Spectral Ratio Index Research Articles

Improving spectral estimation of soil inorganic carbon in urban and suburban areas by coupling continuous wavelet transform with geographical stratification

Urban soils and cultural layers may accumulate carbon over a long period, so certain carbon stocks may be stored beneath cities. Studies have shown that the turnover rate of soil inorganic carbon (SIC) is also fast, and its role in global carbon pool and regulation of atmospheric CO2 should be noticed. Visible and near infrared (vis–NIR) spectroscopy, as a rapid and cost-effective approach, has demonstrated its huge potential for soil characterization. However, little has been reported on the potential of vis–NIR spectra for estimating SIC in urban and suburban areas that are greatly affected by human activities. Here, an urban soil spectral library (SSL) with 3492 samples in Wuhan City, China was established. Our main objective was to evaluate whether stratification of an urban SSL by land-use/land-cover (LULC) strategy can generate better estimation, compared with non-stratified (global) model for predicting SIC. Five modeling approaches were developed for SIC prediction: linear regression using an optimal spectral ratio index, and partial least squares regression, Cubist, convolutional neural network (CNN), and autoencoder-based residual deep network using full-spectrum. Raw absorbance was processed by continuous wavelet transform (CWT), thus decomposing spectral data at six scales. Results demonstrated that in almost all cases, CWT scale 1 obtained better performances in predicting SIC than raw absorbance and other five CWT scales. Stratification by LULC resulted in improved accuracy, compared with non-stratified model, with CWT scale 1 obtaining the highest validation R2 of 0.73. Among the non-stratified modeling, CNN based on CWT scale 1 generated the optimal validation result with an R2 value of 0.69. The most important spectral absorption for SIC was peaked around 2310 nm. This study confirms that vis–NIR technique combined with LULC stratification and machine/deep learning has a huge potential for SIC prediction in urban soils.

Optimized spectral index models for accurately retrieving soil moisture (SM) of winter wheat under water stress

Soil moisture (SM) is an important indicator of the photosynthetic rate and growth status of crops. A few related parameters, such as the red-edge parameters and spectral indices, have been adopted for retrieving the SM of winter wheat. To further study their abilities to detect the SM, field-scale water stress experiments on winter wheat were conducted during the 2018/19 growing season. The spectral ratio index in the near-infrared (NIR) shoulder region (NSRI) (700–1100 nm) was selected by comparing the correlations between the SM and the red edge parameters and spectral indices, and it was optimized using the partial least squares regression (PLSR) method. To assess the performance of the sensitive wavebands of the NSRI in retrieving the SM, three types of spectral index models were established using multiple linear regression (MLR) for the winter wheat from the jointing to the ripening stage. The results indicate that the red-edge parameters are more sensitive to the spectral variation during the jointing and flowering stages. The sensitivity decreased with increasing water stress. The red-edge area (SDr) of winter wheat irrigated in the flowering stage (D1 treatment) and irrigated in the jointing stage (D2 treatment) decreased by 20–30%, respectively. In general, all of the parameters and indices were correlated with the surface SM (0–40 cm depth), especially for the NSRI, with a significant coefficient of determination (R2) of 0.52 in the 10–20 cm depth interval (P < 0.01). Moreover, all of the spectral index models based on the optimized NSRI have good capabilities for retrieving the SM in the jointing stage. The model for one derivative of the logarithm of the NSRI (logarithmic NSRI)' performed best, with R2 and root mean square error (RMSE) values of 0.81–0.92 and 0.17–0.89%, respectively. Finally, the (logarithmic NSRI)' model was used to retrieve the SM in the flowering–ripening stage (R2 =0.85). Overall, the optimized spectral index models can accurately and quickly retrieve the SM and can assist in predicting the effect of drought on the crop yield in the future.

Analyzing spatial variability in night-time lights using a high spatial resolution color Jilin-1 image – Jerusalem as a case study

