan, C. (2020). Identification of Wheat Yellow Rust Using Spectral and Texture Features of Hyperspectral Images. Remote Sens., 12.","DOI":"10.3390\/rs12091419"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Ma, H., Huang, W., Jing, Y., Pignatti, S., Laneve, G., Dong, Y., Ye, H., Liu, L., Guo, A., and Jiang, J. (2019). Identification of Fusarium Head Blight in Winter Wheat Ears Using Continuous Wavelet Analysis. Sensors, 20.","DOI":"10.3390\/s20010020"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Liu, L., Dong, Y., Huang, W., Du, X., and Ma, H. (2020). Monitoring Wheat Fusarium Head Blight Using Unmanned Aerial Vehicle Hyperspectral Imagery. Remote Sens., 12.","DOI":"10.3390\/rs12223811"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Loizzo, R., Guarini, R., Longo, F., Scopa, T., Formaro, R., Facchinetti, C., and Varacalli, G. (2018, January 22\u201327). Prisma: The Italian Hyperspectral Mission. Proceedings of the IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8518512"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Ren, K., Sun, W., Meng, X., Yang, G., and Du, Q. (2020). Fusing China GF-5 Hyperspectral Data with GF-1, GF-2 and Sentinel-2A Multispectral Data: Which Methods Should Be Used?. Remote Sens., 12.","DOI":"10.3390\/rs12050882"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Matsunaga, T., Iwasaki, A., Tsuchida, S., Iwao, K., Tanii, J., Kashimura, O., Nakamura, R., Yamamoto, H., Kato, S., and Obata, K. (2017, January 23\u201328). Current status of Hyperspectral Imager Suite (HISUI) onboard International Space Station (ISS). Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA.","DOI":"10.1109\/IGARSS.2017.8126989"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Mahalingam, S., Srinivas, P., Devi, P.K., Sita, D., Das, S.K., Leela, T.S., and Venkataraman, V.R. (2019, January 17\u201320). Reflectance based vicarious calibration of HySIS sensors and spectral stability study over pseudo-invariant sites. Proceedings of the IEEE Recent Advances in Geoscience and Remote Sensing: Technologies, Standards and Applications (TENGARSS), Grand Hyatt Kochi Bolgatti, Kerala, India.","DOI":"10.1109\/TENGARSS48957.2019.8976044"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Alonso, K., Bachmann, M., Burch, K., Carmona, E., Cerra, D., Reyes, R.D.L., Dietrich, D., Heiden, U., H\u00f6lderlin, A., and Ickes, J. (2019). Data Products, Quality and Validation of the DLR Earth Sensing Imaging Spectrometer (DESIS). Sensors, 19.","DOI":"10.3390\/s19204471"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"8830","DOI":"10.3390\/rs70708830","article-title":"The EnMAP spaceborne imaging spectroscopy mission for earth observation","volume":"7","author":"Guanter","year":"2015","journal-title":"Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Boccia, V., Adams, J., Thome, K.J., Turpie, K.R., Kokaly, R., Bouvet, M., Green, R.O., and Rast, M. (2021). NASA-ESA Cooperation on the SBG and CHIME Hyperspectral Satellite Missions: A roadmap for the joint Working Group on Cal\/Val activities (No. EGU21-15166). EGU General Assembly Conference Abstracts, The European Geosciences Union.","DOI":"10.5194\/egusphere-egu21-15166"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"111511","DOI":"10.1016\/j.rse.2019.111511","article-title":"A review of vegetation phenological metrics extraction using time-series, multispectral satellite data","volume":"237","author":"Zeng","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1016\/j.rse.2005.10.021","article-title":"Improved monitoring of vegetation dynamics at very high latitudes: A new method using MODIS NDVI","volume":"100","author":"Beck","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"656","DOI":"10.1111\/j.1365-2486.2011.02521.x","article-title":"Landscape controls on the timing of spring, autumn, and growing season length in mid-Atlantic forests","volume":"18","author":"Elmore","year":"2012","journal-title":"Glob. Change Biol."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Noormets, A. (2009). Characterizing the seasonal dynamics of plant community photosynthesis across a range of vegetation types. Phenology of Ecosystem Processes, Springer.","DOI":"10.1007\/978-1-4419-0026-5"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.geoderma.2015.07.006","article-title":"Mapping topsoil physical properties at European scale using the LUCAS database","volume":"261","author":"Ballabio","year":"2016","journal-title":"Geoderma"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1609","DOI":"10.13031\/2013.13326","article-title":"Simple model to estimate field-measured soil water limits","volume":"42","author":"Ritchie","year":"1999","journal-title":"Trans. ASAE"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1016\/S1161-0301(02)00107-7","article-title":"The DSSAT cropping system model","volume":"18","author":"Jones","year":"2003","journal-title":"Eur. J. Agron."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"747","DOI":"10.2307\/2401901","article-title":"Solar radiation and productivity in tropical ecosystems","volume":"9","author":"Monteith","year":"1972","journal-title":"J. Appl. Ecol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1098\/rstb.1977.0140","article-title":"Climate and the efficiency of crop production in Britain","volume":"281","author":"Monteith","year":"1977","journal-title":"Philos. Trans. R. Soc. Lond. B Biol. Sci."