Spectral Vegetation Indices as Nondestructive Tools for Determining Durum Wheat Yield

Abstract

Remote sensing measurements may be a useful tool for quantifying crop development and yield. Our objective was to study the potential of using spectral reflectance indices to provide accurate and nondestructive estimates of physiological traits determining yield in durum wheat [Triticum turgidum L. subsp. durum (Desf.) Husn.]. Twenty‐five genotypes were grown under rainfed and irrigated conditions in northeastern Spain. Reflectance from the vegetation at different growth stages was measured and the following spectral indices calculated: simple ratio (SR), normalized difference vegetation index (NDVI), and photochemical reflectance index (PRI). Crop dry mass (CDM), leaf area index (LAI), and green area index (GAI) were measured. All the indices and grain yield were greater under irrigated than rainfed conditions. LAI was the crop growth trait that most closely correlated with the spectral reflectance indices, with SR and PRI being the best and the worst indices, respectively, for the assessment of crop growth and yield. In rainfed conditions, the spectral reflectance indices measured at any crop stage were positively correlated (P < 0.05) with LAI and yield. Under irrigation, correlations were only significant during the second half of the grain filling. The integration of either NDVI, SR, or PRI from heading to maturity explained 52, 59, and 39% of the variability in yield within genotypes in rainfed conditions and 39, 28, and 26% under irrigation. Our results suggest that for durum wheat, the usefulness of the SR and NDVI for calculating green area and grain yield is limited to LAI values < 3.

Management of crop water under drought: a review

“…Beside plant water status, reflectance indices are also used to predict other plant properties, such as biomass growth and yield (e.g. Aparicio et al 2000;Ma et al 2001) and nutritional status of crops (e.g. Filella et al 1995;Graeff and Claupein 2003;Xue et al 2004).…”

Using Ridge Regression Models to Estimate Grain Yield from Field Spectral Data in Bread Wheat (Triticum Aestivum L.) Grown under Three Water Regimes

“…The indices were chosen according to different biophysical criteria. The normalized difference vegetation index or NDVI (R900 − R680)/(R900 + R680) and the simple ratio or SR (R900/R680) are correlated with photosynthetic area [5,17,20,35], the photochemical reflectance index or PRI (R531 − R570)/(R531 + R570) is correlated with radiation use efficiency [36] and the water index or WI (R970/R900) is correlated with water status [37] Thereafter, a regression analysis was generated between each index and grain yield, where the coefficients of the regression were obtained to generate a predictive model for grain yield. Finally, the model fit between observed and predicted grain yields was evaluated using the coefficient of determination (R 2 ).…”

“…Various studies in wheat have associated GY with reflectance data from these zones of the spectral signature [17,21,50].…”

“…By determining the spectral signature of canopy and leaf reflectance with a spectroradiometer, it is possible to indirectly measure agronomic and physiological traits [9,12] such as chlorophyll content [13,14], aerial biomass [15], plant water content [16], or grain yield [17,18]. Indices based on reflectance at different wavelengths, known as spectral reflectance indices (SRIs), have been used for this purpose [17,[19][20][21].…”

“…Indices based on reflectance at different wavelengths, known as spectral reflectance indices (SRIs), have been used for this purpose [17,[19][20][21]. Several of these indices have also been positively correlated (using logarithmic or linear models) with crop dry matter (DM), leaf area index (LAI), green area index (GAI), and potential photosynthetic capacity [5,17,20]. However, these indices only use a limited portion of the spectral information by reducing, simplifying, or not considering the abundant information it contains [22,23].…”