Spectral-temporal characterization of wheat cultivars through NDVI obtained by terrestrial sensors

Carlos E V Cattani,Erivelto Mercante,Jerry A Johann,Murilo R Garcia,Marcus M Correa,Lucas V Oldoni

doi:10.1590/1807-1929/agriambi.v21n11p769-773

Abstract

ABSTRACT Remote sensing applications in agriculture are presented as a very promising reality, but research is still needed for the correct use of spectral data. The objective of this study was to evaluate the spectral-temporal patterns of eleven wheat cultivars (Triticum aestivum L.). The experiment was conducted in Cascavel, PR, in the year 2014. With the help of the GreenSeeker and FieldSpec 4 terrestrial sensors, spectral signatures were determined and the temporal profiles of the Normalized Difference Vegetation Index (NDVI) were created. Statistical differences between wheat cultivars were analysed using analysis of variance (ANOVA) and Scott-Knott test. Grain yields obtained with INSEY (In-Season Estimate of Yield) factors were correlated. NDVI normalized by degree-days accumulated from the Feekes growth stages 2 and 8 showed to be more consistent in the estimation of grain yield, explaining approximately 70% of the variation. At the Feekes stage 10.1, wheat cultivars presented different spectral patterns in the near and medium infrared bands. This suggests that these spectral bands can be used to differentiate wheat cultivars.

Highlights

Remote sensing (RS) has stood out as a technique that allows to monitor agricultural crops along their development cycle (Gao et al, 2017)
Studies have already been conducted demonstrating the strong correlation between normalized difference vegetation index (NDVI) and photosynthetically active radiation (PAR) (Gamon et al, 1995), leaf area index (LAI) (Fassnacht et al, 1997), quantity of biomass and nitrogen of the agricultural crops (Hansen & Schjoerring, 2003)
According to Cordeiro et al (2015) and Farooq et al (2011), the water demand of the wheat crop is in the interval from 400 to 650 mm and the optimal mean ten-day temperature between 19 and 24 °C

Summary

Introduction

Remote sensing (RS) has stood out as a technique that allows to monitor agricultural crops along their development cycle (Gao et al, 2017). For conditions in which there is greater soil cover by plants, NDVI may saturate and become insensitive to alterations in LAI and biomass (Povh et al, 2008). Despite such limitation, efforts have been made to improve the capacity of remote detection of crop yields using NDVI (Mulla, 2013). Raun et al (1999), in studies on the wheat crop, developed the INSEY (In-Season Estimate of Yield) model, which allows to correlate grain yield with NDVI Efforts have been made to improve the capacity of remote detection of crop yields using NDVI (Mulla, 2013). Raun et al (1999), in studies on the wheat crop, developed the INSEY (In-Season Estimate of Yield) model, which allows to correlate grain yield with NDVI

Objectives

Methods

Results

Conclusion