Nondestructive detection of anthocyanin content in fresh leaves of purple maize using hyperspectral data.

Anthocyanins are widely used in the food industry as an additive, improving antioxidant capacity and strengthening the human immune system. However, rapid and nondestructive detection methods are lacking. This study aimed to develop a rapid and nondestructive method to detect anthocyanin content in fresh purple maize leaves using hyperspectral reflectance. Sensitivity bands were screened by analyzing the correlation between the spectrum and anthocyanin, chlorophyll, and moisture content in maize leaves with models constructed. Through a combination of the sensitivity bands of the three components, the interference of chlorophyll and moisture on the spectral detection of anthocyanin in fresh leaves was analyzed. The results showed that the anthocyanin sensitivity band was approximately 550 nm. The determination coefficient and root mean square error of the optimal hyperspectral model were 0.766 and 4.215 mg/g, respectively. After excluding chlorophyll and moisture interference, the anthocyanin content detection accuracy was improved by only 2% compared to that of the original. These results indicate that hyperspectral technology can be used to nondestructively detect anthocyanin content in fresh purple maize leaves with good accuracy. Chlorophyll and moisture in the leaves did not significantly influence anthocyanin content.

Establishing NDRE dynamic models of winter wheat under multi-nitrogen rates based on a field spectral sensor.

Field spectral sensors provide real-time, reliable, quantitative monitoring of crop growth. Fitting the continuous growth in the entire growing period from the measurements of limited frequency is helpful to the comparative analysis of interannual growth and fertilizer management in the field. To exploit this capacity, our work presents a model that uses the normalized difference red edge (NDRE) index derived from the field spectral sensor for real-time monitoring of the canopy growth of winter wheat in the whole growing period. We developed this model from experiments in three counties in Hebei province, China, where we obtained the near-infrared and red edge reflectance, grain yield, and canopy parameters for eight growth stages and for various nitrogen (N) rates. Given the correlation between effective accumulated temperature and crop growth, we used the growing degree-days as an adjustment parameter to develop models for dynamic monitoring of the NDRE of the winter wheat canopy during the entire growing period. The results show that high determination coefficients (R2=0.89 to 0.96) are obtained from all models based on relative NDRE and effective accumulative temperature (independent of N fertilization rates). The model based on the rational function is the best of all models tested, with the accuracy for normal and high N fertilization rates being slightly greater than that for low N fertilization rates. Therefore, a relative-NDRE model with the accumulative growing degree-days since sowing could allow monitoring canopy NDRE of winter wheat at any time, which could be helpful for overcoming the shortage of incomparable growth derived from the differences of sensing date, sowing date, and fertilizer, etc.

Reduction of wheat photosynthesis by fine particulate (PM2.5) pollution over the North China Plain.

The negative impact of rapid urbanization in developing countries has led to a deterioration of urban and regional air quality. Much attention has been given to the impact of fine particulate pollution on urban public health. However, very little attention has been given to its impact on the regional ecosystem such as the agricultural ecosystem. Thus, we evaluate the direct impact of air pollution on the reduction of wheat photosynthesis by fine particulate matter (PM2.5) pollution in the world's most heavily polluted area, the North China Plain, using remote sensing observations and ground measurements. We found the following to be true: (1) Heavy PM2.5 pollution could significantly reduce wheat photosynthesis and cause an expositional relationship between the PM2.5 concentration and wheat photosynthesis (R2 = 0.9824, P < 0.05); (2) Heavy PM2.5 pollution makes up 2% for the reduction in wheat photosynthesis at all wheat-plant farmlands in the North China Plain, approximately covering an area of 354,400 km2; (3) Increasing heavy PM2.5 pollution significantly reduced wheat photosynthesis by 87% in wheat-planted farmland during 1999-2011. We hope the results presented here could draw attention to the effect of PM2.5 pollution on the agricultural ecosystem and encourage further studies to evaluate the feedback of atmospheric pollution on the agricultural ecosystem using remote sensing. Abbreviation: Northern China Plain (NCP); normalized difference vegetation index (NDVI); The Moderate Resolution Imaging Spectroradiometer (MODIS); fine particulate matter (PM2.5).

