Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/31416
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dc.contributor.authorSilva-Perez, Cristianen_UK
dc.contributor.authorMarino, Armandoen_UK
dc.contributor.authorCameron, Iainen_UK
dc.date.accessioned2020-07-10T00:02:53Z-
dc.date.available2020-07-10T00:02:53Z-
dc.date.issued2020-06en_UK
dc.identifier.other1993en_UK
dc.identifier.urihttp://hdl.handle.net/1893/31416-
dc.description.abstractThis paper presents the analysis and a methodology for monitoring asparagus crops from remote sensing observations in a tropical region, where the local climatological conditions allow farmers to grow two production cycles per year. We used the freely available dual-polarisation GRD data provided by the Sentinel-1 satellite, temperature from a ground station and ground truth from January to August of 2019 to perform the analysis. We showed how particularly the VH polarisation can be used for monitoring the canopy formation, density and the growth rate, revealing connections with temperature. We also present a multi-output machine learning regression algorithm trained on a rich spatio-temporal dataset in which each output estimates the number of asparagus stems that are present in each of the pre-defined crop phenological stages. We tested several scenarios that evaluated the importance of each input data source and feature, with results that showed that the methodology was able to retrieve the number of asparagus stems in each crop stage when using information about starting date and temperature as predictors with coefficients of determination (R2) between 0.84 and 0.86 and root mean squared error (RMSE) between 2.9 and 2.7. For the multitemporal SAR scenario, results showed a maximum R2 of 0.87 when using up to 5 images as input and an RMSE that maintains approximately the same values as the number of images increased. This suggests that for the conditions evaluated in this paper, the use of multitemporal SAR data only improved mildly the retrieval when the season start date and accumulated temperature are used to complement the backscatter.en_UK
dc.language.isoenen_UK
dc.publisherMDPI AGen_UK
dc.relationSilva-Perez C, Marino A & Cameron I (2020) Monitoring Agricultural Fields Using Sentinel-1 and Temperature Data in Peru: Case Study of Asparagus (Asparagus officinalis L.). Remote Sensing, 12 (12), Art. No.: 1993. https://doi.org/10.3390/rs12121993en_UK
dc.rightsCopyright 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).en_UK
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en_UK
dc.subjecttropical agricultural monitoringen_UK
dc.subjectcanopy development analysisen_UK
dc.subjectphenology retrievalen_UK
dc.subjectSentinel-1en_UK
dc.subjectmultitemporal SARen_UK
dc.subjectmulti-task machine learningen_UK
dc.titleMonitoring Agricultural Fields Using Sentinel-1 and Temperature Data in Peru: Case Study of Asparagus (Asparagus officinalis L.)en_UK
dc.typeJournal Articleen_UK
dc.identifier.doi10.3390/rs12121993en_UK
dc.citation.jtitleRemote Sensingen_UK
dc.citation.issn2072-4292en_UK
dc.citation.volume12en_UK
dc.citation.issue12en_UK
dc.citation.publicationstatusPublisheden_UK
dc.citation.peerreviewedRefereeden_UK
dc.type.statusVoR - Version of Recorden_UK
dc.citation.date21/06/2020en_UK
dc.contributor.affiliationBiological and Environmental Sciencesen_UK
dc.contributor.affiliationBiological and Environmental Sciencesen_UK
dc.contributor.affiliationEnvironment Systems LTDen_UK
dc.identifier.isiWOS:000554736600001en_UK
dc.identifier.scopusid2-s2.0-85086990315en_UK
dc.identifier.wtid1642874en_UK
dc.contributor.orcid0000-0002-6843-5022en_UK
dc.contributor.orcid0000-0002-4531-3102en_UK
dc.date.accepted2020-06-18en_UK
dc.date.filedepositdate2020-07-09en_UK
Appears in Collections:Biological and Environmental Sciences Journal Articles

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