Leaf Chlorophyll Content Estimation of Winter Wheat Based on Visible and Near-Infrared Sensors

Zhang, Jianfeng; Han, Wenting; Huang, Lu-Qi; Zhang, Zhiyong; Ma, Yimian; Hu, Yamin

doi:10.3390/s16040437

Cited by 36 publications

(28 citation statements)

References 18 publications

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“…For most hyperspectral remote sensing applications [23,47,48,[50][51][52][53][54][55][56][57][58][59][60][61][62], the PCA transformations are always conducted at the spectral dimension based on the high degree of spatial similarity between images at different bands. As illustrated in Figure 1, the spectra of leaves have similar fixed absorption features that are due to pigments and water.…”

Section: Principal Component Analysismentioning

confidence: 99%

“…Some hyperspectral studies have demonstrated that regression models using PCA (over spectral dimension) gave superior results as compared to using VIs [23,[50][51][52][53]59]. For example, Satapathy showed that the retrieved LAI from PCA has higher accuracy (RMSE = 0.137) than the classical NDVI-LAI empirical approach (RMSE = 1.139) [51].…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, PCA methods are also used for selecting subsets of variables used in regression equations of surface properties such as the leaf area index (LAI) [23,[50][51][52][53][54][55][56][57], canopy chlorophyll content [24,58,59], soil properties [60] and the chlorophyll concentration of inland water bodies [61,62]. Some hyperspectral studies have demonstrated that regression models using PCA (over spectral dimension) gave superior results as compared to using VIs [23,[50][51][52][53]59].…”

Section: Introductionmentioning

confidence: 99%

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A Novel Principal Component Analysis Method for the Reconstruction of Leaf Reflectance Spectra and Retrieval of Leaf Biochemical Contents

et al. 2017

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Vegetation variable retrieval from reflectance data is typically grouped into three categories: the statistical-empirical category, the physical category and the hybrid category (physical models applied to statistical models). Based on the similarities between the spectra of leaves in the optical domain, the leaf reflectance spectra can be linearly modelled using a very limited number of principal components (PCs) if the PCA (principal component analysis) transformation is carried out at the sample dimension. In this paper, we present a novel data-driven approach that uses the PCA transformation to reconstruct leaf reflectance spectra and also to retrieve leaf biochemical contents. First, the PCA transformation was carried out on a training dataset simulated by the PROSPECT-5 model. The results showed that the leaf reflectance spectra can be accurately reconstructed using only a few leading PCs, as the ten leading PCs contained 99.999% of the total information in the 3636 training samples. The spectral error between the simulated or measured reflectance and the reconstructed spectra was also investigated using the simulated and measured datasets (ANGERS and LOPEX'93). The mean root mean squared error (RMSE) values varied from 5.56 × 10 −5 to 6.18 × 10 −3 , which is about 3-10 times more accurate than the PROSPECT simulation method for measured datasets. Secondly, the relationship between PCs and leaf biochemical components was investigated, and we found that the PCs are closely related to the leaf biochemical components and to the reflectance spectra. Only when the weighting coefficient of the most sensitive PC was employed to retrieve the leaf biochemical contents, the coefficients of determination for the PCA data-driven model were 0.69, 0.99, 0.94 and 0.68 for the specific leaf weight (SLW), equivalent water thickness (EWT), chlorophyll content (Cab) and carotenoid content (Car), respectively. Finally, statistical models for the retrieval of leaf biochemical contents were developed based on the weighting coefficients of the sensitive PCs, and the PCA data-driven models were validated and compared to the traditional VI-based and physically-based approaches for the retrieval of leaf properties. The results show that the PCA method shows similar or better performance in the estimation of leaf biochemical contents. Therefore, the PCA method provides a new and accurate data-driven method for reconstructing leaf reflectance spectra and also for retrieving leaf biochemical contents.