MiRNAs Mediate GDNF-Induced Proliferation and Migration of Glioma Cells.

BACKGROUND/AIMS: Glial cell line-derived neurotrophic factor (GDNF) is an important factor promoting invasive glioma growth. This study was performed to reveal a unique mechanism of glioma cell proliferation and migration. METHODS: Human U251 glioma cells were used to screen the optimal GDNF concentration and treatment time to stimulate proliferation and migration. MicroRNA (MiRNA) expression profiles were detected by microarray and confirmed by real-time polymerase chain reaction (PCR). The target genes of differentially expressed miRNAs were predicted by miRWalk, and those targeted by multiple miRNAs were screened with Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. A regulatory miRNA network was constructed using ingenuity pathway analysis (IPA). Target gene expression of differentially expressed miRNAs was examined by real-time PCR or mRNA microarray. RESULTS: The results show that 50 ng/mL GDNF for 24 h significantly promotes U251 glioma cell proliferation and migration (P < 0.05). Seven miRNAs (hsa-miR-194-5p, hsa-miR-152-3p, hsa-miR-205-5p, hsa-miR-629-5p, hsa-miR-3609, hsa-miR-183-5p, and hsa-miR-487b-3p) were significantly up-regulated after GDNF treatment (P < 0.05). These miRNAs are primarily involved in signal transduction, cell adhesion and cell cycle through mitogen-activated protein kinase (MAPK) signaling, focal adhesion and glioma signal pathways. Five of these miRNAs (hsa-miR-194-5p, hsa-miR-152-3p, hsa-miR-205-5p, hsa-miR-183-5p, and hsa-miR-487b-3p) co-regulate TP53 and Akt. mRNA expression levels of four genes co-targeted by two or more up-regulated miRNAs were significantly decreased after GDNF treatment (P < 0.05). CONCLUSION: GDNF treatment of U251 glioma cells significantly increased the expression of seven miRNAs involved in cell adhesion and the cell cycle.

Study of enteropathogenic bacteria in children with acute diarrhoea aged from 7 to 10 years in Xuzhou, China.

BACKGROUND: Acute diarrhoea is a common infectious disease among children in many countries and it has different kinds of clinical symptoms including vomiting, abdominal cramps, or fever of 38 °C. Some specific intestinal bacteria and their quantities can result in relevant symptoms. AIM: To analyze the correspondence between enteropathogenic bacteria and acute diarrhoea at family-level using high-throughput sequencing approach. METHODS: Every 30 children of acute diarrhoea with abdominal cramps, vomiting, and fever of 38 °C was regarded as a group, respectively. Stools samples were collected from each group and the DNA of stool was examined by E.Z.N.A.(®) Stool DNA Kit. The 16S rRNA genes sequencing was performed on an Illumina Miseq platform. FINDINGS: The sequencing dataset comprised 65,092 valid reads sequences that affiliated to the 18 phylogenetic families. The four dominant taxonomic groups in all three samples were Streptococcaceae, Veillonellaceae, Enterobacteriaceae and Lactobacillaceae. The stools of children with high fever presented higher pathogenic bacterial diversities and more complex community structures than other two groups. Lactobacillaceae was the enteric predominant microflora that could reduce the severity of acute diarrhoea. CONCLUSION: The reduction of predominant microflora or the aberrant proliferation of sub-dominant microflora can break the intestinal operation mechanism and cause intestinal diseases. What's more, people's living habits are also correlative about acute diarrhoea and parents should prepare light food for their children in order to protect their tender gastrointestinal mucosa.

[Estimation of organic matter content of north fluvo-aquic soil based on the coupling model of wavelet transform and partial least squares].

