First Derivative Reflectance Research Articles

Using Spectroradiometry to Measure Organic Carbon in Carbonate-Containing Soils.

This study explores the feasibility of analyzing soil organic carbon (SOC) in carbonate-rich soils using visible near-infrared spectroscopy (VIS-NIR). Employing a combination of datasets, feature groups, variable selection methods, and regression models, 22 modeling pipelines were developed. Spectral data and spectral data combined with carbonate contents were used as datasets, while raw reflectance, first-derivative (FD) reflectance, and second-derivative (SD) reflectance constituted the feature groups. The variable selection methods included Spearman correlation, Variable Importance in Projection (VIP), and Random Frog (Rfrog), while Partial Least Squares Regression (PLSR), Random Forest Regression (RFR), and Support Vector Regression (SVR) were the regression models. The obtained results indicated that the FD preprocessing method combined with RF, results in the model that is sufficiently robust and stable to be applied to soils rich in calcium carbonate.

Inversion of soil water and salt information based on UAV hyperspectral remote sensing and machine lear-ning.

Accurate diagnosis of water and salt information in saline agricultural lands is crucial for long-term soil quality improvement and arable land conservation. In this study, we extracted field-scale vegetation canopy spectral information by UAV hyperspectral information, transforming the reflectance (R) to standard normal variate transformation (SNV), multiplicative scatter correction (MSC), first derivative of reflectance (FDR) and second derivative of reflectance (SDR). We determined the optimal spectral transformation forms of soil water content (SWC), soil pH, and soil salt content (SSC) by the maximum absolute correlation coefficient (MACC), and extracted the feature bands by competitive adaptive reweighted sampling (CARS). We constructed an inversion model of soil water and salt information by partial least squares regression (PLSR), random forest (RF), and extreme gradient boosting (XGBoost). The results showed that R, FDR and MSC were the best spectral transformation types for soil water content, soil pH, and soil salt content, and the corresponding MACC were 0.730, 0.472 and 0.654, respectively. The CARS algorithm effectively eliminated the irrelevant variables, optimally selecting 16-17 feature bands from 150 spectral bands. Both soil water content and soil pH performed best with XGBoost model, achieving determination coefficient of validation (Rp2) 0.927 and 0.743, and the relative percentage difference (RPD) amounted to 3.93 and 2.45. For soil salt content, the RF model emerged as the best inversion method with Rp2 and RPD of 0.427 and 1.64, respectively. The study could provide a reference solution for the integrated remote sensing monitoring of soil water and salt information in space and sky, serving as a scientific guide for the amelioration and sustainable management of saline lands.

Prediction of Potassium Content in Rice Leaves Based on Spectral Features and Random Forests

The information acquisition about potassium, which affects the quality and yield of crops, is of great significance for crop nutrient management and intelligent decision making in smart agriculture. This article proposes a method for predicting the rice leaf potassium content (LKC) using spectral characteristics and random forests (RF). The method screens spectral characteristic variables based on the linear correlation analysis results of rice LKC and four transformed spectra (original reflectance (R), first derivative reflectance (FDR), continuum-removed reflectance (CRR), and normalized reflectance (NR)) of leaves and the PCA dimensionality reduction results of vegetation indices. Following a second screening of the correlated single band and vegetation index variables of the four transformed spectra, the RF is used to obtain the mixed variable (MV), and regression models are developed to achieve an accurate prediction of rice LKC. Additionally, the effect of potassium spectral sensitivity bands, indices, spectral transformation form, and different modeling methods on rice LKC prediction accuracy is assessed. The results showed that the mixed variable obtained with the second screening using the random forest feature selection method could effectively improve the prediction accuracy of rice LKC. The regression models based on the single band variables (BV) and the vegetation index variables (IV), FDR–RF and IV–RF, with R2 values of 0.62301 and 0.7387 and RMSE values of 0.24174 and 0.15045, respectively, are the best models. In comparison to the previous two models, the MV–RF validation had a higher R2 and a lower RMSE, reaching 0.77817 and 0.14913, respectively. It can be seen that the RF has a better processing ability for the MV that contains vegetation indices and IV than for the BV. Furthermore, the results of different variable screening and regression analyses also revealed that the single band’s range of 1402–1428 nm and 1871–1907 nm, as well as the vegetation indices constituted of reflectance 1799–1881 nm and 2276–2350 nm, are of great significance for predicting rice LKC. This conclusion can provide a reference for establishing a universal vegetation index related to potassium.

