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1.
This study was an experiment to measure the reflectance data of water and oil films at the 0, 1st, 3rd, 12th, and 48th h of weathering and analyzed the spectral reflectance characteristics of the weathered oil films. Based on the experiment and analysis, the measured data were resampled through the spectral response function of the hyperspectral data. The environmental noise equivalent reflectance difference of the image data was then used to assess the sensitivity of each band for the detection of oil films. The bands that were most effective in detecting oil films in various stages of weathering were assessed. Results indicated that the spaceborne hyperspectral sensor employed in this study had the potential to discriminate among oil films in different stages of weathering. At the 0, 1st, 3rd, and 12th h of weathering, discrimination between water and oil films and among oil films in varying stages of weathering was achieved at the visible and near-infrared bands. After 48 h, the potential sensitive bands were distributed mainly in the near-infrared region. This experiment can provide a method to select bands of hyperspectral data for monitoring oil films.  相似文献   

2.
Techniques for mapping and monitoring wetland species are critical for their sustainable management. Papyrus (Cyperus papyrus L.) is one of the most important species-rich habitats that characterize the Greater St. Lucia Wetlands Park (GSWP) in South Africa. This paper investigates whether papyrus could be discriminated from its co-existing species using ASD field spectrometer data ranging from 300 nm to 2500 nm, yielding a total of 2151 bands. Canopy spectral measurements from papyrus and three other species were collected in situ in the Greater St. Lucia Wetlands Park, South Africa. A new hierarchical method based on three integrated analysis levels was proposed and implemented to spectrally discriminate papyrus from other species as well as to reduce and subsequently select optimal bands for the potential discrimination of papyrus. In the first level of the analysis using ANOVA, we found that there were statistically significant differences in spectral reflectance between papyrus and other species on 412 wavelengths located in different portions of the electromagnetic spectrum. Using the selected 412 bands, we further investigated the use of classification and regression trees (CART) in the second level of analysis to identify the most sensitive bands for spectral discrimination. This analysis yielded eight bands which are considered to be practical for upscaling to airborne or space borne sensors for mapping papyrus vegetation. The final sensitivity analysis level involved the application of Jeffries-Matusita (JM) distance to assess the relative importance of the selected eight bands in discriminating papyrus from other species. The results indicate that the best discrimination of papyrus from its co-existing species is possible with six bands located in the red-edge and near-infrared regions of the electromagnetic spectrum. Overall, the study concluded that spectral reflectance of papyrus and its co-existing species is statistically different, a promising result for the use of airborne and satellite sensors for mapping papyrus. The three-step hierarchical approach employed in this study could systematically reduce the dimensionality of bands to manageable levels, a move towards operational implementation with band specific sensors.  相似文献   

3.
基于水稻高光谱遥感数据的PLS波长选择研究   总被引：1，自引：0，他引：1  
对武汉地区不同生长状况下的水稻冠层进行了遥感监测,采用偏最小二乘(PLS)分析方法分别对水稻叶面氮含量、叶绿素含量进行了波长选择研究,并通过构建8种波长组合进行PLS水稻叶面氮含量反演分析和比较,选择出最合适的反演波长。多种波长(波段)组合进行叶面氮含量反演的验证表明,采用552 nm、675 nm7、52 nm7、76 nm的4波长组合是最适合叶面氮含量反演的波长选择结果,同时,采用光谱能量空间变换的形式能较好地改善波长选择的反演效果。  相似文献   

