Monitoring Corn Nitrogen Concentration from Radar (C-SAR), Optical, and Sensor Satellite Data Fusion
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 fu...
| Main Authors: | , , , , , , , |
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| Format: | info:ar-repo/semantics/artículo |
| Language: | Inglés |
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Multidisciplinary Digital Publishing Institute (MDPI)
2023
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.12123/15233 https://www.mdpi.com/2072-4292/15/3/824 https://doi.org/10.3390/rs15030824 |
| _version_ | 1855037278374592512 |
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| author | Lapaz Olveira, Adrián Sainz Rozas, Hernan Rene Castro Franco, Mauricio Carciochi, Walter Nieto, Luciana Balzarini, Mónica Ciampitti, Ignacio Reussi Calvo, Nahuel Ignacio |
| author_browse | Balzarini, Mónica Carciochi, Walter Castro Franco, Mauricio Ciampitti, Ignacio Lapaz Olveira, Adrián Nieto, Luciana Reussi Calvo, Nahuel Ignacio Sainz Rozas, Hernan Rene |
| author_facet | Lapaz Olveira, Adrián Sainz Rozas, Hernan Rene Castro Franco, Mauricio Carciochi, Walter Nieto, Luciana Balzarini, Mónica Ciampitti, Ignacio Reussi Calvo, Nahuel Ignacio |
| author_sort | Lapaz Olveira, Adrián |
| collection | INTA Digital |
| description | 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. |
| format | info:ar-repo/semantics/artículo |
| id | INTA15233 |
| institution | Instituto Nacional de Tecnología Agropecuaria (INTA -Argentina) |
| language | Inglés |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | Multidisciplinary Digital Publishing Institute (MDPI) |
| publisherStr | Multidisciplinary Digital Publishing Institute (MDPI) |
| record_format | dspace |
| spelling | INTA152332023-09-18T10:30:39Z Monitoring Corn Nitrogen Concentration from Radar (C-SAR), Optical, and Sensor Satellite Data Fusion Lapaz Olveira, Adrián Sainz Rozas, Hernan Rene Castro Franco, Mauricio Carciochi, Walter Nieto, Luciana Balzarini, Mónica Ciampitti, Ignacio Reussi Calvo, Nahuel Ignacio Vigilancia Nitrógeno Maíz Teledetección Satélites Sensores Monitoring Nitrogen Maize Remote Sensing Satellites Sensors 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. EEA Balcarce Fil: Lapaz Olveira, Adrián. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina. Fil: Lapaz Olveira, Adrián. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Fil: Saínz Rozas, Hernán. Agencia Nacional de Promoción Científica y Tecnológica; Argentina. 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. Fil: Castro Franco, Mauricio. Universidad de los Llanos. Facultad de Ciencias Agropecuarias y Recursos Naturales; Colombia. Fil: Carciochi, Walter. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina. Fil: Carciochi, Walter. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Fil: Nieto, Luciana. Kansas State University. Department of Agronomy; Estados Unidos. Fil: Balzarini, Mónica. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Fil: Balzarini, Mónica. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias; Argentina. Fil: Balzarini, Mónica. Unidad de Fitopatología Y Modelización Agrícola; Argentina. Fil: Ciampitti, Ignacio. Kansas State University. Department of Agronomy; Estados Unidos. Fil: Reussi Calvo, Nahuel. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina. Fil: Reussi Calvo, Nahuel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. 2023-09-18T10:26:59Z 2023-09-18T10:26:59Z 2023-02 info:ar-repo/semantics/artículo info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://hdl.handle.net/20.500.12123/15233 https://www.mdpi.com/2072-4292/15/3/824 2072-4292 https://doi.org/10.3390/rs15030824 eng info:eu-repograntAgreement/INTA/2019-PE-E9-I177-001, Desarrollo y aplicación de tecnologías de mecanización, precisión y digitalización de la Agricultura info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) Multidisciplinary Digital Publishing Institute (MDPI) Remote Sensing 15 (3) : 824 (2023) |
| spellingShingle | Vigilancia Nitrógeno Maíz Teledetección Satélites Sensores Monitoring Nitrogen Maize Remote Sensing Satellites Sensors Lapaz Olveira, Adrián Sainz Rozas, Hernan Rene Castro Franco, Mauricio Carciochi, Walter Nieto, Luciana Balzarini, Mónica Ciampitti, Ignacio Reussi Calvo, Nahuel Ignacio Monitoring Corn Nitrogen Concentration from Radar (C-SAR), Optical, and Sensor Satellite Data Fusion |
| title | Monitoring Corn Nitrogen Concentration from Radar (C-SAR), Optical, and Sensor Satellite Data Fusion |
| title_full | Monitoring Corn Nitrogen Concentration from Radar (C-SAR), Optical, and Sensor Satellite Data Fusion |
| title_fullStr | Monitoring Corn Nitrogen Concentration from Radar (C-SAR), Optical, and Sensor Satellite Data Fusion |
| title_full_unstemmed | Monitoring Corn Nitrogen Concentration from Radar (C-SAR), Optical, and Sensor Satellite Data Fusion |
| title_short | Monitoring Corn Nitrogen Concentration from Radar (C-SAR), Optical, and Sensor Satellite Data Fusion |
| title_sort | monitoring corn nitrogen concentration from radar c sar optical and sensor satellite data fusion |
| topic | Vigilancia Nitrógeno Maíz Teledetección Satélites Sensores Monitoring Nitrogen Maize Remote Sensing Satellites Sensors |
| url | http://hdl.handle.net/20.500.12123/15233 https://www.mdpi.com/2072-4292/15/3/824 https://doi.org/10.3390/rs15030824 |
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