{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T22:34:25Z","timestamp":1781735665240,"version":"3.54.5"},"reference-count":55,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2022,12,9]],"date-time":"2022-12-09T00:00:00Z","timestamp":1670544000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"ERANETMED Project EO-TIME (Earth Observation Technologies for Irrigation in Mediterranean Environment)","award":["1768\/2019"],"award-info":[{"award-number":["1768\/2019"]}]},{"name":"Italian Ministry University and Research","award":["1768\/2019"],"award-info":[{"award-number":["1768\/2019"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The widespread development of Earth Observation (EO) systems and advances in numerical atmospheric modeling have made it possible to use the newest data sources as input for crop\u2013water balance models, thereby improving the crop water requirements (CWR) and yield estimates from the field to the regional scale. Satellite imagery and numerical weather prediction outputs offer high resolution (in time and space) gridded data that can compensate for the paucity of crop parameter field measurements and ground weather observations, as required for assessments of CWR and yield. In this study, the AquaCrop model was used to assess CWR and yield of tomato on a farm in Southern Italy by assimilating Sentinel-2 (S2) canopy cover imagery and using CM-SAF satellite-based radiation data and ERA5-Land reanalysis as forcing weather data. The prediction accuracy was evaluated with field data collected during the irrigation season (April\u2013July) of 2021. Satellite estimates of canopy cover differed from ground observations, with a RMSE of about 11%. CWR and yield predictions were compared with actual data regarding irrigation volumes and harvested yield. The results showed that S2 estimates of crop parameters represent added value, since their assimilation into crop growth models improved CWR and yield estimates. Reliable CWR and yield estimates can be achieved by combining the ERA5-Land and CM-SAF weather databases with S2 imagery for assimilation into the AquaCrop model.<\/jats:p>","DOI":"10.3390\/rs14246233","type":"journal-article","created":{"date-parts":[[2022,12,9]],"date-time":"2022-12-09T03:23:49Z","timestamp":1670556229000},"page":"6233","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Assessing Crop Water Requirement and Yield by Combining ERA5-Land Reanalysis Data with CM-SAF Satellite-Based Radiation Data and Sentinel-2 Satellite Imagery"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2306-5793","authenticated-orcid":false,"given":"Anna","family":"Pelosi","sequence":"first","affiliation":[{"name":"Department of Civil Engineering, University of Salerno, 84084 Fisciano, SA, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5748-4224","authenticated-orcid":false,"given":"Oscar Rosario","family":"Belfiore","sequence":"additional","affiliation":[{"name":"Department of Agricultural Sciences, University of Naples \u201cFederico II\u201d, 80055 Portici, NA, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0251-4668","authenticated-orcid":false,"given":"Guido","family":"D\u2019Urso","sequence":"additional","affiliation":[{"name":"Department of Agricultural Sciences, University of Naples \u201cFederico II\u201d, 80055 Portici, NA, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9536-4741","authenticated-orcid":false,"given":"Giovanni Battista","family":"Chirico","sequence":"additional","affiliation":[{"name":"Department of Agricultural Sciences, University of Naples \u201cFederico II\u201d, 80055 Portici, NA, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,9]]},"reference":[{"key":"ref_1","unstructured":"(2022, July 12). 2021 EU Strategy on Adaptation to Climate Change. Available online: https:\/\/eur-lex.europa.eu\/legal-content\/EN\/TXT\/?uri=COM:2021:82:FIN."},{"key":"ref_2","unstructured":"(2022, July 12). Irrigation in EU Agriculture. Available online: www.europarl.europa.eu\/thinktank."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1108\/MEQ-01-2015-0017","article-title":"Climate adaptation strategies: Cohesion policy 2014-2020 and prospects for Greek regions","volume":"28","author":"Thoidou","year":"2017","journal-title":"Manag. Environ. Qual."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Pelosi, A., Terribile, F., D\u2019Urso, G., and Chirico, G.B. (2020). Comparison of ERA5-Land and UERRA MESCAN-SURFEX Reanalysis Data with Spatially Interpolated Weather Observations for the Regional Assessment of Reference Evapotranspiration. Water, 12.","DOI":"10.3390\/w12061669"},{"key":"ref_5","unstructured":"Allen, R.G., Pereira, L.S., Raes, D., and Smith, M. (1998). Crop Evapotranspiration\u2014Guidelines for Computing Crop Water Requirements, Irrigation and Drain\u2014FAO Irrigation and Drainage Paper No. 56, FAO."