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Corn (Zea mays L.) nitrogen (N) management requires monitoring plant N concentration (Nc) with remote sensing tools to improve N use, increasing both profitability and sustainability. This work aims to predict the corn Nc during the growing cycle from Sentinel-2 and Sentinel-1 (C-SAR) sensor data fusion. Eleven experiments using five fertilizer N rates (0, 60, 120, 180, and 240 kg N ha−1) were conducted in the Pampas region of Argentina. Plant samples
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dc.contributor.author | Lapaz Olveira, Adrián | |
dc.contributor.author | Sainz Rozas, Hernan Rene | |
dc.contributor.author | Castro Franco, Mauricio | |
dc.contributor.author | Carciochi, Walter | |
dc.contributor.author | Nieto, Luciana | |
dc.contributor.author | Balzarini, Mónica | |
dc.contributor.author | Ciampitti, Ignacio | |
dc.contributor.author | Reussi Calvo, Nahuel Ignacio | |
dc.date.accessioned | 2023-09-18T10:26:59Z | |
dc.date.available | 2023-09-18T10:26:59Z | |
dc.date.issued | 2023-02 | |
dc.identifier.issn | 2072-4292 | |
dc.identifier.other | https://doi.org/10.3390/rs15030824 | |
dc.identifier.uri | http://hdl.handle.net/20.500.12123/15233 | |
dc.identifier.uri | https://www.mdpi.com/2072-4292/15/3/824 | |
dc.description.abstract | Corn (Zea mays L.) nitrogen (N) management requires monitoring plant N concentration (Nc) with remote sensing tools to improve N use, increasing both profitability and sustainability. This work aims to predict the corn Nc during the growing cycle from Sentinel-2 and Sentinel-1 (C-SAR) sensor data fusion. Eleven experiments using five fertilizer N rates (0, 60, 120, 180, and 240 kg N ha−1) were conducted in the Pampas region of Argentina. Plant samples were collected at four stages of vegetative and reproductive periods. Vegetation indices were calculated with new combinations of spectral bands, C-SAR backscatters, and sensor data fusion derived from Sentinel-1 and Sentinel-2. Predictive models of Nc with the best fit (R2 = 0.91) were calibrated with spectral band combinations and sensor data fusion in six experiments. During validation of the models in five experiments, sensor data fusion predicted corn Nc with lower error (MAPE: 14%, RMSE: 0.31 %Nc) than spectral band combination (MAPE: 20%, RMSE: 0.44 %Nc). The red-edge (704, 740, 740 nm), short-wave infrared (1375 nm) bands, and VV backscatter were all necessary to monitor corn Nc. Thus, satellite remote sensing via sensor data fusion is a critical data source for predicting changes in plant N status. | eng |
dc.language.iso | eng | es_AR |
dc.publisher | Multidisciplinary Digital Publishing Institute (MDPI) | es_AR |
dc.relation | info:eu-repograntAgreement/INTA/2019-PE-E9-I177-001, Desarrollo y aplicación de tecnologías de mecanización, precisión y digitalización de la Agricultura | es_AR |
dc.rights | info:eu-repo/semantics/openAccess | es_AR |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/4.0/ | es_AR |
dc.source | Remote Sensing 15 (3) : 824 (2023) | es_AR |
dc.subject | Vigilancia | es_AR |
dc.subject | Monitoring | eng |
dc.subject | Nitrógeno | es_AR |
dc.subject | Nitrogen | eng |
dc.subject | Maíz | es_AR |
dc.subject | Maize | eng |
dc.subject | Teledetección | es_AR |
dc.subject | Remote Sensing | eng |
dc.subject | Satélites | es_AR |
dc.subject | Satellites | eng |
dc.subject | Sensores | es_AR |
dc.subject | Sensors | eng |
dc.title | Monitoring Corn Nitrogen Concentration from Radar (C-SAR), Optical, and Sensor Satellite Data Fusion | es_AR |
dc.type | info:ar-repo/semantics/artículo | es_AR |
dc.type | info:eu-repo/semantics/article | es_AR |
dc.type | info:eu-repo/semantics/publishedVersion | es_AR |
dc.rights.license | Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) | es_AR |
dc.description.origen | EEA Balcarce | es_AR |
dc.description.fil | Fil: Lapaz Olveira, Adrián. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina. | es_AR |
dc.description.fil | Fil: Lapaz Olveira, Adrián. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. | es_AR |
dc.description.fil | Fil: Saínz Rozas, Hernán. Agencia Nacional de Promoción Científica y Tecnológica; Argentina. | es_AR |
dc.description.fil | Fil: Saínz Rozas, Hernán. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto de Innovación para la Producción Agropecuaria y el Desarrollo Sostenible; Argentina. | es_AR |
dc.description.fil | Fil: Castro Franco, Mauricio. Universidad de los Llanos. Facultad de Ciencias Agropecuarias y Recursos Naturales; Colombia. | es_AR |
dc.description.fil | Fil: Carciochi, Walter. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina. | es_AR |
dc.description.fil | Fil: Carciochi, Walter. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. | es_AR |
dc.description.fil | Fil: Nieto, Luciana. Kansas State University. Department of Agronomy; Estados Unidos. | es_AR |
dc.description.fil | Fil: Balzarini, Mónica. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. | es_AR |
dc.description.fil | Fil: Balzarini, Mónica. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias; Argentina. | es_AR |
dc.description.fil | Fil: Balzarini, Mónica. Unidad de Fitopatología Y Modelización Agrícola; Argentina. | es_AR |
dc.description.fil | Fil: Ciampitti, Ignacio. Kansas State University. Department of Agronomy; Estados Unidos. | es_AR |
dc.description.fil | Fil: Reussi Calvo, Nahuel. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina. | es_AR |
dc.description.fil | Fil: Reussi Calvo, Nahuel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. | es_AR |
dc.subtype | cientifico |
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