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 det...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.展开更多
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),...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).展开更多
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 n...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.展开更多
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...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.展开更多
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 ref...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.展开更多
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 spec...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.展开更多
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 Jun...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.展开更多
In the context of global climate change and the increasing need to study plant response to drought,there is a demand for easily,rapidly,and remotely measurable parameters that sensitively reflect leaf water status.Par...In the context of global climate change and the increasing need to study plant response to drought,there is a demand for easily,rapidly,and remotely measurable parameters that sensitively reflect leaf water status.Parameters with this potential include those derived from leaf spectral reflectance(R)and chlorophyll fluorescence.As each of these methods probes completely different leaf characteristics,their sensitivity to water loss may differ in different plant species and/or under different circumstances,making it difficult to choose the most appropriate method for estimating water status in a given situation.Here,we present a simple comparative analysis to facilitate this choice for leaf-level measurements.Using desiccation of tobacco(Nicotiana tabacum L.cv.Samsun)and barley(Hordeum vulgare L.cv.Bojos)leaves as a model case,we measured parameters of spectral R and chlorophyll fluorescence and then evaluated and compared their applicability by means of introduced coefficients(coefficient of reliability,sensitivity,and inaccuracy).This comparison showed that,in our case,chlorophyll fluorescence was more reliable and universal than spectral R.Nevertheless,it is most appropriate to use both methods simultaneously,as the specific ranking of their parameters according to the coefficient of reliability may indicate a specific scenario of changes in desiccating leaves.展开更多
Spectral data can potentially offer a rapid assessment of nutrients in leaves and reveal information about the geologic history of the soil.This study evaluated the capability of the partial least squares regression(P...Spectral data can potentially offer a rapid assessment of nutrients in leaves and reveal information about the geologic history of the soil.This study evaluated the capability of the partial least squares regression(PLSR)for estimating foliar macro-and micronutrients(Ca,Mg,K,P,Mn,and Zn)using spectral data(400 to 2,450 nm).First,filter-based wavelength selection was conducted to reduce the independent variables.PLSR performance was then assessed across 4 geologic materials(coarse glacial till,glaciofluvial,melt-out till,and outwash)and 4 dominant tree genera(Acer,Betula,Fagus,and Quercus)in the northeastern United States.The spectral ranges 400 to 500 nm and 1,800 to 2,450 nm were found to be the most important spectral regions for estimating foliar nutrient concentrations.The developed PLSR model predicted 6 foliar nutrients with moderate to high accuracy(adjusted R^(2) from 0.60 to 0.75).Foliar macronutrient concentrations were estimated with higher accuracy(mean adj.R^(2)=0.69)than micronutrient concentrations(mean adj.R^(2)=0.635).The prediction for the individual tree genera group and the individual geologic materials group outperformed the combined group;for instance,the adj.R^(2) for estimating Ca and P was 39%higher for American beech(Fagus grandifolia)than all tree genera combined.Spectral measurements combined with wavelength selection and PLSR models can potentially be used to quantify foliar macro-and micronutrients at regional scales,and taking into account geologic materials and tree genera will improve this prediction.展开更多
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 nonne...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.展开更多
Soil color changes with water content due to chemical and physical reactions,making it a potential indicator for moisture estimation.By analyzing soil surface images and comparing color variations against laboratory-m...Soil color changes with water content due to chemical and physical reactions,making it a potential indicator for moisture estimation.By analyzing soil surface images and comparing color variations against laboratory-measured water content,a rapid and cost-effective method for moisture determination can be developed.Traditional moisture measurement techniques are time-consuming,so an imaging-based approach would be highly beneficial for quick decision-making.Soil color is also influenced by factors such as particle coarseness,which creates shadows and alters perceived darkness.This research introduces a novel method to isolate true soil color by analyzing the maximum color response in image pixels,minimizing shadow effects.Several equations were derived to correlate color changes with moisture content and were validated against lab measurements to ensure accuracy and simplicity.The most effective equation can be further adapted for satellite imagery by accounting for atmospheric light scattering differences between ground and satellite sensors,enabling large-scale moisture monitoring.The derived equations can be programmed into a software tool,allowing moisture estimation from simple soil surface images.The study involved controlled experiments where soil samples at varying moisture levels were imaged to establish an empirical color-moisture relationship.This method provides a fast,economical,and practical alternative to conventional techniques.However,the approach requires further refinement to account for different soil types globally.Future work should focus on adjusting the model with variables that adapt the color-moisture relationship for diverse soils,ensuring broader applicability.Once optimized,this could significantly improve moisture assessment in agriculture,environmental monitoring,and land management.