内陆水体藻类叶绿素浓度与反射光谱特征的关系

通过研究内陆水体反射光谱特征与藻类叶绿素浓度之间的关系,建立藻类叶绿素高光谱定量遥感模型。在实验地太湖地区,采用高分辨率地物光谱仪实地测量了湖水在可见光和近红外波段（３００－１１００ｎｍ）的反射光谱曲线,并且同时采样分析叶绿素、总悬浮固体物质浓度等水质参数。研究发现在叶绿素浓度较高时（〉５μｇ／Ｌ）,水体光谱反射比Ｒ７０５ｎｍ／Ｒ６７５ｎｍ、叶绿素在７００ｎｍ附近反射峰的位置等与叶绿素浓度有较好的     

水稻叶片不同光谱形式反演叶绿素含量的对比分析研究

通过对常优1号和武粳15两个品种水稻叶片的反射率R、lg(1/R)、反射率一阶微分（FD）和反射率归一化（BN）等光谱形式的测量和计算，分析了叶片光谱不同变化形式与叶绿素含量的相关关系，建立了统计方程，并进行了比较与评价，同时，对反演方程的最佳波段选择进行了探讨。结果表明，叶绿素含量与反射率一阶微分光谱方程的相关性最强，而采用lg(1/R)的光谱形式能够提高遥感反演叶绿素含量的效果。经验证，两个水稻品种叶绿素含量的模拟值与实测值的复相关系数R2分别达到0.641和0. 818。     

Quantitative Inversion of Vegetation Biochemical Components Based on HJ1-A HSI in Coal Mining Area

This study investigates the applicability of estimating chlorophyll and water content at canopy level through empirical models and band combinations. The main goal is to evaluate and compare the accuracy of these two approaches for estimating and mapping canopy chlorophyll and water content through canopy reflectance and spaceborne HJ1-A HSI data acquired over Yanzhou coal mining area. An experiment was carried out. Canopy spectral measurements were acquired in the field using an ASD spectroradiometer along with simultaneous in situ measurements of leaf chlorophyll content. We tested seven variables derived from canopy reflectance for detecting canopy chlorophyll and water content: (1) R, (2) Log(1/R), (3) Log(1/R)′, (4) FDR, (5) SDR, (6) CRR, (7) BD. Stepwise multiple linear regressions were used to select wavelengths from HJ1-A HSI image bands. Correlation analysis was also done between different band combinations and biochemistry. A statistically significant relationship between Log(1/R) and chlorophyll was found at canopy level (R² = 0.516). SDR had the highest correlation with canopy water content (R² = 0.490). In addition, relationship between normalized different band combinations and chlorophyll and water content is also significantly obvious (R² = 0.577 and R² = 0.615). Canopy chlorophyll content was estimated with the intermediate accuracy (R² = 0.4144), while water content was estimated with an acceptable accuracy (R² = 0.4592). Canopy chlorophyll and water content spatial distribution were mapped. Chlorophyll and water stress levels were quantified by comparing different environmental stressors.     

不同钾素处理春玉米叶片营养元素含量变化及其光谱响应

目的是研究不同钾营养水平春玉米典型生育期叶片的光谱响应,探索叶片内营养成分与叶片光谱反射率的相关性。方法是设置了不同梯度钾处理的盆栽试验,按玉米生育期进行光谱测定和取样分析。结果,通过对不同钾处理间玉米叶片养分含量的差异性分析表明,随着施钾的提高,叶片钾含量差异性达到显著水平。分析不同钾营养水平不同生育时期春玉米叶片光谱反射率与叶片钾含量的相关关系,并建立了喇叭口期利用叶片光谱反射率估测叶片钾含量的数学模型;以及分析了该处理下喇叭口期叶片内水分、叶绿素、氮、磷、钙、镁、锌、锰、铜、铁含量与叶片光谱反射率的相关性。结果表明：不同生育时期叶片钾含量与其光谱反射率的相关关系在光谱维方向存在明显差别,730—930nm和960—1100nm两波段为春玉米喇叭口期评价钾营养状况的敏感波段,光谱变量R767＋R1057,（R767＋R1057） /（logR767＋logR1057）和（R767-R1057） /（logR767-logR1057）均能很好的预测喇叭口期叶片钾含量;该时期叶片内不同成分与光谱反射率相关分析表明：550nm,710nm,950nm三波段处是各个相关曲线的突变点;叶片内各成分间高度相关的,它们的光谱相关曲线趋势也极为一致或对称。     