4.
The influence of morphophysiological variation at different growth stages on the performance of vegetation indices for estimating plant N status has been confirmed. However, the underlying mechanisms explaining how this variation impacts hyperspectral measures and canopy N status are poorly understood. In this study, four field experiments involving different N rates were conducted to optimize the selection of sensitive bands and evaluate their performance for modeling canopy N status of rice at various growth stages in 2007 and 2008. The results indicate that growth stages negatively affect hyperspectral indices in different ways in modeling leaf N concentration (LNC), plant N concentration (PNC) and plant N uptake (PNU). Published hyperspectral indices showed serious limitations in estimating LNC, PNC and PNU. The newly proposed best 2-band indices significantly improved the accuracy for modeling PNU (R2 = 0.75–0.85) by using the lambda by lambda band-optimized algorithm. However, the newly proposed 2-band indices still have limitations in modeling LNC and PNC because the use of only 2-band indices is not fully adequate to provide the maximum N-related information. The optimum multiple narrow band reflectance (OMNBR) models significantly increase the accuracy for estimating the LNC (R2 = 0.67–0.71) and PNC (R2 = 0.57–0.78) with six bands. Results suggest the combinations of center of red-edge (735 nm) with longer red-edge bands (730–760 nm) are very efficient for estimating PNC after heading, whereas the combinations of blue with green bands are more efficient for modeling PNC across all stages. The center of red-edge (730–735 nm) paired with early NIR bands (775–808 nm) are predominant in estimating PNU before heading, whereas the longer red-edge (750 nm) paired with the center of “NIR shoulder” (840–850 nm) are dominant in estimating PNU after heading and across all stages. The OMNBR models have the advantage of modeling canopy N status for the entire growth period. However, the best 2-band indices are much easier to use. Alternatively, it is also possible to use the best 2-band indices to monitor PNU before heading and PNC after heading. This study systematically explains the influences of N dilution effect on hyperspectral band combinations in relating to the different N variables and further recommends the best band combinations which may provide an insight for developing new hyperspectral vegetation indices.  相似文献   

5.
The present study was carried out to evaluate the satellite-based hyperspectral data available from Hyperion onboard EO-1 of NASA for agricultural applications. The study was carried out for Daurala block of Meerut district, using data of March 2005. The preliminary data analysis showed that there are 196 usable bands out of a total of 242 bands. Principal component (PC) analysis showed that about 99% of the information explained in 10 PCs. The atmospherically corrected reflectance, derived from satellite data had good agreement with the ground reflectance, observed using handheld spectroradiometer, with r2 ranging from 0.85 to 0.98. A set of twenty most usable bands was selected by the criteria of maximum contribution to first five PCs and the band combinations with least inter-band correlations.  相似文献   

6.
Hyperspectral remote sensing, because of its large number of narrow bands, has shown possibility of discriminating the crops. Current study was carried out to select the optimum bands for discrimination among pulses, cole crops and ornamental plants using the ground-based Hyperspectral data in Patha village, Lalitpur district, Uttar Pradesh state and Kolkata, West Bengal state. The field observations of reflectance were taken using a 512-channel spectroradiometer with a range of 325–1075 nm. The stepwise discriminant analysis was carried out and separability measures, such as Wilks’ lambda and F-Value were used as criteria for identifying the narrow bands. The analysis showed that, the best four bands for pulse crop discrimination lie mostly in NIR and early MIR regions i.e. 750, 800, 940 and 960 nm. Within cole crops discrimination is primarily determined by the green, red and NIR bands of 550, 690, 740, 770 and 980 nm. The separability study showed the bands 420,470,480,570,730,740, 940, 950, 970, 1030 nm are useful for discriminating flowers.  相似文献   

7.
The use of multispectral satellite sensors for generation of hyperspectral indices is restricted because of their coarse spectral resolutions. In this study, we attempted to synthesize a few of these hyperspectral indices, viz. RedEdge Normalized Difference Vegetation Index (NDVI705), Plant Senescence Reflectance Index (PSRI) and Normalized-Difference-Infrared-Index (NDII), for crop stress monitoring at regional scale using multispectral images, simulated from Hyperion data. The Hyperion data were resampled and simulated to corresponding spatial and spectral resolutions of AWiFS, OCM-2 and MODIS sensors using their respective filter function. Different possible combinations of two bands (i.e. simple difference, simple ratio and normalized difference) were computed using synthetic spectral bands of each sensor, and were regressed with NDVI705, PSRI and NDII. Models with highest correlation were selected and inverted on Hyperion data of another date to synthesize respective multispectral indices. Synthetic broad band indices of multispectral sensors with their respective narrow band indices of Hyperion were found to be in good agreement.  相似文献   