展开更多
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 spectroradiomet...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.展开更多
Soil salinization is a land degradation process that leads to reduced agricultural yields. This study investigated the method that can best predict electrical conductivity (EC) in dry soils using individual bands, a n...Soil salinization is a land degradation process that leads to reduced agricultural yields. This study investigated the method that can best predict electrical conductivity (EC) in dry soils using individual bands, a normalized difference salinity index (NDSI), partial least squares regression (PLSR), and bagging PLSR. Soil spectral reflectance of dried, ground, and sieved soil samples containing varying amounts of EC was measured using an ASD FieldSpec spectrometer in a darkroom. Predictive models were computed using a training dataset. An independent validation dataset was used to validate the models. The results showed that good predictions could be made based on bagging PLSR using first derivative reflectance (validation R2 = 0.85), PLSR using untransformed reflectance (validation R2 = 0.70), NDSI (validation R2 = 0.65), and the untransformed individual band at 2257 nm (validation R2 = 0.60) predictive models. These suggested the potential of mapping soil salinity using airborne and/or satellite hyperspectral data during dry seasons.展开更多
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 ...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.展开更多
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 quantitati...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.展开更多
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 h...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.展开更多
Rewardless orchids attract pollinators by food, sexual, and brood-site mimicry, but other forms of sensory deception may also operate. Helmet orchids （Corybas, Nematoceras and related genera） are often assumed to be...Rewardless orchids attract pollinators by food, sexual, and brood-site mimicry, but other forms of sensory deception may also operate. Helmet orchids （Corybas, Nematoceras and related genera） are often assumed to be brood-site deceivers that mimic the colours and scents of mushrooms to fool female fungus gnats （Mycetophilidae） into attempting oviposition and polli- nating flowers. We sampled spectral reflectances and volatile odours of an endemic terrestrial New Zealand orchid Corybas cheesemanii, and co-occurring wild mushrooms. The orchid is scentless to humans and SPME GC-MS analyses did not detect any odours, but more sensitive methods may be required. The orchids reflected strongly across all visible wavelengths （300-700nm） with peaks in the UV （-320nm）, yellow-green （500-600 nm） and red regions （650-700 nm）, whereas mushrooms and surrounding leaf litter reflected predominantly red and no UV. Rather than mimicking mushrooms, these orchids may attract pollinators by exploiting insects＇ strong sensory bias for UV. Modelling spectral reflectances into a categorical fly vision model and a generic tetrachromat vision model provided very different results, but neither suggest any mimicry of mushrooms. However, these models require further assessment and data on fly spectral sensitivity to red wavelengths is lacking - a problem given the predominance of red, fly-pollinated flowers worldwide展开更多
Objective:We applied hyperspectral imaging(HSI)system to distinguish early caries from soundand pigmented areas.It will provide a theoretical basis and technical support,for research anddevelopment of an instrument th...Objective:We applied hyperspectral imaging(HSI)system to distinguish early caries from soundand pigmented areas.It will provide a theoretical basis and technical support,for research anddevelopment of an instrument that could be used for screening and detection of early dentalcaries.Methods:Eighteen extracted human teeth(molars and premolars),with varying degrees ofnatural pathology and no degree of decay involving dentin were obtained.HSI system with awavelength range from 400 to 1000nm was used to obtain images of all 18 teeth containingsound,carious and pigmented areas.We compared the spectra of the wavebands at both 500 nmand 780 nm from the different tooth states,and the reflectance diference bet ween sound versuscarious lesions and sound versus pigmented areas,respectively.Results:There was a slight diference in refectance bet ween carious areas and pigmented areas at500 nm.A substantial difference was additionally noted in refectance bet ween carious areas andpigmented areas at 780 nm.Conclusion:The results have shown that the interference of tooth surface pigment can be elim-inated in the near-infrared(NIR)waveband,and the caries can be effectively identifed from the pigmented areas.Thus,it could be used to detect carious areas of teeth in place of the traditionalvisual inspection method or white light endoscopy.Clinical significance:The NIR difused light signal enables the identification of early caries frompigment and other interference,providing a reasonable detection tool for early detection andearly treatment of teeth diseases.展开更多