A dynamic normalized difference index for estimating soil organic matter concentration using visible and near-infrared spectroscopy

Accurate, rapid, and non-destructive estimation of soil organic matter (SOM) is crucial for soil fertility diagnosis and precision farming. Due to the complicated and unstable spectral characteristics of SOM, few SOM spectral indexes have been proposed and widely used. In this paper, a new dynamic normalized difference index (DNDI) was proposed and constructed to estimate SOM using visible and near-infrared spectroscopy. A dynamic correction factor α was used to adjust the optimal wavelength range to obtain more robust features of SOM. Different spectral pre-processing methods were applied and compared. The support vector machine (SVM) model and Partial least square regression (PLSR) model were calibrated based on DNDI and applied to estimate SOM. To this end, a total of 111 soil samples were collected in the southern coastal plain of Laizhou Bay. The results showed that the DNDI index constructed by wavelength optimization could have a higher correlation with SOM than the two-dimensional normalized difference index (NDI). DNDI had the maximum correlation of 0.88 from the first derivative of reflectance, and the NDI correlations were most improved by standard normal variate transform (SNV), with the maximum correlation reaching 0.81. For SOM estimation models, DNDI exhibited better model performance, yielding a validation R2, RMSE, and RPD of 0.78, 0.17 g·kg−1, and 2.01, respectively. Our algorithm has strong application potential for estimating other soil properties and enhancing the application of ground- and satellite-based sensing.

An Indirect Inversion Scheme for Retrieving Toxic Metal Concentrations Using Ground-Based Spectral Data in a Reclamation Coal Mine, China

A reclamation coal mine in Baishui County of Shaanxi Province, China, was selected as the study area to develop a fast survey method for estimating soil heavy metal concentrations using spectral data. A portable object spectrometer manufactured by Analytical Spectral Devices (ASD) was used to measure soil spectral reflectance, and an X-ray fluorescence device was utilized to obtain the content of heavy metals. The Savitzky-Golay filter, first derivative reflectance (FDR), second derivative reflectance (SDR), continuum removal (CR), and continuous wavelet transform (CWT) were used to transform the original reflectance (OR) spectra for enhancing the spectral characteristics, respectively. Furthermore, correlation analysis was introduced to determine the characteristic bands and the correlations of heavy metals. Partial least squares regression (PLSR), extremely learning machine (ELM), random forest (RF), and support vector machine (SVM) were implemented for quantitatively determining relations between heavy metal contents and spectral reflectance. The outcomes demonstrated that the spectral transformation methods could effectively capture the characteristic bands and increase the relations between heavy metal contents and spectral reflectance. The relation between Fe and Ni was close with a relatively high correlation coefficient (r = 0.741). RF combined with CWT at the decomposition scales of 9 demonstrated the best performance with the highest Rv2 (0.71) and the lowest RMSEv (1019.1 mg/kg) for inferring Fe content. Ni content was inferred based on the close relationship between Fe and Ni. The result of RF was better than other methods with the highest Rv2 (0.69) and the lowest RMSEv (1.94 mg/kg) for estimating Ni concentration. Therefore, the RF model was chosen for mapping Fe and Ni contents in the study area. The present study revealed that the indirect inversion methods using spectral data can be effectively used to predict heavy metal concentrations. The outcomes supply a new perspective for retrieving heavy metal content based on hyperspectral remotely sensed technology.

Mapping Soil Organic Matter Content Based on Feature Band Selection with ZY1-02D Hyperspectral Satellite Data in the Agricultural Region