In recent decades, there has been an increase in artificial lighting in the world due to urbanization and the revolution of LED lighting. Artificial lighting is an indicator of human activity, but can adversely affect natural ecosystems and people due to negative impacts of light pollution. Space-borne and airborne imagery as well as ground-based measurements enable to measure the intensity and spectra of artificial lights. One of the challenges in remote sensing of night-time lights is how to ground truth night-time imagery acquired by satellites, and how much do space-borne measurements represent the brightness as perceived by organisms. Most of the studies on night-time lights to-date were done using panchromatic sensors at large spatial extents, which did not allow to examine intra-urban variation in night light intensity and spectra. The aim of this study was to test the capability of the new Chinese satellite Jilin-1, which is the first commercial satellite to offer multispectral night-light imagery at a spatial resolution below 1 m, to characterize the night-time properties of urban areas. We examined the correspondence between light intensities as measured from different sensors at different spatial resolutions: two Jilin-1 images of the Jerusalem metropolitan area (0.89 m), VIIRS/DNB (500 m), Loujia-1 (130 m), unmanned aerial vehicle (UAV) color image (0.05 m) and hemispherical color photographs taken by a calibrated ground DSLR (digital single-lens reflex camera). In all the comparisons between different remote sensing tools, as the spatial resolution coarsened, the Pearson correlation coefficient increased, reaching r > 0.5 (after resampling to 100 m). Stronger correlations were found for the red band, and weaker correlations were found for the blue band, probably due to atmospheric scattering. By identifying specific objects such as buildings and lightings, we found good correspondence (R2=0.51) between Jilin-1 and the ground-based measurements of night-time brightness. We further examined the variability of night lights within different land use types and within different ethnic/religion composition of statistical areas. We found that residential areas of Orthodox Jews were characterized with the highest brightness at night compared with residential areas of Arabs in the West Bank that had the lowest brightness. At the statistical zone level (n = 299), more than 50% of the variability in night-time brightness, was explained by land cover properties (NDVI), infrastructure (roads and built volume) and the ethnic/religious composition. In addition, we found that the spectral ratio index which was based on the red and green bands, enabled to better distinguish between land use classes, than the spectral ratio index which was based on the green and blue bands. The availability of night-time multi-spectral imagery at fine spatial resolution now enables to study urban land-use and spatial inequality, and to better understand the factors explaining night-time brightness.

Spectral reflectance indices sense desiccation induced changes in the thalli of Antarctic lichen Dermatocarpon polyphyllizum

Lichens, in polar and alpine regions, pass through repetitive dehydration and rehydration events over the years. The harsh environmental conditions affect the plasticity of lichen’s functional and structural features for their survival, in a species-specific way, and, thus, their optical and spectral characteristics. For an understanding on how dehydration affects lichens spectral reflectance, we measured visible (VIS) and near infrared (NIR) reflectance spectra of Dermatocarpon polyphyllizum, a foliose lichen species, from James Ross Island (Antarctica), during gradual dehydration from fully wet (relative water content (RWC) = 100%) to dry state (RWC = 0%), under laboratory conditions, and compared several derived reflectance indices (RIs) to RWC. We found a curvilinear relationship between RWC and range of RIs: water index (WI), photochemical reflectance index (PRI), normalized difference vegetation index (NDVI), modified chlorophyll absorption in reflectance indices (MCARI and MCARI1), simple ratio pigment index (SRPI), normalized pigment chlorophyll index (NPCI), and a new NIR shoulder region spectral ratio index (NSRI). The index NDVI was initially increased with maxima around 70% RWC and it steadily declined with further desiccation, whereas PRI in-creased with desiccation and steeply falls when RWC was below 10%. The curvilinear relationship, for RIs versus RWC, was best fitted by polynomial regressions of second or third degree, and it was found that RWC showed very high correlation with WI (R2 = 0.94) that is followed by MCARI (R2 = 0.87), NDVI (R2 = 0.83), and MCARI (R2 = 0.81). The index NSRI, proposed for accessing structural deterioration, was almost invariable during dehydration with the least value of the coefficient of determination (R2 = 0.28). This may mean that lichen, Dermatocarpon polyphyllizum, activates protection mechanisms initially in response to the progression of dehydration; however, severe dehydration causes deactivation of photosynthesis and associated pigments without much affecting its structure.

Analysis of water quality parameters by hyperspectral imaging in Ganges River

Optical dynamic properties of water have capacity to give a coherent outline of water quality, however its exactness relies on the samples gathered from water bodies. This work was performed to make utilization of remotely detected information for recognizing water quality parameters in view of optical dynamic properties of water. Airborne visible/infrared imaging spectrometer new generation (AVIRIS-NG) was guided over the Ganges River Buxer, Bihar, India gathering hyperspectral band of information. The image taken from nearly 10 km of the Ganges River with sample and line numbers 731 and 3190 respectively, having 425 spectral bands between 380 and 2510 nm wavelength with 5 nm sampling. Water test from 17 areas of Ganges River were gathered and dissected. By the utilization of ground truth information and combination of spectral band got from hyperspectral imaging, different spectral indices were readied which are valuable in evaluating chlorophyll-a, turbidity and aggregate phosphorus. The results show that the Pearson correlation between ground truth data and spectral ratio indices were stronger than the single spectral band. It is also clear that the various indices seem to associate with particular class of ground truth data. Spectral bands having wavelength 677, 702, 705, 671 and 742 nm are dominating the formation of spectral indices. The correlation and R2 for each investigated parameter were greater than 0.6 and 0.5 representing to develop a good linear model.

