Characterizing and Estimating Fungal Disease Severity of Rice Brown Spot with Hyperspectral Reflectance Data

Large-scale farming of agriculture crops requires real-time detection of disease for field pest management. Hyperspectral remote sensing data generally have high spectral resolution, which could be very useful for detecting disease stress in green vegetation at the leaf and canopy levels. In this study, hyperspectral reflectances of rice in the laboratory and field were measured to characterize the spectral regions and wavebands, which were the most sensitive to rice brown spot infected by Bipolaris oryzae （Helminthosporium oryzae Breda. de Hann）. Leaf reflectance increased at the ranges of 450 to 500 nm and 630 to 680 nm with the increasing percentage of infected leaf surface, and decreased at the ranges of 520 to 580 nm, 760 to 790 nm, 1550 to 1750 nm, and 2080 to 2350 nm with the increasing percentage of infected leaf surface respectively. The sensitivity analysis and derivative technique were used to select the sensitive wavebands for the detection of rice brown spot infected by B. oryzae. Ratios of rice leaf reflectance were evaluated as indicators of brown spot. R669/R746 （the reflectance at 669 nm divided by the reflectance at 746 nm, the following ratios may be deduced by analogy）, R702/R718, R692/R530, R692/R732, R535/R746, R521/R718, and R569/R718 increased significantly as the incidence of rice brown spot increased regardless of whether it＇s at the leaf or canopy level. R702/R718, R692/R530, R692/R732 were the best three ratios for estimating the disease severity of rice brown spot at the leaf and canopy levels. This result not only confirms the capability of hyperspectral remote sensing data in characterizing crop disease for precision pest management in the real world, but also testifies that the ratios of crop reflectance is a useful method to estimate crop disease severity.

Quantifying biochemical variables of corn by hyperspectral reflectance at leaf scale

To further develop the methods to remotely sense the biochemical content of plant canopies,we report the results of an experiment to estimate the concentrations of three biochemical variables of corn,i.e.,nitrogen(N),crude fat(EE) and crude fiber(CF) concentrations,by spectral reflectance and the first derivative reflectance at fresh leaf scale. The correlations between spectral reflectance and the first derivative transformation and three biochemical variables were analyzed,and a set of estimation models were established using curve-fitting analyses. Coefficient of determination(R2),root mean square error(RMSE) and relative error of prediction(REP) of estimation models were calculated for the model quality evaluations,and the possible opti-mum estimation models of three biochemical variables were proposed,with R2 being 0.891,0.698 and 0.480 for the estimation models of N,EE and CF concentrations,respectively. The results also indicate that using the first derivative reflectance was better than using raw spectral reflectance for all three biochemical variables estimation,and that the first derivative reflectances at 759 nm,1954 nm and 2370 nm were most suitable to develop the estimation models of N,EE and CF concentrations,respectively. In addition,the high correlation coefficients of the theoretical and the measured biochemical parameters were obtained,especially for nitrogen(r=0.948).

Monitoring Protein and Starch Accumulation in Wheat Grains with Leaf SPAD and Canopy Spectral Reflectance

The research was conducted to determine the relationships of protein and starch accumulation dynamics in grains of wheat to post-heading leaf SPAD values and canopy spectral reflectance. The results showed that leaf nitrogen accumulation was exponentially related to leaf SPAD values and linearly related to canopy spectral reflectance, and that there was negative linear relationship between leaf nitrogen accumulation and grain protein accumulation, but positive linear relationship between post-heading leaf nitrogen transloca-tion and grain protein accumulation at maturity. In addition, leaf SPAD values were parabolically related with and ratio indices R(l 500,610)and R(l 220,560)were exponentially related with protein and starch accumulation in grains. These results indicate that leaf SPAD values and canopy spectral reflectance should be good indicators of quality formation dynamics in wheat grains.