For improving the estimation accuracy of soil organic matter content of the north fluvo-aquic soil, wavelet transform technology is introduced. The soil samples were collected from Tongzhou district and Shunyi district in Beijing city. And the data source is from soil hyperspectral data obtained under laboratory condition. First, discrete wavelet transform efficiently decomposes hyperspectral into approximate coefficients and detail coefficients. Then, the correlation between approximate coefficients, detail coefficients and organic matter content was analyzed, and the sensitive bands of the organic matter were screened. Finally, models were established to estimate the soil organic content by using the partial least squares regression (PLSR). Results show that the NIR bands made more contributions than the visible band in estimating organic matter content models; the ability of approximate coefficients to estimate organic matter content is better than that of detail coefficients; The estimation precision of the detail coefficients fir soil organic matter content decreases with the spectral resolution being lower; Compared with the commonly used three types of soil spectral reflectance transforms, the wavelet transform can improve the estimation ability of soil spectral fir organic content; The accuracy of the best model established by the approximate coefficients or detail coefficients is higher, and the coefficient of determination (R2) and the root mean square error (RMSE) of the best model for approximate coefficients are 0.722 and 0.221, respectively. The R2 and RMSE of the best model for detail coefficients are 0.670 and 0.255, respectively.

[Inversion of organic matter content of the north fluvo-aquic soil based on hyperspectral and multi-spectra].

The present study aims to assess the feasibility of multi-spectral data in monitoring soil organic matter content. The data source comes from hyperspectral measured under laboratory condition, and simulated multi-spectral data from the hyperspectral. According to the reflectance response functions of Landsat TM and HJ-CCD (the Environment and Disaster Reduction Small Satellites, HJ), the hyperspectra were resampled for the corresponding bands of multi-spectral sensors. The correlation between hyperspectral, simulated reflectance spectra and organic matter content was calculated, and used to extract the sensitive bands of the organic matter in the north fluvo-aquic soil. The partial least square regression (PLSR) method was used to establish experiential models to estimate soil organic matter content. Both root mean squared error (RMSE) and coefficient of the determination (R2) were introduced to test the precision and stability of the modes. Results demonstrate that compared with the hyperspectral data, the best model established by simulated multi-spectral data gives a good result for organic matter content, with R2=0.586, and RMSE=0.280. Therefore, using multi-spectral data to predict tide soil organic matter content is feasible.

[Monitoring freeze stress levels on winter wheat from hyperspectral reflectance data using principal component analysis].

In order to detect the freeze injury stress level of winter wheat growing in natural environment fast and accurately, the present paper takes winter wheat as experimental object. First winter wheat canopy hyperspectral data were treated with resampling smooth Second hyperspectral data were analyzed based on principal components analysis (PCA), a freeze injury inversion model was established, stems survival rate was dependent, and principal components of spectral data were chosen as independent variables. Third, the precision of the model was testified. The result showed that the freeze injury inversion model based on 6 principal components can estimate the winter wheat freeze injury accurately with the coefficient of determination (R2) of 0. 697 5, root mean square error (RMSE) of 0. 184 2, and the accuracy of 0. 697 5. And the model was verified. It can be concluded that the PCA technology has been shown to be very promising in detecting winter wheat freeze injury effectively, and provide important reference for detecting other stress on crop.

Research on methods for estimating reference crop evapotranspiration under incomplete meteorological indicators.

