{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T04:47:19Z","timestamp":1743137239729,"version":"3.40.3"},"publisher-location":"Cham","reference-count":35,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030870065"},{"type":"electronic","value":"9783030870072"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-87007-2_5","type":"book-chapter","created":{"date-parts":[[2021,9,10]],"date-time":"2021-09-10T17:02:22Z","timestamp":1631293342000},"page":"63-77","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["MAIA S2 Versus Sentinel 2: Spectral Issues and Their Effects in the Precision Farming Context"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4556-446X","authenticated-orcid":false,"given":"Filippo","family":"Sarvia","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8184-9871","authenticated-orcid":false,"given":"Samuele","family":"De Petris","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6750-0438","authenticated-orcid":false,"given":"Tommaso","family":"Orusa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4570-8013","authenticated-orcid":false,"given":"Enrico","family":"Borgogno-Mondino","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,9,11]]},"reference":[{"key":"5_CR1","doi-asserted-by":"publisher","first-page":"37","DOI":"10.3389\/fdata.2019.00037","volume":"2","author":"E Babaeian","year":"2019","unstructured":"Babaeian, E., et al.: others: A new optical remote sensing technique for high-resolution mapping of soil moisture. Front. Big Data 2, 37 (2019)","journal-title":"Front. Big Data"},{"key":"5_CR2","doi-asserted-by":"publisher","first-page":"3136","DOI":"10.3390\/rs12193136","volume":"12","author":"RP Sishodia","year":"2020","unstructured":"Sishodia, R.P., Ray, R.L., Singh, S.K.: Applications of remote sensing in precision agriculture: A review. Remote Sens. 12, 3136 (2020)","journal-title":"Remote Sens."},{"key":"5_CR3","doi-asserted-by":"publisher","first-page":"7091","DOI":"10.3390\/s20247091","volume":"20","author":"S Monteleone","year":"2020","unstructured":"Monteleone, S., et al.: Exploring the adoption of precision agriculture for irrigation in the context of agriculture 4.0: the key role of internet of things. Sensors. 20, 7091 (2020)","journal-title":"Sensors."},{"key":"5_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1007\/978-3-030-24305-0_15","volume-title":"Computational Science and Its Applications \u2013 ICCSA 2019","author":"E Borgogno-Mondino","year":"2019","unstructured":"Borgogno-Mondino, E., Sarvia, F., Gomarasca, M.A.: Supporting insurance strategies in agriculture by remote sensing: a possible approach at regional level. In: Misra, Sanjay, Gervasi, Osvaldo, Murgante, Beniamino, Stankova, Elena, Korkhov, Vladimir, Torre, Carmelo, Rocha, Ana Maria A C., Taniar, David, Apduhan, Bernady O., Tarantino, Eufemia (eds.) ICCSA 2019. LNCS, vol. 11622, pp. 186\u2013199. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-24305-0_15"},{"key":"5_CR5","doi-asserted-by":"crossref","unstructured":"Sarvia, F., De Petris, S., Borgogno-Mondino, E.: Remotely sensed data to support insurance strategies in agriculture. In: Remote Sensing for Agriculture, Ecosystems, and Hydrology XXI, p. 111491H. International Society for Optics and Photonics (2019)","DOI":"10.1117\/12.2533117"},{"key":"5_CR6","doi-asserted-by":"publisher","first-page":"770","DOI":"10.1080\/15481603.2020.1798600","volume":"57","author":"F Sarvia","year":"2020","unstructured":"Sarvia, F., De Petris, S., Borgogno-Mondino, E.: Multi-scale remote sensing to support insurance policies in agriculture: from mid-term to instantaneous deductions. GISci. Remote Sens. 57, 770\u2013784 (2020). https:\/\/doi.org\/10.1080\/15481603.2020.1798600","journal-title":"GISci. Remote Sens."},{"issue":"1","key":"5_CR7","first-page":"58","volume":"12","author":"S De Petris","year":"2020","unstructured":"De Petris, S., Sarvia, F., Borgogno-Mondino, E.: A new index for assessing tree vigour decline based on sentinel-2 multitemporal data. Application to tree failure risk management. Remote Sens. Lett. 12(1), 58\u201367 (2020)","journal-title":"Remote Sens. Lett."