8.
One of the challenges in fighting plant invasions is the inefficiency of identifying their distribution using field inventory techniques. Remote sensing has the potential to alleviate this problem effectively using spectral profiling for species discrimination. However, little is known about the capability of remote sensing in discriminating between shrubby invasive plants with narrow leaf structures and other cohabitants with similar ecological niche. The aims of this study were therefore to (1) assess the classification performance of field spectroradiometer data among three bushy and shruby plants (Artemesia afra, Asparagus laricinus, and Seriphium plumosum) from the coexistent plant species largely dominated by acacia and grass species, and (2) explore the performance of simulated spectral bands of five space-borne images (Landsat 8, Sentinel 2A, SPOT 6, Pleiades 1B, and WorldView-3). Two machine-learning classifiers (boosted trees classification and support vector machines) were used to classify raw hyperspectral (n = 688) and simulated multispectral wavelengths. Relatively high classification accuracies were obtained for the invasive species using the original hyperspectral bands for both classifiers (overall accuracy, OA = 83–97%). The simulated data resulted in higher accuracies for Landsat 8, Sentinel 2A, and WorldView-3 compared to those computed for bands simulated to SPOT 6 and Pleiades 1B data. These findings suggest the potential of remote-sensing techniques in the discrimination of different plant species with similar morphological characteristics occupying the same niche.  相似文献   

9.
With the emergence of very high spatial and spectral resolution data set, the resolution gap that existed between remote-sensing data set and aerial photographs has decreased. The decrease in resolution gap has allowed accurate discrimination of different tree species. In this study, discrimination of indigenous tree species (n?=?5) was carried out using ground based hyperspectral data resampled to QuickBird bands and the actual QuickBird imagery for the area around Palapye, Botswana. The purpose of the study was to compare the accuracies of resampled hyperspectral data (resampled to QuickBird sensors) with the actual image (QuickBird image) in discriminating between the indigenous tree species. We performed Random Forest (RF) using canopy reflectance taking from ground-based hyperspectral sensor and the reflectance delineated regions of the tree species. The overall accuracies for classifying the five tree species was 79.86 and 88.78% for both the resampled and actual image, respectively. We observed that resampled data set can be upscale to actual image with the same or even greater level of accuracy. We therefore conclude that high spectral and spatial resolution data set has substantial potential for tree species discrimination in savannah environments.  相似文献   

10.
The invasion by Striga in most cereal crop fields in Africa has posed a significant threat to food security and has caused substantial socioeconomic losses. Hyperspectral remote sensing is an effective means to discriminate plant species, providing possibilities to track such weed invasions and improve precision agriculture. However, essential baseline information using remotely sensed data is missing, specifically for the Striga weed in Africa. In this study, we investigated the spectral uniqueness of Striga compared to other co-occurring maize crops and weeds. We used the in-situ FieldSpec® Handheld 2™ analytical spectral device (ASD), hyperspectral data and their respective narrow-band indices in the visible and near infrared (VNIR) region of the electromagnetic spectrum (EMS) and four machine learning discriminant algorithms (i.e. random forest: RF, linear discriminant analysis: LDA, gradient boosting: GB and support vector machines: SVM) to discriminate among different levels of Striga (Striga hermonthica) infestations in maize fields in western Kenya. We also tested the utility of Sentinel-2 waveband configurations to map and discriminate Striga infestation in heterogenous cereal crop fields. The in-situ hyperspectral reflectance data were resampled to the spectral waveband configurations of Sentinel-2 using published spectral response functions. We sampled and detected seven Striga infestation classes based on three flowering Striga classes (low, moderate and high) against two background endmembers (soil and a mixture of maize and other co-occurring weeds). A guided regularized random forest (GRRF) algorithm was used to select the most relevant hyperspectral wavebands and vegetation indices (VIs) as well as for the resampled Sentinel-2 multispectral wavebands for Striga infestation discrimination. The performance of the four discriminant algorithms was compared using classification accuracy assessment metrics. We were able to positively discriminate Striga from the two background endmembers i.e. soil and co-occurring vegetation (maize and co-occurring weeds) based on the few GRRF selected hyperspectral vegetation indices and the GRRF selected resampled Sentinel-2 multispectral bands. RF outperformed all the other discriminant methods and produced the highest overall accuracy of 91% and 85%, using the hyperspectral and resampled Sentinel-2 multispectral wavebands, respectively, across the four different discriminant models tested in this study. The class with the highest detection accuracy across all the four discriminant algorithms, was the “exclusively maize and other co-occurring weeds” (>70%). The GRRF reduced the dimensionality of the hyperspectral data and selected only 9 most relevant wavebands out of 750 wavebands, 6 VIs out of 15 and 6 out of 10 resampled Sentinel-2 multispectral wavebands for discriminating among the Striga and co-occurring classes. Resampled Sentinel-2 multispectral wavebands 3 (green) and 4 (red) were the most crucial for Striga detection. The use of the most relevant hyperspectral features (i.e. wavebands and VIs) significantly (p ≤ 0.05) increased the overall classification accuracy and Kappa scores (±5% and ±0.2, respectively) in all the machine learning discriminant models. Our results show the potential of hyperspectral, resampled Sentinel-2 multispectral datasets and machine learning discriminant algorithms as a tool to accurately discern Striga in heterogenous maize agro-ecological systems.  相似文献   

