A field imaging spectrometer system (FISS; 380–870 nm and 344 bands) was designed for agriculture applications.
AcknowledgmentsThis research was supported by the Natural Science Foundation of Jiangsu Province of China (BK20141091), the Chinese Academy of Sciences Action Plan for the Development of Western China (No. IntroductionPlants cover more than 70% of the global land surface and are among the most important resources on the Earth; their distributions are also intensively and closely related to human activities.
The chlorophyll content was retrieved using a multiple linear regression (MLR), partial least squares (PLS) regression and support vector machine (SVM) regression. Terrestrial plants that can perform photosynthesis are the energy and organic matter providers for almost all ecosystems and are also the main products of the vast majority of terrestrial ecosystems.
Our objective was to verify the performance of FISS in a quantitative spectral analysis through the estimation of chlorophyll content and to determine a proper quantitative spectral analysis method for processing FISS data. The importance of plants has made the extraction of information on plants based on various means and methods a topic of constant interest [1,2]. We thank the anonymous reviewers for their constructive comments and suggestions on our manuscript. Spectroscopic technology provides fast, convenient and non-destructive detection, so it has long been used in fields, such as crop yield assessments [3], vegetation ecology assessments [4–6], light and effective radiation [7], land productivity [8], wetland management [9] and tree species identification [10,11]. For the derivative spectra, there were four regions with relatively high correlation coefficients: 490–550, 600–670, 680–710 and 725–775 nm. Their comments helped greatly to improve the quality of this paper.Author ContributionsBo Liu and Yue-Min Yue designed the experiments.
Compared with the spectral features, the regression methods had smaller effects on the retrieval accuracy. As shown in Figures 6 and 7, the maximal correlation coefficient between the mean derivative spectral reflectance of the FISS data and chlorophyll content was ?0.90 (corresponding to a wavelength of 650 nm). A multivariate linear model could be the ideal model to retrieve chlorophyll information with a small number of significant wavelengths used. Compared with single-point sensor spectrometers, an imaging spectroscopy system can provide tens to several hundreds of spectral channels and a wealth of images and spatial details, and it can perform image analyses and spectral analyses simultaneously. A comparison of the two figures revealed that the correlation coefficient between the FISS spectral derivative and pigment content was overall higher than that between the spectral reflectance and pigment content, indicating that the derivative spectral information was a better pigment content indicator. Such systems can also acquire qualitative and quantitative information, as well as information on positioning, distribution and morphology.
Our study indicates that FISS could obtain both spectral and spatial detailed information of high quality.
The ability to obtain “pure” pixel information not only makes up for the drawbacks of conventional non-imaging spectroscopy instruments, but also greatly expands the potential applications of imaging spectroscopy [13–18]. Chlorophyll Content RetrievalThere were 101 valid samples for chlorophyll retrieval, which included 71 training samples and 30 validation samples.
Its image-spectrum-in-one merit promotes the good performance of FISS in quantitative spectral analyses, and it can potentially be widely used in the agricultural sector.
Ground remote sensing systems have been widely used in agriculture [12–16], food security [17], quality control [18], chemical imaging analysis [19,20], biomedicine [21] geology [22,23], color science, materials science, machine vision and other fields [24,25]. Fast and non-destructive chlorophyll content estimation is an important field of application for spectroscopy technologies. However, most current ground-based studies on chlorophyll retrieval have used non-imaging spectral data obtained by single-point sensors or images formed by a small number of wavelengths that are generated by multi-spectrum systems. The use of imaging spectroscopy systems, which are emerging high-tech detection systems, for the non-destructive acquisition of plant chlorophyll information must still be investigated [26–30].The Chinese Scientific Community has exhibited strong interest in the application of small field imaging spectroscopy systems (FISSs).