Assessing canopy nitrogen and carbon content in maize by canopy spectral reflectance and uninformative variable elimination

Assessing canopy nitrogen content(CNC) and canopy carbon content(CCC) of maize by hyperspectral remote sensing data permits estimating cropland productivity, protecting farmland ecology, and investigating the nitrogen and carbon cycles in the atmosphere. This study aimed to assess maize CNC and CCC using canopy hyperspectral information and uninformative variable elimination(UVE). Vegetation indices(VIs) and wavelet functions were adopted for estimating CNC and CCC under varying water and nitrogen regimes. Linear, nonlinear, and partial least squares(PLS) regression models were fitted to VIs and wavelet functions to estimate CNC and CCC, and were evaluated for their prediction accuracy.UVE was used to eliminate uninformative variables, improve the prediction accuracy of the models, and simplify the PLS regression models(UVE-PLS). For estimating CNC and CCC, the normalized difference vegetation index(NDVI, based on red edge and NIR wavebands) yielded the highest correlation coefficients(r > 0.88). PLS regression models showed the lowest root mean square error(RMSE) among all models. However, PLS regression models required nine VIs and four wavelet functions, increasing their complexity. UVE was used to retain valid spectral parameters and optimize the PLS regression models.UVE-PLS regression models improved validation accuracy and resulted in more accurate CNC and CCC than the PLS regression models. Thus, canopy spectral reflectance integrated with UVE-PLS can accurately reflect maize leaf nitrogen and carbon status.

Simple method for extracting the seasonal signals of photochemical reflectance index and normalized difference vegetation index measured using a spectral reflectance sensor

A spectral reflectance sensor(SRS)fixed on the near-surface ground was developed to support the continuous monitoring of vegetation indices such as the normalized difference vegetation index(NDVI)and photochemical reflectance index(PRI).NDVI is useful for indicating crop growth/phenology,whereas PRI was developed for observing physiological conditions.Thus,the seasonal change patterns of NDVI and PRI are two valuable pieces of information in a crop-monitoring system.However,capturing the seasonal patterns is considered challenging because the vegetation index values estimated by the reflection from vegetation are often governed by meteorological conditions,such as solar irradiance and precipitation.Further,unlike growth/phenology,the physiological condition has diurnal changes as well as seasonal characteristics.This study proposed a novel filtering method for extracting the seasonal signals of SRS-based NDVI and PRI in paddy rice,barley,and garlic.First,the measurement accuracy of SRSs was compared with handheld spectrometers,and the R^(2)values between the two devices were 0.96 and 0.81 for NDVI and PRI,respectively.Second,the experimental study of threshold criteria with respect to meteorological variables(i.e.,insolation,cloudiness,sunshine duration,and precipitation)was conducted,and sunshine duration was the most useful one for excluding distorted values of the vegetation indices.After data processing based on sunshine duration,the R^(2)values between the measured vegetation indices and the extracted seasonal signals of vegetation indices increased by approximately 0.002–0.004(NDVI)and 0.065–0.298(PRI)on the three crops,and the seasonal signals of vegetation indices became noticeably improved.This method will contribute to an agricultural monitoring system by identifying the seasonal changes in crop growth and physiological conditions.

Predicting Surface Roughness and Moisture of Bare Soils Using Multi- band Spectral Reflectance Under Field Conditions

Soil surface roughness, denoted by the root mean square height(RMSH), and soil moisture(SM) are critical factors that affect the accuracy of quantitative remote sensing research due to their combined influence on spectral reflectance(SR). In regards to this issue, three SM levels and four RMSH levels were artificially designed in this study; a total of 12 plots was used, each plot had a size of 3 m × 3 m. Eight spectral observations were conducted from 14 to 30 October 2017 to investigate the correlation between RMSH, SM, and SR. On this basis, 6 commonly used bands of optical satellite sensors were selected in this study, which are red(675 nm), green(555 nm), blue(485 nm), near infrared(845 nm), shortwave infrared 1(1600 nm), and shortwave infrared 2(2200 nm). A negative correlation was found between SR and RMSH, and between SR and SM. The bands with higher coefficient of determination R^2 values were selected for stepwise multiple nonlinear regression analysis. Four characterized bands(i.e., blue, green, near infrared, and shortwave infrared 2) were chosen as the independent variables to estimate SM with R^2 and root mean square error(RMSE) values equal to 0.62 and 2.6%, respectively. Similarly, the four bands(green, red, near infrared, and shortwave infrared 1) were used to estimate RMSH with R^2 and RMSE values equal to 0.48 and 0.69 cm, respectively. These results indicate that the method used is not only suitable for estimating SM but can also be extended to the prediction of RMSH. Finally, the evaluation approach presented in this paper highly restores the real situation of the natural farmland surface on the one hand, and obtains high precision values of SM and RMSH on the other. The method can be further applied to the prediction of farmland SM and RMSH based on satellite and unmanned aerial vehicle(UAV) optical imagery.

