Spatio-Temporal Generalization of VIS-NIR-SWIR Spectral Models for Nitrogen Prediction in Sugarcane Leaves
Nitrogen fertilization is a challenging task that usually requires intensive use of resources, such as fertilizers, management and water. This study explored the potential of VIS-NIR-SWIR remote sensing for quantifying leaf nitrogen content (LNC) in sugarcane from different regions and vegetative st...
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2024-11-01
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| author | Carlos Augusto Alves Cardoso Silva Rodnei Rizzo Marcelo Andrade da Silva Matheus Luís Caron Peterson Ricardo Fiorio |
| author_facet | Carlos Augusto Alves Cardoso Silva Rodnei Rizzo Marcelo Andrade da Silva Matheus Luís Caron Peterson Ricardo Fiorio |
| author_sort | Carlos Augusto Alves Cardoso Silva |
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| description | Nitrogen fertilization is a challenging task that usually requires intensive use of resources, such as fertilizers, management and water. This study explored the potential of VIS-NIR-SWIR remote sensing for quantifying leaf nitrogen content (LNC) in sugarcane from different regions and vegetative stages. Conducted in three regions of São Paulo, Brazil (Jaú, Piracicaba and Santa Maria), the research involved three experiments, one per location. The spectral data were obtained at 140, 170, 200, 230 and 260 days after cutting (DAC). From the hyperspectral data, clustering analysis was performed to identify the patterns between the spectral bands for each region where the spectral readings were made, using the Partitioning Around Medoids (PAM) algorithm. Then, the LNC values were used to generate spectral models using Partial Least Squares Regression (PLSR). Subsequently, the generalization of the models was tested with the leave-one-date-out cross-validation (LOOCV) technique. The results showed that although the variation in leaf N was small, the sensor demonstrated the ability to detect these variations. Furthermore, it was possible to determine the influence of N concentrations on the leaf spectra and how this impacted cluster formation. It was observed that the greater the average variation in N content in each cluster, the better defined and denser the groups formed were. The best time to quantify N concentrations was at 140 DAC (R<sup>2</sup> = 0.90 and RMSE = 0.74 g kg<sup>−1</sup>). From LOOCV, the areas with sandier soil texture presented a lower model performance compared to areas with clayey soil, with R<sup>2</sup> < 0.54. The spatial generalization of the models recorded the best performance at 140 DAC (R<sup>2</sup> = 0.69, RMSE = 1.18 g kg<sup>−1</sup> and dr = 0.61), decreasing in accuracy at the crop-maturation stage (260 DAC), R<sup>2</sup> of 0.05, RMSE of 1.73 g kg<sup>−1</sup> and dr of 0.38. Although the technique needs further studies to be improved, our results demonstrated potential, which tends to provide support and benefits for the quantification of nutrients in sugarcane in the long term. |
| format | Article |
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| institution | OA Journals |
| issn | 2072-4292 |
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| publishDate | 2024-11-01 |
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| spelling | doaj-art-466ba3a84afc44b689ea1d8beec953f32025-08-20T01:53:57ZengMDPI AGRemote Sensing2072-42922024-11-011622425010.3390/rs16224250Spatio-Temporal Generalization of VIS-NIR-SWIR Spectral Models for Nitrogen Prediction in Sugarcane LeavesCarlos Augusto Alves Cardoso Silva0Rodnei Rizzo1Marcelo Andrade da Silva2Matheus Luís Caron3Peterson Ricardo Fiorio4Department of Biosystems Engineering, “Luiz de Queiroz” College of Agriculture, University of São Paulo, Piracicaba 13418900, SP, BrazilDepartment of Exact Science, “Luiz de Queiroz” College of Agriculture, University of São Paulo, Piracicaba 13418900, SP, BrazilDepartment of Exact Science, “Luiz de Queiroz” College of Agriculture, University of São Paulo, Piracicaba 13418900, SP, BrazilDepartment of Biosystems Engineering, “Luiz de Queiroz” College of Agriculture, University of São Paulo, Piracicaba 13418900, SP, BrazilDepartment of Biosystems Engineering, “Luiz de Queiroz” College of Agriculture, University of São Paulo, Piracicaba 13418900, SP, BrazilNitrogen fertilization is a challenging task that usually requires intensive use of resources, such as fertilizers, management and water. This study explored the potential of VIS-NIR-SWIR remote sensing for quantifying leaf nitrogen content (LNC) in sugarcane from different regions and vegetative stages. Conducted in three regions of São Paulo, Brazil (Jaú, Piracicaba and Santa Maria), the research involved three experiments, one per location. The spectral data were obtained at 140, 170, 200, 230 and 260 days after cutting (DAC). From the hyperspectral data, clustering analysis was performed to identify the patterns between the spectral bands for each region where the spectral readings were made, using the Partitioning Around Medoids (PAM) algorithm. Then, the LNC values were used to generate spectral models using Partial Least Squares Regression (PLSR). Subsequently, the generalization of the models was tested with the leave-one-date-out cross-validation (LOOCV) technique. The results showed that although the variation in leaf N was small, the sensor demonstrated the ability to detect these variations. Furthermore, it was possible to determine the influence of N concentrations on the leaf spectra and how this impacted cluster formation. It was observed that the greater the average variation in N content in each cluster, the better defined and denser the groups formed were. The best time to quantify N concentrations was at 140 DAC (R<sup>2</sup> = 0.90 and RMSE = 0.74 g kg<sup>−1</sup>). From LOOCV, the areas with sandier soil texture presented a lower model performance compared to areas with clayey soil, with R<sup>2</sup> < 0.54. The spatial generalization of the models recorded the best performance at 140 DAC (R<sup>2</sup> = 0.69, RMSE = 1.18 g kg<sup>−1</sup> and dr = 0.61), decreasing in accuracy at the crop-maturation stage (260 DAC), R<sup>2</sup> of 0.05, RMSE of 1.73 g kg<sup>−1</sup> and dr of 0.38. Although the technique needs further studies to be improved, our results demonstrated potential, which tends to provide support and benefits for the quantification of nutrients in sugarcane in the long term.https://www.mdpi.com/2072-4292/16/22/4250digital agriculturePLS regressionnitrogen sensingmanagement |
| spellingShingle | Carlos Augusto Alves Cardoso Silva Rodnei Rizzo Marcelo Andrade da Silva Matheus Luís Caron Peterson Ricardo Fiorio Spatio-Temporal Generalization of VIS-NIR-SWIR Spectral Models for Nitrogen Prediction in Sugarcane Leaves Remote Sensing digital agriculture PLS regression nitrogen sensing management |
| title | Spatio-Temporal Generalization of VIS-NIR-SWIR Spectral Models for Nitrogen Prediction in Sugarcane Leaves |
| title_full | Spatio-Temporal Generalization of VIS-NIR-SWIR Spectral Models for Nitrogen Prediction in Sugarcane Leaves |
| title_fullStr | Spatio-Temporal Generalization of VIS-NIR-SWIR Spectral Models for Nitrogen Prediction in Sugarcane Leaves |
| title_full_unstemmed | Spatio-Temporal Generalization of VIS-NIR-SWIR Spectral Models for Nitrogen Prediction in Sugarcane Leaves |
| title_short | Spatio-Temporal Generalization of VIS-NIR-SWIR Spectral Models for Nitrogen Prediction in Sugarcane Leaves |
| title_sort | spatio temporal generalization of vis nir swir spectral models for nitrogen prediction in sugarcane leaves |
| topic | digital agriculture PLS regression nitrogen sensing management |
| url | https://www.mdpi.com/2072-4292/16/22/4250 |
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