11.
罗征宇  孙林 《测绘科学》2012,(1):54-55,63
监督分类方法是海冰遥感监测中常用的有效方法,但不同的监督分类方法以及波段选择,在海冰识别中的精度有较大差异。为提高海冰监测的精度,本文使用高光谱传感器提供的可见光、近红外波段的连续成像光谱信息,对比了不同的波段组合在多种分类方法中海冰提取的精度,分析了不同波段组合、不同分类方法在海冰监测上的优缺点,最终得出海冰监测中最佳的波段组合以及最适宜的监督分类方法。  相似文献   

12.
The rice disease is one of the most serious injurious factors that cause major loss of rice production and subsequent economy in agricultural industry. This study explored a new method for obtaining information of the rice disease in a short term through model regression methods. The spectrum characteristics of rice leaves under different disease damage were firstly analyzed for its relationship with rice disease level. The sensitive bands of the spectrum for accurately supervising rice diseases were selected with principal component analysis (PCA). The stepwise regression method and BP neural network were both used to establish the spectrum-based models for recognizing rice diseases. Results showed that five major characteristic bands were determined by PCA (990, 1850, 660, 1921, and 1933 nm) for monitoring foliar rice diseases, among which the edge area for red light had the best correlation with rice disease level was also selected as the parameter to establish the model. Specifically, the composite reflectivity of wavelengths between 990 and 1933 nm was negatively related to rice brown spot diseases stress, which was then used to establish the model. Parameters of the red edge area and the ranged reflectivity between 660 and 990 nm were used to establish models for monitoring rice sheath blight diseases. Totally, there were 60 samples employed to build models for identifying the two diseases by the stepwise regression method and the BP neural network method, and the rest 41 ones were used for further model verification. Compared with the stepwise regression analysis, BP neural network was evaluated to perform better with characteristic bands at 660, 990, and 1933 nm. In conclusion, the establishment of the function model in our study can be implemented to monitor rice diseases, which provided a theoretical basis for indirect and rapid monitoring rice diseases.  相似文献   

13.
A field experiment was conducted to study the effect of vegetation cover on soil spectra and relationship of spectral indices with vegetation cover. Multi-date spectral measurements were carried out on twelve wheat fields. Five sets of measurements were taken during the growth period of wheat crop. Field reflectance data were collected in the range 350 to 1800 nm using ASD spectroradiometer. Analysis of data was done to select narrow spectral bands for estimation of ground cover. The ratio of reflectance from vegetation covered soil and reflectance from bare soil indicated that spectral reflectance at 670 and 710 nm are the most sensitive bands. Two bands in visible (670 and 560 nm), three bands in near infrared (710, 870 and 1100 nm) and three bands in middle infrared (1480, 1700 and 1800 nm) were found highly correlated with fractional cover. Vegetation indices developed using narrow band spectral data have been found to be better than those developed using broad- band data for estimation of ground cover.  相似文献   

14.
This study investigates the applicability of estimating chlorophyll and water content at canopy level through empirical models and band combinations. The main goal is to evaluate and compare the accuracy of these two approaches for estimating and mapping canopy chlorophyll and water content through canopy reflectance and spaceborne HJ1-A HSI data acquired over Yanzhou coal mining area. An experiment was carried out. Canopy spectral measurements were acquired in the field using an ASD spectroradiometer along with simultaneous in situ measurements of leaf chlorophyll content. We tested seven variables derived from canopy reflectance for detecting canopy chlorophyll and water content: (1) R, (2) Log(1/R), (3) Log(1/R)′, (4) FDR, (5) SDR, (6) CRR, (7) BD. Stepwise multiple linear regressions were used to select wavelengths from HJ1-A HSI image bands. Correlation analysis was also done between different band combinations and biochemistry. A statistically significant relationship between Log(1/R) and chlorophyll was found at canopy level (R2 = 0.516). SDR had the highest correlation with canopy water content (R2 = 0.490). In addition, relationship between normalized different band combinations and chlorophyll and water content is also significantly obvious (R2 = 0.577 and R2 = 0.615). Canopy chlorophyll content was estimated with the intermediate accuracy (R2 = 0.4144), while water content was estimated with an acceptable accuracy (R2 = 0.4592). Canopy chlorophyll and water content spatial distribution were mapped. Chlorophyll and water stress levels were quantified by comparing different environmental stressors.  相似文献   