},{"key":"ref_6","first-page":"205","article-title":"Evaporation and environment. The State and Movement of Water in Living Organisms","volume":"19","author":"Monteith","year":"1965","journal-title":"Symposia of the Society for Experimental Biology"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"106357","DOI":"10.1016\/j.agwat.2020.106357","article-title":"Soil water balance models for determining crop water and irrigation requirements and irrigation scheduling focusing on the FAO56 method and the dual Kc approach","volume":"241","author":"Pereira","year":"2020","journal-title":"Agric. Water Manag."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Novelli, F., Spiegel, H., Sand\u00e9n, T., and Vuolo, F. (2019). Assimilation of Sentinel-2 Leaf Area Index Data into a Physically-Based Crop Growth Model for Yield Estimation. Agronomy, 9.","DOI":"10.3390\/agronomy9050255"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"426437","DOI":"10.2134\/agronj2008.0139s","article-title":"AquaCrop\u2014The FAO crop model for predicting yield response to water: I. Concepts and underlying principles","volume":"101","author":"Steduto","year":"2009","journal-title":"Agron. J."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"509","DOI":"10.2134\/agronj2008.0166s","article-title":"Assessment of AquaCrop, CropSyst, and WOFOST Models in the Simulation of Sunflower Growth under Different Water Regimes","volume":"101","author":"Todorovic","year":"2009","journal-title":"Agron. J."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"475","DOI":"10.1016\/j.jhydrol.2014.03.071","article-title":"Comparisons of satellite-based models for estimating evapotranspiration fluxes","volume":"513","author":"Consoli","year":"2014","journal-title":"J. Hydrol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.agwat.2014.08.004","article-title":"Satellite-based irrigation advisory services: A common tool for different experiences from Europe to Australia","volume":"147","author":"Vuolo","year":"2015","journal-title":"Agric. Water Manag."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Calera, A., Campos, I., Osann, A., D\u2019Urso, G., and Menenti, M. (2017). Remote Sensing for Crop Water Management: From ET Modelling to Services for the End Users. Sensors, 17.","DOI":"10.3390\/s17051104"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"702","DOI":"10.1017\/S0021859618000084","article-title":"Forecasting potential evapotranspiration by combining numerical weather predictions and visible and near-infrared satellite images: An application in southern Italy","volume":"156","author":"Chirico","year":"2018","journal-title":"J. Agric. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Pelosi, A., Chirico, G.B., Falanga Bolognesi, S., De Michele, C., and D\u2019Urso, G. (2019, January 24\u201326). Forecasting crop evapotranspiration under standard conditions in precision farming. Proceedings of the 2019 IEEE International Workshop on Metrology for Agriculture and Forestry, MetroAgriFor 2019\u2014Proceedings, Portici, Italy.","DOI":"10.1109\/MetroAgriFor.2019.8909263"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Pelosi, A., Villani, P., Falanga Bolognesi, S., Chirico, G.B., and D\u2019Urso, G. (2020). Predicting Crop Evapotranspiration by Integrating Ground and Remote Sensors with Air Temperature Forecasts. Sensors, 20.","DOI":"10.3390\/s20061740"},{"key":"ref_17","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_18","doi-asserted-by":"crossref","first-page":"107609","DOI":"10.1016\/j.agrformet.2019.06.008","article-title":"Assimilation of remote sensing into crop growth models: Current status and perspectives","volume":"276\u2013277","author":"Huang","year":"2019","journal-title":"Agric. For. Meteorol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"101182","DOI":"10.1016\/j.ejrh.2022.101182","article-title":"Comparing the use of ERA5 reanalysis dataset and ground-based agrometeorological data under different climates and topography in Italy","volume":"42","author":"Vanella","year":"2022","journal-title":"J. Hydrol. Reg. Stud."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"29879","DOI":"10.3402\/tellusa.v68.29879","article-title":"High-resolution precipitation reanalysis system for climatological purposes","volume":"68","author":"Soci","year":"2016","journal-title":"Tellus A"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2378","DOI":"10.1002\/joc.4852","article-title":"Assessing reference evapotranspiration estimation from reanalysis weather products. An application to the Iberian Peninsula","volume":"37","author":"Martins","year":"2017","journal-title":"Int. J. Climatol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1016\/j.agwat.2018.08.003","article-title":"Accuracy of daily estimation of grass reference evapotranspiration using ERA-Interim reanalysis products with assessment of alternative bias correction schemes","volume":"210","author":"Paredes","year":"2018","journal-title":"Agric. Water Manag."