{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,12]],"date-time":"2024-09-12T00:00:44Z","timestamp":1726099244248},"publisher-location":"Cham","reference-count":42,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030588106"},{"type":"electronic","value":"9783030588113"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"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":[[2020]]},"DOI":"10.1007\/978-3-030-58811-3_53","type":"book-chapter","created":{"date-parts":[[2020,9,28]],"date-time":"2020-09-28T08:04:50Z","timestamp":1601280290000},"page":"737-751","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["A Methodological Proposal to Support Estimation of Damages from Hailstorms Based on Copernicus Sentinel 2 Data Times Series"],"prefix":"10.1007","author":[{"given":"F.","family":"Sarvia","sequence":"first","affiliation":[]},{"given":"S.","family":"De Petris","sequence":"additional","affiliation":[]},{"given":"E.","family":"Borgogno-Mondino","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,9,29]]},"reference":[{"key":"53_CR1","unstructured":"FAO: Damage and losses from climate-related disasters in agricultural sectors (2016)"},{"key":"53_CR2","first-page":"35","volume":"3","author":"FG Santeramo","year":"2012","unstructured":"Santeramo, F.G., Di Pasquale, J., Cont\u00f2, F., Tudisca, S., Sgroi, F.: Analyzing risk management in mediterranean countries: the Syrian perspective. New Medit 3, 35\u201340 (2012)","journal-title":"New Medit"},{"issue":"3","key":"53_CR3","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1257\/jep.9.3.103","volume":"9","author":"J Morduch","year":"1995","unstructured":"Morduch, J.: Income smoothing and consumption smoothing. J. Econ. Perspect. 9(3), 103\u2013114 (1995)","journal-title":"J. Econ. Perspect."},{"issue":"2","key":"53_CR4","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1016\/j.jdeveco.2010.08.003","volume":"96","author":"S Dercon","year":"2011","unstructured":"Dercon, S., Christiaensen, L.: Consumption risk, technology adoption and poverty traps: evidence from Ethiopia. J. Dev. Econ. 96(2), 159\u2013173 (2011)","journal-title":"J. Dev. Econ."},{"issue":"3","key":"53_CR5","doi-asserted-by":"publisher","first-page":"363","DOI":"10.1093\/aepp\/pps029","volume":"34","author":"VH Smith","year":"2012","unstructured":"Smith, V.H., Glauber, J.W.: Agricultural insurance in developed countries: where have we been and where are we going? Appl. Econ. Perspect. Policy 34(3), 363\u2013390 (2012)","journal-title":"Appl. Econ. Perspect. Policy"},{"key":"53_CR6","unstructured":"Glauber, J.W.: Agricultural insurance and the world trade organization (2015)"},{"issue":"3","key":"53_CR7","doi-asserted-by":"publisher","first-page":"643","DOI":"10.1111\/0002-9092.00184","volume":"83","author":"BK Goodwin","year":"2001","unstructured":"Goodwin, B.K.: Problems with market insurance in agriculture. Am. J. Agr. Econ. 83(3), 643\u2013649 (2001)","journal-title":"Am. J. Agr. Econ."},{"key":"53_CR8","doi-asserted-by":"crossref","unstructured":"Serra, T., Goodwin, B.K., Featherstone, A.M.: Modeling changes in the US demand for crop insurance during the 1990s (2003)","DOI":"10.1108\/00215030380001144"},{"key":"53_CR9","doi-asserted-by":"crossref","unstructured":"Goodwin, B.K., Mahul, O.: Risk modeling concepts relating to the design and rating of agricultural insurance contracts. The World Bank (2004)","DOI":"10.1596\/1813-9450-3392"},{"issue":"5","key":"53_CR10","doi-asserted-by":"publisher","first-page":"1450","DOI":"10.1093\/ajae\/aaw046","volume":"98","author":"ND Jensen","year":"2016","unstructured":"Jensen, N.D., Barrett, C.B., Mude, A.G.: Index insurance quality and basis risk: evidence from northern Kenya. Am. J. Agr. Econ. 98(5), 1450\u20131469 (2016)","journal-title":"Am. J. Agr. Econ."