15.
In the present study, field based hyperspectral data was used to estimate the tea (Camellia sinensis L.) polyphenol at Deha Tea garden of Assam state, India. Leaf reflectance spectra were first filtered for noise and then transformed into normalized and first derivative reflectance for further analysis. Stepwise discriminant analysis was carried out to select sensitive bands for a range of polyphenol concentration by minimizing the effects of other factors such as age of the bushes and management practices. The wavelengths at 358, 369, 484, 845, 916, 1387, 1420, 1435, 1621 and 2294 nm were identified as sensitive to tea polyphenol, among which 2294 nm was found to be the most recurring band. The noise removed selected bands, their transformed derivatives and principal components were regressed with the tea polyphenol using univariate and multi-variate analysis. In univariate analysis the correlation was very poor with RMSE more than 3.0. A significant improvement in R2 values were observed when multivariate analyses like stepwise multiple linear regression (SMLR) and partial least square regression (PLSR) was carried out. The PLSR of first derivative reflectance was most accurate (R2 = 0.81 and RMSE = 1.39 mg g−1) among all the uni- and multivariate analysis for predicting the polyphenol of fresh tea leaves.  相似文献   

16.
基于高光谱遥感反射比的太湖水体叶绿素a含量估算模型   总被引：19，自引：1，他引：19  
旨在寻找叶绿素a的高光谱遥感敏感波段并建立其定量估算模型。通过对太湖水体的连续监测，获得了从2004年6月到8月3个月的太湖水体高光谱数据和水质化学分析数据。利用实测的高光谱数据分析计算太湖水体的离水辐亮度和遥感反射比；然后，通过相关分析寻找反演叶绿素a浓度的高光谱敏感波段，进而建立反演太湖水体叶绿素a浓度的高光谱遥感定量估算模型，并用相关数据对模型进行精度分析。研究发现，水体的遥感反射比光谱在719nm和725nm存在两个峰，其中719nm处的峰更明显且稳定。通过模型的对比分析，发现用这两个峰值处的遥感反射比参与建模可以提高叶绿素a的估算精度；并且认为由反射比比值变量R719／R670所建立的线性模型对叶绿素a浓度的估算精度最理想。  相似文献   

17.
Remote sensing allows monitoring heavy metal pollution in crops for agricultural production and food security. This paper presents an approach to wavelet-fractal analysis for exploring a set of sensitive spectral parameters to monitor the heavy metal pollution levels in rice crops from hyperspectral reflectance data. Hyperspectral and biochemical data were collected from three study farms in Changchun, Jilin Province, China. Our study explored the fractal dimension of reflectance with wavelet transform (FDWT) that demonstrated a better performance than other existing methods. Our results obtained in this study show that the red edge position (REP) was the most sensitive indicator for monitoring the heavy metal pollution levels in rice crops among common indices. As compared with REP, the FDWT is more sensitive to biochemical composition, namely with respect to chlorophyll concentrations, N, Cu and Cd. The established linear models showed a correlation coefficient (R2) above 0.70, model efficiency (ME) above 0.65 and a root mean square error (RMSE) below 3.5. Minimum FDWT values occurred in rice with Level II pollution followed by Level I pollution, and finally the safe level. This study suggests that wavelet transform is well suited as a spectral analysis method to eliminate noise and amplify the stress information from heavy metals. The wavelet transform in conjunction with fractal analysis is promising for detecting heavy metal-induced stress in rice crops.  相似文献   