The number of features and accuracy in each case are shown in Table 2 for comparison.ASD spectrometer was used as well to obtain the spectra data of these samples simultaneously. A series of FISSs have been developed by the Chinese Academy of Sciences, including two visible light systems and one shortwave infrared system [14,31,32]. Scientific and technical challenges in remote sensing of plant canopy reflectance and fluorescence. In the present study, one of these visible FISSs (the first FISS in China that is suitable for agricultural research) was used to obtain 101 FISS images of soybean leaves and to extract their reflectance, derivative spectra and other information. The chlorophyll content was retrieved using multiple linear regression (MLR), partial least squares (PLS) regression and support vector machine (SVM) regression.


In order to compare the performance of imaging spectrometer and traditional single-point sensor spectrometer, we also used an ASD spectrometer to obtain the spectral data of these soybean leaf samples simultaneously.
Comparative Analysis of the Accuracy of Chlorophyll Content Retrieval Based on Imaging Spectrometer and Non-Imaging SpectrometerFrom the comparison of Table 2 with Table 3, it is easy to find that chlorophyll content retrieval accuracies based on the imaging spectrometer are higher than those based on a traditional single-point sensor spectrometer. A similar result can be found in the case where derivative spectral reflectance is the model feature. Detection of crude protein, crude starch, and amylase for rice by hyperspectral reflectance. FISSFISS consist of a multi-purpose platform, electronic system, opto-mechanical system, computer system and auxiliary equipment (Figure 1) [31].
The opto-mechanical system, which is the key component of the FISS, consists of a scanning mirror, optical lenses, spectroscopic devices (ImSpector V9, Spectral Imaging, Ltd., Qulu, Finland) and a charge-coupled device (CCD) camera. Its image-spectrum-in-one merit can provide more information about the whole leaf and is more conducive to chlorophyll content estimation. Meanwhile, the imaging spectrometer can accurately obtain the spectral information of any valid pixel in the entire leaf and accurately locate the target. The motor control circuit is primarily used to control the rotation of the scanning mirror, synchronize the beam splitter and receiver and collect and store the data. A single-point sensor spectrometer can only obtain information in the sensor's field of view and not the complete spectral information of the entire leaf. This could lead to the fact that its spectral signals cannot be precisely mapped to the target objects. Predicting water stress induced by Thaumastocoris pergrinus infestations in plantation forest using field spectroscopy and neural networks. The instrument operating software and data acquisition software are used for setting the instrument parameters (integration time, aperture, field of view, cooling temperature, etc.) and can display images and spectra in real time.
In our cases, the entire leaf was used for chlorophyll extraction, while we cannot get the complete spectral information of the entire leaf using a non-imaging spectrometer.
The primary functions of the data processing software are data format conversion, geometric correction, radiometric correction, image segmentation, interception and image stitching.The FISS acquires spatial data by rotating the scanning mirror, and the spatial resolution varies with the platform height. The imaging spectrometer can overcome this shortcoming and, thus, obtain better results for chlorophyll content estimation. Figure 3 shows the whole system, and the main technical specifications are listed in Table 1. Figure 4 shows a sample of the hyperspectral data cube obtained by FISS in a mulberry field [31,32]. Vegetation indices simulated from different sensors for the estimation of biophysical variables of common bean.
Experimental DesignSample collection: The experiment was conducted in the Huanjiang Observation and Research Station for Karst Ecosystem, Chinese Academy of Sciences.
When only a small number of wavelengths were used, the difference was particularly significant, and the mean reduction was 22.7%.
Several different fertilization gradients were set up, and fertilizers with different nutrient gradients were applied in an experimental soybean plot with the aim of studying nutrients in rain-fed lands.
This result demonstrates that the derivative spectrum is a more sensitive chlorophyll content indicator that can extract content information more efficiently than the raw spectral reflectance.From Table 3, it can be found that the derivative spectrum serves as a better indicator of chlorophyll content generally compared to raw spectral reflectance for ASD data. It holds true for both FISS and ASD data that derivative spectra have better performance than reflectance spectra for quantifying chlorophyll.