Soil organic matter (SOM) is an essential nutrient for crop growth and development. Hyperspectral satellite images with comprehensive spectral band coverage and high spectral resolution can be used to estimate and draw a spatial distribution map of SOM content in the region, which can provide a scientific management basis for precision agriculture. This study takes Xinzheng City, Henan Province’s agricultural area, as the research object. Based on ZY1-02D hyperspectral satellite image data, the first derivative of reflectance (FDR) was processed on the original reflectance (OR). The SOM characteristic spectral bands were extracted using the correlation coefficient (CC) and least absolute shrinkage and selection operator (Lasso) methods. The prediction model of SOM content was established by multiple linear regression (MLR), partial least squares regression (PLSR), and random forest (RF) algorithms. The results showed that: (1) FDR processing can enhance SOM spectral features and reduce noise; (2) the Lasso feature band extraction method can reduce the model’s input variables and raise the estimation precision; (3) the SOM content prediction ability of the RF model was significantly better than that of the MLR and PLSR models. The FDR-Lasso-RF model was the best SOM content prediction model, and the validation set R2 = 0.921, MAEV = 0.512 g/kg, RMSEV = 0.645 g/kg; (4) compared with laboratory hyperspectral data-SOM prediction methods, hyperspectral satellite data can achieve accurate, rapid, and large-scale SOM content prediction and mapping. This study provides an efficient, accurate, and feasible method for predicting and mapping SOM content in an agricultural region.

Prediction of Soluble Solids and Lycopene Content of Processing Tomato Cultivars by Vis-NIR Spectroscopy.

Tomato-based products are significant components of vegetable consumption. The processing tomato industry is unquestionably in need of a rapid definition method for measuring soluble solids content (SSC) and lycopene content. The objective was to find the best chemometric method for the estimation of SSC and lycopene content from visible and near-infrared (Vis-NIR) absorbance and reflectance data so that they could be determined without the use of chemicals in the process. A total of 326 Vis-NIR absorbance and reflectance spectra and reference measurements were available to calibrate and validate prediction models. The obtained spectra can be manipulated using different preprocessing methods and multivariate data analysis techniques to develop prediction models for these two main quality attributes of tomato fruits. Eight different method combinations were compared in homogenized and intact fruit samples. For SSC prediction, the results showed that the best root mean squared error of cross-validation (RMSECV) originated from raw absorbance (0.58) data and with multiplicative scatter correction (MSC) (0.59) of intact fruit in Vis-NIR, and first derivatives of reflectance (R2 = 0.41) for homogenate in the short-wave infrared (SWIR) region. The best predictive ability for lycopene content of homogenate in the SWIR range (R2 = 0.47; RMSECV = 17.95 mg kg–1) was slightly lower than that of Vis-NIR (R2 = 0.68; 15.07 mg kg–1). This study reports the suitability of two Vis-NIR spectrometers, absorbance/reflectance spectra, preprocessing methods, and partial least square (PLS) regression to predict SSC and lycopene content of intact tomato fruit and its homogenate.

Monitoring Leaf Nitrogen Accumulation With Optimized Spectral Index in Winter Wheat Under Different Irrigation Regimes.

Rapid and non-destructive estimation of leaf nitrogen accumulation (LNA) is essential to field nitrogen management. Currently, many vegetation indices have been used for indicating nitrogen status. Few studies systematically analyzed the performance of vegetation indices of winter wheat in estimating LNA under different irrigation regimes. This study aimed to develop a new spectral index for LNA estimation. In this study, 2 years of field experiments with different irrigation regimes were conducted from 2015 to 2017. The original reflectance (OR) and three transformed spectra [e.g., the first derivative reflectance (FDR), logarithm of the reciprocal of the spectra (Log(1/R)), and continuum removal (CR)] were used to calculate two- and three-band spectral indices. Correlation analyses and univariate linear and non-linear regression between transformed-based spectral indices and LNA were performed. The performance of the optimal spectral index was evaluated with classical vegetation index. The results showed that FDR was the most stable transformation method, which can effectively enhance the relationships to LNA and improve prediction performance. With a linear relationship with LNA, FDR-based three-band spectral index 1 (FDR-TBI1) (451, 706, 688) generated the best performance with coefficient of determination (R2) of 0.73 and 0.79, the root mean square error (RMSE) of 1.267 and 1.266 g/m2, and the ratio of performance to interquartile distance (RPIQ) of 2.84 and 2.71 in calibration and validation datasets, respectively. The optimized spectral index [FDR-TBI1 (451, 706, 688)] is more effective and might be recommended as an indicator for estimating winter wheat LNA under different irrigation regimes.