},{"key":"53_CR11","unstructured":"Greatrex, H., et al.: Scaling up index insurance for smallholder farmers: recent evidence and insights (2015)"},{"issue":"2","key":"53_CR12","doi-asserted-by":"publisher","first-page":"597","DOI":"10.1093\/qje\/qju002","volume":"129","author":"D Karlan","year":"2014","unstructured":"Karlan, D., Osei, R., Osei-Akoto, I., Udry, C.: Agricultural decisions after relaxing credit and risk constraints. Q. J. Econ. 129(2), 597\u2013652 (2014)","journal-title":"Q. J. Econ."},{"key":"53_CR13","doi-asserted-by":"publisher","first-page":"421","DOI":"10.1146\/annurev-resource-100516-053352","volume":"9","author":"M Carter","year":"2017","unstructured":"Carter, M., de Janvry, A., Sadoulet, E., Sarris, A.: Index insurance for developing country agriculture: a reassessment. Ann. Rev. Resour. Econ. 9, 421\u2013438 (2017)","journal-title":"Ann. Rev. Resour. Econ."},{"key":"53_CR14","unstructured":"Kramer, B., Hellin, J., Hansen, J., Rose, A., Braun, M.: Building resilience through climate risk insurance: insights from agricultural research for development (2019)"},{"key":"53_CR15","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1016\/j.ecolind.2017.08.054","volume":"105","author":"B Zhang","year":"2019","unstructured":"Zhang, B., Jin, P., Qiao, H., Hayat, T., Alsaedi, A., Ahmad, B.: Exergy analysis of Chinese agriculture. Ecol. Ind. 105, 279\u2013291 (2019)","journal-title":"Ecol. Ind."},{"key":"53_CR16","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1016\/j.rse.2012.11.009","volume":"130","author":"JC Brown","year":"2013","unstructured":"Brown, J.C., Kastens, J.H., Coutinho, A.C., de Castro Victoria, D., Bishop, C.R.: Classifying multiyear agricultural land use data from Mato Grosso using time-series MODIS vegetation index data. Remote Sens. Environ. 130, 39\u201350 (2013)","journal-title":"Remote Sens. Environ."},{"issue":"1","key":"53_CR17","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1080\/22797254.2017.1328269","volume":"50","author":"E Borgogno Mondino","year":"2017","unstructured":"Borgogno Mondino, E., Gajetti, M.: Preliminary considerations about costs and potential market of remote sensing from UAV in the Italian viticulture context. Eur. J. Remote Sens. 50(1), 310\u2013319 (2017)","journal-title":"Eur. J. Remote Sens."},{"key":"53_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jdeveco.2018.09.003","volume":"136","author":"RV Hill","year":"2019","unstructured":"Hill, R.V., et al.: Ex ante and ex post effects of hybrid index insurance in Bangladesh. J. Dev. Econ. 136, 1\u201317 (2019)","journal-title":"J. Dev. Econ."},{"key":"53_CR19","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, S., et al. (eds.) ICCSA 2019. LNCS, vol. 11622, pp. 186\u2013199. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-24305-0_15"},{"key":"53_CR20","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. International Society for Optics and Photonics (2019)","DOI":"10.1117\/12.2533117"},{"key":"53_CR21","doi-asserted-by":"crossref","unstructured":"De Petris, S., Berretti, R., Sarvia, F., Borgogno-Mondino, E.: Precision arboriculture: a new approach to tree risk management based on geomatics tools. In: Remote Sensing for Agriculture, Ecosystems, and Hydrology XXI. International Society for Optics and Photonics (2019)","DOI":"10.1117\/12.2532778"},{"key":"53_CR22","doi-asserted-by":"crossref","unstructured":"Orusa, T., Mondino, E.B.: Landsat 8 thermal data to support urban management and planning in the climate change era: a case study in Torino area, NW Italy. In: Remote Sensing Technologies and Applications in Urban Environments IV. International Society for Optics and Photonics (2019)","DOI":"10.1117\/12.2533110"},{"issue":"5","key":"53_CR23","doi-asserted-by":"publisher","first-page":"1241","DOI":"10.1111\/j.1467-8276.2007.01091.x","volume":"89","author":"BJ Barnett","year":"2007","unstructured":"Barnett, B.J., Mahul, O.: Weather index insurance for agriculture and rural areas in lower-income countries. Am. J. Agr. Econ. 89(5), 1241\u20131247 (2007)","journal-title":"Am. J. Agr. Econ."