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

Abstract

A B S T R A C TRemote 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.

Sugarcane spatial-temporal monitoring and crop coefficient estimation through NDVI

“…An important application, among others, in the analysis with NDVI is related to the estimation of crop coefficient (kc). As the attribution of kc values is directly related to crop phenological cycle, some studies have suggested that temporal profiles of NDVI can be used to obtained kc values (Singh & Irmak, 2009;Kamble et al, 2013;Cattani et al, 2017).…”

Annual cropland mapping using data mining and OLI Landsat-8

“…The NDVI of annual agricultural crops range from values close to zero (beginning of lifecycle) to one -maximum vegetative development (flowering, fruiting and grain-filling); then, they decrease to values near zero again (senescence, remains and bare soil), being followed by a new annual crop cycle with the same trend (Cattani et al, 2017). There is little spectral-temporal variation in targets such as cities, reforestation areas and forests, which show mean NDVI values near 1.0 for reforestation and forest, and values close to 0.5 for urban areas.…”

“…DM approach has tools to analyze large amounts of data, allowing the development of a learning mechanism (Vintrou et al, 2013). Another procedure to assist in the multispectral classification of images is the multi-temporal analysis of Normalized Difference Vegetation Index (NDVI) (Rouse et al, 1974) since spectral-temporal profiles are strongly tied to agriculture dynamics (Cattani et al, 2017). This type of approach has been used to classify crop types (Chen et al, 2018) and land cover (Jia et al, 2014).…”

Landsat and Sentinel-2 images as a tool for the effective estimation of winter and spring cultivar growth and yield prediction in the Czech Republic

“…Similarly, while it may be difficult to choose the best available plant species and cultivar for a specific sloping plot, for example, a study of five wheat cultivars by Grohs et al (2009) confirmed that the difference in cultivar reflectance values in the near infrared and red spectra does not sufficiently indicate their productive potential. Cattani et al (2017) then added results based on the In-Season Estimate of Yield model, where NDVI is normalized by degree-day accumulated from Feekes growth stages 2 and 8. This appears to be a more consistent method of estimating grain yield, and in the context of climate change, this model may be more reliable and accurate for grain yield estimation in areas with higher temperatures and a lack of precipitation than solely calculating the spectral index, as occurred in this study.…”