Background: Accurate estimation of reference crop evapotranspiration (ET0) is crucial for farmland hydrology, crop water requirements, and precision irrigation decisions. The Penman-Monteith (PM) model has high accuracy in estimating ET0, but it requires many uncommon meteorological data inputs. Therefore, an ideal method is needed that minimizes the number of input data variables without compromising estimation accuracy. This study aims to analyze the performance of various methods for estimating ET0 in the absence of some meteorological indicators. The Penman-Monteith (PM) model, known for its high accuracy in ET0 estimation, served as the standard value under conditions of adequate meteorological indicators. Comparative analyses were conducted for the Priestley-Taylor (PT), Hargreaves (H-A), McCloud (M-C), and FAO-24 Radiation (F-R) models. The Bayesian estimation method was used to improve the ET estimation model. Results: Results indicate that, compared to the PM model, the F-R model performed best with inadequate meteorological indicators. It demonstrates higher average correlation coefficients (R2) at daily, monthly, and 10-day scales: 0.841, 0.937, and 0.914, respectively. The corresponding root mean square errors (RMSE) are 1.745, 1.329, and 1.423, and mean absolute errors (MAE) are 1.340, 1.159, and 1.196, with Willmott's Index (WI) values of 0.843, 0.862, and 0.859. Following Bayesian correction, R2 values remained unchanged, but significant reductions in RMSE were observed, with average reductions of 15.81%, 29.51%, and 24.66% at daily, monthly, and 10-day scales, respectively. Likewise, MAE decreased significantly, with average reductions of 19.04%, 34.47%, and 28.52%, respectively, and WI showed improvement, with average increases of 5.49%, 8.48%, and 10.78%, respectively. Conclusion: Therefore, the F-R model, enhanced by the Bayesian estimation method, significantly enhances the estimation accuracy of ET0 in the absence of some meteorological indicators.

[Change of LAI and spectral response for rice under flood and waterlogging stress].

In order to provide the foundational theoretical support for flood loss estimation of rice with RS, the change of leaf area index (LAI) and canopy spectral response during four developmental stages and three waterlogging depths were studied, and the LAI estimation model was established with spectra characteristics parameter using regression analysis method. The results show that LAI value decreases as water depth increases in tillering, jointing and heading stages, and LAI value under complete submergence decreased by 36. 36% than CK in jointing stages. "Double-Peak" presented in the canopy first derivative spectra of 680-760 nm where the red edge parameters existed, and the main peak is located in the 724-737 nm with 701 and 718 nm exhibiting secondary peak. With water depth increasing, "Triple-Peak" emerges especially. The red edge position moves to long-wavelength direction in each developmental stage. Blue shift of red edge amplitude and red edge area was detected in tillering, jointing and filling stages, while red shift appeared in heading stage. The relationship between spectra characteristics parameters and LAI were investigated during 4 growth stages, results were not consistently significant at any wavelengths, and the leaf area indices were significantly correlative to the spectra parameters before heading stage, so the spectra parameters before heading stage can be used to estimate the leaf area indices, and a regression model based on parameter D(lambda737)/D(lambda718) was recommended. Therefore the variation range of LAI for rice could response to the stress intensity directly, and the regression model LAI= 3. 138(D(lambda737)/D(lambda718))-0. 806 can precisely estimate the leaf area index under flooding and waterlogging stress.

Hyperspectral remote sensing for tobacco quality estimation, yield prediction, and stress detection: A review of applications and methods.

Tobacco is an important economic crop and the main raw material of cigarette products. Nowadays, with the increasing consumer demand for high-quality cigarettes, the requirements for their main raw materials are also varying. In general, tobacco quality is primarily determined by the exterior quality, inherent quality, chemical compositions, and physical properties. All these aspects are formed during the growing season and are vulnerable to many environmental factors, such as climate, geography, irrigation, fertilization, diseases and pests, etc. Therefore, there is a great demand for tobacco growth monitoring and near real-time quality evaluation. Herein, hyperspectral remote sensing (HRS) is increasingly being considered as a cost-effective alternative to traditional destructive field sampling methods and laboratory trials to determine various agronomic parameters of tobacco with the assistance of diverse hyperspectral vegetation indices and machine learning algorithms. In light of this, we conduct a comprehensive review of the HRS applications in tobacco production management. In this review, we briefly sketch the principles of HRS and commonly used data acquisition system platforms. We detail the specific applications and methodologies for tobacco quality estimation, yield prediction, and stress detection. Finally, we discuss the major challenges and future opportunities for potential application prospects. We hope that this review could provide interested researchers, practitioners, or readers with a basic understanding of current HRS applications in tobacco production management, and give some guidelines for practical works.