},{"key":"5_CR8","doi-asserted-by":"publisher","first-page":"126862","DOI":"10.1016\/j.ufug.2020.126862","volume":"55","author":"S De Petris","year":"2020","unstructured":"De Petris, S., Sarvia, F., Borgogno-Mondino, E.: RPAS-based photogrammetry to support tree stability assessment: Longing for precision arboriculture. Urban Forest. Urban Green. 55, 126862 (2020). https:\/\/doi.org\/10.1016\/j.ufug.2020.126862","journal-title":"Urban Forest. Urban Green."},{"key":"5_CR9","doi-asserted-by":"publisher","first-page":"110","DOI":"10.3390\/agronomy11010110","volume":"11","author":"F Sarvia","year":"2021","unstructured":"Sarvia, F., Xausa, E., Petris, S.D., Cantamessa, G., Borgogno-Mondino, E.: A possible role of copernicus sentinel-2 data to support common agricultural policy controls in agriculture. Agronomy 11, 110 (2021)","journal-title":"Agronomy"},{"key":"5_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"737","DOI":"10.1007\/978-3-030-58811-3_53","volume-title":"Computational Science and Its Applications \u2013 ICCSA 2020","author":"F Sarvia","year":"2020","unstructured":"Sarvia, F., De Petris, S., Borgogno-Mondino, E.: A methodological proposal to support estimation of damages from hailstorms based on copernicus sentinel 2 data times series. In: Gervasi, O., et al. (eds.) ICCSA 2020. LNCS, vol. 12252, pp. 737\u2013751. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58811-3_53"},{"key":"5_CR11","doi-asserted-by":"publisher","first-page":"1030","DOI":"10.3390\/rs13051030","volume":"13","author":"S De Petris","year":"2021","unstructured":"De Petris, S., Sarvia, F., Gullino, M., Tarantino, E., Borgogno-Mondino, E.: Sentinel-1 polarimetry to map apple orchard damage after a storm. Remote Sens. 13, 1030 (2021). https:\/\/doi.org\/10.3390\/rs13051030","journal-title":"Remote Sens."},{"key":"5_CR12","unstructured":"De Petris, S., Sarvia, F., Borgogno-Mondino, E.: Multi-temporal mapping of flood damage to crops using sentinel-1 imagery: a case study of the Sesia River (October 2020), (2021)"},{"key":"5_CR13","doi-asserted-by":"publisher","first-page":"555","DOI":"10.3390\/agronomy11030555","volume":"11","author":"F Sarvia","year":"2021","unstructured":"Sarvia, F., De Petris, S., Borgogno-Mondino, E.: Exploring climate change effects on vegetation phenology by MOD13Q1 data: the Piemonte region case study in the period 2001\u20132019. Agronomy 11, 555 (2021). https:\/\/doi.org\/10.3390\/agronomy11030555","journal-title":"Agronomy"},{"key":"5_CR14","doi-asserted-by":"publisher","first-page":"3542","DOI":"10.3390\/rs12213542","volume":"12","author":"T Orusa","year":"2020","unstructured":"Orusa, T., Orusa, R., Viani, A., Carella, E., Borgogno Mondino, E.: Geomatics and EO data to support wildlife diseases assessment at landscape level: a pilot experience to map infectious Keratoconjunctivitis in Chamois and phenological trends in Aosta Valley (NW Italy). Remote Sens. 12, 3542 (2020)","journal-title":"Remote Sens."},{"key":"5_CR15","doi-asserted-by":"publisher","first-page":"105709","DOI":"10.1016\/j.compag.2020.105709","volume":"177","author":"T van Klompenburg","year":"2020","unstructured":"van Klompenburg, T., Kassahun, A., Catal, C.: Crop yield prediction using machine learning: a systematic literature review. Comput. Electron. Agric. 177, 105709 (2020)","journal-title":"Comput. Electron. Agric."},{"key":"5_CR16","doi-asserted-by":"publisher","first-page":"49","DOI":"10.3390\/jimaging3040049","volume":"3","author":"A Lessio","year":"2017","unstructured":"Lessio, A., Fissore, V., Borgogno-Mondino, E.: Preliminary tests and results concerning integration of Sentinel-2 and Landsat-8 OLI for crop monitoring. J. Imaging 3, 49 (2017)","journal-title":"J. Imaging"},{"issue":"3","key":"5_CR17","doi-asserted-by":"publisher","first-page":"149","DOI":"10.5194\/isprs-archives-XLII-3-W3-149-2017","volume":"42","author":"E Nocerino","year":"2017","unstructured":"Nocerino, E., Dubbini, M., Menna, F., Remondino, F., Gattelli, M., Covi, D.: Geometric calibration and radiometric correction of the MAIA multispectral camera. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. 