Suitability of different multivariate analysis methods for monitoring leaf N accumulation in winter wheat using in situ hyperspectral data

Leaf nitrogen accumulation (LNA) is an important indicator of crop growth, and the real-time and nondestructive estimation of LNA by hyperspectral remote sensing is crucial for crop N management. In this study, five field experiments were conducted over four successive years at three ecological sites (Xinyang, Zhengzhou and Zhoukou) in Henan, China, incorporating different N amounts and wheat varieties. The canopy spectral data were obtained by a ground-based spectrometer, and the original spectral reflectance (R) was transformed using continuum removal (CR), first-derivative reflectance (FDR) and inverse-log reflectance (log (1/R)) methods. The effective bands of the above four transformed reflectance data were selected by the variable importance in projection (VIP) scores of the partial least square regression method (PLSR). Then, four multivariate regression methods (PLSR, stepwise multiple linear regression (SMLR), extreme learning machine (ELM) and support vector machine (SVM)) were used to develop an optimal forecasting model for LNA in winter wheat. The results show that FDR spectral data had the best comprehensive performance and were a better method for spectral data preprocessing. In the comparison of different nonparametric algorithms, the SVM model performed the best using the full wavelengths, in which R2cal/val and RMSEcal/val were 0.927–0.963/0.899–0.925 and 0.725–1.066/1.161–1.331 g m−2, respectively. The characteristic bands of FDR were mainly distributed in the visible light and red edge regions (445, 501, 546, 683, 747 and 771 nm, respectively), and these effective bands were consistent under different field conditions. The SVM regression method using the effective wavelengths of FDR exhibited excellent performance for LNA prediction with R2val = 0.875 and RMSEval = 1.294. In addition, the FDR-PLSR-SVM model produced satisfactory inversion results under different field conditions. The accuracy of the filling period was better than that of the reviving-anthesis growth period, which might be due to diminished N dilution and stable canopy structure during the reproductive stage. R2val and RMSEval of the FDR-PLSR-SVM model during the filling period were 0.914 and 1.156 g m−2 in Xinyang, 0.825 and 1.542 g m−2 in Zhengzhou and 0.796 and 1.902 g m−2 in Zhoukou, respectively. Considering the advantages of simple and efficient computer calculations, the FDR-PLSR-SVM algorithm is strongly recommended to predict the LNA in winter wheat. This study provides a reference basis for the selection of effective wavelengths for N nutrition diagnosis by unmanned aerial vehicles and satellite remote sensing in the future.

Estimating chlorophyll and nitrogen contents in maize leaves (Zea mays L.) with spectroscopic analysis

The relationship between reflectance and chlorophyll (Chl) and nitrogen (N) contents in maize leaves was analyzed to identify useful spectral indices for diagnosing the nutritional status of crops in terms of N. An experiment was carried out in random blocks with five treatments of nitrogen fertilizer (0, 50, 100, 150, 200 kg ha-1) and four repetitions and the foliar spectral responses were measured with a FieldSpec 4 spectroradiometer in three phenological stages. Several spectral indices and values of red edge position (REP) were calculated using various methods. Red-edge position linear interpolation (REP-L), Red-edge position linear extrapolation (REP-LE), REP-Inverted Gaussian fitting technique (REP-IG), REP-Polynomial fitting technique (REP-P) and NDVI had the best relationship with chlorophyll and nitrogen contents. The first derivative of reflectance, between 560 and 760 nm, transformed by the normal state variable (SNV) also had highly significant correlation coefficients with the N, Chl, and yield. Additionally, the corn yield showed highly significant correlations with the N and Chl contents. From the point of view of the diagnosis of the nutritional status of corn, the spectral indices and REP values were suitable for establishing the nutritional status of corn in relation to N in the phenological stages V8 and R1.

Applicability of optimized hyperspectral indices for estimating Betalain content in Suaeda salsa

