scholarly journals Quantitative Estimating Salt Content of Saline Soil Using Laboratory Hyperspectral Data Treated by Fractional Derivative

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Dong Zhang ◽  
Tashpolat Tiyip ◽  
Jianli Ding ◽  
Fei Zhang ◽  
Ilyas Nurmemet ◽  
...  
Keyword(s):  
Fractional Derivative ◽  
Spectral Reflectance ◽  
Salt Content ◽  
Hyperspectral Data ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Lake Basin ◽  
Soil Salt Content ◽  
Soil Salt

Most present researches on estimation of soil salinity by hyperspectral data have focused on the spectral reflectance or their integer derivatives but ignored the fractional derivative information of hyperspectral data. Motivated by this situation, the selected study area is the Ebinur Lake basin located in the southwest border in the Xinjiang Uygur Autonomous Region, China, with severe salinization. The field work was conducted from 15 to 25 October, 2014, and a total of 180 soil samples were collected from 45 sampling sites; after measuring the soil salt content and spectral reflectance in the laboratory, the range from 0 to 2 was divided into 11 orders (interval 0.2) and then the hyperspectral data were treated by 4 kinds of mathematical transformations and 11 orders of fractional derivatives. Combined with the soil salt content, partial least square regression method was applied for model calibrations and predictions and some indexes were used to evaluate the performance of models. The results showed that the retrieval model built up by 250 bands based on 1.2-order derivative of 1/lg⁡R had excellent capacity of estimating soil salt content in the study area (RMSEC=14.685 g/kg, RMSEP=14.713 g/kg, R2C=0.782, R2P=0.768, and RPD = 2.080). This study provides an application reference for quantitative estimations of other land surface parameters and some other applications on hyperspectral technology.

scholarly journals Hyperspectral reflectance models for soil salt content by filtering methods and waveband selection

2016 ◽  
Vol 23 (1) ◽  
pp. 117-130 ◽  
Author(s):  
Wen-Zhi Zeng ◽  
Jie-Sheng Huang ◽  
Chi Xu ◽  
Tao Ma ◽  
Jing-Wei Wu
Keyword(s):  
Salt Content ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Irrigation District ◽  
Hyperspectral Reflectance ◽  
Soil Salt Content ◽  
Soil Salt ◽  
Orthogonal Signal Correction ◽  
Signal Correction

Abstract For improving the understanding of interactions between hyperspectral reflectance and soil salinity, in situ hyperspectral inversion of soil salt content at a depth of 0-10 cm was conducted in Hetao Irrigation District, Inner Mongolia, China. Six filtering methods were used to preprocess soil reflectance data, and waveband selection combined by VIP (variable importance in projection) and b-coefficients (regression coefficients of model) was also applied to simplify model. Then statistical methods of partial least square regression (PLS) and orthogonal projection to latent structures (OPLS) were processed to establish the inversion models. Our findings indicate that the selected sensitive wavebands for the 6 filtering methods are different, among which the multiplicative signal correction (MSC) and standard normal variate methods (SNV) have some similar sensitive wavebands with unfiltered data. Derivatives (DF1 and DF2) could characterize sensitive wavebands along the scale of VNIR (350-1100 nm), especially the second derivative (DF2). The sensitive wavebands for continuum-removed reflectance method (CR) have protruded many narrow absorption features. For orthogonal signal correction method (OSC), the selected wavebands are centralized in the range of 565-1013 nm. The calibration and evaluation processes have demonstrated the second order derivate filtering method (DF2) combined with waveband selection is superior to other processes, for it has high R2 (larger than 0.7) both in PLS and OPLS models for calibration and evaluation, by choosing only 156 wavebands from the whole 700 wavebands. Meanwhile, OPLS method was considered to be more suitable for the analyzing than PLS in most of our situations.

scholarly journals LEAF AREA INDEX RETRIEVED FROM THERMAL HYPERSPECTRAL DATA

2016 ◽  
Vol XLI-B7 ◽  
pp. 99-105
Author(s):  
Elnaz Neinavaz ◽  
Andrew K. Skidmore ◽  
Roshanak Darvishzadeh ◽  
Thomas A. Groen
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Narrow Band ◽  
Hyperspectral Data ◽  
Partial Least Square ◽  
Least Square ◽  
Growth Potential ◽  
Partial Least Square Regression ◽  
Area Index ◽  
Thermal Data

