Comparison between vegetation indices for detecting spatial and temporal variabilities in soybean crop using canopy sensors

• Autor(a) principal: Morlin Carneiro, Franciele [UNESP]
• Data de Publicação: 2020
• Outros Autores: Angeli Furlani, Carlos Eduardo [UNESP], Zerbato, Cristiano [UNESP], Candida de Menezes, Patricia, da Silva Gírio, Lucas Augusto, Freire de Oliveira, Mailson [UNESP]
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1007/s11119-019-09704-3; http://hdl.handle.net/11449/201397
• topic: Active optical sensor; Control charts; Glycine max; Precision agriculture; Remote sensing

Crop monitoring through remote sensing techniques enable greater knowledge of average variability in crop growth. Canopy sensors help provide information on the variability of crop through the use of vegetation indices. The objective of this work was to compare the potential and performance of three vegetation indices used for monitoring soybean variability with canopy sensors was compared. The optimal time for sensor readings was determined during the soybean crop development stages. Also, the quality of the readings between vegetation indices [the normalized difference vegetation index (NDVI), normalized difference red-edge (NDRE), and inverse ratio (IRVI)] was compared through control charts and the saturation detection index. The experimental design was based on statistical quality control and comprised 65 sampling points within a 30 × 30 m grid. At 30, 45, 60, 75, and 90 days after sowing (DAS), the parameters used as quality indicators, such as fresh and dry biomass, canopy width, chlorophyll index, plant height, yield, and the vegetation indices were assessed using canopy sensors. The optimal time for canopy sensor readings, based mainly on the NDRE, was at 45 and 60 DAS. The lower variability exhibited by NDRE led to higher process quality when compared with those for NDVI and IRVI. The control charts proved to be promising in identifying the moment when saturation occurs for the indices more susceptible to saturation, such as the NDVI.