ABSTRACT Betalains (Bt) are a collective group of natural, edible, red-yellow plant pigments, and are an important biochemical parameter in vegetable growth. Betalains are believed to have fungicidal activity and their presence in plants is a key to explaining the physiological response and resistance of plants caused by different environmental stress factors or seasonal fluctuations. Hyperspectral remote sensing has proven potential to understand many biochemical processes widely used in investigating vegetation growth condition. To better understand vegetation containing betalain pigments, for growth monitoring and agronomic decision-making, a novel betalain source, Suaeda salsa (S. salsa) was studied by extracting betalain at different phenological periods, and registering the results with time-course measurements of canopy spectral reflectance via hyperspectral remote imaging. Partial least squares regression (PLSR) analysis was undertaken with three spectral transformation methods (the raw hyperspectral reflectance (R), first derivative reflectance (FDR) and second derivative reflectance (SDR). The variable importance in projection (VIP) score resulting from PLSR model was used to determine the key spectral wavelengths and reduce the dimensionality of the hyperspectral reflectance data matrix. The study results demonstrated that Bt content had a significant correlation with the effective wavelength (FD-R 436nm, R 847nm, R 848nm, R 860nm, R 871nm, R 971nm and SD-R 437nm, R 496nm, R 499nm, R 508nm, R 740nm, R 772nm, R 774nm, R 780nm) and optimized spectral indices (simple normalized difference spectral indices (NDSI(802nm,408nm), NDSI(781nm,776nm)), normalized polarization indices (NPDI(781nm,775nm), NPDI(781nm,776nm)), ratio spectral indices (RSI(781nm,776nm), RSI(781nm,775nm)), chlorophyll indices(CI(781nm,776nm), CI(781nm, 775nm)) derived from first derivative spectral reflectance. The PLSR-5 (constructed with the spectral indices) model resulted in an R 2 val (determination coefficient) of 0.79 and root mean square error (RMSEval) was 0.81 μg cm−2. Compared to PLSR-3 model built by sensitive bands, the PLSR-5 model estimation accuracy increased 15%. These results suggest the possibility of estimating Bt content using hyperspectral indices, which contain sufficient information for a successful estimation. Study findings will help researchers in deciding suitable Bt indices for S. salsa growth status and stress assessment, and will facilitate detection growth condition of the vegetables with betalain pigments.

Improving the accuracy of soil organic carbon content prediction based on visible and near-infrared spectroscopy and machine learning

Choosing appropriate multivariate calibration and preprocessing transformation techniques is important in the determination of soil organic carbon (SOC) content based on visible and near-infrared (Vis–NIR) spectroscopy. The performance levels of partial least-squares regression (PLSR), support vector machine regression (SVMR), and wavelet neural network (WNN) calibration methods coupled with different preprocessing approaches were compared using three kinds of criteria, including the coefficient of determination (R2), root mean square error (RMSE), and residual prediction deviation (RPD). A total of 328 soil samples collected from the south bank of Hangzhou Bay were used as the dataset for the calibration–validation procedure and SOC content inversion. The effects of spectra preprocessing transformation methods were evaluated for raw spectra, Savitzky–Golay smoothing with the first derivatives of reflectance (FDR) and Savitzky–Golay smoothing with logarithm of reciprocal of the reflectance (log R−1). The results indicate that the SVMR is superior to the PLSR, and WNN models for SOC content prediction. The combination of the SVMR model with FDR provided the best prediction results for the SOC content, with R2p = 0.92, RPDP = 2.82, RMSEP = 0.36%, and a kappa correlation coefficient of interpolation as high as 0.97. The FDR of Vis–NIR spectroscopy combined with the SVMR model is recommended over the PLSR and WNN modeling techniques for the high-accuracy determination of the SOC content.

Estimation of Leaf Chlorophyll a, b and Carotenoid Contents and Their Ratios Using Hyperspectral Reflectance

Japanese horseradish (wasabi) grows in very specific conditions, and recent environmental climate changes have damaged wasabi production. In addition, the optimal culture methods are not well known, and it is becoming increasingly difficult for incipient farmers to cultivate it. Chlorophyll a, b and carotenoid contents, as well as their allocation, could be an adequate indicator in evaluating its production and environmental stress; thus, developing an in situ method to monitor photosynthetic pigments based on reflectance could be useful for agricultural management. Besides original reflectance (OR), five pre-processing techniques, namely, first derivative reflectance (FDR), continuum-removed (CR), de-trending (DT), multiplicative scatter correction (MSC), and standard normal variate transformation (SNV), were compared to assess the accuracy of the estimation. Furthermore, five machine learning algorithms—random forest (RF), support vector machine (SVM), kernel-based extreme learning machine (KELM), Cubist, and Stochastic Gradient Boosting (SGB)—were considered. To classify the samples under different pH or sulphur ion concentration conditions, the end of the red edge bands was effective for OR, FDR, DT, MSC, and SNV, while a green-peak band was effective for CR. Overall, KELM and Cubist showed high performance and incorporating pre-processing techniques was effective for obtaining estimated values with high accuracy. The best combinations were found to be DT–KELM for chl a (RPD = 1.511–5.17, RMSE = 1.23–3.62 μg cm−2) and chl a:b (RPD = 0.73–3.17, RMSE = 0.13–0.60); CR–KELM for chl b (RPD = 1.92–5.06, RMSE = 0.41–1.03 μg cm−2) and chl a:car (RPD = 1.31–3.23, RMSE = 0.26–0.50); SNV–Cubist for car (RPD = 1.63–3.32, RMSE = 0.31–1.89 μg cm−2); and DT–Cubist for chl:car (RPD = 1.53–3.96, RMSE = 0.27–0.74).