Choosing the Minqin Oasis, located downstream of the Shiyang River in Northwest China, as the study area, we used field-measured hyperspectral data and laboratory-measured soil salt content data to analyze the charact...Choosing the Minqin Oasis, located downstream of the Shiyang River in Northwest China, as the study area, we used field-measured hyperspectral data and laboratory-measured soil salt content data to analyze the characteristics of saline soil spectral reflectance and its transformation in the area, and elucidated the relations between the soil spectral re-flectance, reflectance transformation, and soil salt content. In addition, we screened sensitive wavebands. Then, a multiple linear regression model was established to predict the soil salt content based on the measured spectral data, and the accuracy of the model was verified using field-measured salinity data. The results showed that the overall shapes of the spectral curves of soils with different degrees of salinity were consistent, and the reflectance in visible and near-infrared bands for salinized soil was higher than that for non-salinized soil. After differential transformation, the correlation coefficient between the spectral reflectance and soil salt content was obviously improved. The first-order differential transformation model based on the logarithm of the reciprocal of saline soil spectral reflectance produced the highest accuracy and stability in the bands at 462 and 636 nm; the determination coefficient was 0.603, and the root mean square error was 5.407. Thus, the proposed model provides a good reference for the quantitative extraction and monitoring of regional soil salinization.展开更多
This study is aimed to explore the spectrum reflection characteristics of summer corn leaves in different fertilization conditions.Using hyperspectral remote sensing technology,the experiments were conducted in fields...This study is aimed to explore the spectrum reflection characteristics of summer corn leaves in different fertilization conditions.Using hyperspectral remote sensing technology,the experiments were conducted in fields to collect the hyperspectral images of Denghai 605( DH605) and Ludan 981( LD981) in different growth period under five fertilization treatments,and then the reflectance of corn ear leaves was extracted by ENVI software. The five fertilization treatments included the control( CK) with no fertilization,40 kg and 30 kg of controlled-release fertilizer per 666. 67 m2 as base( K40 and K30),50 kg and 40 kg of compound fertilizer per 666. 67 m2 as base with 15 kg urea as seed fertilizer( F50 + N and F40 + N). The reflectance spectrums of the two corn cultivars under different fertilization treatments showed the approximately same changing trend with a reflection peak at green band( 550 nm) and a higher reflection platform at near infrared band( 760 nm-1050 nm). At the heading to filling stage,the reflectance of DH605 and LD981 was the highest under the CK,followed by the K30 and F40 + N respectively. At the filling to dough stage,the reflectance of DH605 and LD981 was the highest under the treatment K30 and F40 + N respectively,which was obviously higher than that of the other treatments. In the conditions of compound fertilizer,except the late filling stage,LD981 had little higher reflectance than DH605 at the other stages. In the conditions of controlled-release fertilizer and at dough to mature stage,LD981 had obviously higher reflectance compared to the other stages,and also higher than that of DH605; there was not obvious difference in reflectance LD981 and DH605 at the other stages.展开更多
基金supported by the National High Technology Research and Development Program of China (Grant No. 2006AA10Z203) the National Natural Science Foundation of China (Grant No. 40571115).
文摘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.
基金Project supported by the National Natural Science Foundation of China (No. 40271078)the Basic Research Program of Science and Technology Department of China (No. 2003DEA2C010-13)
文摘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).
基金supported by the National High Tech R&D Program,China(863 Program,2002AA243011)the National Natural Science Foundation of China(30030090)the Natural Science Foundation of Jiangsu Province,China(BK2003079).
文摘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.
基金supported by the National Key Research and Development Program of China (2016YFD0300602)China Agricultural Research System (CARS-04-PS19)Chengdu Science and Technology Project (2020-YF09-00033-SN)。
文摘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.
基金supported by the Rural Development Administration(PJ013821032020),Republic of Korea。
文摘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.
基金Under the auspices of the Excellent Youth Talent Project of Jilin Science and Technology Development Program(No.20170520078JH)Science and Technology Basic Work of Science and Technology(No.2014FY210800–4)National Natural Science Foundation of China(No.41601382)
文摘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.
