Performance of different Sentinel-2A vegetation indices to estimate soybean yield in precision farming
Precision agriculture (PA) aims to identify crop and soil variability to improve management and optimize the use of inputs. Yield maps become a relevant tool for PA planning. Vegetation indices (VI) from remote sensing allow monitoring the spatio-temporal variation of crops in the growing season....
Guardado en:
| Autores principales: | , , , , , |
|---|---|
| Formato: | Artículo revista |
| Lenguaje: | Español |
| Publicado: |
Facultad de Ciencias Agropecuarias
2021
|
| Materias: | |
| Acceso en línea: | https://revistas.unc.edu.ar/index.php/agris/article/view/25148 |
| Aporte de: |
| Sumario: | Precision agriculture (PA) aims to identify crop and soil variability to improve management and optimize the use of inputs. Yield maps become a relevant tool for PA planning. Vegetation indices (VI) from remote sensing allow monitoring the spatio-temporal variation of crops in the growing season. The objective of this work was to evaluate the performance of different Sentinel-2A VIs to estimate soybean (Glycine max (L.) Merril) yield within the framework of PA. The study was carried out using a soybean yield map during the 2017/2018 season from a plot located to the south of Córdoba city, Argentina. Every two bands were taken from a Sentinel-2A image from 4/Feb/2018 and VI were calculated using differences, ratios and normalized differences. The absolute value of the Pearson linear correlation coefficient (|r|) between the different IVs and soybean yield was computed. The highest value of |r| (0.726) corresponded to the difference between bands 8 (NIR) and 12 (SWIR), allowing to reproduce with sufficient precision the spatial variability of yields in the plot. |
|---|