Assessing the Spectral Properties of Sunlit and Shaded Components in Rice Canopies with Near-Ground Imaging Spectroscopy Data

Abstract

Monitoring the components of crop canopies with remote sensing can help us understand the within-canopy variation in spectral properties and resolve the sources of uncertainties in the spectroscopic estimation of crop foliar chemistry. To date, the spectral properties of leaves and panicles in crop canopies and the shadow effects on their spectral variation remain poorly understood due to the insufficient spatial resolution of traditional spectroscopy data. To address this issue, we used a near-ground imaging spectroscopy system with high spatial and spectral resolutions to examine the spectral properties of rice leaves and panicles in sunlit and shaded portions of canopies and evaluate the effect of shadows on the relationships between spectral indices of leaves and foliar chlorophyll content. The results demonstrated that the shaded components exhibited lower reflectance amplitude but stronger absorption features than their sunlit counterparts. Specifically, the reflectance spectra of panicles had unique double-peak absorption features in the blue region. Among the examined vegetation indices (VIs), significant differences were found in the photochemical reflectance index (PRI) between leaves and panicles and further differences in the transformed chlorophyll absorption reflectance index (TCARI) between sunlit and shaded components. After an image-level separation of canopy components with these two indices, statistical analyses revealed much higher correlations between canopy chlorophyll content and both PRI and TCARI of shaded leaves than for those of sunlit leaves. In contrast, the red edge chlorophyll index (CIRed-edge) exhibited the strongest correlations with canopy chlorophyll content among all vegetation indices examined regardless of shadows on leaves. These findings represent significant advances in the understanding of rice leaf and panicle spectral properties under natural light conditions and demonstrate the significance of commonly overlooked shaded leaves in the canopy when correlated to canopy chlorophyll content.

Assessing the Impact of Spatial Resolution on the Estimation of Leaf Nitrogen Concentration Over the Full Season of Paddy Rice Using Near-Surface Imaging Spectroscopy Data

“…The EVI is an optimized vegetation index with improved vegetation monitoring through minimizing the effects of background influences and atmosphere influences (Liu and Huete, 1995 ). Its discrimination capacity for separating non-vegetation background and vegetation pixels has been proved in previous studies (Pinto et al, 2016 ; Zhou et al, 2017 ). Afterwards, we constructed the classification decision tree developed in Zhou et al ( 2017 ) by applying photochemical reflectance index (PRI) (Gamon et al, 1992 ) and transformed chlorophyll absorption reflectance index (TCARI) (Haboudane et al, 2002 ) thresholds at two sequential steps for discriminating all the pixels of sunlit and shaded canopy leaves and panicles in the images.…”

“…The image preprocessing procedures including subtraction of sensor electronic noise (dark current) and radiometric correction were implemented within the specVIEW software (Specim, Oulu, Finland). The final relative reflectance values were converted from the original digital number (DN) values (i.e., pixel brightness values) using the calibration equation as follows (Zhou et al, 2017 ):…”

“…To investigate the relationships between LNC and VIs derived from pure leaf pixels across the whole image at 1.3 mm spatial resolutions, we firstly identified vegetation pixels applying a threshold of the enhanced vegetation index (EVI) (Pinto et al, 2016 ; Zhou et al, 2017 ) (EVI > 0.45). The EVI is an optimized vegetation index with improved vegetation monitoring through minimizing the effects of background influences and atmosphere influences (Liu and Huete, 1995 ).…”

Specim IQ: Evaluation of a New, Miniaturized Handheld Hyperspectral Camera and Its Application for Plant Phenotyping and Disease Detection

“…In the present study, the characteristics of the novel hyperspectral camera Specim IQ are presented, and a direct qualitative comparison on radiometric accuracy with the well-established sensor Specim HS-V10E-CL-30 [ 21 , 40 , 41 , 46 , 47 ] (denoted in the manuscript as Specim V10E (Specim Ltd., Oulu, Finland)) was conducted. Further details on this sensor are provided in Section 3 .…”

Estimating Rice Agronomic Traits Using Drone-Collected Multispectral Imagery

“…Most other studies for estimating rice agronomic traits employ near-ground measurements, either using hyperspectral sensors or active canopy ones, which however do not need to account for the complex background phenomena arising for the lower resolution of the UAV-collected imagery. In the latter case, techniques for removing the background [57] are difficult to be employed. This also means that the results of previous studies are not necessarily transferable to UAV-collected multispectral data.…”