基金Financial support was given by the CAS International Partnership Project "The Basic Research for Water Issues of Inland River Basin in Arid Region," (CXTD-Z2005-2)National Natural Science Funds of China for Distinguished Young Scholar (40525001)National Basic Research Program of China (2005CB422003)
文摘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.
基金supported from the project TowArds Next GENeration Crops(TANGENC),reg.no.CZ.02.01.01/00/22_008/0004581,of the ERDF Programme Johannes Amos Comenius.
文摘In the context of global climate change and the increasing need to study plant response to drought,there is a demand for easily,rapidly,and remotely measurable parameters that sensitively reflect leaf water status.Parameters with this potential include those derived from leaf spectral reflectance(R)and chlorophyll fluorescence.As each of these methods probes completely different leaf characteristics,their sensitivity to water loss may differ in different plant species and/or under different circumstances,making it difficult to choose the most appropriate method for estimating water status in a given situation.Here,we present a simple comparative analysis to facilitate this choice for leaf-level measurements.Using desiccation of tobacco(Nicotiana tabacum L.cv.Samsun)and barley(Hordeum vulgare L.cv.Bojos)leaves as a model case,we measured parameters of spectral R and chlorophyll fluorescence and then evaluated and compared their applicability by means of introduced coefficients(coefficient of reliability,sensitivity,and inaccuracy).This comparison showed that,in our case,chlorophyll fluorescence was more reliable and universal than spectral R.Nevertheless,it is most appropriate to use both methods simultaneously,as the specific ranking of their parameters according to the coefficient of reliability may indicate a specific scenario of changes in desiccating leaves.
基金supported by the University of Massachusetts Amherst,College of Natural Sciences by Award 1801 to J.B.R.
文摘Spectral data can potentially offer a rapid assessment of nutrients in leaves and reveal information about the geologic history of the soil.This study evaluated the capability of the partial least squares regression(PLSR)for estimating foliar macro-and micronutrients(Ca,Mg,K,P,Mn,and Zn)using spectral data(400 to 2,450 nm).First,filter-based wavelength selection was conducted to reduce the independent variables.PLSR performance was then assessed across 4 geologic materials(coarse glacial till,glaciofluvial,melt-out till,and outwash)and 4 dominant tree genera(Acer,Betula,Fagus,and Quercus)in the northeastern United States.The spectral ranges 400 to 500 nm and 1,800 to 2,450 nm were found to be the most important spectral regions for estimating foliar nutrient concentrations.The developed PLSR model predicted 6 foliar nutrients with moderate to high accuracy(adjusted R^(2) from 0.60 to 0.75).Foliar macronutrient concentrations were estimated with higher accuracy(mean adj.R^(2)=0.69)than micronutrient concentrations(mean adj.R^(2)=0.635).The prediction for the individual tree genera group and the individual geologic materials group outperformed the combined group;for instance,the adj.R^(2) for estimating Ca and P was 39%higher for American beech(Fagus grandifolia)than all tree genera combined.Spectral measurements combined with wavelength selection and PLSR models can potentially be used to quantify foliar macro-and micronutrients at regional scales,and taking into account geologic materials and tree genera will improve this prediction.
基金The Pre-Research Foundation of National Ministries andCommissions (No9140A16050109DZ01)the Scientific Research Program of the Education Department of Shanxi Province (No09JK701)
文摘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.
文摘Soil color changes with water content due to chemical and physical reactions,making it a potential indicator for moisture estimation.By analyzing soil surface images and comparing color variations against laboratory-measured water content,a rapid and cost-effective method for moisture determination can be developed.Traditional moisture measurement techniques are time-consuming,so an imaging-based approach would be highly beneficial for quick decision-making.Soil color is also influenced by factors such as particle coarseness,which creates shadows and alters perceived darkness.This research introduces a novel method to isolate true soil color by analyzing the maximum color response in image pixels,minimizing shadow effects.Several equations were derived to correlate color changes with moisture content and were validated against lab measurements to ensure accuracy and simplicity.The most effective equation can be further adapted for satellite imagery by accounting for atmospheric light scattering differences between ground and satellite sensors,enabling large-scale moisture monitoring.The derived equations can be programmed into a software tool,allowing moisture estimation from simple soil surface images.The study involved controlled experiments where soil samples at varying moisture levels were imaged to establish an empirical color-moisture relationship.This method provides a fast,economical,and practical alternative to conventional techniques.However,the approach requires further refinement to account for different soil types globally.Future work should focus on adjusting the model with variables that adapt the color-moisture relationship for diverse soils,ensuring broader applicability.Once optimized,this could significantly improve moisture assessment in agriculture,environmental monitoring,and land management.