Spectral detection of stress-related pigments in salt-lake succulent halophytic shrubs

The spectral detection of vegetation pigment concentrations has a high potential value, but it is still underdeveloped, especially for pigments other than chlorophylls. In this study, the seasonal pigment dynamics of two Tecticornia species (samphires; halophytic shrubs) from north-western Australia were correlated with spectral indices that best document the pigment changes over time. Pigment dynamics were assessed by analysing betacyanin, chlorophyll and carotenoid concentrations at plant level and by measuring reflectance at contrasting seasonal dates. Plant reflectance was used to define a new reflectance index that was most sensitive to the seasonal shifts in Tecticornia pigment concentrations. The two Tecticornia species turned from green to red-pinkish for the period March–August 2012 when betacyanins increased almost nine times in both species. Chlorophyll levels showed the opposite pattern to that of betacyanins, whereas carotenoid levels were relatively stable. Normalised difference indices correlated well with betacyanin (r = 0.805, using bands at 600 and 620 nm) and chlorophyll (r = 0.809, using bands at 737 and 726 nm). Using knowledge of chlorophyll concentrations slightly improved the ability of the spectral index to predict betacyanin concentration (r = 0.822 at bands 606 and 620 nm, in the case of chemically determined chlorophyll, r = 0.809 when using remotely sensed chlorophyll). Our results suggest that this new spectral index can reliably detect changes in betacyanin concentrations in vegetation, with potential applications in ecological studies and environmental impact monitoring.     

Potential of Detecting the Sulfur Dioxide Stress on Landscape Plants in Spectral Reflectance Data

Sulfur dioxide (SO₂) exhibits a powerful implication on the air condition and responsible for increasing the acidity of rainfall which plays negative effects on plant growth. It is a big problem to quantitatively access the stress degrees of sulfur dioxide on landscape plants. This study aims to find a non-destructive way to detect the degrees of SO₂ stress by using the spectral reflectance data. Five different landscape plants were selected and a simulated SO₂ stress environment by using fumigation box was built in this experiment. Landscape plants were grown on at this simulated SO₂ environment, and the leaf reflectance, chlorophyll and sulfur concentration were measured at 0, 2, 4, 6, 8, 10 and 12 h respectively. The spectral, chlorophyll response of five different plants were examined and the red edge position (REP) shift obtained from the reflectance were used to evaluate the SO₂ stress degrees at this paper. The results showed leaf chlorophyll content generally decreased and leaf sulfur content generally increased of all of these five landscape plants as though the chlorophyll and sulfur content disturbing during the whole stress time. However, compared with the sulfur content changed in leaves, chlorophyll content did not significantly changed when suffering from SO₂. The shift of REP performed well to indicate the severity of SO₂ fumigation stress and different species showed the different REP shift. The determined coefficient R² of REP shift and the relative changed sulfur content in leaves can up to 0.85. And the results also indicated that the different species maintained different resistance to SO₂.     

Proximal Remote Sensing of Herbicide and Drought Stress in Field Grown Colocasia and Sweet Potato Plants by Sunlight-Induced Chlorophyll Fluorescence Imaging

Chlorophyll fluorescence is an indicator of plant photosynthetic activity and has been used to monitor the health status of vegetation. Several studies have exploited the application of red/far-red chlorophyll fluorescence ratio in detecting the impact of various types of stresses in plants. Recently, sunlight-induced chlorophyll fluorescence imaging has been used to detect and discriminate different stages of mosaic virus infection in potted cassava plants with a multi-spectral imaging system (MSIS). In this study, the MSIS is used to investigate the impact of drought and herbicide stress in field grown crop plants. Towards this control and treatment groups of colocasia and sweet potato plants were grown in laterite soil beds and the reflectance images of these crop plants were recorded up to 14-days of treatment at the Fraunhofer lines of O₂ B at 687 nm and O₂ A at 759.5 nm and the off-lines at 684 and 757.5 nm. The recorded images were analyzed using the Fraunhofer Line Discrimination technique to extract the sunlight-induced chlorophyll fluorescence component from the reflectance images of the plant leaves. As compared to the control group, the chlorophyll fluorescence image ratio (F ₆₈₇/F ₇₆₀) in the treatment groups of both the plant varieties shows an increasing trend with increase in the extent of stress. Further, the F ₆₈₇/F ₇₆₀ ratio was found to correlate with the net photosynthetic rate (Pn) and stomatal conductance (g_s) of leaves. The correlation coefficient (R ²) for the relationship of F ₆₈₇/F ₇₆₀ ratio with Pn were found to be 0.78, 0.79 and 0.78, respectively for the control, herbicide treated and drought treated colocasia plants, while these were 0.77, 0.86 and 0.88, respectively for sweet potato plants. The results presented show the potential of proximal remote sensing and the application F ₆₈₇/F ₇₆₀ fluorescence image ratio for effective monitoring of stress-induced changes in field grown plants.     

