Predicting Nitrogen Status of Rice Using Multispectral Data at Canopy Scale

Two field experiments were conducted in Jiashan and Yuhang towns of Zhejiang Province, China, to study the feasibility of predicting N status of rice using canopy spectral reflectance. The canopy spectral reflectance of rice grown with different levels of N inputs was determined at several important growth stages. Statistical analyses showed that as a result of the different levels of N supply, there were significant differences in the N concentrations of canopy leaves at different growth stages. Since spectral reflectance measurements showed that the N status of rice was related to reflectance in the visible and NIR (near-infrared) ranges, observations for rice in 1 nm bandwidths were then converted to bandwidths in the visible and NIR spectral regions with IKONOS (space imaging) bandwidths and vegetation indices being used to predict the N status of rice. The results indicated that canopy reflectance measurements converted to ratio vegetation index (RVI) and normalized difference vegetation index (NDVI) for simulated IKONOS bands provided a better prediction of rice N status than the reflectance measurements in the simulated IKONOS bands themselves. The precision of the developed regression models using RVI and NDVI proved to be very high with R2 ranging from 0.82 to 0.94, and when validated with experimental data from a different site, the results were satisfactory with R2 ranging from 0.55 to 0.70. Thus, the results showed that theoretically it should be possible to monitor N status using remotely sensed data.

Selection of Spectral Data for Classification of Steels Using Laser-Induced Breakdown Spectroscopy

Principal component analysis （PCA） combined with artificial neural networks was used to classify the spectra of 27 steel samples acquired using laser-induced breakdown spectroscopy. Three methods of spectral data selection, selecting all the peak lines of the spectra, selecting intensive spectral partitions and the whole spectra, were utilized to compare the infiuence of different inputs of PCA on the classification of steels. Three intensive partitions were selected based on experience and prior knowledge to compare the classification, as the partitions can obtain the best results compared to all peak lines and the whole spectra. We also used two test data sets, mean spectra after being averaged and raw spectra without any pretreatment, to verify the results of the classification. The results of this comprehensive comparison show that a back propagation network trained using the principal components of appropriate, carefully selecred spectral partitions can obtain the best results accuracy can be achieved using the intensive spectral A perfect result with 100% classification partitions ranging of 357-367 nm.

Monitoring Soil Salt Content Using HJ-1A Hyperspectral Data: A Case Study of Coastal Areas in Rudong County, Eastern China

Hyperspectral data are an important source for monitoring soil salt content on a large scale. However, in previous studies, barriers such as interference due to the presence of vegetation restricted the precision of mapping soil salt content. This study tested a new method for predicting soil salt content with improved precision by using Chinese hyperspectral data, Huan Jing-Hyper Spectral Imager(HJ-HSI), in the coastal area of Rudong County, Eastern China. The vegetation-covered area and coastal bare flat area were distinguished by using the normalized differential vegetation index at the band length of 705 nm(NDVI705). The soil salt content of each area was predicted by various algorithms. A Normal Soil Salt Content Response Index(NSSRI) was constructed from continuum-removed reflectance(CR-reflectance) at wavelengths of 908.95 nm and 687.41 nm to predict the soil salt content in the coastal bare flat area(NDVI705 < 0.2). The soil adjusted salinity index(SAVI) was applied to predict the soil salt content in the vegetation-covered area(NDVI705 ≥ 0.2). The results demonstrate that 1) the new method significantly improves the accuracy of soil salt content mapping(R2 = 0.6396, RMSE = 0.3591), and 2) HJ-HSI data can be used to map soil salt content precisely and are suitable for monitoring soil salt content on a large scale.

Some Peculiarities of the Preprocessing of Spectral Data and Images

Remotely sensed spectral data and images are acquired under significant additional effects accompanying their major formation process, which greatly determine measurement accuracy. In order to be used in subsequent quantitative analysis and assessment, this data should be subject to preliminary processing aiming to improve its accuracy and credibility. The paper considers some major problems related with preliminary processing of remotely sensed spectral data and images. The major factors are analyzed, which affect the occurrence of data noise or uncertainties and the methods for reduction or removal thereof. Assessment is made of the extent to which available equipment and technologies may help reduce measurement errors.

