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Crop Monitoring Strategy Based on Remote Sensing Data (Sentinel-2 and Planet), Study Case in a Rice Field after Applying Glycinebetaine

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Crop Monitoring Strategy Based on Remote Sensing Data (Sentinel-2 and Planet), Study Case in a Rice Field after Applying Glycinebetaine

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dc.contributor.author San Bautista Primo, Alberto es_ES
dc.contributor.author Fita-Silvestre, David es_ES
dc.contributor.author Franch, Belen es_ES
dc.contributor.author Castiñeira Ibáñez, Sergio es_ES
dc.contributor.author Arizo, Patricia es_ES
dc.contributor.author Sánchez-Torres, María José es_ES
dc.contributor.author Becker-Reshef, Inbal es_ES
dc.contributor.author Uris Martínez, Antonio es_ES
dc.contributor.author Rubio Michavila, Constanza es_ES
dc.date.accessioned 2023-10-25T18:02:30Z
dc.date.available 2023-10-25T18:02:30Z
dc.date.issued 2022-03 es_ES
dc.identifier.uri http://hdl.handle.net/10251/198811
dc.description.abstract [EN] World agriculture is facing a great challenge since it is necessary to find a sustainable way to increase food production. Current trends in advancing the agriculture sector are based on leveraging remote sensing technology and the use of biostimulants. However, the fficient implementation of both of these on a commercial scale for the purposes of productivity improvement remains a challenge. Thus, by proposing a crop monitoring strategy based on remote sensing data, this paper aims to verify and anticipate the impact of applying a Glycinebetaine biostimulant (GB) on the final yield. The study was carried out in a rice-producing area in Eastern Spain (Valencia) in 2021. GB was applied by drone 33 days after sowing (tillering phase). Phenology was monitored and crop production parameters were determined. Regarding satellite data, Sentinel-2 cloud-free images were obtained from sowing to harvest, using the bands at 10 m. Planet data were used to evaluate the results from Sentinel-2. The results show that GB applied 33 days after sowing improves both crop productive parameters and commercial yield (13.06% increase). The design of the proposed monitoring strategy was based on the dynamics and correlations between the visible (green and red) and NIR bands. The analysis showed differences when comparing the GB and control areas, and permitted the determination of the moment in which the effect of GB on yield (tillering and maturity) may be greater. In addition, an index was constructed to verify the crop monitoring strategy, its mathematical expression being: NCMI = (NIR ¿ (red + green))/(NIR + red + green). Compared with the other VIs (NDVI, GNDVI and EVI2), the NCMI presents a greater sensitivity to changes in the green, red and NIR bands, a lower saturation phenomenon than NDVI and a better monitoring of rice phenology and management than GNDVI and EVI2. These results were evaluated with Planet images, obtaining similar results. In conclusion, in this study, we confirm the improvement in rice crop productivity by improving sustainable plant nutrition with the use of biostimulants and by increasing the components that define crop yield (productive tillers, spikelets and grains). Additionally, crop monitoring using remote sensing technology permits the anticipation and understanding of the productive behavior. es_ES
dc.description.sponsorship This research was funded by the program Generacio Talent of the Generalitat Valenciana (CIDEGENT/2018/009) and the Cooperative Agreement NASA Harvest, grant number 80NSSC18M0039. es_ES
dc.language Inglés es_ES
dc.publisher MDPI es_ES
dc.relation.ispartof Agronomy es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Agronomy es_ES
dc.subject Remote sensing es_ES
dc.subject Glycinebetaine es_ES
dc.subject Sentinel-2 es_ES
dc.subject Yield es_ES
dc.subject Crop monitoring es_ES
dc.subject Vegetation index es_ES
dc.subject Rice es_ES
dc.subject.classification PRODUCCION VEGETAL es_ES
dc.subject.classification FISICA APLICADA es_ES
dc.title Crop Monitoring Strategy Based on Remote Sensing Data (Sentinel-2 and Planet), Study Case in a Rice Field after Applying Glycinebetaine es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.3390/agronomy12030708 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/GVA//CIDEGENT%2F2018%2F009//Generacio Talent of the Generalitat Valenciana/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/NASA//80NSSC18M0039/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Escuela Técnica Superior de Ingenieros Industriales - Escola Tècnica Superior d'Enginyers Industrials es_ES
dc.contributor.affiliation Universitat Politècnica de València. Escuela Técnica Superior de Ingeniería Agronómica y del Medio Natural - Escola Tècnica Superior d'Enginyeria Agronòmica i del Medi Natural es_ES
dc.contributor.affiliation Universitat Politècnica de València. Escuela Técnica Superior de Ingenieros de Telecomunicación - Escola Tècnica Superior d'Enginyers de Telecomunicació es_ES
dc.description.bibliographicCitation San Bautista Primo, A.; Fita-Silvestre, D.; Franch, B.; Castiñeira Ibáñez, S.; Arizo, P.; Sánchez-Torres, MJ.; Becker-Reshef, I.... (2022). Crop Monitoring Strategy Based on Remote Sensing Data (Sentinel-2 and Planet), Study Case in a Rice Field after Applying Glycinebetaine. Agronomy. 12(3):1-23. https://doi.org/10.3390/agronomy12030708 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.3390/agronomy12030708 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 23 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 12 es_ES
dc.description.issue 3 es_ES
dc.identifier.eissn 2073-4395 es_ES
dc.relation.pasarela S\460238 es_ES
dc.contributor.funder Generalitat Valenciana es_ES
dc.contributor.funder Universitat Politècnica de València es_ES
dc.contributor.funder National Aeronautics and Space Administration, EEUU es_ES
upv.costeAPC 1800 es_ES


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