Hyperspectral remote sensing and inversion of biochemical parameters at the leaf and canopy levels

Ecosystems play a vital role in the exchange of water, energy, and greenhouse gases among soil, vegetation, and atmosphere. The ability to detect changes in ecosystem processes such as carbon sequestration, nutrient cycling, net primary productivity, and litter decomposition is an essential component of defining global biogeochemical cycles and identifying climate change. In forest ecosystem models, these processes are associated with canopy biochemical content, particularly nitrogen, lignin, and cellulose concentrations.

therefore, Understanding the pigments present in plant leaves is an important prerequisite for improving the assessment and quantification of plant physiology and vitality.Because estimating canopy chemical composition using traditional field sampling methods is time-consuming and unsuitable for large-scale regional and global studies, remote sensing technology for leaf biochemistry (nutrients, pigments), as an indicator of stress phenomena or pests, plant diseases and other disasters, has long been of interest to forest managers and ecologists.

Figure 1. Detection of biochemical parameters at the forest and canopy levels.

Three types of pigments determine leaf color through selective light absorption: chlorophyll (two major types in higher plants, chlorophyll a and chlorophyll b), carotenoids (including β-carotene and xanthophyll), and anthocyanins. The content and composition of these pigments are related to the physiological state of the leaves and exist in different proportions during leaf differentiation and senescence. Certain conditions can hinder their formation, such as nutrient deficiencies and the effects of abiotic stresses—ozone or sulfur dioxide air pollution, heavy metals, viral attacks, or water shortages—which can lead to changes in the optical properties of vegetation. Chlorophyll concentration indicates photosynthesis and potential maximum carbon dioxide assimilation rate. Carotenoids help capture light but also play a photoprotective role and indicate reduced photosynthesis.Nitrogen and lignin are correlated with carbon allocation and nutrient cycling rates, respectively, through their effects on decomposition rates. Chlorophyll concentration is also highly correlated with nitrogen, thus it has become a key parameter for studying plant canopies.

Numerous studies have demonstrated a correlation between data acquired by imaging spectrometers and leaf biochemical properties. Early research primarily used stepwise regression to establish predictive relationships between narrow-spectral-band remote sensing data and biochemical variables of interest (e.g., cellulose, chlorophyll, lignin, nitrogen, and starch). Subsequent studies have proposed alternative techniques, namely estimating biochemical variables using reflectance models.

The transition from leaf to canopy level introduces strong interference effects, such as the influence of canopy structure (leaf area index, leaf angle distribution) and soil background. Compared to controlled laboratory conditions, airborne or spaceborne data are further affected by factors such as variable sunlight and atmospheric conditions, as well as instrument characteristics (signal-to-noise ratio, spectral bandwidth, observation geometry). These factors collectively lead to many problems in determining leaf biochemical composition using remote sensing data. Nevertheless, related studies have shown that spectral irradiance measurements of plant canopies are correlated with changes in canopy chemical composition, including lignin, nitrogen, and cellulose concentrations.

Figure 2. Scatter plot showing the relationship between various reflectance indices and total chlorophyll content.