Lithological mapping with multispectral data–setup and application of a spectral database for rocks in the Balakot area, Northern Pakistan

In the frame of landslide susceptibility assessment, a spectral library was created to support the identification of materials confined to a particular region using remote sensing images. This library, called Pakistan spectral library(pklib) version 0.1, contains the analysis data of sixty rock samples taken in the Balakot region in Northern Pakistan.The spectral library is implemented as SQLite database. Structure and naming are inspired by the convention system of the ASTER Spectral Library. Usability, application and benefit of the pklib were evaluated and depicted taking two approaches, the multivariate and the spectral based. The spectral information were used to create indices. The indices were applied to Landsat and ASTER data tosupportthespatial delineation of outcropping rock sequences instratigraphic formations. The application of the indices introduced in this paper helps to identify spots where specific lithological characteristics occur. Especially in areas with sparse or missing detailed geological mapping, the spectral discrimination via remote sensing data can speed up the survey. The library can be used not only to support the improvement of factor maps for landslide susceptibility analysis, but also to provide a geoscientific basisto further analyze the lithological spotin numerous regions in the Hindu Kush.

Research on Estimation Models of Chlorophyll Content in Apple Leaves Based on Imaging Hyperspectral Data

In view of the shortage of using traditional methods to monitor chlorophyll content, hyperspectral technology was used to estimate the chlorophyll content of apple leaves rapidly, accurately and non-destructively. Based on the data of hyperspectral reflectivity and SPAD value of normal apple leaves and the leaves under the stress of red spiders collected from the Wanjishan base in Tai an, the correlations of SPAD value with the original spectral reflectivity of apple leaves and its first derivative and between SPAD value and high spectral value were analyzed to select sensitive bands, and the estimation models of chlorophyll content in apple leaves based on hyperspectral reflectivity were established. The sensitive bands of chlorophyll content in normal apple leaves were 513-539, 564-585, 694, 699 and 720 nm , and the best estimation model of chlorophyll content was SPAD =152.450-1 884.851 R 377 . The sensitive bands of chlorophyll content in the leaves under the stress of red spiders were 961, 972 and 720 nm, and the best estimation model of chlorophyll content was SPAD =49.371-46 428.473 R 972.

Spectral-spatial target detection based on data field modeling for hyperspectral data

Target detection is always an important application in hyperspectral image processing field. In this paper, a spectral-spatial target detection algorithm for hyperspectral data is proposed.The spatial feature and spectral feature were unified based on the data filed theory and extracted by weighted manifold embedding. The novelties of the proposed method lie in two aspects. One is the way in which the spatial features and spectral features were fused as a new feature based on the data field theory, and the other is that local information was introduced to describe the decision boundary and explore the discriminative features for target detection. The extracted features based on data field modeling and manifold embedding techniques were considered for a target detection task.Three standard hyperspectral datasets were considered in the analysis. The effectiveness of the proposed target detection algorithm based on data field theory was proved by the higher detection rates with lower False Alarm Rates（FARs） with respect to those achieved by conventional hyperspectral target detectors.

Study on shallow groundwater information extraction technology based on multi-spectral data and spatial data

Aimed at solving the difficulties,such as low efficiency and limited exploration range encountered in finding groundwater with the traditional methods,a new method was presented by using remote sensing technology in this paper.Based on multi-spectral data(ETM data) and spatial data(SRTM data),a forecasting model was built to produce a probability rating map for finding shallow groundwater in the arid and semi-arid areas.According to investigations,a conclusion is drawn that the results of the model are satisfied,which have been testified by the later geophysical exploration and drilling.Thus,the model can serve as a guide for finding groundwater in the arid and semi-arid regions.

Study on the biomass change derived from the hyperspectral data of cotton leaves in canopy under moisture stress

在这研究，在美国做的一个 ASD 分光计被用来在北方 Xinjiang 在华盖导出棉花叶子的 hyperspectral 数据。红边的不可分的区域变量被用来在华盖在棉花叶子估计全部的氮(TN ) 内容。反射系列微分的第一份订单被执行。分析方法基于光谱位置变量从第一份订单被导出微分光谱数据。在红边的不可分的区域之间的关联上的分析( SDr ，认为是独立变量)并且 TN 内容(认为是功能)被执行，并且在在棉花变化的华盖叶子的红边和 TN 内容的不可分的区域之间的数学模型作为 Xinluzao 说出 No.6 的关联被开发。在在单个棉花的叶绿素内容和 TN 内容之间的关联上的分析在华盖与不同的水体积在灌溉下面成长离开被执行。结果证明在叶绿素内容和 TN 内容之间有重要积极关联(R = 0.8723， n = 39 ) ，并且叶绿素内容的数据能被用来在单个棉花叶子估计 TN 内容；在在在华盖的棉花叶子的红边和 TN 内容的不可分的区域变量之间的关联是重要的，他们的关联系数是 0.7394 ( n = 40 )，在作为 Xinluzao 号码 6 和号码 8 罐头说出的棉花变化的华盖叶子的 TN 内容被使用发达模型精确地估计，并且他们的 RMSE 价值分别地是 0.3859 和 0.4272 。在研究以后，有一个适用的潜力使用红边的不可分的区域变量在华盖在棉花叶子估计 TN 内容，这被考虑，并且数学模型与第三方面的区域变量发展了让高适用在在庄稼华盖导出 TN 内容珍视。认出应力由研究移动和红边的变化程度由棉花植物承受了的潮湿是可行的，这也被考虑，并且钥匙是开发相应合理识别索引系统。

