Remote and Proximal Sensing-Derived Spectral Indices and Biophysical Variables for Spatial Variation Determination in Vineyards
Remote-sensing measurements are crucial for smart-farming applications, crop monitoring, and yield forecasting, especially in fields characterized by high heterogeneity. Therefore, in this study, Precision Viticulture (PV) methods using proximal- and remote-sensing technologies were exploited and co...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-04-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/11/4/741 |
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| author | Nicoleta Darra Emmanouil Psomiadis Aikaterini Kasimati Achilleas Anastasiou Evangelos Anastasiou Spyros Fountas |
| author_facet | Nicoleta Darra Emmanouil Psomiadis Aikaterini Kasimati Achilleas Anastasiou Evangelos Anastasiou Spyros Fountas |
| author_sort | Nicoleta Darra |
| collection | DOAJ |
| description | Remote-sensing measurements are crucial for smart-farming applications, crop monitoring, and yield forecasting, especially in fields characterized by high heterogeneity. Therefore, in this study, Precision Viticulture (PV) methods using proximal- and remote-sensing technologies were exploited and compared in a table grape vineyard to monitor and evaluate the spatial variation of selected vegetation indices and biophysical variables throughout selected phenological stages (multi-seasonal data), from veraison to harvest. The Normalized Difference Vegetation Index and the Normalized Difference Red-Edge Index were calculated by utilizing satellite imagery (Sentinel-2) and proximal sensing (active crop canopy sensor Crop Circle ACS-470) to assess the correlation between the outputs of the different sensing methods. Moreover, numerous vegetation indices and vegetation biophysical variables (VBVs), such as the Modified Soil Adjusted Vegetation Index, the Normalized Difference Water Index, the Fraction of Vegetation Cover, and the Fraction of Absorbed Photosynthetically Active Radiation, were calculated, using the satellite data. The vegetation indices analysis revealed different degrees of correlation when using diverse sensing methods, various measurement dates, and different parts of the cultivation. The results revealed the usefulness of proximal- and remote-sensing-derived vegetation indices and variables and especially of Normalized Difference Vegetation Index and Fraction of Absorbed Photosynthetically Active Radiation in the monitoring of vineyard condition and yield examining, since they were demonstrated to have a very high degree of correlation (coefficient of determination was 0.87). The adequate correlation of the vegetation indices with the yield during the latter part of the veraison stage provides valuable information for the future estimation of production in broader areas. |
| first_indexed | 2024-03-10T12:26:10Z |
| format | Article |
| id | doaj.art-848dd9237c0d4aee826205f06aed7a1e |
| institution | Directory Open Access Journal |
| issn | 2073-4395 |
| language | English |
| last_indexed | 2024-03-10T12:26:10Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj.art-848dd9237c0d4aee826205f06aed7a1e2023-11-21T15:03:37ZengMDPI AGAgronomy2073-43952021-04-0111474110.3390/agronomy11040741Remote and Proximal Sensing-Derived Spectral Indices and Biophysical Variables for Spatial Variation Determination in VineyardsNicoleta Darra0Emmanouil Psomiadis1Aikaterini Kasimati2Achilleas Anastasiou3Evangelos Anastasiou4Spyros Fountas5Laboratory of Agricultural Machinery, Department of Natural Resources Management and Agricultural Engineering, School of Environment and Agricultural Engineering, Agricultural University of Athens, 75 Iera Odos Str., Votanikos, 11855 Athens, GreeceLaboratory of Mineralogy and Geology, Department of Natural Resources Management and Agricultural Engineering, School of Environment and Agricultural Engineering, Agricultural University of Athens, 75 Iera Odos Str., Votanikos, 11855 Athens, GreeceLaboratory of Agricultural Machinery, Department of Natural Resources Management and Agricultural Engineering, School of Environment and Agricultural Engineering, Agricultural University of Athens, 75 Iera Odos Str., Votanikos, 11855 Athens, GreeceLaboratory