[Temporal and Spatial Variation Characteristics of Heavy Metal in Atmospheric Deposition in China from 2000 to 2018].

Owing to the significant impact of heavy metals in atmospheric deposition on soil, clear knowledge on the present situation and temporal and spatial variation in fluxes of heavy metals in atmospheric deposition all around China is urgently needed. In this study, we collected 99 published papers on deposition fluxes of heavy metals from 2001 to 2021 based on the CNKI and Web of Science database and extracted 718 to 1672 monitoring points from these papers. The Meta-analysis method was used to calculate the weighted average of deposition fluxes of heavy metals, and the spatial-temporal characteristics in different periods from 2000 to 2018 were studied by subgroup analysis, which compared the differences between different types of areas, such as agricultural and rural areas and urban and industrial areas. The results showed that the annual fluxes of heavy metals in atmospheric deposition[mg·(m2·a)-1] in China were as follows:Zn (96.75)>Pb (23.37)>Cu (12.77)>Cr (11.04)>Ni (6.61)>As (2.97)>Cd (0.48)>Hg (0.05). Overall, the estimated value of deposition fluxes in China from 2000 to 2018 was higher than that of rural areas in England from 1995 to 1998. The deposition fluxes in industrial areas and urban areas were much higher than those in the agricultural and rural areas, especially the industrial areas where the heavy metal pollution was more serious. The deposition fluxes of As and Cd in the Changsha-Zhuzhou-Xiangtan area were relatively high, whereas the atmospheric deposition of heavy metals in Northeast China, the Pearl River Delta, and North China Plain was more serious than that in the other areas. In the past 20 years, the annual deposition fluxes of Cd fluctuated around the overall average, without an obviously declining trend, whereas the deposition fluxes of Cd in the urban, agricultural, and rural areas showed a trend of growth. These results suggested that precise and risk control measures of atmospheric emissions should be established based on the characteristics of regional industrial structure, which should cover all levels, all types, and all regions. In addition, more restrictive measures should be taken to solve the current problem caused by the higher deposition flux of Cd in atmospheric deposition.

[The analysis of consistency between HJ-1B and Landsat 5 TM for retrieving LST based on the single-channel algorithm].

To ascertain whether the thermal infrared image of HJ-1B which has the similar sensor parameter and setting to Landsat 5 TM6 image is applicable for retrieving the land surface temperature (LST), a comparison of retrieved LST between two types of sensors was conducted. Two scenes of thermal infrared images that came from different sensors were acquired in 5th, Apr 2009, which covered the same region in Beijing. To retrieve LST, a generalized single-channel algorithm developed by Jiménez-Muñoz and Sobrino was applied. The LST of study area for both images was thus generated. Based on the LST mapping results and corresponding statistics, an apparent trend could be observed which indicated the consistency in both LST value and its spatial distribution. Consequently, the performance of HJ-IB IRS serving as the data source for LST retrieval was assessed and illustrated in this study. Besides, a high temporal resolution as well as wide swath of the HJ-IRS data suggested its potential in application.

[Monitoring stripe rust of winter wheat using PHI based on sensitive bands].

Forty six points representing different severity degree of stripe rust were established in winter wheat field. The canopy reflectance was collected by an ASD hand-held spectrometer at each point. Meanwhile, the diseases index was investigated. These data were used for the following analysis. Firstly, the relationships between diseases index and reflectance of bands in the range of 300-1500 nm were analyzed. The sensitive bands were selected for stripe rust detecting. Secondly, considering the character of PHI image, red bands (620-718 nm) and near infrared bands (770-805 nm) were assigned as the best bands. Finally, the mean reflectance of red bands (620-718 nm) and near infrared bands (770-805 nm) was calculated respectively to construct the reverse model with the observed diseases indexes: DI = 19.241 R1 - 2.20667 R2 + 12.2744. With this model, the severity degree of stripe rust of winter wheat was monitored successfully in PHI image.

Crosstalk between DNA methylation and histone acetylation triggers GDNF high transcription in glioblastoma cells.