Crop Discrimination Using Field Hyper Spectral Remotely Sensed Data

Crop discrimination through satellite imagery is still problematic. Accuracy of crop classification for high spatial resolution satellite imagery in the intensively cultivated lands of the Egyptian Nile delta is still low. Therefore, the main objective of this research is to determine the optimal hyperspectral wavebands in the spectral range of (400 - 2500 nm) to discriminate between two winter crops (Wheat and Clover) and two summer crops (Maize and Rice). This is considered as a first step to improve crop classification through satellite imagery in the intensively cultivated areas in Egypt. Hyperspectral ground measurements of ASD field Spec3 spectroradiometer was used to monitor the spectral reflectance profile during the period of the maximum growth stage of the four crops. 1-nm-wide was aggregated to 10-nm-wide bandwidths. After accounting for atmospheric windows and/or areas of significant noise, a total of 2150 narrow bands in 400 - 2500 nm were used in the analysis. Spectral reflectance was divided into six spectral zones: blue, green, red, near-infrared, shortwave infrared-I and shortwave infrared-II. One Way ANOVA and Tukey’s HSD post hoc analysis was performed to choose the optimal spectral zone that could be used to differentiate the different crops. Then, linear regression discrimination (LDA) was used to identify the specific optimal wavebands in the spectral zones in which each crop could be spectrally identified. The results of Tukey’s HSD showed that blue, NIR, SWIR-1 and SWIR-2 spectral zones are more sufficient in the discrimination between wheat and clover than green and red spectral zones. At the same time, all spectral zones were quite sufficient to discriminate between rice and maize. The results of (LDA) showed that the wavelength zone (727:1299 nm) was the optimal to identify clover crop while three zones (350:712, 1451:1562, 1951:2349 nm) could be used to identify wheat crop. The spectral zone (730:1299 nm) was the optimal to identify maize crop while three spectral zones were the best to identify rice crop (350:713, 1451:1532, 1951:2349 nm). An average of thirty measurements for each crop was considered in the process. These results will be used in machine learning process to improve the performance of the existing remote sensing software’s to isolate the different crops in intensive cultivated lands. The study was carried out in Damietta governorate of Egypt.

Use of Linear Spectral Mixture Model to Estimate Rice Planted Area Based on MODIS Data

MODIS （Moderate Resolution Imaging Spectroradiometer） is a key instrument aboard the Terra （EOS AM） and Aqua （EOS PM） satellites. Linear spectral mixture models are applied to MOIDS data for the sub-pixel classification of land covers. Shaoxing county of Zhejiang Province in China was chosen to be the study site and early rice was selected as the study crop. The derived proportions of land covers from MODIS pixel using linear spectral mixture models were compared with unsupervised classification derived from TM data acquired on the same day, which implies that MODIS data could be used as satellite data source for rice cultivation area estimation, possibly rice growth monitoring and yield forecasting on the regional scale.

Utilization of Landsat Data for Quantifying and Predicting Land Cover Change in the Bumbuna Watershed in Sierra Leone