of Agricultural Machinery, Department of Natural Resources Management and Agricultural Engineering, School of Environment and Agricultural Engineering, Agricultural University of Athens, 75 Iera Odos Str., Votanikos, 11855 Athens, GreeceLaboratory of Agricultural Machinery, Department of Natural Resources Management and Agricultural Engineering, School of Environment and Agricultural Engineering, Agricultural University of Athens, 75 Iera Odos Str., Votanikos, 11855 Athens, GreeceLaboratory of Agricultural Machinery, Department of Natural Resources Management and Agricultural Engineering, School of Environment and Agricultural Engineering, Agricultural University of Athens, 75 Iera Odos Str., Votanikos, 11855 Athens, GreeceRemote-sensing measurements are crucial for smart-farming applications, crop monitoring, and yield forecasting, especially in fields characterized by high heterogeneity. Therefore, in this study, Precision Viticulture (PV) methods using proximal- and remote-sensing technologies were exploited and compared in a table grape vineyard to monitor and evaluate the spatial variation of selected vegetation indices and biophysical variables throughout selected phenological stages (multi-seasonal data), from veraison to harvest. The Normalized Difference Vegetation Index and the Normalized Difference Red-Edge Index were calculated by utilizing satellite imagery (Sentinel-2) and proximal sensing (active crop canopy sensor Crop Circle ACS-470) to assess the correlation between the outputs of the different sensing methods. Moreover, numerous vegetation indices and vegetation biophysical variables (VBVs), such as the Modified Soil Adjusted Vegetation Index, the Normalized Difference Water Index, the Fraction of Vegetation Cover, and the Fraction of Absorbed Photosynthetically Active Radiation, were calculated, using the satellite data. The vegetation indices analysis revealed different degrees of correlation when using diverse sensing methods, various measurement dates, and different parts of the cultivation. The results revealed the usefulness of proximal- and remote-sensing-derived vegetation indices and variables and especially of Normalized Difference Vegetation Index and Fraction of Absorbed Photosynthetically Active Radiation in the monitoring of vineyard condition and yield examining, since they were demonstrated to have a very high degree of correlation (coefficient of determination was 0.87). The adequate correlation of the vegetation indices with the yield during the latter part of the veraison stage provides valuable information for the future estimation of production in broader areas.https://www.mdpi.com/2073-4395/11/4/741precision viticultureSentinel-2crop circle ACS-470vegetation indicesvegetation biophysical variables |
| spellingShingle | Nicoleta Darra Emmanouil Psomiadis Aikaterini Kasimati Achilleas Anastasiou Evangelos Anastasiou Spyros Fountas Remote and Proximal Sensing-Derived Spectral Indices and Biophysical Variables for Spatial Variation Determination in Vineyards Agronomy precision viticulture Sentinel-2 crop circle ACS-470 vegetation indices vegetation biophysical variables |
| title | Remote and Proximal Sensing-Derived Spectral Indices and Biophysical Variables for Spatial Variation Determination in Vineyards |
| title_full | Remote and Proximal Sensing-Derived Spectral Indices and Biophysical Variables for Spatial Variation Determination in Vineyards |
| title_fullStr | Remote and Proximal Sensing-Derived Spectral Indices and Biophysical Variables for Spatial Variation Determination in Vineyards |
| title_full_unstemmed | Remote and Proximal Sensing-Derived Spectral Indices and Biophysical Variables for Spatial Variation Determination in Vineyards |
| title_short | Remote and Proximal Sensing-Derived Spectral Indices and Biophysical Variables for Spatial Variation Determination in Vineyards |
| title_sort | remote and proximal sensing derived spectral indices and biophysical variables for spatial variation determination in vineyards |
| topic | precision viticulture Sentinel-2 crop circle ACS-470 vegetation indices vegetation biophysical variables |
| url | https://www.mdpi.com/2073-4395/11/4/741 |
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