},{"key":"53_CR24","unstructured":"European Space Agency. Sentinel-2 User Handbook. ESA (2015)"},{"key":"53_CR25","unstructured":"Colwell, H., Carneggie, D., Croxton, R., Manzer, F., Simonett, D., Steiner, D.: Applications of remote sensing in agriculture and forestry. Applications of remote sensing in agriculture and forestry (1970)"},{"key":"53_CR26","volume-title":"Applications of Remote Sensing in Agriculture","author":"MD Steven","year":"2013","unstructured":"Steven, M.D., Clark, J.A.: Applications of Remote Sensing in Agriculture. Elsevier, Amsterdam (2013)"},{"key":"53_CR27","first-page":"848","volume":"108","author":"RN Sahoo","year":"2015","unstructured":"Sahoo, R.N., Ray, S.S., Manjunath, K.R.: Hyperspectral remote sensing of agriculture. Curr. Sci. 108, 848\u2013859 (2015)","journal-title":"Curr. Sci."},{"key":"53_CR28","unstructured":"Shanmugapriya, P., Rathika, S., Ramesh, T., Janaki, P.: Applications of remote sensing in agriculture. A review. Int. J. Curr. Microbiol. Appl. Sci. 8, 2270\u20132283 (2019)"},{"key":"53_CR29","doi-asserted-by":"publisher","first-page":"111402","DOI":"10.1016\/j.rse.2019.111402","volume":"236","author":"M Weiss","year":"2020","unstructured":"Weiss, M., Jacob, F., Duveiller, G.: Remote sensing for agricultural applications: a meta-review. Remote Sens. Environ. 236, 111402 (2020)","journal-title":"Remote Sens. Environ."},{"issue":"4","key":"53_CR30","first-page":"420","volume":"10","author":"X Zhang","year":"2005","unstructured":"Zhang, X., Zhang, B., Wei, Z., Chen, Z.C., Zheng, L.F.: Study on spectral indices of MODIS for wheat growth monitoring. J. Image Graph. 10(4), 420\u2013424 (2005)","journal-title":"J. Image Graph."},{"issue":"1\u20133","key":"53_CR31","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1007\/s10661-005-9006-7","volume":"119","author":"PY Chen","year":"2006","unstructured":"Chen, P.Y., Fedosejevs, G., Tiscareno-Lopez, M., Arnold, J.G.: Assessment of MODIS-EVI, MODIS-NDVI and VEGETATION-NDVI composite data using agricultural measurements: an example at corn fields in western Mexico. Environ. Monit. Assess. 119(1\u20133), 69\u201382 (2006). https:\/\/doi.org\/10.1007\/s10661-005-9006-7","journal-title":"Environ. Monit. Assess."},{"key":"53_CR32","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1016\/j.agrformet.2014.06.007","volume":"197","author":"NT Son","year":"2014","unstructured":"Son, N.T., Chen, C.F., Chen, C.R., Minh, V.Q., Trung, N.H.: A comparative analysis of multitemporal MODIS EVI and NDVI data for large-scale rice yield estimation. Agric. For. Meteorol. 197, 52\u201364 (2014)","journal-title":"Agric. For. Meteorol."},{"key":"53_CR33","doi-asserted-by":"crossref","unstructured":"Lu, J., Carbone, G.J., Gao, P.: Mapping the agricultural drought based on the long-term AVHRR NDVI and North American Regional Reanalysis (NARR) in the United States, 1981\u20132013. Appl. Geogr. 104, 10\u201320 (2019)","DOI":"10.1016\/j.apgeog.2019.01.005"},{"key":"53_CR34","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1016\/j.jaridenv.2019.01.019","volume":"164","author":"L Nanzad","year":"2019","unstructured":"Nanzad, L., Zhang, J., Tuvdendorj, B., Nabil, M., Zhang, S., Bai, Y.: NDVI anomaly for drought monitoring and its correlation with climate factors over Mongolia from 2000 to 2016. J. Arid Environ. 164, 69\u201377 (2019)","journal-title":"J. Arid Environ."},{"issue":"4","key":"53_CR35","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1080\/02757259409532250","volume":"10","author":"C Leprieur","year":"1994","unstructured":"Leprieur, C., Verstraete, M.M., Pinty, B.: Evaluation of the performance of various vegetation indices to retrieve vegetation cover from AVHRR data. Remote Sens. Rev. 10(4), 265\u2013284 (1994)","journal-title":"Remote Sens. Rev."