Rural communities in third world countries are concerned over land use changes resulting from resource exploitation. This is the case for the Bumbuna watershed in Sierra Leone following impoundment of the Bumbuna reservoir in 2009. Farmers have increased activities along the riparian zones in protest against inundation of their farmlands. The dam operators warn this practice would threaten sustainable power supply;the farmers contend the reservoir is increasing and taking over their farms. However, it is difficult to resolve this issue without a means of quantifying the change and developing early warning systems for land cover in the watershed. This research presents a case for the use of remotely sensed Landsat data for quantification of land cover change and the development of predictive models to inform preparedness for imminent problems that may arise from land use practices. In situ water loggers, in combination with manual readings, recorded water levels in 30-minute intervals since 2009. These datasets combined with spectral values of Landsat 7 and Landsat 8 for the development of regression algorithms for predictive purposes. Digital photographs and satellite imagery illustrated the changes in land cover over time (a 33% water rise and 44% NDVI change from 2009 to 2015). These visual and spectral pictures confirm the usefulness of remotely sensed data for early warning systems in the watershed. Results of the regression analysis show Band 1 (Blue) and Band 4 (NIR) as statistically significant predictors for water level in the reservoir. The tests accounted for 84% (R2) of the data with p-values less than α at the 0.05 confidence level. However, future trials of the model will consider reducing the 4.6 error margin to minimize deviations from the observed data.

Correlation Analysis of the Fluorescence Data of Some Benzaldehyde Derivatives by the Dual-Parameter Equation

The fluorescence emission wavelength [lambda(max(em))] values of three types of benzaldehyde derivatives, namely, ethylene acetals (1-Ys), 4-nitrophenylhydrazones (2-Ys) and phenylhydrazones (3-Ys), have been measured. Correlation analyses by the dual-parameter equation show that the lambda(max(em)) values of 1-Ys are mainly affected by the spin-delocalization effects of the substituents, while those of 2-Ys are mainly affected by the polar effects. However, those of 3-Ys are independent of the substituents.

NMR Spectral Analysis and Hydrolysis Studies of Rebaudioside N, a Minor Steviol Glycoside of <i>Stevia rebaudiana</i>Bertoni

The complete proton and carbon NMR spectral assignments of a diterpene glycoside isolated from the commercial extract of the leaves of Stevia rebaudiana Bertoni, 13-[(2-O-β-D-glucopyranosyl-3-O-β-D-glucopyranosyl-β-D-glucopyranosyl)oxy] entkaur-16-en-19-oic acid-[(2-O-α-L-rhamnopyranosyl-3-O-β-D-glucopyranosyl-β-D-glucopyranosyl) ester] (1);also known as rebaudioside N, was achieved by the extensive 1D and 2D NMR (1H and 13C, COSY, HMQC, HMBC) as well as mass spectral data. Further, hydrolysis studies were performed on rebaudioside N using acid and enzymatic studies to identify aglycone and sugar residues in its structure.

Analysis of Spectral Characteristics Based on Optical Remote Sensing and SAR Image Fusion

Because of cloudy and rainy weather in south China, optical remote sens-ing images often can＇t be obtained easily. With the regional trial results in Baoying, Jiangsu province, this paper explored the fusion model and effect of ENVISAT/SAR and HJ-1A satel ite multispectral remote sensing images. Based on the ARSIS strat-egy, using the wavelet transform and the Interaction between the Band Structure Model （IBSM）, the research progressed the ENVISAT satel ite SAR and the HJ-1A satel ite CCD images wavelet decomposition, and low/high frequency coefficient re-construction, and obtained the fusion images through the inverse wavelet transform. In the light of low and high-frequency images have different characteristics in differ-ent areas, different fusion rules which can enhance the integration process of self-adaptive were taken, with comparisons with the PCA transformation, IHS transfor-mation and other traditional methods by subjective and the corresponding quantita-tive evaluation. Furthermore, the research extracted the bands and NDVI values around the fusion with GPS samples, analyzed and explained the fusion effect. The results showed that the spectral distortion of wavelet fusion, IHS transform, PCA transform images was 0.101 6, 0.326 1 and 1.277 2, respectively and entropy was 14.701 5, 11.899 3 and 13.229 3, respectively, the wavelet fusion is the highest. The method of wavelet maintained good spectral capability, and visual effects while improved the spatial resolution, the information interpretation effect was much better than other two methods.

Black Soils Classification by Ground Spectral Process and Analysis

1 Introduction Black soils are a soil type with good properties and high fertility,which is very suitable for plant growth(Liu et al.,2015).Black soil resources are widely distributed in North America,Eurasia,and South America,and cover about 916million ha around the world,35 million ha of this in northeast China(Liu et al.,2012).

