Leaf chlorophyll content (LCC) and equivalent water thickness (EWT) are critical indicators of plant physiological state and photosynthetic capacity. Hyperspectral measurements provide technical support for the efficient estimation of these leaf biochemical parameters. However, most current studies ignored the influence of leaf surface specular reflection, which reduces estimation accuracy, in the multi-angular measurements. This study proposed the spectral angle cosine-band depth indices (SACIBD), a method that transforms additive specular reflection into multiplicative differences using band depth (BD) and eliminates them via spectral angle cosine (SAC). Based on a multi-angular calibration dataset, the optimal spectral intervals (OSI) for SACIBDwere identified. Then, SACIBDwas validated across five independent leaf datasets and canopy imaging datasets obtained through close-range camera and LESS simulated scene. Results demonstrated that SACIBDefficiently captured biochemical-sensitive spectral absorption shape features (OSILCC: 658-748 nm and OSIEWT: 1872-2026 nm) and eliminated the specular reflection influence on biochemical estimation. Strong linear relationships with both LCC (R2=0.9) and EWT (R2=0.94) as well as validation results of five leaf datasets (LCC: RMSE = 5.98 μg/cm2, n = 2143; EWT: RMSE = 0.0031 g/cm2, n = 1919) confirmed the applicability of SACIBDfor various measurement strategies and plant species in the estimation of leaf biochemical parameters. When the study was extended to the canopy level, SACIBDachieved similarly good accuracy (LCC: RMSE = 6.98 μg/cm², n = 104; EWT: RMSE = 0.0044 g/cm², n = 34). Thus, the novel combination of SAC and BD provides an effective tool for quantitative plant biochemistry research.

PROSPECT is an effective method to retrieve leaf biochemical constituents, such as chlorophyll (Cab), carotenoid (Cxc), water (Cw), and dry matter (Cm). Several published PROSPECT inversion approaches have been performed using spectral bidirectional reflectance factor (BRF) measured at fixed illumination-viewing geometry. However, considering random leaf orientation, varied solar illumination angles, and wide field of view of the sensor, angular reflection is actually used to retrieve leaf biochemical constituents, which may affect the performance of these inversion approaches. Based on multi-angular spectral BRFs of 497 leaves from 13 plant species in the hemispheric space or the principal plane, we evaluated the performance of seven PROSPECT inversion approaches, including PROSPECT, PROREF, sPROCOSINE, PROCWT-S4, PROCWT-S5, PROFED, and PROSED. Significant angular effects was found on the original PROSPECT inversion, which showed low and angle-dependent retrieval accuracy. PROREF, sPROCOSINE, PROCWT-S4, and PROSED improved certain leaf biochemical constituent retrievals compared with PROSPECT. Interestingly, PROCWT-S5 and PROFED improved the retrievals of Cab, Cxc, Cw, and Cm. Among seven inversion approaches, PROFED not only produced the best accuracy in retrieving Cab(R2= 0.92, RMSEadj= 5.92 μg/cm2), Cxc(R2= 0.46, RMSEadj= 2.86 μg/cm2), Cw(R2= 0.91, RMSEadj= 0.0031 g/cm2), and Cm(R2= 0.25, RMSEadj= 0.0017 g/cm2), but also was insensitive to viewing angles. These results suggest that PROFED could effectively retrieve leaf biochemical constituents across a wide range of plant species at random illumination-viewing geometry, which is beneficial for ecology and botany studies.