Significant reductions of the estimation RMSEs were observed by comparing the results derived from derivative and reflectance spectra of the FISS data. The imaging spectral data of the soybean leaves were then obtained with the FISS and an artificial cold light source (halogen light). However, the corresponding improvements are not so obvious for ASD data by the fact that RSMEs reductions are mainly observed in the calibration dataset, while no significant reductions are found in the validation dataset in our cases.If all 344 of the wavelengths are used in the models, then the advantages of using the derivative spectra become less significant, because the derivative spectra are essentially a method of enhancing information, while effectively suppressing noise at specific wavelengths.
Airborne hyperspectral images and ground-level optical sensors as assessment tools for maize nitrogen fertilization.
Therefore, the derivative spectra do not increase the amount of information carried by the FISS data.
Fresh leaves were cut into pieces, and 0.2 g of the pieces were weighed and placed in a 25-mL colorimetric tube for extraction for 18–24 h in the dark.


When all of the wavelengths are used, the spectral reflectance and derivative spectra carry the same amount of information. Consequently, the derivative spectra cannot significantly improve the estimation accuracy with all bands used. Namely, spectral reflectance was calculated as the ratio of leaf spectrum to the standard panel spectrum. The leaves were then separated from the background according to the threshold set, and shadows on the leaves were separated and removed (Figure 5). Because the lighting condition was stable in the lab, the contrast of spectra between leaf and shadow was quite remarkable.
Leaves had much higher reflectance values of green wavelengths, while shadow had much lower reflectance values. Here, we use the reflectance value of the green band (550 nm) as the indicator and set a threshold to separate the leaf and shadow.
Among the four models, the accuracy achieved by the LR model was generally the lowest because the LR model used fewer characteristic variables and, therefore, had insufficient information.When using three or seven wavelengths, the SVM (a nonlinear method) did not outperform the MLR model, indicating that the relationship between the chlorophyll content and these significant wavelengths was closer to a linear relationship than to a complex non-linear relationship. Specifically, this conclusion holds true as well for ASD data by comparing the different regression models (Table 3). General DescriptionThe aim of this study is to evaluate the performance of the FISS through estimation of plant leaf chlorophyll contents. ConclusionsAn FISS was used to study the retrieval of soybean leaf chlorophyll content in the present study, and the performance of the FISS system in a quantitative spectral analysis was assessed. LAI retrieval using PROSAIL model and optimal angel combination of multi-angular data in wheat.
Whether this new system can be used for quantifying chlorophyll or even outperform traditional non-imaging spectrometer becomes the focus.
As a typical non-imaging spectrometer, ASD is widely used in the remote sensing community to perform spectral experiments and quantify biochemicals, including chlorophyll. Detailed comparative analyses of chlorophyll estimation based on the two different spectrometers are presented in Section 4. The impact of the model selection on accuracy was smaller than that of the spectral feature. Optimum pixel size for hyperspectral studies of ecosystem function in southern California chaparral and grassland. Derivative spectra and reflectance spectra are used to estimate chlorophyll contents with ASD and FISS data.
Regression methods only required information from several significant wavelengths to retrieve chlorophyll content, and they can be used in multivariate linear models.
The same analysis methods and techniques are applied on both FISS and ASD data to make the comparison sound and comprehensive. This conclusion holds true for both FISS and ASD data.(4)Because of its unique measurement method and image-spectrum-in-one feature, FISS data can be used to acquire accurate chlorophyll content information with higher accuracies than non-imaging spectral data. The ability to enhance these changes can also help eliminate certain interfering factors and can better reflect the essential characteristics of vegetation.
Evaluation of Sentinel-2 red-edge bands for empirical estimation of green LAI and chlorophyll content.
NIR hyperspectral imaging spectroscopy and chemometrics for the detection of undesirable substances in food and feed. Discrimination of grassland species and their classification inbotanical families by laboratory scale NIR hyperspectral imaging: Preliminary results. Use of spatial struture analysis of hyperspectral data cubes for detection of insect-induced stress in wheat plants. Recent advances in emerging imaging techniques for non-destructive detection of food quality and safety.



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