BACKGROUND: Glial cell line-derived neurotrophic factor (GDNF) is highly expressed in glioblastoma (GBM) and blocking its expression can inhibit the initiation and development of GBM. GDNF is a dual promoter gene, and the promoter II with two enhancers and two silencers plays a major role in transcription initiation. We had previously reported that histone hyperacetylation and DNA hypermethylation in GDNF promoter II region result in high transcription of GDNF in GBM cells, but the mechanism remains unclear. In this study, we investigated whether these modifications synergistically regulate high GDNF transcription in GBM. RESULTS: Cyclic AMP response element binding protein (CREB) expression and phosphorylation at S133 were significantly increased in human GBM tissues and GBM cell lines (U251 and U343). In U251 GBM cells, high expressed CREB significantly enhanced GDNF transcription and promoter II activity. CREB regulated GDNF transcription via the cyclic AMP response elements (CREs) in enhancer II and silencer II of GDNF promoter II. However, the two CREs played opposite regulatory roles. Interestingly, hypermethylation of CRE in silencer II occurred in GBM tissues and cells which led to decreased and increased phosphorylated CREB (pCREB) binding to silencer II and enhancer II, respectively. Moreover, pCREB recruited CREB binding protein (CBP) with histone acetylase activity to the CRE of GDNF enhancer II, thereby increasing histone H3 acetylation and RNA polymerase II recruitment there and at the transcription start site (TSS), and promoted GDNF high transcription in U251 cells. The results indicated that high GDNF transcription was attributable to DNA hypermethylation in CRE of GDNF silencer II increasing pCREB binding to CRE in enhancer II, which enhanced CBP recruitment, histone H3 acetylation, and RNA polymerase II recruitment there and at the TSS. CONCLUSIONS: Our results demonstrate that pCREB-induced crosstalk between DNA methylation and histone acetylation at the GDNF promoter II enhanced GDNF high transcription, providing a new perspective for GBM treatment.

Differential expression of glial cell line-derived neurotrophic factor splice variants in the mouse brain.

Glial cell line-derived neurotrophic factor (GDNF) plays a critical role in neuronal survival and function. GDNF has two major splice variants in the brain, α-pro-GDNF and ß-pro-GDNF, and both isoforms have strong neuroprotective effects on dopamine neurons. However, the expression of the GDNF splice variants in dopaminergic neurons in the brain remains unclear. Therefore, in this study, we investigated the mRNA and protein expression of α- and ß-pro-GDNF in the mouse brain by real-time quantitative polymerase chain reaction, using splice variant-specific primers, and western blot analysis. At the mRNA level, ß-pro-GDNF expression was significantly greater than that of α-pro-GDNF in the mouse brain. In contrast, at the protein level, α-pro-GDNF expression was markedly greater than that of ß-pro-GDNF. To clarify the mechanism underlying this inverse relationship in mRNA and protein expression levels of the GDNF splice variants, we analyzed the expression of sorting protein-related receptor with A-type repeats (SorLA) by real-time quantitative polymerase chain reaction. At the mRNA level, SorLA was positively associated with ß-pro-GDNF expression, but not with α-pro-GDNF expression. This suggests that the differential expression of α- and ß-pro-GDNF in the mouse brain is related to SorLA expression. As a sorting protein, SorLA could contribute to the inverse relationship among the mRNA and protein levels of the GDNF isoforms. This study was approved by the Animal Ethics Committee of Xuzhou Medical University, China on July 14, 2016.

Monitoring Maize Lodging Grades via Unmanned Aerial Vehicle Multispectral Image.

