Retrieval of Crop Variables from Proximal Multispectral UAV Image Data Using PROSAIL in Maize Canopy

• Published in: Remote sensing (Basel, Switzerland) Vol. 14; no. 5; p. 1247
• Main Authors: Chakhvashvili, Erekle, Siegmann, Bastian, Muller, Onno, Verrelst, Jochem, Bendig, Juliane, Kraska, Thorsten, Rascher, Uwe
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 03.03.2022
• Subjects: Canopies; Cereal crops; Chlorophyll; Corn; Crops; Developmental stages; Growing season; Image resolution; inversion; Leaf area; Leaf area index; Leaves; Machine learning; Mapping; multispectral; Pixels; Plant breeding; Plot (Narrative); PROSAIL; Radiation; Radiative transfer; radiative transfer model; Reflectance; Regression analysis; Regression models; Remote sensing; Retrieval; Sensors; Silage; Soils; UAV; Unmanned aerial vehicles; Variables; Vegetation; Vegetation index; radiative transfer model; PROSAIL; leaf chlorophyll content; inversion; canopy chlorophyll content; leaf area index; UAV; multispectral
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs14051247

Mapping crop variables at different growth stages is crucial to inform farmers and plant breeders about the crop status. For mapping purposes, inversion of canopy radiative transfer models (RTMs) is a viable alternative to parametric and non-parametric regression models, which often lack transferability in time and space. Due to the physical nature of RTMs, inversion outputs can be delivered in sound physical units that reflect the underlying processes in the canopy. In this study, we explored the capabilities of the coupled leaf-canopy RTM PROSAIL applied to high-spatial-resolution (0.015 m) multispectral unmanned aerial vehicle (UAV) data to retrieve the leaf chlorophyll content (LCC), leaf area index (LAI) and canopy chlorophyll content (CCC) of sweet and silage maize throughout one growing season. Two different retrieval methods were tested: (i) applying the RTM inversion scheme to mean reflectance data derived from single breeding plots (mean reflectance approach) and (ii) applying the same inversion scheme to an orthomosaic to separately retrieve the target variables for each pixel of the breeding plots (pixel-based approach). For LCC retrieval, soil and shaded pixels were removed by applying simple vegetation index thresholding. Retrieval of LCC from UAV data yielded promising results compared to ground measurements (sweet maize RMSE = 4.92 μg/cm , silage maize RMSE = 3.74 μg/cm ) when using the mean reflectance approach. LAI retrieval was more challenging due to the blending of sunlit and shaded pixels present in the UAV data, but worked well at the early developmental stages (sweet maize RMSE = 0.70m /m , silage RMSE = 0.61m /m across all dates). CCC retrieval significantly benefited from the pixel-based approach compared to the mean reflectance approach (RMSEs decreased from 45.6 to 33.1 μg/m ). We argue that high-resolution UAV imagery is well suited for LCC retrieval, as shadows and background soil can be precisely removed, leaving only green plant pixels for the analysis. As for retrieving LAI, it proved to be challenging for two distinct varieties of maize that were characterized by contrasting canopy geometry.