Advancing the PROSPECT-5 Model to Simulate the Spectral Reflectance of Copper-Stressed Leaves

Abstract

This paper proposes a modified model based on the PROSPECT-5 model to simulate the spectral reflectance of copper-stressed leaves. Compared with PROSPECT-5, the modified model adds the copper content of leaves as one of input variables, and the specific absorption coefficient related to copper (K cu ) was estimated and fixed in the modified model. The specific absorption coefficients of other biochemical components (chlorophyll, carotenoid, water, dry matter) were the same as those in PROSPECT-5. Firstly, based on PROSPECT-5, we estimated the leaf structure parameters (N), using biochemical contents (chlorophyll, carotenoid, water, and dry matter) and the spectra of all the copper-stressed leaves (samples). Secondly, the specific absorption coefficient related to copper (K cu ) was estimated by fitting the simulated spectra to the measured spectra using 22 samples. Thirdly, other samples were used to verify the effectiveness of the modified model. The spectra with the new model are closer to the measured spectra when compared to that with PROSPECT-5. Moreover, for all the datasets used for validation and calibration, the root mean square errors (RMSEs) from the new model are less than that from PROSPECT-5. The differences between simulated reflectance and measured reflectance at key wavelengths with the new model are nearer to zero than those with the PROSPECT-5 model. This study demonstrated that the modified model could get more accurate spectral reflectance from copper-stressed leaves when compared with PROSPECT-5, and would provide theoretical support for monitoring the vegetation stressed by copper using remote sensing.

Characterizing the Variability of the Structure Parameter in the PROSPECT Leaf Optical Properties Model

“…A few studies exist that examine the variation of N s as a function of vegetation physiological status (i.e., water stress) and phenology [13,22,27,42], but no prior study presents a comprehensive analysis of this relationship. Zhang et al [13] established an indirect relationship between leaf reflectance and copper stress through the N s parameter in two crop species at two vegetative growth stages.…”

“…A few studies exist that examine the variation of N s as a function of vegetation physiological status (i.e., water stress) and phenology [13,22,27,42], but no prior study presents a comprehensive analysis of this relationship. Zhang et al [13] established an indirect relationship between leaf reflectance and copper stress through the N s parameter in two crop species at two vegetative growth stages. Jacquemoud and Baret [22] used a limited set of greenhouse measurements to state that a difference exists between estimated N s in monocotyledon and dicotyledon leaves; Jacquemoud et al [27] found that this difference might not be observed on leaves grown outdoors, but neither study took phenology into consideration.…”

“…The systematic application of vegetation indices for quantitative biochemical component estimation, however, is hampered by large uncertainties. Spectral indices are inevitably sensitive to multiple biochemical and structural characteristics of vegetative canopy systems, and are constrained by the representativeness of the training data needed by this approach [11][12][13]. Alternatively, physical-based approaches may be applied to overcome transferability problems that arise during systemic application [14].…”

“…The leaf Propriétés Spectrales radiative transfer model (PROSPECT), which simulates hemispherical reflectance and transmittance of a leaf in reflective wavelengths in the 400-2500 nm spectral region [22], is one of the most widely applied physical-based models for leaf trait estimation [23,24]. The model has been successfully used to estimate leaf biochemical components by inverting leaf reflectance measurements [13,24,25].…”

Recent Progress in Quantitative Land Remote Sensing in China

“…Although 40 articles cannot comprehensively characterize different aspects of quantitative land remote sensing in China, they clearly represent the current level of research in this area by Chinese scientists. These papers are related to various satellite data products, such as incident solar radiation [38][39][40], chlorophyll fluorescence [41], surface directional reflectance [42][43][44], aerosol optical depth [45], albedo [46,47], land surface temperature [48][49][50], upward longwave radiation [51], leaf area index [52][53][54][55], fractional vegetation cover [56], forest biomass [57], precipitation [58], evapotranspiration [59][60][61], freeze/thaw [62], snow cover [63], vegetation productivity [64][65][66][67][68], phenology [69,70], biodiversity indicators [71], drought monitoring [72], forest disturbance [55], air-quality monitoring [73], sensor design [74], and sampling strategy [75] for validation with in situ measurements. Most of these papers are based on optical-thermal remotely-sensed observations, but a few papers are also based on microwave [62,63] and Lidar…”