18.
There is an urgent necessity to monitor changes in the natural surface features of earth. Compared to broadband multispectral data, hyperspectral data provides a better option with high spectral resolution. Classification of vegetation with the use of hyperspectral remote sensing generates a classical problem of high dimensional inputs. Complexity gets compounded as we move from airborne hyperspectral to Spaceborne technology. It is unclear how different classification algorithms will perform on a complex scene of tropical forests collected by spaceborne hyperspectral sensor. The present study was carried out to evaluate the performance of three different classifiers (Artificial Neural Network, Spectral Angle Mapper, Support Vector Machine) over highly diverse tropical forest vegetation utilizing hyperspectral (EO-1) data. Appropriate band selection was done by Stepwise Discriminant Analysis. The Stepwise Discriminant Analysis resulted in identifying 22 best bands to discriminate the eight identified tropical vegetation classes. Maximum numbers of bands came from SWIR region. ANN classifier gave highest OAA values of 81% with the help of 22 selected bands from SDA. The image classified with the help SVM showed OAA of 71%, whereas the SAM showed the lowest OAA of 66%. All the three classifiers were also tested to check their efficiency in classifying spectra coming from 165 processed bands. SVM showed highest OAA of 80%. Classified subset images coming from ANN (from 22 bands) and SVM (from 165 bands) are quite similar in showing the distribution of eight vegetation classes. Both the images appeared close to the actual distribution of vegetation seen in the study area. OAA levels obtained in this study by ANN and SVM classifiers identify the suitability of these classifiers for tropical vegetation discrimination.  相似文献   

19.
Spectral reflectance can be used to assess large-scale performances of plants in the field based on plant nutrient balance as well as composition of defence compounds. However, plant chemical composition is known to vary with season – due to its phenology – and it may even depend on the succession stage of its habitat. Here we investigate (i) how spectral reflectance could be used to discriminate successional and phenological stages of Jacobaea vulgaris in both leaf and flower organs and (ii) if chemical content estimation by reflectance is flower or leaf dependent.We used J. vulgaris, which is a natural outbreak plant species on abandoned arable fields in north-western Europe and studied this species in a chronosequence representing successional development during time since abandonment. The chemical content and reflectance between 400 and 2500 nm wavelengths of flowers and leaves were measured throughout the season in fields of different successional ages. The data were analyzed with multivariate statistics for temporal discrimination and estimation of chemical contents in both leaf and flower organs.Two main effects were revealed by spectral reflectance measurements: (i) both flower and leaf spectra show successional and seasonal changes, but the pattern is complex and organ specific (ii) flower head pyrrolizidine alkaloids, which are involved in plant defence against herbivores, can be detected through hyperspectral reflectance.We conclude that spectral reflectance of both leaves and flowers can provide information on plant performance during season and successional stages. As a result, remote sensing studies of plant performance in complex field situations will benefit from considering hyperspectral reflectance of different plant organs. This approach may enable more detailed studies on the link between spectral information and plant defence dynamics both aboveground and belowground.  相似文献   

20.
The presence of salt in the soil profile negatively affects the growth and development of vegetation. As a result, the spectral reflectance of vegetation canopies varies for different salinity levels. This research was conducted to (1) investigate the capability of satellite-based hyperspectral vegetation indices (VIs) for estimating soil salinity in agricultural fields, (2) evaluate the performance of 21 existing VIs and (3) develop new VIs based on a combination of wavelengths sensitive for multiple stresses and find the best one for estimating soil salinity. For this purpose a Hyperion image of September 2, 2010, and data on soil salinity at 108 locations in sugarcane (Saccharum officina L.) fields were used. Results show that soil salinity could well be estimated by some of these VIs. Indices related to chlorophyll absorption bands or based on a combination of chlorophyll and water absorption bands had the highest correlation with soil salinity. In contrast, indices that are only based on water absorption bands had low to medium correlations, while indices that use only visible bands did not perform well. From the investigated indices the optimized soil-adjusted vegetation index (OSAVI) had the strongest relationship (R2 = 0.69) with soil salinity for the training data, but it did not perform well in the validation phase. The validation procedure showed that the new salinity and water stress indices (SWSI) implemented in this study (SWSI-1, SWSI-2, SWSI-3) and the Vogelmann red edge index yielded the best results for estimating soil salinity for independent fields with root mean square errors of 1.14, 1.15, 1.17 and 1.15 dS/m, respectively. Our results show that soil salinity could be estimated by satellite-based hyperspectral VIs, but validation of obtained models for independent data is essential for selecting the best model.  相似文献   

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