Leaf area index (LAI) is an important essential biodiversity variable due to its role in many terrestrial ecosystem processes such as evapotranspiration, energy balance, and gas exchanges as well as plant growth potential. A novel approach presented here is the retrieval of LAI using thermal infrared (8–14 μm, TIR) measurements. Here, we evaluate LAI retrieval using TIR hyperspectral data. Canopy emissivity spectral measurements were recorded under controlled laboratory conditions using a MIDAC (M4401-F) illuminator Fourier Transform Infrared spectrometer for two plant species during which LAI was destructively measured. The accuracy of retrieval for LAI was then assessed using partial least square regression (PLSR) and narrow band index calculated in the form of normalized difference index from all possible combinations of wavebands. The obtained accuracy from the PLSR for LAI retrieval was relatively higher than narrow-band vegetation index (0.54 < R<sup>2</sup> < 0.74). The results demonstrated that LAI may successfully be estimated from hyperspectral thermal data. The study highlights the potential of hyperspectral thermal data for retrieval of vegetation biophysical variables at the canopy level for the first time.

Comparison of Three Regression Methods for the Winter Wheat Biomass Estimation Using Hyperspectral Data

2013 ◽  
Vol 380-384 ◽  
pp. 1843-1846
Author(s):  
Yuan Yuan Fu ◽  
Ji Hua Wang ◽  
Gui Jun Yang ◽  
Hai Kuan Feng
Keyword(s):  
Winter Wheat ◽  
Principal Component ◽  
Hyperspectral Data ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Estimation Accuracy ◽  
Biomass Estimation ◽  
Regression Methods ◽  
Continuum Removal

To make full use of the hyperspectral data, the strong multi-collinearity in the data is supposed to be taken into account. With this study we evaluated three multivariate regression methods which are principal component regression, partial least square regression (PLSR) and stepwise multiple linear regression. Furthermore, to identify reliable winter wheat biomass predictive models, two different types of spectral transformations (continuum removal, first derivative) were combined with the three regression methods, respectively. Amongst these combinations, the respective combination of three regression methods and continuum removal got the highest estimation accuracy, especially, the combination of PLSR and continuum removal (R2=0.715, RMSE=0.218kg/m2). The experimental results demonstrated that PLSR is recommended for highly multi-collinear data sets. The combination of continuum removal and PLSR could improve the estimation accuracy of winter wheat biomass.

scholarly journals LEAF AREA INDEX RETRIEVED FROM THERMAL HYPERSPECTRAL DATA

2016 ◽  
Vol XLI-B7 ◽  
pp. 99-105 ◽  
Author(s):  
Elnaz Neinavaz ◽  
Andrew K. Skidmore ◽  
Roshanak Darvishzadeh ◽  
Thomas A. Groen
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Narrow Band ◽  
Hyperspectral Data ◽  
Partial Least Square ◽  
Least Square ◽  
Growth Potential ◽  
Partial Least Square Regression ◽  
Area Index ◽  
Thermal Data

Leaf area index (LAI) is an important essential biodiversity variable due to its role in many terrestrial ecosystem processes such as evapotranspiration, energy balance, and gas exchanges as well as plant growth potential. A novel approach presented here is the retrieval of LAI using thermal infrared (8–14 μm, TIR) measurements. Here, we evaluate LAI retrieval using TIR hyperspectral data. Canopy emissivity spectral measurements were recorded under controlled laboratory conditions using a MIDAC (M4401-F) illuminator Fourier Transform Infrared spectrometer for two plant species during which LAI was destructively measured. The accuracy of retrieval for LAI was then assessed using partial least square regression (PLSR) and narrow band index calculated in the form of normalized difference index from all possible combinations of wavebands. The obtained accuracy from the PLSR for LAI retrieval was relatively higher than narrow-band vegetation index (0.54 &lt; R&lt;sup&gt;2&lt;/sup&gt; &lt; 0.74). The results demonstrated that LAI may successfully be estimated from hyperspectral thermal data. The study highlights the potential of hyperspectral thermal data for retrieval of vegetation biophysical variables at the canopy level for the first time.

scholarly journals Assessment of the Hyperspectral Data Analysis as a Tool to Diagnose Xylella fastidiosa in the Asymptomatic Leaves of Olive Plants