Potential of spectral ratio indices derived from hyperspectral LiDAR and laser-induced chlorophyll fluorescence spectra on estimating rice leaf nitrogen contents.

Accurate estimation of leaf nitrogen contents (LNCs) is essential for nutrition management in monitoring crop growth status. The aim of this study was to compare the potential of hyperspectral LiDAR (HSL) and laser-induced chlorophyll fluorescence (LIF) data in accurately predicting rice LNC. First of all, the intensity values of HSL at 694 and 742 nm and LIF at ~685 and ~740 nm were selected as the characteristic variables to analyze rice LNC using data collected in 2014 and 2015, respectively. Second, spectral indices derived from HSL (only) and LIF (only) were utilized to estimate LNC of rice, respectively. Third, a combined ratio indices (the ratio indices of reflectance to fluorescence and NDVI-based indices at the above four wavelengths) was developed and evaluated in estimating rice LNC. The statistical method of linking these spectral indices to rice LNC was the artificial neural network, which was to obtain the optimum performance in LNC estimation of rice. The results demonstrated that the combined ratio indices, especially the ratio of reflectance to fluorescence at ~740 nm, showed a moderate relationship with rice LNC (R2 = 0.736, 0.704, and 0.713 for the 2014 first experiment, 2014 second experiment, and 2015 experiment, respectively).

Assessing soil organic matter of reclaimed soil from a large surface coal mine using a field spectroradiometer in laboratory

Soil organic matter (SOM) for topsoil is one of most important indicators to support the success of mine ecological reclamation. SOM varies along the artificial mine landscape characterized by different bench-slopes of dump. Reflectance using field spectroscopy can provide useful information on the assessment of punctual soil variation, and has the advantages of speed and efficiency. The aims of this study were to 1) explore the characteristic spectrum of reclaimed soil of different landforms, 2) develop a key spectral-ratio index for evaluating SOM content, and 3) establish a SOM prediction model using the Partial Least Square Regression-Support Vector Machine (PLS-SVM) method. Based on comprehensive analysis of the relationship between SOM content and corresponding spectral reflectance in soils from different landforms, the results showed a new derived spectral index would be useful for estimating SOM. The ratio spectral index (R2294nm/R2286nm), calculated using available wavebands in the 350–2500nm region, was proposed for use in the reliable estimation of SOM from downslope and midslope. The PLS-SVM calibration model for the raw spectrum, showed a high predictive accuracy for estimating the SOM content, with cross-validated R2 of 0.95, and RMSE of 0.12. These outcomes provide a theoretical basis and technical support for estimations of SOM content using visible/near-infrared spectra in reclamation areas. It is proposed that the spectral difference index and model undergo further testing and optimization prior to wider application for observation of mine-reclamation ecosystems.

Evaluation of hyperspectral indices for retrieval of canopy equivalent water thickness and gravimetric water content

ABSTRACTThere are two main parameters describing the amount of water in vegetation: the gravimetric water content (GWC) and the equivalent water thickness (EWT). In this study, we investigated the applicability of hyperspectral water-sensitive indices from canopy spectra for estimating canopy EWT (CEWT) and GWC. First, the spectral reflectance’s response to different levels of canopy water content was analysed and a noticeable increase in the slope of the near-infrared (NIR) shoulder of the canopy spectrum was observed. Next, the correlation between the CEWT and various hyperspectral water-sensitive indices was investigated. It was found that all of the indices could retrieve the CEWT of winter wheat well, with the coefficients of determination (R2) all being higher than 0.80. Finally, the retrieval performance of these indices for canopy GWC was evaluated and no significant correlation was observed between canopy GWC and the water-sensitive indices except for the spectral ratio index in the NIR shoulder region (NSRI). These results showed that the traditional water-sensitive vegetation indices are more sensitive to CEWT than to GWC, especially when the LAI is not highly correlated with the GWC, and that the NSRI is a potential vegetation index for use in the retrieval of GWC.