基金Project supported by the National Natural Science Foundation of China (No. 40571130)the Natural Science Foundation of Shanghai, China (No. 07ZR14032)
文摘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.
基金Project supported by the Agricultural Research Council-Institute for Soil, Climate and Water (ARC-ISCW) of South Africa (No.GW51/072)the National Research Foundation (NRF) of South Africa (No.GW 51/083/01)the Water Research Commission (WRC)of South Africa (No.K5/1849)
文摘Soil salinization is a land degradation process that leads to reduced agricultural yields. This study investigated the method that can best predict electrical conductivity (EC) in dry soils using individual bands, a normalized difference salinity index (NDSI), partial least squares regression (PLSR), and bagging PLSR. Soil spectral reflectance of dried, ground, and sieved soil samples containing varying amounts of EC was measured using an ASD FieldSpec spectrometer in a darkroom. Predictive models were computed using a training dataset. An independent validation dataset was used to validate the models. The results showed that good predictions could be made based on bagging PLSR using first derivative reflectance (validation R2 = 0.85), PLSR using untransformed reflectance (validation R2 = 0.70), NDSI (validation R2 = 0.65), and the untransformed individual band at 2257 nm (validation R2 = 0.60) predictive models. These suggested the potential of mapping soil salinity using airborne and/or satellite hyperspectral data during dry seasons.
基金Project supported by the National Natural Science Foundation of China (Nos. 30070444 and 40201021)the British Council (No. SHA/992/308)the Doctor Foundation of Qingdao University of Science and Technology.
文摘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.
基金Project supported by the National Key Technology Research and Development Program of China (Nos.40801167 and 2006BAD05B05)the Knowledge Innovation Program of the Chinese Academy of Sciences (No.KZCX3-SW-356)the Foundation of the Chinese Academy of Sciences for the Field Stations of Resources and Environment
文摘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.
基金the National High-Tech R&D Program of China(2012AA12A30701)the National Natural Science Foundation of China(91125003,41222008)
文摘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.
文摘Rewardless orchids attract pollinators by food, sexual, and brood-site mimicry, but other forms of sensory deception may also operate. Helmet orchids （Corybas, Nematoceras and related genera） are often assumed to be brood-site deceivers that mimic the colours and scents of mushrooms to fool female fungus gnats （Mycetophilidae） into attempting oviposition and polli- nating flowers. We sampled spectral reflectances and volatile odours of an endemic terrestrial New Zealand orchid Corybas cheesemanii, and co-occurring wild mushrooms. The orchid is scentless to humans and SPME GC-MS analyses did not detect any odours, but more sensitive methods may be required. The orchids reflected strongly across all visible wavelengths （300-700nm） with peaks in the UV （-320nm）, yellow-green （500-600 nm） and red regions （650-700 nm）, whereas mushrooms and surrounding leaf litter reflected predominantly red and no UV. Rather than mimicking mushrooms, these orchids may attract pollinators by exploiting insects＇ strong sensory bias for UV. Modelling spectral reflectances into a categorical fly vision model and a generic tetrachromat vision model provided very different results, but neither suggest any mimicry of mushrooms. However, these models require further assessment and data on fly spectral sensitivity to red wavelengths is lacking - a problem given the predominance of red, fly-pollinated flowers worldwide
基金supported by the National Natural Science Foundation of China 62175153the Shanghai Science and Technology Commission 21S902700.