Anisotropy index (ANIX), which is defined as the ratio between the maximum and minimum reflectance factors in the principal plane, has been applied in characterizing the optical properties of vegetation. But it is seldom used to estimate the chlorophyll content in leaf level. In this study, we found the leaf spectral anisotropy index (ANIX) depended on the variation of leaf chlorophyll content (LCC). Newly proposed indices, (ASRI: A750/A720 and ANDI: (A750-A720)/(A750+A720), A is the modified ANIX (mANIX=ANIX-1)), were derived from the format of the existing reflectance-based vegetation indices: simple ratio (SR) and normalized difference (ND) indices. The diffuse reflection, closely related to LCC, can be extracted from the two new indices (or directly obtained as the minimum bidirectional reflectance factor). They have strong linear relationships with LCC (R2=0.88 and 0.89), and have good estimation accuracy using an independent dataset (RMSE=6.39 μg/cm2 and 6.10 μg/cm2). We also found the ASRI (ANDI) had similar LCC estimation accuracy as the range-ASRIs or range-ANDIs (RANDIs or RANDIs), which were calculated by the random combination of reflectance factors in the forward scattering directions (ranged from -30° to -50°) and near the backward scattering directions (ranged from 0° to 20°). The wide and effective ranges of viewing angles strengthen the usability of ASRI and ANDI to estimate LCC in the practical measurements.

Vegetation water status, an important physiological characteristic of vegetation, lacked a global-scale estimate method. In this study, a global vegetation moisture relative index (VMRI) was established based on the vegetation optical depth (VOD) and leaf area index and compared to live fuel moisture content (LFMC) in situ measurements and environmental factors (soil moisture from different depths, precipitation, vapor pressure deficit, ratio of actual to potential evapotranspiration, and self-calibrating Palmer drought severity index). Validation using LFMC measurements indicated that VMRI could characterize vegetation water status ($R_{\mathrm {median}}$= 0.37) and that the VMRI establishment method could eliminate the influence of aboveground biomass in VOD. The results of the correlated comparison between VMRI and environmental factors showed positive significant correlations in most regions. In addition, the VMRI was more correlated with environmental factors in shrublands and grasslands (e.g.,$R_{\mathrm {mean}}$= 0.38 in multidepth soil moisture) than in forests and savannas ($R_{\mathrm {mean}}$= 0.15), and the correlations between the VMRI and environmental factors in water limited regions ($R_{\mathrm {mean}}$= 0.33) were higher than those in nonwater limited regions ($R_{\mathrm {mean}}$= 0.18). Moreover, deeper soil moisture provided more information to the VMRI in regions above 60°N. Furthermore, a comparison of soil moisture trends and VMRI trends displayed more synchronization, with about 60% of pixels showing the same trend and about 85% of the same-trend pixels showing decreasing trends. Particularly, interannual variations in forests showed time-lagged responses to environmental drought. Overall, VMRI provides a new in situ measurement-independent estimation for vegetation water status affected by multiple environmental factors at the global scale.

Leaf chlorophyll content (LCC) is a key indicator of plant photosynthesis and can be estimated by the optical properties of leaves. Due to the random distribution of leaf angles and the change of incident light angle, it is necessary to reduce the effects of specular reflection when estimating LCC under different measurement geometries. Because the polarized reflectance factor can account for specular reflection, which does not relate to LCC, it is possible to improve LCC estimation using spectral indices when the polarized reflectance is removed from the total reflectance. In this study, polarimetric measurements of leaves from three different plant species were performed with different measurement geometries in both laboratory and field conditions. We tested all possible waveband combinations in the 400–1000 nm range with two types of spectral indices: simple ratio (SR) ($R_{\lambda 1}/R_{\lambda 2})$and normalized difference vegetation index (NDVI) ($R_{\lambda \mathrm {i}} - R_{\lambda \mathrm {j}}$)/($R_{\lambda \mathrm {i}} + R_{\lambda \mathrm {j}}$), using both total intensity [defined as$I$parameter reflectance factor (IpRF)] and non-polarized [defined as non-polarized reflectance factor (NpRF)] information. By comparing the LCC estimation accuracy based on IpRF with that based on NpRF, we found that NpRF increased the number of bands that can estimate LCC with relatively high accuracy. These results indicate that the simple two-band indices based on the NpRF are robust and accurate for estimating LCC at leaf scale, and the broad effective wavelength range of NpRF may have the ability to overcome bandwidth limitations. The results of this study support future vegetation indices design and model development.

