Sensitivity Analysis of the Spectral Response of the Plant Leaf to the Biophysical-Biochemical Variables Using the PROSPECT Radiative Transfer Model

Document Type : Original Article

Authors

1 Assistant Professor, Remote Sensing and GIS, Faculty of Geography and Environmental Sciences, Hakim Sabzevari University, Sabzevar, Iran

2 Remote Sensing and GIS Center, Dep. of Earth Science, Shahid Beheshti University, Tehran, Iran

3 Assistant Professor, Dep. of Remote Sensing and GIS, Faculty of Planning and Environmental Sciences, University of Tabriz, Tabriz, Iran

4 Associate Professor, Dep. of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran, Iran

Abstract


Background and aim: Determining how the leaf biochemical content affects its reflectance and spectral behavior through remote sensing can contribute to understanding the ecosystem process and its parameters, such as plant water stress. The optical properties of the canopy are strongly dependent on the optical properties of the leaves and the soil. Also, due to the non-availability of detailed information on the optical properties of leaves, the interpretation of spectral data collected through remote sensing has faced limitations. The internal structure of the leaf controls the amount of reflectance and transmission in the entire electromagnetic spectrum, but physical models have been developed to obtain detailed information and a comprehensive description of the optical properties of the leaf. In addition to calculating the leaf’s spectral response, reversible models such as PROSPECT can calculate a small number of internal characteristics of the leaf, such as the amount of chlorophyll, leaf water content, and leaf structure. Using the reversibility property, it is possible to quantitatively determine the amount of water in the leaves and a small amount of biomass from the spectrum collected through the sensors. Therefore, the use of these models and their integration with remote sensing data, which are non-destructive for the plant and provide the possibility of monitoring in time and space, can open the way for studies and modeling related to the internal characteristics of plant leaves.
Materials and methods: In this research, the effect of leaf biophysical-biochemical variables, including leaf chlorophyll content (LCC), leaf structure, and leaf water content on reflectance, were quantitatively analyzed. To do so, the PROSPECT leaf radiative transfer model, which was developed to simulate the spectral behavior of plant leaves, was used. As a result, the effect of the quantity of leaf parameters, including chlorophyll, leaf water content, and leaf structure, on the shape of the spectral curve of the leaf has been investigated. The study employed two other parameters considered constant to study the effects caused by each parameter. By changing the value of the desired parameter, the spectral curves corresponding to the selected values are extracted using the PROSPECT model. The effect of the desired parameter on the leaf’s spectral reflectance was investigated by comparing and analyzing the resulting curves.
Results and discussion: The research results indicate that the increase of leaf chlorophyll with the effect of reducing the reflectance leads to the rise in triangular plant indices. Based on the leaf structure and inner layers in the near-infrared (NIR) spectrum, it is possible to distinguish monocots, dicots, and old plants. Also, in the NIR spectrum, the amount of reflectance in old, dicotyledonous, and monocots decreases, respectively. In dicots with spongy parenchyma, more reflectance is expected in the NIR spectrum than in monocots. Monocots can be distinguished from other plants due to their lower reflectance in the 1400 to 1900 nm range. The influence of water content on leaf spectral reflectance starts from the wavelength of 1000 nm and continues until the end of the reflective range, 2500 nm, and with the increase of water content, the reflectance decreases. The drying of the plant does not have much effect on the reflectance, but drying more than a certain amount of leaf water content (0.03 to 0.04 ) causes a significant increase in the reflectance, especially outside the water-absorbing bands. Therefore, finding the critical points of the reflectance curve against the water content can help detect severe water stress in plants. By examining the graphs, we can see that the critical point occurs around the leaf water content of 0.03 to 0.04 .
Conclusion: Finding the critical points of the reflectance curve against leaf water content can be used to detect severe water stress in plants. In the PROSPECT model, the effect of the ground soil on the spectral reflectance of plants is not considered. Thus, it is suggested that models such as SAIL and SLC be used, which have been improved for this purpose. Also, considering that the PROSPECT model output is the plant’s leaf spectral curve, canopy radiative transfer models such as the SAIL and SLC can also be employed.

Keywords

References

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