IR Spectral Analysis of Diterpene Glycosides Isolated from <i>Stevia rebaudiana</i>

The objective is to identify the Infra-Red (IR) spectral analysis of the diterpene glycosides present in the commercial extracts of Stevia rebaudiana was achieved by PerkinElmer Spectrum 400 Fourier Transform (FT) spectrometer employing a PerkinElmer Universal Attenuated Total Reflection (ATR) accessory. Using this technique the IR spectral pattern of 15 steviol glycosides which belongs to three different classes of ent-kaurane diterpene glycosides namely ent-13-hydroxykaur-16-en-19-oic acid, ent-13-hydroxykaur-15-en-19-oic acid, and 13-methyl-16-oxo-17-norent- kauran-19-oic acid were identified. From the wave numbers found for all 15 steviol glycosides, it was observed that that though there are differences in the number of sugar units, nature of sugar units, and their attachments;there are not any notable differences in the IR values.

3D Modelling from New and Existing Gravity Data of an Intrusive Body in the Northern Part of Kribi-Campo Sub-Basin in Cameroon

A new gravity survey was carried out in the northern part of the onshore Kribi- Campo sub-basin in Cameroon. The data were incorporated to the existing ones and then analyzed and modeled in order to elucidate the subsurface structure of the area. The area is characterized in its north-western part by considerably high positive anomalies indicative of the presence of a dense intrusive body. We find, 1) from the analysis of the gravity residual anomaly map, the high positive anomalies observed are the signature of a shallow dense structure;2) from the multi-scale analysis of the maxima of the horizontal gradient, the structure is confined between depths of 0.5 km and 5 km;3) from the quantitative interpretation of residual anomalies by spectral analysis, the depth to the upper surface of the intrusive body is not uniform, the average depth of the bottom is h1 = 3.6 km and the depths to particular sections of the roof of the intrusion are h2 = 1.6 km and h3 = 0.5 km;4) and the 3D modeling gives results that are suggestive of the presence of contacts between rocks of different densities at different depths and a dense intrusive igneous body in the upper crust of the Kribi zone. From the 3D model the dense intrusive igneous block is surrounded by sedimentary formations to the south-west and metamorphic formations to the north-east. Both formations have a density of about 2.74 g/cm3. The near surface portions of this igneous block lie at a depth range of 0.5 km to 1.5 km while its lower surface has a depth range of 3.6 km to 5.2 km. The shape of the edges and the bottom of the intrusive body are suggestive of the fact that it forms part of a broader structure underlying the Kribi-Campo sub-basin with a great influence on the sedimentary cover.

¹³C-NMR Data from Coumarins from Moraceae Family

Species from Moraceae family stand out in popular medicine and phytotherapy, have been for example used as expectorants, bronchodilators, anthelmintics and treatment of skin diseases, such as vitiligo, due to the presence of compounds with proven biological activity, as the coumarins. Coumarins are lactones with 1,2-benzopyrone basic structure, and are widely distributed in the plant kingdom, both in free form, and in glycosylated form. This work reports a literature review, describing the data of 13C NMR from 53 coumarins isolated from the family Moraceae, and data comparison between genera who presented photochemical studies, in order to contribute to the chemotaxonomy of this family.

On the Accuracy Assessment of the Latest Releases of GOCE Satellite-Based Geopotential Models with EGM2008 and Terrestrial GPS/Levelling and Gravity Data over Egypt

The Global Geopotential Models (GGMs) of GOCE (Gravity Recovery and steady- state Ocean Circulation Explorer) differ globally as well as regionally in their accuracy and resolution based on the maximum degree and order (d/o) of the fully normalized spherical harmonic (SH) coefficients, which express each GGM. The main idea of this study is to compare the free-air gravity anomalies and quasi geoid heights determined from several recent GOCE-based GGMs with the corresponding ones from the Earth Gravitational Model 2008 (EGM2008) over Egypt on the one hand and with ground-based measurements on the other hand. The results regarding to the comparison of GOCE-based GGMs with terrestrial gravity and GPS/levelling data provide better improvement with respect to EGM2008. The 4th release GOCE-based GGM developed with the use of space-wise solution strategy (SPW_R4) approximates the gravity field well over the Egyptian region. The SPW_R4 model is accordingly suggested as a reference model for recovering the long wavelength (up to SH d/o 200) components of quasi geoid heights when modelling the gravimetric quasi-geoid over the Egypt. Finally, three types of transformation models: Four-, Five- and Seven-parameter transformations have been applied to reduce the data biases and to provide a better fitting of quasi geoid heights obtained from the studied GOCE-based GGMs to those from GPS/levelling data. These models reveal that the standard deviation of vertical datum over Egypt is at the level of about 32 cm.