Optical remote sensing is one of the most popular methods for estimating leaf nitrogen concentration (LNC). This nondestructive approach based on reflected intensity measurements has been applied to estimate the variation and distribution of nitrogen concentration in leaf and canopy levels in numerous studies. However, both intensity and polarization are necessary to describe the optical properties of light reflected from leaves and to estimate LNC estimation. In this study, based on the Stokes parameters, the total reflectance, the polarized reflectance, and the nonpolarized reflectance factors (NpRFs) were simultaneously obtained through polarimetric hyperspectral measurements under varied source-viewing geometries in both laboratory and field conditions. Several published hyperspectral indices based on the NpRF showed much better LNC estimation accuracy than those using the total reflectance factor. A clear improvement was found in the viewing directions dominated by specular reflection. Thus, using multi-angular polarimetric hyperspectral measurements not only improves the accuracy of hyperspectral indices on LNC estimation using the NpRF, but also enables the hyperspectral indices to be effective for a wide range of viewing angles. Moreover, polarimetric measurements deepen the understanding of the optical properties of light reflected from leaves. These results indicate that the combination of multiangular, polarimetric, and hyperspectral measurements may play a key role in the estimation of LNC.

Plant leaf chlorophyll content (LCC) plays a key role in the assessment of plant stress and plant functioning. To date, accurate estimation of LCC over a wide range of plant species (trees, bushes, and lianas) under different measurement conditions is still challenging for nondestructive methods. Based on multiangular hyperspectral reflection of 706 leaves (ten plant species), several popular spectral indices were evaluated for a general estimation of LCC. The modified difference ratio index (MDRI) had the strongest linear relationship ($R^{2}=0.92$) to LCC among all the tested spectral indices. The regression algorithm was then used to estimate LCC in other datasets from different regions across the globe. Comparing with the machine learning techniques and PROSPECT model, validation results from 2024 leaves (114 plant species) confirmed that the linear algorithm derived from the MDRI was the most effective for estimating LCC (root-mean-square error (RMSE)$=6.72\,\,\mu \text{g}$/cm2) across a wide range of plant species under different measurement conditions. The MDRI does not require parameterization for each plant species and has the potential to estimate LCC from a simple handheld laboratory or field instrument at any arbitrary direction. The generality of the approach makes it convenient for botanical and ecological studies under different measurement conditions that need accurate LCC estimates.

Leaf BRDF (bidirectional reflectance distribution function) is partly controlled by the specular reflection from the leaf surface, which does not convey information about leaf biochemical parameters. Therefore, vegetation remote sensing can benefit from understanding the effects of BRDF on leaf biochemical parameters estimation and attempting to reduce them. In this study, polarimetric measurements, which simultaneously derive intensity and polarization information, were taken on the leaves of four plant species in the hemispheric space. The results showed that there are distinct BRDF effects on leaf biochemical parameters (leaf chlorophyll content (LCC) and equivalent water thickness (EWT)) estimated using spectral indices based on the intensity (IpRF: I parameter reflectance factor) reflected from leaves. The relationships between four types of spectral indices (single reciprocal, difference between two wavelengths or two reciprocal, simple ratio, and normalized difference indices) and leaf biochemical parameters were found to be weak and dependent on the BRDF of leaves, especially in the forward scattering directions, which are dominated by the specular reflection. After separating the bidirectional polarized reflectance factor (BPRF) from the reflectance intensity (IpRF), the non-polarized reflectance factor (Rn-p) significantly reduces the BRDF effects on biochemical parameters estimation near the specular reflection directions, and a relatively improved estimation is found in either individual viewing directions or over all viewing directions. This study demonstrates that polarimetric measurements are able to reduce BRDF effects on leaf biochemical parameters estimation, and therefore are useful to improve the leaf biochemical parameters estimation using spectral indices in the hemispheric space.