42(3), 149\u2013156 (2017)","journal-title":"Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci."},{"key":"5_CR18","first-page":"87","volume":"19","author":"F Marinello","year":"2017","unstructured":"Marinello, F.: Last generation instrument for agriculture multispectral data collection. Agric. Eng. Int. CIGR J. 19, 87\u201393 (2017)","journal-title":"Agric. Eng. Int. CIGR J."},{"key":"5_CR19","doi-asserted-by":"crossref","unstructured":"Boccardo, P., Mondino, E.B., Tonolo, F.G.: High resolution satellite images position accuracy tests. In: IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No. 03CH37477), pp. 2320\u20132322. IEEE (2003)","DOI":"10.1109\/IGARSS.2003.1294428"},{"key":"5_CR20","doi-asserted-by":"publisher","first-page":"243","DOI":"10.5721\/EuJRS20124522","volume":"45","author":"EB Mondino","year":"2012","unstructured":"Mondino, E.B., Perotti, L., Piras, M.: High resolution satellite images for archeological applications: the Karima case study (Nubia region, Sudan). Eur. J. Remote Sens. 45, 243\u2013259 (2012)","journal-title":"Eur. J. Remote Sens."},{"key":"5_CR21","doi-asserted-by":"publisher","first-page":"C03020","DOI":"10.1088\/1748-0221\/13\/03\/C03020","volume":"13","author":"CG Ryan","year":"2018","unstructured":"Ryan, C.G., et al.: MAIA mapper: high definition XRF imaging in the lab. J. Instrum. 13, C03020 (2018)","journal-title":"J. Instrum."},{"key":"5_CR22","doi-asserted-by":"publisher","first-page":"641","DOI":"10.3390\/agronomy10050641","volume":"10","author":"J Segarra","year":"2020","unstructured":"Segarra, J., Buchaillot, M.L., Araus, J.L., Kefauver, S.C.: Remote sensing for precision agriculture: sentinel-2 improved features and applications. Agronomy 10, 641 (2020)","journal-title":"Agronomy"},{"key":"5_CR23","unstructured":"Maggiore, P., Greco, A.: Development of the SmartGimbal Control System for the SmartBay Platform (2019)."},{"key":"5_CR24","doi-asserted-by":"crossref","unstructured":"Gascon, F., Cadau, E., Colin, O., Hoersch, B., Isola, C., Fern\u00e1ndez, B.L., Martimort, P.: Copernicus sentinel-2 mission: products, algorithms and Cal\/Val. In: Earth Observing Systems XIX, p. 92181E. International Society for Optics and Photonics (2014)","DOI":"10.1117\/12.2062260"},{"key":"5_CR25","doi-asserted-by":"crossref","unstructured":"Dechoz, C., et al.: Sentinel 2 global reference image. In: Image and Signal Processing for Remote Sensing XXI, p. 96430A. International Society for Optics and Photonics (2015)","DOI":"10.1117\/12.2195046"},{"key":"5_CR26","series-title":"Mechanisms and Machine Science","doi-asserted-by":"publisher","first-page":"483","DOI":"10.1007\/978-3-319-61276-8_51","volume-title":"Advances in Service and Industrial Robotics","author":"E Borgogno-Mondino","year":"2018","unstructured":"Borgogno-Mondino, E.: Remote sensing from RPAS in agriculture: an overview of expectations and unanswered questions. In: Ferraresi, C., Quaglia, G. (eds.) RAAD 2017. MMS, vol. 49, pp. 483\u2013492. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-61276-8_51"},{"key":"5_CR27","doi-asserted-by":"publisher","first-page":"427","DOI":"10.1016\/j.isprsjprs.2008.01.006","volume":"63","author":"MA Brovelli","year":"2008","unstructured":"Brovelli, M.A., Crespi, M., Fratarcangeli, F., Giannone, F., Realini, E.: Accuracy assessment of high resolution satellite imagery orientation by leave-one-out method. ISPRS J. Photogramm. Remote. Sens. 63, 427\u2013440 (2008)","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"key":"5_CR28","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1016\/j.isprsjprs.2018.09.018","volume":"146","author":"C Lanaras","year":"2018","unstructured":"Lanaras, C., Bioucas-Dias, J., Galliani, S., Baltsavias, E., Schindler, K.: Super-resolution of Sentinel-2 images: learning a globally applicable deep neural network. ISPRS J. Photogramm. Remote. Sens. 146, 305\u2013319 (2018)","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"key":"5_CR29","doi-asserted-by":"publisher","first-page":"79","DOI":"10.3354\/cr030079","volume":"30","author":"CJ Willmott","year":"2005","unstructured":"Willmott, C.J., Matsuura, K.: Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate Res. 30, 79\u201382 (2005)","journal-title":"Climate Res."