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.1744-7348.2005.04088.x","article-title":"Thermal time\u2014Concepts and utility","volume":"146","author":"Trudgill","year":"2005","journal-title":"Ann. Appl. Biol."},{"key":"ref_38","unstructured":"Wallach, D., Makowski, D., Jones, J.W., and Brun, F. (2014). Working with Dynamic Crop Models: Methods, Tools and Examples for Agriculture and Environment, Academic Press. [2nd ed.]."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1115\/1.3662552","article-title":"A new approach to linear filtering and prediction problems","volume":"82","author":"Kalman","year":"1960","journal-title":"J. Basic Eng."},{"key":"ref_40","unstructured":"(2021, April 01). The MathWorks, Inc, MATLAB (Version R2019b, Academic Use). Available online: https:\/\/www.mathworks.com\/."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.rse.2019.04.005","article-title":"Field-level crop yield mapping with Landsat using a hierarchical data assimilation approach","volume":"228","author":"Kang","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_42","unstructured":"Greer, G., and Saunders, C. (2012). The costs of Psa-V to the New Zealand Kiwifruit Industry and the Wider Community, Agribusiness and Economics Research Unit 62."},{"key":"ref_43","first-page":"107","article-title":"Dragon 4\u2014Satellite based analysis of diseases on permanent and row crops in Italy and China","volume":"3","author":"Laneve","year":"2020","journal-title":"J. Geod. Geoinf. Sci."},{"key":"ref_44","first-page":"246","article-title":"Detection of Pseudomonas syringae pv. actinidiae in kiwifruit pollen samples","volume":"64","author":"Vanneste","year":"2011","journal-title":"N. Z. Plant Prot."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1080\/01140671.2014.894543","article-title":"Early season detection and mapping of Pseudomonas syringae pv. actinidae infected kiwifruit (Actinidia sp.) orchards","volume":"42","author":"Taylor","year":"2014","journal-title":"N. Z. J. Crop Hortic. Sci."},{"key":"ref_46","unstructured":"ProMed Posting (no. 20110822.2550) (2011, August 22). Bacterial Canker, Kiwifruit\u2014New Zealand, Italy: Spread. Available online: www.promedmail.org."},{"key":"ref_47","first-page":"66","article-title":"L\u2019actinidia in Italia e nel mondo tra concorrenza e nuove opportunit\u00e0","volume":"12","author":"Palmieri","year":"2014","journal-title":"Riv. Fruttic."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/bs.agron.2017.01.003","article-title":"Delineation of soil management zones for variable-rate fertilization: A review","volume":"143","author":"Nawar","year":"2017","journal-title":"Adv. Agron."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Peng, X., Han, W., Ao, J., and Wang, Y. (2021). Assimilation of LAI Derived from UAV Multispectral Data into the SAFY Model to Estimate Maize Yield. Remote Sens., 13.","DOI":"10.3390\/rs13061094"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Kasampalis, D.A., Alexandridis, T.K., Deva, C., Challinor, A., Moshou, D., and Zalidis, G. (2018). Contribution of remote sensing on crop models: A review. J. Imaging, 4.","DOI":"10.3390\/jimaging4040052"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Tolomio, M., and Casa, R. (2020). Dynamic Crop Models and Remote Sensing Irrigation Decision Support Systems: A Review of Water Stress Concepts for Improved Estimation of Water Requirements. Remote Sens., 12.","DOI":"10.3390\/rs12233945"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Pascucci, S., Carfora, M.F., Palombo, A., Pignatti, S., Casa, R., Pepe, M., and Castaldi, F. (2018). A comparison between standard and functional clustering methodologies: Application to agricultural fields for yield pattern assessment. Remote Sens., 10.","DOI":"10.3390\/rs10040585"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/j.eja.2011.06.004","article-title":"A strategic and tactical management approach to select optimal N fertilizer rates for wheat in a spatially variable field","volume":"35","author":"Basso","year":"2011","journal-title":"Eur. J. Agron."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"106207","DOI":"10.1016\/j.agwat.2020.106207","article-title":"Dynamic Management Zones for Irrigation Scheduling","volume":"238","author":"Fontanet","year":"2020","journal-title":"Agric. Water Manag."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.eja.2017.11.002","article-title":"A review of data assimilation of remote sensing and crop models","volume":"92","author":"Jin","year":"2018","journal-title":"Eur. J. Agron."},{"key":"ref_56","unstructured":"EFSA (2016). Workshop on Xylella Fastidiosa, John Wiley & Sons, Inc., European Distribution Centre. Knowledge Gaps and Research Priorities for the EU: 2016."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/15\/2889\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:33:52Z","timestamp":1760164432000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/15\/2889"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,23]]},"references-count":56,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2021,8]]}},"alternative-id":["rs13152889"],"URL":"https:\/\/doi.org\/10.3390\/rs13152889","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,23]]}}}