The reflection property of targets is the fundamental signal for applications of optical remote sensing of the earth's surface. In this paper, we measured the photometric and polarimetric characteristics of 15 leaves with different properties from one plant species (i.e., Pachira aquatica) using a laboratory goniospectrometer system and used the bidirectional reflectance factor (BRF) and the bidirectional polarized reflectance factor (BPRF) to describe the reflection of these leaf samples. The results illustrated that the BRF can be replaced by the I parameter reflectance factor (IpRF) when the extinction of the polarizer is considered. Subsequently, the BRF model was fit to the IpRF measurements at selected wavelengths, and the inverted refractive index was used in a BPRF model, which has been proposed to simulate the polarization of surfaces. We found that the modeled photometric results of all the leaves matched well with our measurement results over all the measurement directions, while the modeled polarimetric results of the leaves gave a good agreement with the measurements at the forward scattering directions. Moreover, the degree of linear polarization (Dolp) of the leaf, which is derived from the ratio between BPRF and IpRF, can also be effectively computed by the combination of BPRF and BRF models in the forward scattering directions. These findings suggested that more attention should be dedicated to the combination of BRF and BPRF of leaves in the future because we can completely describe the essential optical properties of the light reflected by leaves via the polarimetric measurement. This paper indicates that the polarimetric measurement is a beneficial method for optical remote sensing applications and helps us deepen the understanding of the optical properties of leaves.

A lot of spectral indices were developed based on the relationship between the spectral reflectance of the upper leaf surface and chlorophyll content. But the lower leaf surface may influence reflectance spectra because of canopy structure or the inclination of leaves. The results of this study showed that structural differences of the two leaf sides may result in differences in reflectance and spectral indices. Among the 30 vegetation indices tested, R672/(R550× R708) had the smallest difference (4.66% for white poplar 2.30% for Chinese elm) between the two blade surfaces of the same leaf in both species. However, linear regression analysis showed that several vegetation indices (R850-R710)/(R850-R680), VOG2, D730, and D740, had high coefficients of determination (R2> 0.8) and varied little between the two leaf surfaces of the plants we sampled. This demonstrated these four vegetation indices were readily available and were little influenced by the differences in the two leaf surfaces during the estimation of leaf chlorophyll content.

Many hyperspectral vegetation indices can be used to estimate the biochemical contents such as pigment content, nondestructively. These reflectance indices are influenced by some leaf surface structure, such as wax, and the existence of the wax affects the performance of the indices on the estimation of the biochemical contents. This research studied the possible effects of the leaf surface wax on the reflectance of the same leaf before and after removal of leaf wax. We found that dewaxing had decreased the reflectance between wavelength 400 and 1000nm, and the decrease depends on the wavelength. The changes of 37 hyperspectral indices before and after the wax removal were compared. The results revealed that some indices such as PSNDb, R672/R550, SIPI and WBI were not affected much by the dewaxing process and are thought relatively robust to estimate the biochemical contents.

Weeping love grass (Eragrostis curvula) has become a well-established invasive species along the Kinu River, Japan and is now considered a problematic invasive weed species. The aim of this study was to map the probability of the establishment of this invasive grass in the Shore of the Kinu River using airborne hyperspectral imagery. Binary logistic regression analysis was used to model the probable presence/absence of weeping love grass. This study tried entering two types of input variables, original reflectance bands and MNF (Minimum Noise Fraction) transformed bands, into the regression model. No available variable of original reflectance data was selected, but two bands of MNF were selected in the regression analysis. The final classification, using the selected MNF bands, has distinguished weeping love grass from pseudo-absence pixels with user's and producer's accuracies of 100% and 66.7% respectively. The kappa coefficient was 0.74. These results indicate that the MNF transformed hyperspectral bands are more suitable than the original reflectance data to estimate the distribution of invasive weeping love grass in the Shore of the Kinu River.