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Pelosi, A., Falanga Bolognesi, S., D\u2019Urso, G., and Chirico, G.B. (2021, January 3\u20135). Assessing crop evapotranspiration by combining ERA5-Land meteorological reanalysis data and visible and near-infrared satellite imagery. Proceedings of the 2021 IEEE International Workshop on Metrology for Agriculture and Forestry, MetroAgriFor 2021\u2014Proceedings, Trento-Bolzano, Italy.","DOI":"10.1109\/MetroAgriFor52389.2021.9628640"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"107169","DOI":"10.1016\/j.agwat.2021.107169","article-title":"Regional assessment of daily reference evapotranspiration: Can ground observations be replaced by blending ERA5-Land meteorological reanalysis and CM-SAF satellite-based radiation data?","volume":"258","author":"Pelosi","year":"2021","journal-title":"Agric. Water Manag."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"10654","DOI":"10.1016\/j.agwat.2020.106543","article-title":"Daily grass reference evapotranspiration with Meteosat Second Generation shortwave radiation and reference ET products","volume":"248","author":"Paredes","year":"2021","journal-title":"Agric. Water Manag."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2725","DOI":"10.1080\/01431161003743199","article-title":"The satellite application facility on land surface analysis","volume":"32","author":"Trigo","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2692","DOI":"10.1016\/j.rse.2010.06.010","article-title":"Improving the spatio-temporal distribution of surface radiation data by merging ground and satellite measurements","volume":"114","author":"Bertrand","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.rse.2017.07.013","article-title":"Extensive validation of CM SAF surface radiation products over Europe","volume":"199","author":"Urraca","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.agwat.2016.09.015","article-title":"Probabilistic forecasting of reference evapotranspiration with a limited area ensemble prediction system","volume":"178","author":"Pelosi","year":"2016","journal-title":"Agric. Water Manag."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Dalla Marta, A., Chirico, G.B., Falanga Bolognesi, S., Mancini, M., D\u2019Urso, G., Orlandini, S., De Michele, C., and Altobelli, F. (2019). Integrating Sentinel-2 Imagery with AquaCrop for Dynamic Assessment of Tomato Water Requirements in Southern Italy. Agronomy, 9.","DOI":"10.3390\/agronomy9070404"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1007\/s00484-011-0438-1","article-title":"Photographic assessment of temperate forest understory phenology in relation to springtime meteorological drivers","volume":"56","author":"Liang","year":"2012","journal-title":"Int. J. Biometeorol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1007\/s00442-006-0657-z","article-title":"Use of digital webcam images to track spring green-up in a deciduous broadleaf forest","volume":"152","author":"Richardson","year":"2007","journal-title":"Oecologia"},{"key":"ref_33","unstructured":"VALERI (2022, June 04). Land European Remote-Sensing Instruments Field Protocol. Available online: http:\/\/w3.avignon.inra.fr\/valeri\/."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Pasqualotto, N., D\u2019Urso, G., Bolognesi, S.F., Belfiore, O.R., Wittenberghe, S.V., Delegido, J., Pezzola, A., Winschel, C., and Moreno, J. (2019). Retrieval of evapotranspiration from Sentinel-2: Comparison of vegetation indices, semi-empirical models and SNAP biophysical processor approach. Agronomy, 9.","DOI":"10.3390\/agronomy9100663"},{"key":"ref_35","unstructured":"(2021, June 04). ESA\u2014Sentinel Online. Available online: https:\/\/sentinels.copernicus.eu\/web\/sentinel\/technical-guides\/sentinel-2-msi\/msi-instrument."},{"key":"ref_36","unstructured":"Theia (2021, June 04). Theia French Land Data Center. Available online: www.theia-land.fr."},{"key":"ref_37","unstructured":"Hagolle, O., Huc, M., Desjardins, C., Auer, S., and Richter, R. (2017). MAJA Algorithm Theoretical Basis Document (1.0). Zenodo. Available online: https:\/\/zenodo.org\/record\/1209633#.Y5HljX1BxPY."