Distinct contribution of the blue spectral region and far-red solar-induced fluorescence to needle nitrogen and phosphorus assessment in coniferous nutrient trials with hyperspectral imagery
Peiye Li , Tomas Poblete , Alberto Hornero , Jagannath Aryal , Pablo J. Zarco-Tejada
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引用次数: 0
Abstract
Accurate monitoring of plant nutrient status, especially nitrogen (N) and phosphorus (P) content, via remote sensing can facilitate precision forestry, with environmental and management benefits. In previous studies, plant traits derived from hyperspectral data via radiative transfer models (RTMs) and solar-induced chlorophyll fluorescence (SIF) effectively explained the observed variability in leaf N concentrations in crops. However, their contribution to leaf P concentration is unknown. Furthermore, such an approach might not be transferrable to coniferous stands, which are structurally complex and heterogeneous. We evaluated the potential of using physiological plant traits derived from airborne hyperspectral imagery to explain the observed variability in needle N and P concentrations in Pinus radiata D. Don (radiata pine) with four datasets collected over three years in established nutrient trials. RTM-derived data on pigment content in needles, including chlorophyll a + b (Cab), carotenoid (Car), and anthocyanin contents (Anth), as well as SIF quantified at the O2A absorption band (SIF760), explained variability in N (R2 = 0.67–0.97 and NRMSE = 0.07–0.30) and P concentrations (R2 = 0.60–0.95 and NRMSE = 0.09–0.27) in needles. Although Cab was the most important predictor of needle N concentration (ranking Cab > Anth > SIF760 > Car), SIF760 contributed the most to explain the variability of needle P concentration (SIF760 > Anth > Cab > Car). Moreover, the blue spectral region was essential for assessing P but not for explaining N variability in needles. Among all reflectance-based indices and inverted traits evaluated, the blue indices best explained the variability in needle P concentration, followed by Cab, Car, and Anth. The study revealed the distinct contribution of far-red SIF vs. the blue spectral region for needle P compared to needle N, describing new insights for the physiological assessment of nutrient levels in forest stands using hyperspectral imagery.
期刊介绍:
Remote Sensing of Environment (RSE) serves the Earth observation community by disseminating results on the theory, science, applications, and technology that contribute to advancing the field of remote sensing. With a thoroughly interdisciplinary approach, RSE encompasses terrestrial, oceanic, and atmospheric sensing.
The journal emphasizes biophysical and quantitative approaches to remote sensing at local to global scales, covering a diverse range of applications and techniques.
RSE serves as a vital platform for the exchange of knowledge and advancements in the dynamic field of remote sensing.