Variations of albedo and spectral reflectance on Qiyi Glacier in Qilian Mountains during the ablation season

Based on the data observed at two sites (site H1, 4,473 m a.s.l., and site H2, 4,696 m a.s.l.) on Qiyi Glacier in Qilian Mountains, China, by automatic weather station and spectral pyranometer during the period of June 9 through September 27, 2006, we investigated the temporal and spatial variations in surface albedo and spectral reflectance on the glacier. At site H1, the daily mean surface albedos fluctuated between 0.233 and 0.866, which were significantly affected by the air temperature on the glacier. It was found that the albedos clearly showed a diurnal cycle with the lowest value at noon at the two observation sites over the study period, and the difference of albedos between the upper site H2 and the lower site H1 also showed diurnal cycle but with the highest value at noon. The reflectance on the glacier was higher in the ultraviolet (0.28–0.4 μm) and visible (0.4–0.76 μm) wavelengths, lower in the near infrared wavelength (0.76–3 μm), which is quite contrary to the spectral reflectance on other ground surfaces. At the two observation sites, the spectral reflectance declined in all wavelengths with the ablation of snow generally. However, it declined drastically in ultraviolet (0.28–0.4 μm) and 0.6–0.7 μm wavelength, and declined less in 0.4–0.5 μm wavelength. On fresh snow surface, the spectral reflectance had the high values of 0.983 and 0.815 in the ultraviolet and visible (0.4–0.76 μm) wavelengths, respectively; but it had a relatively lower value of 0.671 in near infrared (0.76–3 μm) wavelengths. However, on dirty and melting ice surfaces, the reflectance had the very low values of 0.305 and 0.256 in the ultraviolet and visible wavelengths, with the lowest value of 0.082 in near infrared wavelengths. The spectral reflectance also showed a diurnal cycle like that of albedo. The diurnal variations of spectral reflectance on snow surface in ultraviolet and visible wavelength changed to a greater degree than that on ice surface. The diurnal variation curves were asymmetrical before and after the local noontime, but the curves on ice surfaces in every wavelength were relatively flat and symmetrical. Especially, the surface reflectance in near infrared wavelength was flat and symmetry on both snow and ice surfaces. The studies of relations between the snow albedo and snow density and impurity, and the impact of glacier albedo on the glacier runoff are also described in this paper.

Derivation of salt content in salinized soil from hyperspectral reflectance data: A case study at Minqin Oasis, Northwest China

Soil salinization is a serious ecological and environmental problem because it adversely affects sustainable development worldwide, especially in arid and semi-arid regions. It is crucial and urgent that advanced technologies are used to efficiently and accurately assess the status of salinization processes. Case studies to determine the relations between particular types of salinization and their spectral reflectances are essential because of the distinctive characteristics of the reflectance spectra of particular salts. During April 2015 we collected surface soil samples(0–10 cm depth) at 64 field sites in the downstream area of Minqin Oasis in Northwest China, an area that is undergoing serious salinization. We developed a linear model for determination of salt content in soil from hyperspectral data as follows. First, we undertook chemical analysis of the soil samples to determine their soluble salt contents. We then measured the reflectance spectra of the soil samples, which we post-processed using a continuum-removed reflectance algorithm to enhance the absorption features and better discriminate subtle differences in spectral features. We applied a normalized difference salinity index to the continuum-removed hyperspectral data to obtain all possible waveband pairs. Correlation of the indices obtained for all of the waveband pairs with the wavebands corresponding to measured soil salinities showed that two wavebands centred at wavelengths of 1358 and 2382 nm had the highest sensitivity to salinity. We then applied the linear regression modelling to the data from half of the soil samples to develop a soil salinity index for the relationships between wavebands and laboratory measured soluble salt content. We used the hyperspectral data from the remaining samples to validate the model. The salt content in soil from Minqin Oasis were well produced by the model. Our results indicate that wavelengths at 1358 and 2382 nm are the optimal wavebands for monitoring the concentrations of chlorine and sulphate compounds, the predominant salts at Minqin Oasis. Our modelling provides a reference for future case studies on the use of hyperspectral data for predictive quantitative estimation of salt content in soils in arid regions. Further research is warranted on the application of this method to remotely sensed hyperspectral data to investigate its potential use for large-scale mapping of the extent and severity of soil salinity.