},{"key":"5_CR30","doi-asserted-by":"publisher","first-page":"235","DOI":"10.5194\/isprs-archives-XLII-2-W13-235-2019","volume":"42","author":"S Chauhan","year":"2019","unstructured":"Chauhan, S., et al.: Wheat lodging assessment using multispectral UAV data. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. 42, 235\u2013240 (2019)","journal-title":"Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci."},{"key":"5_CR31","doi-asserted-by":"publisher","first-page":"581","DOI":"10.1007\/s11119-012-9270-9","volume":"13","author":"D G\u00f3mez-Cand\u00f3n","year":"2012","unstructured":"G\u00f3mez-Cand\u00f3n, D., L\u00f3pez-Granados, F., Caballero-Novella, J.J., Pe\u00f1a-Barrag\u00e1n, J.M., Garc\u00eda-Torres, L.: Understanding the errors in input prescription maps based on high spatial resolution remote sensing images. Precision Agric. 13, 581\u2013593 (2012). https:\/\/doi.org\/10.1007\/s11119-012-9270-9","journal-title":"Precision Agric."},{"key":"5_CR32","doi-asserted-by":"publisher","unstructured":"Bates, T., Dresser, J., Eckstrom, R., Badr, G., Betts, T., Taylor, J.: Variable-rate mechanical crop adjustment for crop load balance in \u201cConcord\u201d vineyards. In: Presented at the 2018 IoT Vertical and Topical Summit on Agriculture - Tuscany, IOT Tuscany 2018 (2018). https:\/\/doi.org\/10.1109\/IOT-TUSCANY.2018.8373046","DOI":"10.1109\/IOT-TUSCANY.2018.8373046"},{"issue":"2","key":"5_CR33","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1007\/s11119-017-9510-0","volume":"19","author":"E Borgogno-Mondino","year":"2017","unstructured":"Borgogno-Mondino, E., Lessio, A., Tarricone, L., Novello, V., de Palma, L.: A comparison between multispectral aerial and satellite imagery in precision viticulture. Precision Agric. 19(2), 195\u2013217 (2017). https:\/\/doi.org\/10.1007\/s11119-017-9510-0","journal-title":"Precision Agric."},{"key":"5_CR34","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1016\/j.proenv.2015.03.032","volume":"24","author":"CA Rokhmana","year":"2015","unstructured":"Rokhmana, C.A.: The potential of UAV-based remote sensing for supporting precision agriculture in Indonesia. Procedia Environ. Sci. 24, 245\u2013253 (2015)","journal-title":"Procedia Environ. Sci."},{"key":"5_CR35","doi-asserted-by":"publisher","first-page":"137","DOI":"10.5721\/EuJRS20164908","volume":"49","author":"E Borgogno-Mondino","year":"2016","unstructured":"Borgogno-Mondino, E., Lessio, A., Gomarasca, M.A.: A fast operative method for NDVI uncertainty estimation and its role in vegetation analysis. Eur. J. Remote Sens. 49, 137\u2013156 (2016)","journal-title":"Eur. J. Remote Sens."}],"container-title":["Lecture Notes in Computer Science","Computational Science and Its Applications \u2013 ICCSA 2021"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-87007-2_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,8]],"date-time":"2024-09-08T05:43:55Z","timestamp":1725774235000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-87007-2_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030870065","9783030870072"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-87007-2_5","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"11 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCSA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Science and Its Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Cagliari","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccsa2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iccsa.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Customed version of CyberChair 4","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1588","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"466","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"18","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"29% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2,5","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"8","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}