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

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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.

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Source: DAVIS meteorological station of the Instrumentation, Automation and Processing Laboratory in the Department of Rural Engineering, Unesp/Fcav, Jaboticabal, State of São Paulo, Brazil

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Sources: Ciampitti et al. (2014), K-State Research and Extension, adapted from Carneiro (2018)

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Authors and Affiliations

  1. Department of Rural Engineering, School of Agricultural and Veterinarian Sciences, São Paulo State University (Unesp), Jaboticabal, 14884900, Brazil

    Franciele Morlin Carneiro, Carlos Eduardo Angeli Furlani, Cristiano Zerbato & Mailson Freire de Oliveira

  2. Federal Institute of Education, Science and Technology of Rondônia (IFRO), Colorado Do Oeste, 76993000, Brazil

    Patricia Candida de Menezes

  3. Federal Institute of Education, Science and Technology of Farroupilha (IFFar), Jaguari, Rio Grande do Sul state, 97760-000, Brazil

    Lucas Augusto da Silva Gírio

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  1. Franciele Morlin Carneiro
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Morlin Carneiro, F., Angeli Furlani, C.E., Zerbato, C. et al. Comparison between vegetation indices for detecting spatial and temporal variabilities in soybean crop using canopy sensors. Precision Agric 21, 979–1007 (2020). https://doi.org/10.1007/s11119-019-09704-3

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