Critical evaluation of spectroscopic indices for organic matter source tracing via end member mixing analysis based on two contrasting sources

Despite the wide use of absorption and fluorescence spectroscopy for tracking the sources of dissolved organic matter (DOM), there are limited studies on evaluating their source discrimination capabilities at variable solution chemistry (pH, NaCl, Ca2+, and DOM concentration). For this study, we compared the applicability of several well-known spectroscopic indices via end member mixing analysis based on two contrasting DOM sources (Suwannee River fulvic acid and an algal DOM). The absorption coefficients and the intensities of fluorescent components from parallel factor analysis (PARAFAC) showed linear relationships with increasing algal carbon fraction in the mixture of the two DOMs. In contrast, although they still behaved conservatively, spectral ratio indices such as spectral slopes, ratios of PARAFAC components, humification index, and fluorescence index changed in nonlinear patterns with the mixing ratios. The indices based on PARAFAC results exhibited strong discrimination capabilities, as indicated by high susceptibility to the changes in DOM sources relative to the analytical precision. While variable NaCl concentrations had limited effects, most fluorescence indices were considerably affected by other solution chemistry such as pH, Ca2+, and DOM level. Our study demonstrated that the applicability of the source discrimination indices should be critically examined especially in the environments with notable changes in the solution chemistry. The solution chemistry effects could be minimized by adjusting samples to a constant condition prior to the measurements or otherwise the effects should be fully taken into account in interpreting the field observations.

Detection of Internal Leaf Structure Deterioration Using a New Spectral Ratio Index in the Near-Infrared Shoulder Region

Spectral reflectance in the near-infrared (NIR) shoulder (750-900 nm) region is affected by internal leaf structure, but it has rarely been investigated. In this study, a dehydration treatment and three paraquat herbicide applications were conducted to explore how spectral reflectance and shape in the NIR shoulder region responded to various stresses. A new spectral ratio index in the NIR shoulder region (NSRI), defined by a simple ratio of reflectance at 890 nm to reflectance at 780 nm, was proposed for assessing leaf structure deterioration. Firstly, a wavelength-independent increase in spectral reflectance in the NIR shoulder region was observed from the mature leaves with slight dehydration. An increase in spectral slope in the NIR shoulder would be expected only when water stress developed sufficiently to cause severe leaf dehydration resulting in an alteration in cell structure. Secondly, the alteration of leaf cell structure caused by Paraquat herbicide applications resulted in a wavelength-dependent variation of spectral reflectance in the NIR shoulder region. The NSRI in the NIR shoulder region increased significantly under an herbicide application. Although the dehydration process also occurred with the herbicide injury, NSRI is more sensitive to herbicide injury than the water-related indices (water index and normalized difference water index) and normalized difference vegetation index. Finally, the sensitivity of NSRI to stripe rust in winter wheat was examined, yielding a determination coefficient of 0.61, which is more significant than normalized difference vegetation index (NDVI), water index (WI) and normalized difference water index (NDWI), with a determination coefficient of 0.45, 0.36 and 0.13, respectively. In this study, all experimental results demonstrated that NSRI will increase with internal leaf structure deterioration, and it is also a sensitive spectral index for herbicide injury or stripe rust in winter wheat.

Relationships between Spectral Reflectance and Pigment Concentrations in Stacks of Deciduous Broadleaves

This article presents the findings of a laboratory experiment using stacks of leaves obtained from four species of deciduous tree at various stages of senescence. This methodology generated a wide variation in pigment concentrations (per unit leaf area and per unit ground area) and leaf area index, as may be expected at the plant canopy scale, and provided independent variation between the chlorophylls and carotenoids and leaf area index. Spectral reflectance was measured for each leaf increment of each stack using a spectroradiometer and these data were used to evaluate the nature and strength of relationships between a number of existing and novel spectral transformations and the concentrations of pigments (per unit ground area) within the leaf stacks. Reflectance in narrow wavebands within the visible region was moderately related to chlorophyll concentrations, while ratio indices, employing wavebands in the near- infrared and visible, particularly the green, were more successful. While the wavelength position of the red edge was related to chlorophyll concentration, characteristics of the amplitude of the first and second derivatives of reflectance and pseudo absorbance were more strongly correlated with chlorophyll. No transformation of spectral reflectance that was tested was strongly related to the concentration of carotenoids but two ratio indices were highly correlated with the ratio of carotenoids to chlorophyll a. Interestingly, leaf area index (LAI) was unrelated to all spectral ratio indices including the broad-band normalized-difference and the simple ratio vegetation indices. This indicates that spectral indices may have limited applicability for estimating the LAI of vegetation canopies when the leaves that comprise these canopies have differing chlorophyll concentrations (per unit leaf area).