Exploring relevant wavelength regions for estimating soil total carbon contents of rice fields in Madagascar from Vis-NIR spectra with sequential application of backward interval PLS

ABSTRACT Laboratory visible and near-infrared (Vis-NIR) spectroscopy with partial least squares (PLS) regression can be used to determine the soil carbon (C) content, and the waveband selection procedures can refine the predictive ability. However, individually selected wavebands are not always the same depending on the location, scale, and approach. To simplify the variable selection issue, some methods for selecting wavelength regions instead of individual wavebands have been proposed. In this study, we explore relevant wavelength regions for predicting the total carbon (TC) content of lowland and upland soils in Madagascar from Vis-NIR spectroscopy using a dynamic version of backward interval PLS (biPLS) regression. The predictive ability of dynamic biPLS was compared with that of standard full-spectrum PLS (FS-PLS) using the cross-validated coefficient of determination (R 2), root mean squared error (RMSE), and ratio of performance to interquartile distance (RPIQ). The biPLS models using reflectance (R 2 = 0.877, RMSE = 0.690) and first derivative reflectance (FDR) (R 2 = 0.940, RMSE = 0.494) data sets showed better predictive accuracy than the FS-PLS models using reflectance (R 2 = 0.826, RMSE = 0.809) and FDR (R 2 = 0.933, RMSE = 0.518) data sets, the spectral efficiency was improved. By using biPLS to predict soil TC, the model was simplified by using only four selected wavelength regions in the reflectance (400–490, 1402–1440, 1846–1980 and 2151–2283 nm) and FDR (652–687, 1322–1443, 1856–1985, and 2290–2400 nm) data sets, which yielded reliable (RPIQ > 2.5) predictions.

Multivariate Analysis Models Based on Full Spectra Range and Effective Wavelengths Using Different Transformation Techniques for Rapid Estimation of Leaf Nitrogen Concentration in Winter Wheat.

To develop a stable estimation model and identify effective wavelengths that could explain the variations in leaf nitrogen (N) concentration with different N supplies, growing seasons, ecological locations, growth stages, and wheat cultivars. Four field experiments were performed during two consecutive years (2017–2019) at three sites (Yuanyang, Hebi, and Wenxian) in Henan, China. In situ canopy spectral reflectance data under the aforementioned N supply conditions were obtained over a range of 400–950 nm (visible and near-infrared region). On the basis of the canopy raw spectral reflectance data and their subsequent transformation by two different techniques, first-derivative reflectance (FDR) and continuum removal (CR), four multivariate regression methods were comparatively analyzed and used to develop predictive models for estimating leaf N concentration: multiple linear regression (MLR), principal component regression (PCR), partial least square (PLS), and support vector machine (SVM). Results showed that leaf N concentration and canopy reflectance significantly varied with the levels of N fertilization, and a good correlation was observed for all the spectral techniques. Seven wavelengths with relatively higher r values than the bands of the raw spectra centered at 508, 525, 572, 709, 780, 876, and 925 nm were specified using the FDR technique. Based on the full wavelengths, the FDR-SVM model exhibited a good performance for leaf N concentration estimation, with coefficients of determination (r2val) for the validation datasets and corresponding relative percent deviations (RPDval) values of 0.842 and 2.383, respectively. However, the FDR-PLS yielded a more accurate assessment of the leaf N concentration than did the other methods, with r2val and RPDval values of 0.857 and 2.535, respectively. The variable importance in projection (VIP) scores from the FDR-PLS with the all canopy spectral region were used to screen the effective wavelengths of the spectral data. Therefore, six effective wavelengths centered at 525, 573, 710, 780, 875, and 924 nm were identified for leaf N concentration estimation. The SVM regression method with the effective wavelengths showed excellent performance for leaf N concentration estimation with r2val = 0.823 and RPDval = 2.280. These results demonstrated that the in situ canopy spectral technique is promising for the estimation of leaf N concentration in winter wheat based on the FDR-PLS regression model and the effective wavelengths identified.

