Hyperspectral field reflectance measurements to estimate wheat grain yield and plant height

Abstract

Hyperspectral crop reflectance data are useful for several remote sensing applications in agriculture, but there is still a need for studies to define optimal wavebands to estimate crop biophysical parameters. The objective of this work is to analyze the use of narrow and broad band vegetation indices (VI) derived from hyperspectral field reflectance measurements to estimate wheat (Triticum aestivum L.) grain yield and plant height. A field study was conducted during the winter growing season of 2003 in Campinas, São Paulo State, Brazil. Field canopy reflectance measurements were acquired at six wheat growth stages over 80 plots with four wheat cultivars (IAC-362, IAC-364, IAC-370, and IAC-373), five levels of nitrogen fertilizer (0, 30, 60, 90, and 120 kg of N ha-1) and four replicates. The following VI were analyzed: a) hyperspectral or narrow-band VI (1. optimum multiple narrow-band reflectance, OMNBR; 2. narrow-band normalized difference vegetation index, NB_NDVI; 3. first- and second-order derivative of reflectance; and 4. four derivative green vegetation index); and b) broad band VI (simple ratio, SR; normalized difference vegetation index, NDVI; and soil-adjusted vegetation index, SAVI). Hyperspectral indices provided an overall better estimate of biophysical variables when compared to broad band VI. The OMNBR with four bands presented the highest R² values to estimate both grain yield (R² = 0.74; Booting and Heading stages) and plant height (R² = 0.68; Heading stage). Best results to estimate biophysical variables were observed for spectral measurements acquired between Tillering II and Heading stages.

Spectroscopic analysis of soil metal contamination around a derelict mine site in the Blue Mountains, Australia

“…Derivatives are usually applied to enhance the spectra appearance (Bogrekci and Lee, 2005). The first derivative of the reflectance measuring the slope of the spectra between the reflectance of two wavelengths (Richardson and Berlyn, 2002) was derived using the following equation (Xavier et al, 2006):…”

“…This means that the reflectance derivative of λ i is derived using the calculation of the spectral slope of the reflectance of the next wavelength (λ i+1 ) and reflectance of the wavelength before (λ i-1 ). In order to calculate the second derivative of the spectra, the following equation was applied (Xavier et al, 2006):…”

Estimation of optimal size of plots for experiments with radiometer in beans

“…Doblinski et al (2010) adopted a 2 m² area in the study of diffuse pollution of swine wastewater on the beans. To estimate the productivity of grains and wheat plant height using reflectance measurements Xavier et al (2006) adopted 3.6 m² plots (3 m long by 1.2 m wide). In order to quantify the damage and the relationship between severity, reflectance and productivity in the pathosystem of Asian soybean rust, Hikishima et al (2010) adopted as experimental unit an area of 6.75 m², or 3 lines of 5 m in length.…”

Simulated multipolarized MAPSAR images to distinguish agricultural crops

“…Due to the continental dimension and the intense agricultural involvement of Brazil, information acquired from remote sensing images are important to various agricultural applications, such as crop monitoring, mapping and estimation of crop production (Galvão et al, 2009;Rizzi and Rudorff, 2007;Rudorff et al, 2010;Soares et al, 2008;Xavier et al, 2006). However, the availability of cloud free images during the primary growth period of summer crops is limited, particularly in tropical regions and, therefore, restricting the use of optical remote sensing imagery to monitor agricultural activities.…”