Leaf Age Matters in Remote Sensing: Taking Ground Truth for Spectroscopic Studies in Hemiboreal Deciduous Trees with Continuous Leaf Formation

Abstract

We examined the seasonal changes in biophysical, anatomical, and optical traits of young leaves, formed throughout the vegetative season due to sylleptic growth, and mature leaves formed by proleptic growth in spring. Leaf developmental categories contribute to the top-of-canopy reflectance and should be considered when taking ground truth for remote sensing studies (RS). Deciduous tree species, Betula pendula, Populus tremula, and Alnus incana, were sampled from May to October 2018 in an Estonian hemiboreal forest. Chlorophyll and carotenoid content were detected biochemically; leaf anatomical traits (leaf, palisade, and spongy mesophyll thickness) were measured on leaf cross-sections; leaf reflectance was measured by a spectroradiometer with an integrating sphere (350–2500 nm). Biophysical and anatomical leaf traits were related to 64 vegetation indices (VIs). Linear models based on VIs for all tested leaf traits were more robust if both juvenile and mature leaves were included. This study provides information on which VIs are interchangeable or independent. Pigment and leaf thickness sensitive indices formed PC1; water and structural trait related VIs formed an independent group associated with PC3. Type of growth and leaf age could affect the validation of biophysical and anatomical leaf trait retrieval from the optical signal. It is, therefore, necessary to sample both leaf developmental categories—young and mature—in RS, especially if sampling is only once within the vegetation season.

Field-Deployed Spectroscopy from 350 to 2500 nm: A Promising Technique for Early Identification of Powdery Mildew Disease (Erysiphe necator) in Vineyards

“…Moreover, the reflectance of mature leaves may be lower due to several factors, including structural, chemical and photochemical changes. As the leaf ages, there is a variation in the ratio of chlorophyll to carotenoids, changes in the leaf cuticle and adaptations to light exposure that can affect how the leaf reflects light [52][53][54]. In addition, the consistency in the Weighted Regression Coefficients of PLS across both datasets underscores the reliability of spectral reflectance as an indicator of PM infestation, irrespective of the temporal development of the disease.…”

On the Utility of Longwave-Infrared Spectral Imaging for Remote Botanical Identification

“…Both the detection and ID success rate of these two deciduous taxa fell dramatically for the spring acquisitions at those facilities, suggesting that future investigations should include systematic phenological analyses of foliar LWIR spectral traits and the consequent impact on ID accuracy throughout the growth cycle [35]. In this respect, the LWIR spectral region is subject to similar constraints as the VSWIR [51][52][53][54][55].…”

Development of High-Fidelity Automotive LiDAR Sensor Model with Standardized Interfaces

“…)” sensor models, use the object list provided by the simulation framework in the world coordinate system as an input. The term ground truth is borrowed from remote sensing, where it refers to information collected on location for data calibration [ 21 ]. These models’ output is a filtered object list as per the sensor-specific FoV [ 22 ].…”