Vis-SWIR spectral prediction model for soil organic matter with different grouping strategies

The effect of soil organic matter (SOM) on the spectral characteristics of soil differs among soil samples of different soil types with various mineral contents or mechanical compositions. SOM prediction models with visible and near-infrared and shortwave infrared (Vis-SWIR) spectral variables for grouping soil samples are more accurate than global modeling methods. However, the optimal grouping method must be explored. We measured the Vis-SWIR (400–2500 nm) spectral reflectance of 274 soil samples from the Songnen Plain and applied different grouping strategies, including traditional soil grouping, spectral clustering grouping and decision tree grouping considering both traditional grouping information and spectral information. The original reflectance, continuum removal, and first-derivative reflectance were analyzed with competitive adaptive reweighted sampling (CARS), and the results were used as inputs to establish a random forest model for SOM prediction. The following results were obtained: (1) the decision tree grouping method is the optimal grouping strategy for the SOM prediction model; (2) among different inputs, first-derivative reflectance is optimal; and (3) CARS is an effective way to reduce the number of inputs and improve the prediction accuracy of SOM. This study provides an effective soil grouping methodology for SOM estimation at the soil great group level.

Advantages of first-derivative reflectance spectroscopy in the VNIR-SWIR for the quantification of olivine and hematite

The focus of this paper is to study the application of the first order derivative method for the estimation of minerals rates in different mineral mixtures. The primary goal with this is to find robust spectral features of specific minerals that are not severely influenced by the spectral features of the other minerals in a mixture. Results were used to select appropriate spectral features to be applied for quantifying the minerals in upcoming studies. Mixtures of different terrestrial minerals equivalent to those dominating the Martian surface with a grain size <0.25 ​mm were prepared and analyzed in the laboratory by reflectance spectroscopy in the VNIR-SWIR range. The first derivatives were computed and correlated with the mineral concentration at specific wavelengths using the Unscrambler X software. The results indicated the first derivatives near 2300 ​nm, that is a characteristic absorption feature of olivine rich in magnesium and iron silicate, correlate strongly to the olivine content for all the mixtures containing olivine, binary and ternary, with regression coefficients ranging between 0.93 and 0.98. Additionally, the main advantage of this work is that first derivative spectra of mixtures with different olivine ratio highlights in the overlapping regions of the spectra the wavelengths where the first derivative values correlate strongly to the amount of olivine in the mixtures. The region near 1050–1300 ​nm was identified as a promising one for hematite-olivine mixtures and 785–900 ​nm for magnetite olivine, with a regression coefficient mean of 0.97 and 0.98, respectively. The study of hematite-plagioclase mixtures demonstrates that wavelengths near 785–858 ​nm and 940–989 ​nm lying within the overlapping regions of hematite and plagioclase exhibit robust correlation to the hematite content with a regression coefficient mean of 0.98 for both areas.

Regional soil organic carbon prediction model based on a discrete wavelet analysis of hyperspectral satellite data

