High-Resolution Airborne Hyperspectral Imagery for Assessing Yield, Biomass, Grain N Concentration, and N Output in Spring Wheat

Abstract

Remote sensing allows fast assessment of crop monitoring over large areas; however, questions regarding uncertainty in crop parameter prediction and application to nitrogen (N) fertilization remain open. The objective of this study was to optimize of remote sensing spectral information for its application to grain yield (GY), biomass, grain N concentration (GNC), and N output assessment, and decision making on spring wheat fertilization. Spring wheat (Triticum turgidum L.) field experiments testing two tillage treatments, two irrigation levels and six N treatments were conducted in Northwest Mexico over four consecutive years. Hyperspectral images were acquired through 27 airborne flight campaigns. At harvest, GY, biomass, GNC and N output were determined. Spectral exploratory analysis was used to identify the best wavelength combinations, the most suitable vegetation indices (VIs) and the best growth stages to assess the agronomic variables. The relationship between the spectral information and the agronomic measurements was evaluated by the coefficient of determination (R2) and the root mean square error (RMSE). The ability of the indices to guide fertilizer recommendation was assessed through an error analysis based on the N sufficiency index. GY was better assessed from the end of flowering to the early milk stage by VIs based on the combination of bands from near infrared radiation/visible and from near infrared radiation/red-edge regions (R2 > 0.6; RMSE < 700 kg ha−1). N output was efficiently assessed by a combination of bands from near infrared radiation/red-edge at booting (R2 > 0.7; RMSE < 9 kg N ha−1). The GNC was better estimated by VIs combining bands in near infrared radiation/red-edge at early milk, but with great variability among the years studied. Some VIs were promising for guiding fertilizer recommendation for increasing GNC, but there was not a single index providing reliable recommendations every year. This study highlights the potential of remote sensing imagery to assess GY and N output in spring wheat, but the identification of GNC responsive sites needs to be improved.

Winter wheat genotype ability to recover nitrogen supply by precedent crops under combined nitrogen and water scenarios

“…In Exp 2 in which the N provided by the precedent pea crop was high, the Cellule genotype showed its high capacity to translocate the absorbed N to the grain and increase GY, achieving the highest N output and AE N among all genotypes. Non-destructive techniques (i.e., sensors) that allow monitoring crop N status from flowering to grain filling should be implemented to detect different performances between genotypes in the N translocation (Raya-Sereno et al, 2021).…”

Optimization of soil background removal to improve the prediction of wheat traits with UAV imagery

“…The Normalized Difference Red-Edge index (NDRE) (Barnes et al, 2000) includes the red-edge spectral region (690-730 nm) in its formulation. Several studies (Jiang et al, 2021;Marszalek et al, 2022;Raya-Sereno et al, 2021) support that the rededge channel is more sensitive to chlorophyll content. The Blue-Red (BRI1) ratio is sensitive to changes or disturbances in the photosynthetic apparatus (Li et al, 2014;Lichtenthaler et al, 1996;Zarco-Tejada et al, 2005a), enabling the identification of early stress in plants.…”

Estimation of Rice Aboveground Biomass by Combining Canopy Spectral Reflectance and Unmanned Aerial Vehicle-Based Red Green Blue Imagery Data

“…Researchers have developed various data-processing methods and mathematical models for remotely sensed data. Canopy spectral reflectance, multispectral imagery, and RGB imagery are often used to extract vegetation indices (VIs) for estimating AGB in maize, rice, and wheat ( Jiang et al, 2019 ; Yue et al, 2019 ; Ma et al, 2020 ; Raya-Sereno et al, 2021 ). Spectral VIs (SVIs), such as the ratio vegetation index (RVI), difference vegetation index (DVI), and normalized difference vegetation index (NDVI), have proven to have close relationships with rice biomass ( Gnyp et al, 2014 ).…”