{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T10:01:08Z","timestamp":1782813668554,"version":"3.54.5"},"reference-count":73,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2020,8,18]],"date-time":"2020-08-18T00:00:00Z","timestamp":1597708800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Space Agency: Dragon 4 Programme","award":["4000121195"],"award-info":[{"award-number":["4000121195"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Crop growth models play an important role in agriculture management, allowing, for example, the spatialized estimation of crop yield information. However, crop model parameter calibration is a mandatory step for their application. The present work focused on the regional calibration of the Aquacrop-OS model for durum wheat by assimilating high spatial and temporal resolution canopy cover data retrieved from VEN\u00b5S satellite images. The assimilation procedure was implemented using the Bayesian approach with the recent implementation of the Markov chain Monte Carlo (MCMC)-based Differential Evolution Adaptive Metropolis (DREAM) algorithm DREAM(KZS). The fraction of vegetation cover (fvc) was retrieved from the VEN\u00b5S satellite images for two years, during the durum wheat growing seasons of 2018 and 2019 in Central Italy. The retrieval was based on a hybrid method using PROSAIL Radiative Transfer Model (RTM) simulations for training a Gaussian Process Regression (GPR) algorithm, combined with Active Learning to reduce the computational cost. The Aquacrop-OS model was calibrated with the fvc data of 2017\u20132018 for the Maccarese farm in Central Italy and validated with the 2018\u20132019 data. The retrieval accuracy of the fvc from the VEN\u00b5S images were the Coefficient of Determination (R2) = 0.76, Root Mean Square Error (RMSE) = 0.09, and Relative Root Mean Square Error (RRMSE) = 11.6%, when compared with the ground-measured fvc. The MCMC results are presented in terms of Gelman\u2013Rubin R statistics and MR statistics, Markov chains, and marginal posterior distribution functions, which are summarized with the mean values for the most sensitive crop parameters of the Aquacrop-OS model subjected to calibration. When validating for the fvc, the R2 of the model for year (2018\u20132019) ranged from 0.69 to 0.86. The RMSE, Relative Error (RE), Relative Variability (\u03b1), and Relative Bias (\u03b2) ranged from 0.15 to 0.44, 0.19 to 2.79, 0.84 to 1.45, and 0.91 to 1.95, respectively. The present work shows the importance of the calibration of the Aquacrop-OS (AOS) crop water productivity model for durum wheat by assimilating remote sensing information from VEN\u00b5S satellite data.<\/jats:p>","DOI":"10.3390\/rs12162666","type":"journal-article","created":{"date-parts":[[2020,8,18]],"date-time":"2020-08-18T11:15:27Z","timestamp":1597749327000},"page":"2666","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Bayesian Calibration of the Aquacrop-OS Model for Durum Wheat by Assimilation of Canopy Cover Retrieved from VEN\u00b5S Satellite Data"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8936-1889","authenticated-orcid":false,"given":"Deepak","family":"Upreti","sequence":"first","affiliation":[{"name":"Universit\u00e0 della Tuscia, DAFNE, Via San Camillo de Lellis, 01100 Viterbo, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0587-8926","authenticated-orcid":false,"given":"Stefano","family":"Pignatti","sequence":"additional","affiliation":[{"name":"Consiglio Nazionale delle Richerche, Institute of Methodologies for Environmental Analysis (CNR, IMAA), Via del Fosso del Cavaliere, 100, 00133 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8311-8615","authenticated-orcid":false,"given":"Simone","family":"Pascucci","sequence":"additional","affiliation":[{"name":"Consiglio Nazionale delle Richerche, Institute of Methodologies for Environmental Analysis (CNR, IMAA), Via del Fosso del Cavaliere, 100, 00133 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9377-6307","authenticated-orcid":false,"given":"Massimo","family":"Tolomio","sequence":"additional","affiliation":[{"name":"Universit\u00e0 della Tuscia, DAFNE, Via San Camillo de Lellis, 01100 Viterbo, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenjiang","family":"Huang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3091-7680","authenticated-orcid":false,"given":"Raffaele","family":"Casa","sequence":"additional","affiliation":[{"name":"Universit\u00e0 della Tuscia, DAFNE, Via San Camillo de Lellis, 01100 Viterbo, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,8,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.fcr.2012.08.008","article-title":"The use of satellite data for crop yield gap analysis","volume":"143","author":"Lobell","year":"2013","journal-title":"Field Crop. 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