{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T00:53:01Z","timestamp":1742950381070,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":19,"publisher":"Springer Singapore","isbn-type":[{"type":"print","value":"9789811566332"},{"type":"electronic","value":"9789811566349"}],"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"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-981-15-6634-9_40","type":"book-chapter","created":{"date-parts":[[2020,7,17]],"date-time":"2020-07-17T14:27:09Z","timestamp":1594996029000},"page":"439-448","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Agricultural Field Analysis Using Satellite Surface Reflectance Data and Machine Learning Technique"],"prefix":"10.1007","author":[{"given":"Medha","family":"Wyawahare","sequence":"first","affiliation":[]},{"given":"Pranesh","family":"Kulkarni","sequence":"additional","affiliation":[]},{"given":"Aditya","family":"Kulkarni","sequence":"additional","affiliation":[]},{"given":"Ankit","family":"Lad","sequence":"additional","affiliation":[]},{"given":"Jayant","family":"Majji","sequence":"additional","affiliation":[]},{"given":"Aayush","family":"Mehta","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,7,18]]},"reference":[{"key":"40_CR1","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1023\/A:1009928431735","volume":"2","author":"C Yang","year":"2000","unstructured":"Yang, C., Anderson, G.L.: mapping grain sorghum yield variability using airborne digital videography. Precis. Agric. 2, 7\u201323 (2000). \nhttps:\/\/doi.org\/10.1023\/A:1009928431735","journal-title":"Precis. Agric."},{"issue":"1","key":"40_CR2","doi-asserted-by":"publisher","first-page":"80","DOI":"10.17521\/cjpe.2015.0267","volume":"40","author":"L Chang","year":"2016","unstructured":"Chang, L., et al.: A review of plant spectral reflectance response to water physiological changes. Chin. J. Plant Ecol. 40(1), 80\u201391 (2016). \nhttps:\/\/doi.org\/10.17521\/cjpe.2015.0267","journal-title":"Chin. J. Plant Ecol."},{"key":"40_CR3","doi-asserted-by":"publisher","unstructured":"\u00dcstuner, M., Sanli, F.B., Abdikan, S., Esetlili, M.T., Kurucu, Y.: Crop type classification using vegetation indices of rapideye imagery. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. XL-7, 195\u2013198 (2014). \nhttps:\/\/doi.org\/10.5194\/isprsarchives-xl-7-195-2014","DOI":"10.5194\/isprsarchives-xl-7-195-2014"},{"key":"40_CR4","doi-asserted-by":"publisher","unstructured":"Burchfield, E., Nay, J.J., Gilligan, J.: Application of machine learning to the prediction of vegetation health. Int. Arch. Photogramm. Remote Sensing Spatial Inf. Sci. XLI-B2 (2016). \nhttps:\/\/doi.org\/10.5194\/isprsarchives-xli-b2-465-2016","DOI":"10.5194\/isprsarchives-xli-b2-465-2016"},{"key":"40_CR5","doi-asserted-by":"publisher","unstructured":"Han, H., Bai, J., Yan, J., Yang, H., Ma, G.: A combined drought monitoring index based on multi-sensor remote sensing data and machine learning. Geocarto Int. 1\u201316 (2019). \nhttps:\/\/doi.org\/10.1080\/10106049.2019.1633423","DOI":"10.1080\/10106049.2019.1633423"},{"key":"40_CR6","doi-asserted-by":"publisher","unstructured":"Alexander, C.: Normalized difference spectral indices and urban land cover as indicators of land surface temperature (LST). Int. J. Appl. Earth Observ. Geoinf. 86, 102013 (2020). \nhttps:\/\/doi.org\/10.1016\/j.jag.2019.102013\n\n, ISSN 0303-2434","DOI":"10.1016\/j.jag.2019.102013"},{"key":"40_CR7","unstructured":"Veeraswamy, G., Nagaraju, A., Balaji, E., Sridhar, Y.: land use land cover studies of using remotesensing and gis a case study in gudur area nellore district, andhrapradesh. Int. J. Res. 4 (2017)"},{"issue":"1","key":"40_CR8","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1080\/24751839.2019.1694765","volume":"4","author":"N Kobayashi","year":"2020","unstructured":"Kobayashi, N., Tani, H., Wang, X., Sonobe, R.: Crop classification using spectral indices derived from Sentinel-2A imagery. J. Inf. Telecommun. 4(1), 67\u201390 (2020). \nhttps:\/\/doi.org\/10.1080\/24751839.2019.1694765","journal-title":"J. Inf. Telecommun."},{"key":"40_CR9","doi-asserted-by":"publisher","unstructured":"Romero, M., Luo, Y., Su, B., Fuentes, S.: Vineyard water status estimation using multispectral imagery from an UAV platform and machine learning algorithms for irrigation scheduling management, Comput. Electron. Agric. 