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Section: Principal Component Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Novel Principal Component Analysis Method for the Reconstruction of Leaf Reflectance Spectra and Retrieval of Leaf Biochemical Contents

et al. 2017

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“…Plant phenotyping based on hyperspectral imaging techniques has been successfully used mainly in discriminate genotypes with different contents of leaf biochemical components. Frequently, the genotypes are screened for differences in content of leaf chlorophyll (Ferri et al, 2004;Wu et al, 2011;Bauriegel and Herppich, 2014;Zhang et al, 2016), anthocyanins (Gitelson et al, 2002;Shi et al, 2012) or nitrogen (Yu et al, 2014). Sytar et al (2015) analyzed the flavonoid contents in the leaves of 30 plant species using the multiplex fluorimetric sensor.…”

Section: Integration Of Hyperspectral Analyses Into Salt-stress Relatmentioning

confidence: 99%

Applying hyperspectral imaging to explore natural plant diversity towards improving salt stress tolerance

Sytar

Brestič

Živčák

et al. 2017

Science of The Total Environment

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• Salinity represents an abiotic stress constraint affecting growth and productivity of plants • Better solutions is to improve the level of salt resistance using natural genetic variability within crop species • Phenomic methodology employing different non-invasive imaging systems for detecting quantitative and qualitative changes caused by salt stress at the whole plant and canopy level. Hyperspectral imaging techniques provide unique opportunities for fast and reliable evaluation of numerous characteristics associated both with various structural, biochemical and physiological traits • Salt-soil-plant interaction and sustainable coastal agriculture need powerful phenotyping tools Salinity represents an abiotic stress constraint affecting growth and productivity of plants in many regions of the world. One of the possible solutions is to improve the level of salt resistance using natural genetic variability within crop species. In the context of recent knowledge on salt stress effects and mechanisms of salt tolerance, this review present useful phenomic approach employing different non-invasive imaging systems for detection of quantitative and qualitative changes caused by salt stress at the plant and canopy level. The focus is put on hyperspectral imaging technique, which provides unique opportunities for fast and reliable estimate of numerous characteristics associated both with various structural, biochemical Science of the Total Environment 578 (2017) 90-99 ⁎ Corresponding authors at:

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“…In recent years, the method of measurement of chlorophyll relative content based on multi-spectrum has attracted the attention of many researchers, which has applied to many crops, including rice nitrogen fertilizer recommendation [12,13], potato [14,15], wheat [16,17], and corn [18]. Zhang et al [19] proposed the quantitative analysis model for the relation between the leaf relative chlorophyll content and the reflectance spectra through researching the non-destructive testing of leaf chlorophyll content of winter wheat, and preprocessing using MSC and SNV improved the accuracy. Liu et al [20] pointed out that when the plant was in the process of photosynthesis, it mainly absorbed red light and blue light from the sun; two wavelengths of light selected as the characteristic wavelengths light sources were the infrared region (650 nm) and near-infrared region (940 nm); the chlorophyll content of leaves was determined by comparing the light intensity of the reflected light and the transmitted light through the leaves.…”

Section: Design Of the Portable Farmland Information Collection Symentioning

confidence: 99%

A Portable Farmland Information Collection System with Multiple Sensors

Zhang¹,

Hu²,

Huang³

et al. 2016

Sensors

Self Cite

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Precision agriculture is the trend of modern agriculture, and it is also one of the important ways to realize the sustainable development of agriculture. In order to meet the production requirements of precision agriculture—efficient use of agricultural resources, and improving the crop yields and quality—some necessary field information in crop growth environment needs to be collected and monitored. In this paper, a farmland information collection system is developed, which includes a portable farmland information collection device based on STM32 (a 32-bit comprehensive range of microcontrollers based on ARM Crotex-M3), a remote server and a mobile phone APP. The device realizes the function of portable and mobile collecting of multiple parameters farmland information, such as chlorophyll content of crop leaves, air temperature, air humidity, and light intensity. UM220-III (Unicore Communication Inc., Beijing, China) is used to realize the positioning based on BDS/GPS (BeiDou Navigation Satellite System, BDS/Global Positioning System, GPS) dual-mode navigation and positioning system, and the CDMA (Code Division Multiple Access, CDMA) wireless communication module is adopted to realize the real-time remote transmission. The portable multi-function farmland information collection system is real-time, accurate, and easy to use to collect farmland information and multiple information parameters of crops.

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Leaf Chlorophyll Content Estimation of Winter Wheat Based on Visible and Near-Infrared Sensors

Cited by 36 publications

References 18 publications

A Novel Principal Component Analysis Method for the Reconstruction of Leaf Reflectance Spectra and Retrieval of Leaf Biochemical Contents

A Novel Principal Component Analysis Method for the Reconstruction of Leaf Reflectance Spectra and Retrieval of Leaf Biochemical Contents

Applying hyperspectral imaging to explore natural plant diversity towards improving salt stress tolerance

A Portable Farmland Information Collection System with Multiple Sensors

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