Plants ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 683
Author(s):  
Carmela Riefolo ◽  
Ilaria Antelmi ◽  
Annamaria Castrignanò ◽  
Sergio Ruggieri ◽  
Ciro Galeone ◽  
...  
Keyword(s):  
Lignin Content ◽  
Early Stage ◽  
Xylella Fastidiosa ◽  
Confusion Matrix ◽  
Hyperspectral Data ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Diagnostic Tools ◽  
Reference Analysis

Xylella fastidiosa is a bacterial pathogen affecting many plant species worldwide. Recently, the subspecies pauca (Xfp) has been reported as the causal agent of a devastating disease on olive trees in the Salento area (Apulia region, southeastern Italy), where centenarian and millenarian plants constitute a great agronomic, economic, and landscape trait, as well as an important cultural heritage. It is, therefore, important to develop diagnostic tools able to detect the disease early, even when infected plants are still asymptomatic, to reduce the infection risk for the surrounding plants. The reference analysis is the quantitative real time-Polymerase-Chain-Reaction (qPCR) of the bacterial DNA. The aim of this work was to assess whether the analysis of hyperspectral data, using different statistical methods, was able to select with sufficient accuracy, which plants to analyze with PCR, to save time and economic resources. The study area was selected in the Municipality of Oria (Brindisi). Partial Least Square Regression (PLSR) and Canonical Discriminant Analysis (CDA) indicated that the most important bands were those related to the chlorophyll function, water, lignin content, as can also be seen from the wilting symptoms in Xfp-infected plants. The confusion matrix of CDA showed an overall accuracy of 0.67, but with a better capability to discriminate the infected plants. Finally, an unsupervised classification, using only spectral data, was able to discriminate the infected plants at a very early stage of infection. Then, in phase of testing qPCR should be performed only on the plants predicted as infected from hyperspectral data, thus, saving time and financial resources.

scholarly journals Integration of Partial Least Squares Regression and Hyperspectral Data Processing for the Nondestructive Detection of the Scaling Rate of Carp (Cyprinus carpio)

Foods ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 500
Author(s):  
Huihui Wang ◽  
Kunlun Wang ◽  
Xinyu Zhu ◽  
Peng Zhang ◽  
Jixin Yang ◽  
...  
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Hyperspectral Data ◽  
Correlation Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Coefficient Of Determination ◽  
Mean Square

The scaling rate of carp is one of the most important factors restricting the automation and intelligence level of carp processing. In order to solve the shortcomings of the commonly-used manual detection, this paper aimed to study the potential of hyperspectral technology (400–1024.7 nm) in detecting the scaling rate of carp. The whole fish body was divided into three regions (belly, back, and tail) for analysis because spectral responses are different for different regions. Different preprocessing methods, including Savitzky–Golay (SG), first derivative (FD), multivariate scattering correction (MSC), and standard normal variate (SNV) were applied for spectrum pretreatment. Then, the successive projections algorithm (SPA), regression coefficient (RC), and two-dimensional correlation spectroscopy (2D-COS) were applied for selecting characteristic wavelengths (CWs), respectively. The partial least square regression (PLSR) models for scaling rate detection using full wavelengths (FWs) and CWs were established. According to the modeling results, FD-RC-PLSR, SNV-SPA-PLSR, and SNV-RC-PLSR were determined to be the optimal models for predicting the scaling rate in the back (the coefficient of determination in calibration set (RC2) = 96.23%, the coefficient of determination in prediction set (RP2) = 95.55%, root mean square error by calibration (RMSEC) = 6.20%, the root mean square error by prediction (RMSEP)= 7.54%, and the relative percent deviation (RPD) = 3.98), belly (RC2 = 93.44%, RP2 = 90.81%, RMSEC = 8.05%, RMSEP = 9.13%, and RPD = 3.07) and tail (RC2 = 95.34%, RP2 = 93.71%, RMSEC = 6.66%, RMSEP = 8.37%, and RPD = 3.42) regions, respectively. It can be seen that PLSR integrated with specific pretreatment and dimension reduction methods had great potential for scaling rate detection in different carp regions. These results confirmed the possibility of using hyperspectral technology in nondestructive and convenient detection of the scaling rate of carp.

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