Most studies have the achieved rapid and accurate determination of soil organic carbon (SOC) using laboratory spectroscopy; however, it remains difficult to map the spatial distribution of SOC. To predict and map SOC at a regional scale, we obtained fourteen hyperspectral images from the Gaofen-5 (GF-5) satellite and decomposed and reconstructed the original reflectance (OR) and the first derivative reflectance (FDR) using discrete wavelet transform (DWT) at different scales. At these different scales, as inputs, we selected the 3 optimal bands with the highest weight coefficient using principal component analysis and chose the normalized difference index (NDI), ratio index (RI) and difference index (DI) with the strongest correlation with the SOC content using a contour map method. These inputs were then used to build regional-scale SOC prediction models using random forest (RF), support vector machine (SVM) and back-propagation neural network (BPNN) algorithms. The results indicated that: 1) at a low decomposition scale, DWT can effectively eliminate the noise in satellite hyperspectral data, and the FDR combined with DWT can improve the SOC prediction accuracy significantly; 2) the method of selecting inputs using principal component analysis and a contour map can eliminate the redundancy of hyperspectral data while retaining the physical meaning of the inputs. For the model with the highest prediction accuracy, the inputs were all derived from the wavelength range of SOC variations; 3) the differences in prediction accuracy among the different prediction models are small; and 4) the SOC prediction accuracy using hyperspectral satellite data is greatly improved compared with that of previous SOC prediction studies using multispectral satellite data. This study provides a highly robust and accurate method for predicting and mapping regional SOC contents.

Estimating leaf nitrogen concentration considering unsynchronized maize growth stages with canopy hyperspectral technique

Abstract Accurate monitoring of the leaf nitrogen concentration (LNC) in maize can provide a fundamental basis for effective N management. In recent decades, many spectral indices and algorithms can accurately estimate the crop N status during different growth stages of maize. However, the effect of unsynchronized maize growth stages on these spectral models is rarely considered. The objectives of this study were to verify the predictive ability of the published vegetation indices (VIs), the partial least squares (PLS) regression and the two-band optimal combinations algorithms, and to determine the most accurate method for assessing the LNC of unsynchronized growth stages in maize. Canopy raw and first-derivative reflectance (FDR) spectra, and destructive measurements of the maize LNC were collected in 2016–2018 in different ecological areas, unsynchronized growth stages, cultivars, plant densities, and N rates. The published VIs and new 2-band VIs and their band combinations varied across different growth stages and were not affected by unsynchronized growth stages. The red-edge chlorophyll index (CIred edge), which was identified across growth stages, performed quite well for LNC estimation, but performed unsatisfactorily when compared to the new 2-band VIs that were developed in this study. The best spectral index of the green or red and red-edge or near-infrared band combination based on FDR spectra had a good diagnostic effect for LNC of maize across the four growth stages, in which the novel normalized difference spectral indices (NDSI) effect was optimal in the V9 (9-leaf stage), VT (tasseling stage) and R1 (silking stage) stage and the novel ratio spectral indices (RSI) was the best in the R3 (milk stage) stage. Compared to PLS regression based on raw full-range hyperspectral data, the PLS regression for estimating LNC across four growth stages based on FDR full-range hyperspectral data showed a higher accuracy, with an average coefficient of determination (rval2) of 0.87 and average root mean square error (RMSEval) of 0.18. In addition, the average rval2 for the PLS regression based on selected FDR wavelengths increased by 2.40% and the average RMSEval decreased by 14.8%, compared with the best performing VIs during the four growth stages. It is concluded that the best 2-band VIs and the PLS regression based on selected FDR wavelengths provide a useful explorative tool for estimating LNC of maize across years, ecological areas, and unsynchronized growth stages.

Spectral Deconvolution for Dimension Reduction and Differentiation of Seagrasses: Case Study of Gulf St. Vincent, South Australia

Seagrasses are a vulnerable and declining coastal habitat, which provide shelter and substrate for aquatic microbiota, invertebrates, and fishes. More accurate mapping of seagrasses is imperative for their sustainability but is hindered by the lack of data on reflectance spectra representing the optical signatures of individual species. Objectives of this study are: (1) To determine distinct characteristics of spectral profiles for sand versus three temperate seagrasses (Posidonia, Amphibolis, and Heterozostera); (2) to evaluate the most efficient derivative analysis method of spectral reflectance profiles for determining benthic types; and to assess the influences of (3) site location and (4) the water column on spectral responses. Results show that 566:689 and 566:600 bandwidth ratios are useful in separating seagrasses from sand and from detritus and algae, respectively; first-derivative reflectance spectra generally is the most efficient method, especially with deconvolution analyses further helping to reveal and isolate 11 key wavelength dimensions; and differences between sites and water column composition, which can include suspended particulate matter, both have no effect on endmembers. These findings helped develop a spectral reflectance library that can be used as an endmember reference for remote sensing, thereby providing continued monitoring, assessment, and management of seagrasses.