Lodging is one of the main factors affecting the quality and yield of crops. Timely and accurate determination of crop lodging grade is of great significance for the quantitative and objective evaluation of yield losses. The purpose of this study was to analyze the monitoring ability of a multispectral image obtained by an unmanned aerial vehicle (UAV) for determination of the maize lodging grade. A multispectral Parrot Sequoia camera is specially designed for agricultural applications and provides new information that is useful in agricultural decision-making. Indeed, a near-infrared image which cannot be seen with the naked eye can be used to make a highly precise diagnosis of the vegetation condition. The images obtained constitute a highly effective tool for analyzing plant health. Maize samples with different lodging grades were obtained by visual interpretation, and the spectral reflectance, texture feature parameters, and vegetation indices of the training samples were extracted. Different feature transformations were performed, texture features and vegetation indices were combined, and various feature images were classified by maximum likelihood classification (MLC) to extract four lodging grades. Classification accuracy was evaluated using a confusion matrix based on the verification samples, and the features suitable for monitoring the maize lodging grade were screened. The results showed that compared with a multispectral image, the principal components, texture features, and combination of texture features and vegetation indices were improved by varying degrees. The overall accuracy of the combination of texture features and vegetation indices is 86.61%, and the Kappa coefficient is 0.8327, which is higher than that of other features. Therefore, the classification result based on the feature combinations of the UAV multispectral image is useful for monitoring of maize lodging grades.

[Remote sensing resource monitoring on Atractylodes lancea].

Remote sensing technology was used for investigation of the resources of Atractylodes lancea. Firstly, the general situation of Jiangshu Maoshan and A. lancea in Maoshan was introduced; Secondly, the methods of remote sensing on the resource of the wild drugs were explained. Thirdly, the TM images were interpret according to the differences of the objects reflex spectrum, and growth environments in Damao mountain, Ermao mountain and Xiaomao mountain were divided into different sub-areas according to the results of the field investigations. Finally, the resource of A. lancea in Jiangshu Maoshan was estimated.

Estimating foliar anthocyanin content of purple corn via hyperspectral model.

To date, the foliar anthocyanin content was either determined via the pH differential or HPLC methods, both of which are slow and destructive. Here, a hyperspectral model was established to estimate the foliar anthocyanin content of purple corn (Zea mays L. var. Jingzi No. 1). The reflectivity (P) of the foliar hyperspectral was inverted to 1/P, lg P, 1/lg P, P' , 1/P' , lgP' , and 1/lgP' . The correlation coefficient between these inversions and the foliar anthocyanin content was plotted against the hyperspectral wavelength. The wavelength of inversions around 650 nm was sensitive to the foliar anthocyanin content. The hyperspectral model was fitted via linear, polynomial, power, exponential, and logarithmic functions with the sensitive band as independent variable and the anthocyanin content as function. The hyperspectral model (y = 3,000,000,000 × W6854.5896) fitted via inversion of lgP' showed the highest determination coefficients (0.768) among all models. The hyperspectral model was well validated with a determination coefficient of 0.932 and an RMSE of 0.0065. Moreover, the accuracy and stability of the hyperspectral model were further enhanced with a determination coefficient of 0.954 and RMSE of 0.0047 when the anthocyanin content of the sample was below 20 mg/g. Hence, the hyperspectral model estimated the foliar anthocyanin content of purple corn quickly and nondestructively.

[Study on dynamical changes of Ginkgo biloba resources in Pizhou city of Jiangsu Province base on RS and GIS].

OBJECTIVE: To analyse the dynamical changes of the Ginkgo biloba's resources from 2001 to 2006, in Pizhou city, Jiangshu province by useing spatial analytical function of GIS and RS technology. METHOD: Use the GIS and RS technology, extracted the information of G. biloba by scientific investigation, researched the spatial distribution and dynamical changes of G. biloba based on landsat 5 TM: the Apr. 3rd, 2001; Jan. 16th, 2005; July 30th, 2006. RESULT: Ginkgo biloba's resource was 1.61 x 10(5) hm2 in 2001, 1.84 x 10(5) hm2 in 2005, 1.88 x 10(5) hm2 in 2006. CONCLUSION: Ginkgo biloba's resource rised from 1.61 x 10(5) hm2 to 1.88 x 10(5) hm2 from 2001 to 2006, showed the gradually rise.