Study on the key technology of spectral reflectivity reconstruction based on sparse prior by a single-pixel detector

By studying the traditional spectral reflectance reconstruction method, spectral reflectance and the relative spectral power distribution of a lighting source are sparsely decomposed, and the orthogonal property of the principal component orthogonal basis is used to eliminate basis; then spectral reflectance data are obtained by solving a sparse coefficient. After theoretical analysis, the spectral reflectance reconstruction based on sparse prior knowledge of the principal component orthogonal basis by a single-pixel detector is carried out by software simulation and experiment. It can reduce the complexity and cost of the system, and has certain significance for the improvement of multispectral image acquisition technology.

Spectral Beam Combining of Fiber Lasers by Using Reflecting Volume Bragg Gratings

By employing three reflecting volume Bragg gratings, a near-infrared 4-channel spectral-beam-combining system is demonstrated to present 720 W combined power with a combining efficiency of 94.7%. The combined laser beam is near-diffraction-limited with a beam factor M^2-1.54. During this 4-channel beam-combining process, no special active cooling measures are used to evaluate the volume Bragg gratings as combining elements are under the higher power laser operation. Thermal expansion and period distortion are verified in a 2 k W 2-channel beam-combining process, and the heat issue in the transmission case is found to be more remarkable than that in the diffraction e-se. Transmitted and diffracted beams experience wave-front aberrations with different degrees, thus leading to distinct beam deterioration.

Spectral dispersion of cloud droplet size distributions and radar threshold reflectivity for drizzle

From first principles, we find that the radar threshold reflectivity between nonprecipitating clouds and precipitating clouds is strongly related to not only the cloud droplet number concentration but also the spectral dispersion of cloud droplet size distributions. The further investigation indicates that the threshold value is an increasing function of spectral dispersion and cloud droplet number concentration. These results may improve our understanding of the cloud-precipitation interaction and the aerosol indirect effect.

Spectral reflectance indices as proxies for yield potential and heat stress tolerance in spring wheat: heritability estimates and marker-trait associations

The application of spectral reflectance indices(SRIs) as proxies to screen for yield potential(YP) and heat stress(HS) is emerging in crop breeding programs.Thus, a comparison of SRIs and their associations with grain yield(GY) under YP and HS conditions is important.In this study, we assessed the usefulness of 27 SRIs for indirect selection for agronomic traits by evaluating an elite spring wheat association mapping initiative(WAMI) population comprising 287 elite lines under YP and HS conditions.Genetic and phenotypic analysis identified 11 and 9 SRIs in different developmental stages as efficient indirect selection indices for yield in YP and HS conditions,respectively.We identified enhanced vegetation index(EVI) as the common SRI associated with GY under YP at booting, heading and late heading stages, whereas photochemical reflectance index(PRI) and normalized difference vegetation index(NDVI) were the common SRIs under booting and heading stages in HS.Genomewide association study(GWAS) using 18704 single nucleotide polymorphisms(SNPs) from Illumina i Select90 K identified 280 and 43 marker-trait associations for efficient SRIs at different developmental stages under YP and HS, respectively.Common genomic regions for multiple SRIs were identified in 14 regions in 9 chromosomes: 1 B(60–62 cM), 3 A(15, 85–90, 101–105 cM), 3 B(132–134 cM), 4 A(47–51 cM), 4 B(71–75 cM), 5 A(43–49, 56–60, 89–93 cM), 5 B(124–125 cM),6 A(80–85 cM), and 6 B(57–59, 71 cM).Among them,SNPs in chromosome 5 A(89–93 cM) and 6 A(80–85 cM)were co-located for yield and yield related traits.Overall,this study highlights the utility of SRIs as proxies for GY under YP and HS.High heritability estimates and identification of marker-trait associations indicate that SRIs are useful tools for understanding the genetic basis of agronomic and physiological traits.