147, 109\u2013117 (2018). \nhttps:\/\/doi.org\/10.1016\/j.compag.2018.02.013\n\n, ISSN 0168-1699","DOI":"10.1016\/j.compag.2018.02.013"},{"key":"40_CR10","doi-asserted-by":"publisher","unstructured":"Schwalbert, R.A., Amado, T., Corassa, G., Pott, L.P., Prasad, P.V., Ciampitti, I.A.: Satellite-based soybean yield forecast: integrating machine learning and weather data for improving crop yield prediction in southern Brazil. Agric. Forest Meteorol. 284, 107886 (2020). \nhttps:\/\/doi.org\/10.1016\/j.agrformet.2019.107886\n\n, ISSN 0168-1923","DOI":"10.1016\/j.agrformet.2019.107886"},{"key":"40_CR11","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1007\/978-981-32-9915-3_3","volume-title":"Manual Digit. Earth","author":"W Fu","year":"2020","unstructured":"Fu, W., Ma, J., Chen, P., Chen, F.: Remote sensing satellites for digital earth. In: Guo, H., Goodchild, M.F., Annoni, A. (eds.) Manual Digit. Earth, pp. 55\u2013123. Springer, Singapore (2020). \nhttps:\/\/doi.org\/10.1007\/978-981-32-9915-3_3"},{"key":"40_CR12","doi-asserted-by":"publisher","unstructured":"Anyamba, A., Tucker, C.: Historical perspectives on AVHRR NDVI and vegetation drought monitoring. Remote Sensing of Drought: Innovative Monitoring Approaches (2012). \nhttps:\/\/doi.org\/10.1201\/b11863","DOI":"10.1201\/b11863"},{"key":"40_CR13","unstructured":"Brecht. Remote Sensing Indices (2018). \nhttps:\/\/medium.com\/regen-network\/remote-sensing-indices-389153e3d947"},{"key":"40_CR14","unstructured":"Jena, J., Misra, S., Tripathi, K.: Normalized Difference Vegetation Index (NDVI) and its role in Agriculture (2019)"},{"key":"40_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2017\/1353691","volume":"2017","author":"J Xue","year":"2017","unstructured":"Xue, J., Baofeng, S.: Significant remote sensing vegetation indices: a review of developments and applications. J. Sensors 2017, 1\u201317 (2017). \nhttps:\/\/doi.org\/10.1155\/2017\/1353691","journal-title":"J. Sensors"},{"key":"40_CR16","unstructured":"Google earth engine website. \nhttps:\/\/earthengine.google.com\/"},{"key":"40_CR17","doi-asserted-by":"publisher","unstructured":"Barsi, J.A., Schott, J.R., Hook, S.J., Raqueno, N.G., Markham, B.L., Radocinski, R.G.: Landsat-8 thermal infrared sensor (TIRS) vicarious radiometric calibration. Remote Sensing 6, 11607\u201311626 (2014). \nhttps:\/\/doi.org\/10.3390\/rs61111607","DOI":"10.3390\/rs61111607"},{"key":"40_CR18","doi-asserted-by":"publisher","unstructured":"Bao, W., Lianju, N., Yue, K.: Integration of unsupervised and supervised machine learning algorithms for credit risk assessment. Exp. Syst. Appl. 128, 301\u2013315 (2019). \nhttps:\/\/doi.org\/10.1016\/j.eswa.2019.02.033\n\n, ISSN 0957-4174","DOI":"10.1016\/j.eswa.2019.02.033"},{"key":"40_CR19","doi-asserted-by":"publisher","unstructured":"Li, Y., Wu, H.: A clustering method based on k-means algorithm. Phys. Proc. 1104\u20131109 (2012). \nhttps:\/\/doi.org\/10.1016\/j.phpro.2012.03.206","DOI":"10.1016\/j.phpro.2012.03.206"}],"container-title":["Communications in Computer and Information Science","Advances in Computing and Data Sciences"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-15-6634-9_40","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,7,17]],"date-time":"2020-07-17T14:42:50Z","timestamp":1594996970000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-981-15-6634-9_40"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9789811566332","9789811566349"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-981-15-6634-9_40","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"18 July 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICACDS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Advances in Computing and Data Sciences","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Valletta","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Malta","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":"24 April 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 April 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icacds2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icacds.com\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"354","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":"46","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":"0","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":"13% - 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":"3","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":"2","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)"}}]}}