文摘Objective:We applied hyperspectral imaging(HSI)system to distinguish early caries from soundand pigmented areas.It will provide a theoretical basis and technical support,for research anddevelopment of an instrument that could be used for screening and detection of early dentalcaries.Methods:Eighteen extracted human teeth(molars and premolars),with varying degrees ofnatural pathology and no degree of decay involving dentin were obtained.HSI system with awavelength range from 400 to 1000nm was used to obtain images of all 18 teeth containingsound,carious and pigmented areas.We compared the spectra of the wavebands at both 500 nmand 780 nm from the different tooth states,and the reflectance diference bet ween sound versuscarious lesions and sound versus pigmented areas,respectively.Results:There was a slight diference in refectance bet ween carious areas and pigmented areas at500 nm.A substantial difference was additionally noted in refectance bet ween carious areas andpigmented areas at 780 nm.Conclusion:The results have shown that the interference of tooth surface pigment can be elim-inated in the near-infrared(NIR)waveband,and the caries can be effectively identifed from the pigmented areas.Thus,it could be used to detect carious areas of teeth in place of the traditionalvisual inspection method or white light endoscopy.Clinical significance:The NIR difused light signal enables the identification of early caries frompigment and other interference,providing a reasonable detection tool for early detection andearly treatment of teeth diseases.
基金financially supported by the National Natural Science Foundation of China (No. 41401109)Foundation for Excellent Youth Scholars of CAREERI, CAS (No. Y551D21001)the Open Fund Project of the Key Laboratory of Desert and Desertification, CAS (No. Y452J71001)
文摘Choosing the Minqin Oasis, located downstream of the Shiyang River in Northwest China, as the study area, we used field-measured hyperspectral data and laboratory-measured soil salt content data to analyze the characteristics of saline soil spectral reflectance and its transformation in the area, and elucidated the relations between the soil spectral re-flectance, reflectance transformation, and soil salt content. In addition, we screened sensitive wavebands. Then, a multiple linear regression model was established to predict the soil salt content based on the measured spectral data, and the accuracy of the model was verified using field-measured salinity data. The results showed that the overall shapes of the spectral curves of soils with different degrees of salinity were consistent, and the reflectance in visible and near-infrared bands for salinized soil was higher than that for non-salinized soil. After differential transformation, the correlation coefficient between the spectral reflectance and soil salt content was obviously improved. The first-order differential transformation model based on the logarithm of the reciprocal of saline soil spectral reflectance produced the highest accuracy and stability in the bands at 462 and 636 nm; the determination coefficient was 0.603, and the root mean square error was 5.407. Thus, the proposed model provides a good reference for the quantitative extraction and monitoring of regional soil salinization.
基金Supported by Key Research and Development Program of Shandong Province(2016CYJS03A01-1)Key Technology Research of Precision Agriculture(CXGC2017B04)+1 种基金Key Application Technological Innovation Project of Agriculture in Shandong Province"Research and Application of Key Technologies for Early Rapid Identification of Wheat Diseases"Young Scholar Scientific Research Foundation of Shandong Academy of Agricultural Sciences"Study on Rapid Extraction Technology for County-wide Farmland Based on High Resolution Remote Sensing Image"(2015YQN47)
文摘This study is aimed to explore the spectrum reflection characteristics of summer corn leaves in different fertilization conditions.Using hyperspectral remote sensing technology,the experiments were conducted in fields to collect the hyperspectral images of Denghai 605( DH605) and Ludan 981( LD981) in different growth period under five fertilization treatments,and then the reflectance of corn ear leaves was extracted by ENVI software. The five fertilization treatments included the control( CK) with no fertilization,40 kg and 30 kg of controlled-release fertilizer per 666. 67 m2 as base( K40 and K30),50 kg and 40 kg of compound fertilizer per 666. 67 m2 as base with 15 kg urea as seed fertilizer( F50 + N and F40 + N). The reflectance spectrums of the two corn cultivars under different fertilization treatments showed the approximately same changing trend with a reflection peak at green band( 550 nm) and a higher reflection platform at near infrared band( 760 nm-1050 nm). At the heading to filling stage,the reflectance of DH605 and LD981 was the highest under the CK,followed by the K30 and F40 + N respectively. At the filling to dough stage,the reflectance of DH605 and LD981 was the highest under the treatment K30 and F40 + N respectively,which was obviously higher than that of the other treatments. In the conditions of compound fertilizer,except the late filling stage,LD981 had little higher reflectance than DH605 at the other stages. In the conditions of controlled-release fertilizer and at dough to mature stage,LD981 had obviously higher reflectance compared to the other stages,and also higher than that of DH605; there was not obvious difference in reflectance LD981 and DH605 at the other stages.