Application of Spectral Angle Mapper Classification to Discriminate Hydrothermal Alteration in Southwest Birjand, Iran, Using Advanced Spaceborne Thermal Emission and Reflection Radiometer Image Processing

The purpose of this study is to evaluate the Spectral Angle Mapper （SAM） classification method for determining the optimum threshold （maximum spectral angle） to unveil the hydrothermal mineral assemblages related to mineral deposits. The study area indicates good potential for Cu-Au porphyry, epithermal gold deposits and hydrothermal alteration well developed in arid and semiarid climates, which makes this region significant for Advanced Spaceborne Thermal Emission and Reflection Radiometer （ASTER） image processing analysis. Given that achieving an acceptable mineral mapping requires knowing the alteration patterns, petrochemistry and petrogenesis of the igneous rocks while considering the effect of weathering, overprinting of supergene alteration, overprinting of hypogene alteration and host rock spectral mixing, SAM classification was implemented for argillic, sericitic, propylitic, alunitization, silicification and iron oxide zones of six previously known mineral deposits： Maherabad, a Cu-Au porphyry system; Sheikhabad, an upper part of Cu-Au porphyry system; Khoonik, an Intrusion related Au system; Barmazid, a low sulfidation epithermal system; Khopik, a Cu-Au porphyry system; and Hanish, an epithermal Au system. Thus, the investigation showed that although the whole alteration zones are affected by mixing, it is also possible to produce a favorable hydrothermal mineral map by such complementary data as petrology, petrochemistry and alteration patterns.

Low-dimensional multi-spectral space for color reproduction based on nonnegative constrained principal component analysis

In order to overcome the shortcomings that the reconstructed spectral reflectance may be negative when using the classic principal component analysis （PCA）to reduce the dimensions of the multi-spectral data, a nonnegative constrained principal component analysis method is proposed to construct a low-dimensional multi-spectral space and accomplish the conversion between the new constructed space and the multispectral space. First, the reason behind the negative data is analyzed and a nonnegative constraint is imposed on the classic PCA. Then a set of nonnegative linear independence weight vectors of principal components is obtained, by which a lowdimensional space is constructed. Finally, a nonlinear optimization technique is used to determine the projection vectors of the high-dimensional multi-spectral data in the constructed space. Experimental results show that the proposed method can keep the reconstructed spectral data in [ 0, 1 ]. The precision of the space created by the proposed method is equivalent to or even higher than that by the PCA.

不同分数阶微分形式在盐渍土电导率高光谱反演中的适用性研究

近年来,部分学者将分数阶微分理论应用到盐渍土电导率高光谱反演中,取得了较为显著的效果。然而,已有研究多采用Grünwald-Letnikov分数阶微分定义形式,对于Riemann-Liouville、Caputo分数阶微分定义形式的应用研究较少,Riemann-Liouville、Caputo分数阶微分定义形式在盐渍土电导率高光谱反演中的适用性尚不明确。基于实测土壤电导率和高光谱数据,考虑常见的Grünwald-Letnikov、Riemann-Liouville和Caputo三类分数阶微分定义形式,通过软件编程实现Grünwald-Letnikov、Riemann-Liouville、Caputo分数阶微分处理函数,比较分析了不同分数阶微分定义形式下,土样高光谱数据在同阶微分处理后的差异性以及随阶数增加后的变化特征。结果表明:土样光谱反射率曲线在不同分数阶微分定义形式下,同阶微分处理后表现出较大差异;在0.1~1阶范围内,随微分阶数的增加,Grünwald-Letnikov、Riemann-Liouville、Caputo分数阶微分处理后高度变异波段数量均呈现出增加趋势;当微分阶数趋于1时,光谱反射率微分值逐渐降低且向0值逼近,波动范围逐渐减小,而光谱数据的变异性却随着波动范围的减小而增强;Grünwald-Letnikov分数阶微分处理在0.6、0.7阶相关系数提升9.5%、6.7%,Riemann-Liouville和Caputo分数阶微分处理后,相关系数分别在0.8~0.9阶、0.7~0.9阶提升1%左右。该研究为高光谱数据预处理提供了一种新的研究思路,为更好的将分数阶微分理论应用到土壤盐渍化遥感反演中提供参考。

Estimation of As and Cu Contamination in Agricultural Soils Around a Mining Area by Reflectance Spectroscopy:A Case Study