},{"issue":"4","key":"53_CR36","doi-asserted-by":"publisher","first-page":"901","DOI":"10.1175\/WCAS-D-19-0014.1","volume":"11","author":"CG Turvey","year":"2019","unstructured":"Turvey, C.G., Shee, A., Marr, A.: Addressing fractional dimensionality in the application of weather index insurance and climate risk financing in agricultural development: a dynamic triggering approach. Weather Climate Soc. 11(4), 901\u2013915 (2019)","journal-title":"Weather Climate Soc."},{"issue":"4","key":"53_CR37","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1111\/rmir.12133","volume":"22","author":"TK Jensen","year":"2019","unstructured":"Jensen, T.K., Johnson, R.R., McNamara, M.J.: Funding conditions and insurance stock returns: do insurance stocks really benefit from rising interest rate regimes? Risk Manag. Insur. Rev. 22(4), 367\u2013391 (2019)","journal-title":"Risk Manag. Insur. Rev."},{"issue":"1","key":"53_CR38","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1108\/AFR-12-2014-0044","volume":"75","author":"RD Bacchini","year":"2015","unstructured":"Bacchini, R.D., Miguez, D.F.: Agricultural risk management using NDVI pasture index-based insurance for livestock producers in south west Buenos Aires province. Agric. Financ. Rev. 75(1), 77\u201391 (2015)","journal-title":"Agric. Financ. Rev."},{"key":"53_CR39","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1016\/j.compag.2018.07.021","volume":"152","author":"A Haghverdi","year":"2018","unstructured":"Haghverdi, A., Washington-Allen, R.A., Leib, B.G.: Prediction of cotton lint yield from phenology of crop indices using artificial neural networks. Comput. Electron. Agric. 152, 186\u2013197 (2018)","journal-title":"Comput. Electron. Agric."},{"key":"53_CR40","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.rse.2018.10.006","volume":"219","author":"F Zambrano","year":"2018","unstructured":"Zambrano, F., Vrieling, A., Nelson, A., Meroni, M., Tadesse, T.: Prediction of drought-induced reduction of agricultural productivity in Chile from MODIS, rainfall estimates, and climate oscillation indices. Remote Sens. Environ. 219, 15\u201330 (2018)","journal-title":"Remote Sens. Environ."},{"key":"53_CR41","doi-asserted-by":"publisher","first-page":"406","DOI":"10.1016\/j.compag.2016.06.019","volume":"127","author":"J Zhou","year":"2016","unstructured":"Zhou, J., Pavek, M.J., Shelton, S.C., Holden, Z.J., Sankaran, S.: Aerial multispectral imaging for crop hail damage assessment in pota-to. Comput. Electron. Agric. 127, 406\u2013412 (2016)","journal-title":"Comput. Electron. Agric."},{"issue":"1","key":"53_CR42","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(1), 137\u2013156 (2016)","journal-title":"Eur. J. Remote Sens."}],"container-title":["Lecture Notes in Computer Science","Computational Science and Its Applications \u2013 ICCSA 2020"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-58811-3_53","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,23]],"date-time":"2021-04-23T13:07:17Z","timestamp":1619183237000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-58811-3_53"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030588106","9783030588113"],"references-count":42,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-58811-3_53","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"29 September 2020","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":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 July 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 July 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccsa2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.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":"Cyber chair 4","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1450","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":"32","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":"32% - 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":"6","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Conference was held virtually due to COVID-19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}