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2668","DOI":"10.3390\/rs70302668","article-title":"A Multi-Temporal and Multi-Spectral Method to Estimate Aerosol Optical Thickness over Land, for the Atmospheric Correction of FormoSat-2, LandSat, VENuS and Sentinel-2 Images","volume":"7","author":"Hagolle","year":"2015","journal-title":"Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2077","DOI":"10.1080\/01431160500486690","article-title":"An automatic atmospheric correction algorithm for visible\/NIR imagery","volume":"27","author":"Richter","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_40","unstructured":"Weiss, M., and Baret., F. (2021, July 12). S2ToolBox Level 2 Products: LAI, FAPAR, FCOVER, Version 1.1. ESA Contract nr 000110612\/14\/I-BG 2016, 52. Available online: https:\/\/step.esa.int\/docs\/extra\/ATBD_S2ToolBox_L2B_V1.1.pdf."},{"key":"ref_41","unstructured":"Weiss, M., Baret, F., and Jay, S. (2021, July 12). S2ToolBox Level 2 Products: LAI, FAPAR, FCOVER, Version 2.0. Available online: http:\/\/step.esa.int\/docs\/extra\/ATBD_S2ToolBox_V2.0.pdf."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Rolle, M., Tamea, S., and Claps, P. (2021). ERA5-based global assessment of irrigation requirement and validation. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0250979"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"107319","DOI":"10.1016\/j.agwat.2021.107319","article-title":"Reference crop evapotranspiration for data-sparse regions using reanalysis products","volume":"262","author":"Nouri","year":"2022","journal-title":"Agric. Water Manag."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1038\/nature14956","article-title":"The quiet revolution of numerical weather prediction","volume":"525","author":"Bauer","year":"2015","journal-title":"Nature"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1016\/j.jhydrol.2018.05.029","article-title":"Medium-range reference evapotranspiration forecasts for the contiguous United States based on multi-model numerical weather predictions","volume":"562","author":"Medina","year":"2018","journal-title":"J. Hydrol."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1999","DOI":"10.1002\/qj.3803","article-title":"The ERA5 Global Reanalysis","volume":"146","author":"Hersbach","year":"2020","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_47","unstructured":"Mu\u00f1oz Sabater, J. (2020, July 18). ERA5-Land Hourly Data from 1981 to Present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). Available online: https:\/\/doi.org\/10.24381\/cds.e2161bac."},{"key":"ref_48","unstructured":"Pfeifroth, U., Trentmann, J., Hollmann, R., Selbach, N., Werscheck, M., and Meirink, J.F. (2021, January 04). ICDR SEVIRI Radiation\u2014Based on SARAH-2 Methods. Satellite Application Facility on Climate Monitoring. Available online: https:\/\/wui.cmsaf.eu\/safira\/action\/viewICDRDetails?acronym=SARAHV002ICDR."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"622","DOI":"10.3390\/rs4030622","article-title":"A New Algorithm for the Satellite-Based Retrieval of Solar Surface Irradiance in Spectral Bands","volume":"4","author":"Mueller","year":"2012","journal-title":"Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1012","DOI":"10.1016\/j.rse.2009.01.012","article-title":"The CM SAF operational scheme for the satellite-based retrieval of solar surface irradiance\u2014A LUT based eigenvector approach","volume":"113","author":"Mueller","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1007\/BF00977785","article-title":"Two algorithms for constructing a Delaunay triangulation","volume":"9","author":"Lee","year":"1980","journal-title":"Internat. Comput. Inform. Sci."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"4837","DOI":"10.1175\/MWR-D-17-0084.1","article-title":"Adaptive Kalman filtering for post-processing of ensemble numerical weather predictions","volume":"145","author":"Pelosi","year":"2017","journal-title":"Mon. Weather Rev."},{"key":"ref_53","unstructured":"(2021, September 12). AquaCrop, the Crop Water Productivity Model. Available online: https:\/\/www.fao.org\/documents\/card\/en\/c\/f34330d3-592e-42e1-883e-1db3506c8c8d\/."},{"key":"ref_54","unstructured":"Reference Manual (2022, November 25). Chapter 1 FAO Crop-Water Productivity Model to Simulate YIELD response to Water. Available online: https:\/\/www.fao.org\/3\/br246e\/br246e.pdf."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"452","DOI":"10.1016\/j.rse.2018.06.035","article-title":"Capability of Sentinel-2 data for estimating maximum evapotranspiration and irrigation requirements for tomato crop in Central Italy","volume":"215","author":"Vanino","year":"2018","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/24\/6233\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:37:02Z","timestamp":1760146622000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/24\/6233"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,9]]},"references-count":55,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["rs14246233"],"URL":"https:\/\/doi.org\/10.3390\/rs14246233","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,9]]}}}