Concentrations of Iron (Fe), As, and Cu in soil samples from the fields near the Baoshan Mine in Hunan Province, China, were analyzed and soil spectral reflectance was measured with an ASD FieldSpec FR spectroradiometer (Analytical Spectral Devices, Inc., USA) under laboratory condition. Partial least square regression (PLSR) models were constructed for predicting soil metal concentrations. The data pre-processing methods, first and second derivatives (FD and SD), baseline correction (BC), standard normal variate (SNV), multiplicative scatter correction (MSC), and continuum removal (CR), were used for the spectral reflectance data pretreatments. Then, the prediction results were evaluated by relative root mean square error (RRMSE) and coefficients of determination (R 2 ). According to the criteria of minimal RRMSE and maximal R 2 , the PLSR models with the FD pretreatment (RRMSE = 0.24, R 2 = 0.61), SNV pretreatment (RRMSE = 0.08, R 2 = 0.78), and BC-pretreatment (RRMSE = 0.20, R 2 = 0.41) were considered as the final models for predicting As, Fe, and Cu, respectively. Wavebands at around 460, 1 400, 1 900, and 2 200 nm were selected as important spectral variables to construct final models. In conclusion, concentrations of heavy metals in contaminated soils could be indirectly assessed by soil spectra according to the correlation between the spectrally featureless components and Fe; therefore, spectral reflectance would be an alternative tool for monitoring soil heavy metals contamination.

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.

Detection of Internal Leaf Structure Deterioration Using a New Spectral Ratio Index in the Near-Infrared Shoulder Region

Spectral reflectance in the near-infrared （NIR） shoulder （750-900 nm） region is affected by internal leaf structure, but it has rarely been investigated. In this study, a dehydration treatment and three paraquat herbicide applications were conducted to explore how spectral reflectance and shape in the NIR shoulder region responded to various stresses. A new spectral ratio index in the NIR shoulder region （NSRI）, defined by a simple ratio of reflectance at 890 nm to reflectance at 780 nm, was proposed for assessing leaf structure deterioration. Firstly, a wavelength-independent increase in spectral reflectance in the NIR shoulder region was observed from the mature leaves with slight dehydration. An increase in spectral slope in the NIR shoulder would be expected only when water stress developed sufficiently to cause severe leaf dehydration resulting in an alteration in cell structure. Secondly, the alteration of leaf cell structure caused by Paraquat herbicide applications resulted in a wavelength-dependent variation of spectral reflectance in the NIR shoulder region. The NSRI in the NIR shoulder region increased significantly under an herbicide application. Although the dehydration process also occurred with the herbicide injury, NSRI is more sensitive to herbicide injury than the water-related indices （water index and normalized difference water index） and normalized difference vegetation index. Finally, the sensitivity of NSRI to stripe rust in winter wheat was examined, yielding a determination coefficient of 0.61, which is more significant than normalized difference vegetation index （NDVI）, water index （WI） and normalized difference water index （NDWI）, with a determination coefficient of 0.45, 0.36 and 0.13, respectively. In this study, all experimental results demonstrated that NSRI will increase with internal leaf structure deterioration, and it is also a sensitive spectral index for herbicide injury or stripe rust in winter wheat.

Quantitative Analysis of Moisture Effect on Black Soil Reflectance

Several studies have demonstrated that soil reflectance decreases with increasing soil moisture content, or increases when the soil moisture reaches a certain content; however, there are few analyses on the quantitative relationship between soil reflectance and its moisture, especially in the case of black soils in northeast China. A new moisture adjusting method was developed to obtain soil reflectance with a smaller moisture interval to describe the quantitative relationship between soil reflectance and moisture. For the soil samples with moisture contents ranging from air-dry to saturated, the changes in soil reflectance with soil moisture can be depicted using a cubic equation. Both moisture threshold (MT) and moisture inflexion (MI) of soil reflectance can also be determined by the equation. When the moisture range was smaller than MT, soil reflectance can be simulated with a linear model. However, for samples with different soil organic matter (OM), the parameters of the linear model varied regularly with the OM content. Based on their relationship, the soil moisture can be estimated from soil reflectance in the black soil region.

Study on Hyperspectral Estimation Model of Chlorophyll Content in Grape Leaves

In view of the shortage of using traditional methods to monitor chlorophyll content, hyperspectral technology was used to estimate the chlorophyll content of grape leaves rapidly, accurately and non-destructively. Based on the data of hyperspectral reflectivity and SPAD value of grape leaves collected from Wanjishan grape planting base in Tai an, the correlations of SPAD value with the original spectral reflectivity of grape leaves and its first derivative were analyzed to select sensitive bands, and an estimation model of chlorophyll content in grape leaves based on hyperspectral reflectivity was established. The best model was SPAD = 59.352＋ 44 836.313 R 601 .