Non-destructive estimation of foliar chlorophyll and carotenoid contents: Focus on informative spectral bands

Leaf pigment content provides valuable insight into the productivity, physiological and phenological status of vegetation. Measurement of spectral reflectance offers a fast, nondestructive method for pigment estimation. A number of methods were used previously for estimation of leaf pigment content, however, spectral bands employed varied widely among the models and data used. Our objective was to find informative spectral bands in three types of models, vegetation indices (VI), neural network (NN) and partial least squares (PLS) regression, for estimating leaf chlorophyll (Chl) and carotenoids (Car) contents of three unrelated tree species and to assess the accuracy of the models using a minimal number of bands. The bands selected by PLS, NN and VIs were in close agreement and did not depend on the data used. The results of the uninformative variable elimination PLS approach, where the reliability parameter was used as an indicator of the information contained in the spectral bands, confirmed the bands selected by the VIs, NN, and PLS models. All three types of models were able to accurately estimate Chl content with coefficient of variation below 12% for all three species with VI showing the best performance. NN and PLS using reflectance in four spectral bands were able to estimate accurately Car content with coefficient of variation below 14%. The quantitative framework presented here offers a new way of estimating foliar pigment content not requiring model re-parameterization for different species. The approach was tested using the spectral bands of the future Sentinel-2 satellite and the results of these simulations showed that accurate pigment estimation from satellite would be possible.     

南海水色遥感的主因子分析

分析了叶绿素、黄色物质和无机悬浮颗粒等要素对海水光谱反射率的贡献,并由这些要素的光学特性正演光谱反射率,与实测结果符合较好。在此基础上,利用主成分分析方法,通过对光谱反射率数据的特征向量变换和主因子回归,建立了反演南海海水叶绿素和溶解有机碳的遥感算法。与实测结果的比较表明,叶绿素和溶解有机碳的相对误差分别达１７５％和３７４％。     

Area between Peaks Feature in the Derivative Reflectance Curve as a Sensitive Indicator of Change in Chlorophyll Concentration

Vegetation spectral features can detect chlorophyll concentrations. Two key spectral features evident in the first derivative (FD) of reflectance constitute the two main peaks: one located around 685-705 nm and the other near 710-725 nm. We propose that the area between peaks (ABP) can be used as a sensitive indicator of changes in the photosynthetic pigments at leaf level and demonstrate it using a high-spectral-resolution dataset of maize leaves collected by Gitelson and coworkers (2005). We find significant high positive correlations (r ² > 0.90) between chlorophyll concentrations and both the ABP and its continuum length feature.     

基于主成分分析和BP神经网络的法国梧桐叶绿素含量高光谱反演研究

利用ASD便携式野外光谱仪和SPAD-502叶绿素计实测了落叶阔叶树法国梧桐叶片的高光谱反射率与叶片绿度,并对原始光谱反射率及一阶导数光谱与叶片绿度进行了相关分析;综合分析了10个常见光谱植被指数与法国梧桐叶绿素含量的相关性与预测性;最后利用主成分分析对光谱数据进行降维,将得到的主成分得分作为BP人工神经网络模型的输入变量进行了法国梧桐叶绿素含量的估算。结果表明:法国梧桐的叶片反射光谱数据与叶绿素含量的相关性在可见光区域显著,导数光谱数据在绿黄光区和红光区的部分波段与叶绿素含量的相关系数大于对应波段光谱反射率与叶绿素含量的相关关系。在所列举的10个常用植被指数中归一化植被指数与叶绿素含量的关系最密切,相关系数达到了0.7957。主成分分析的BP神经网络模型可以容纳更多的波段信息进行叶绿素含量的估算,预测值与实测值之间的线性回归的确定性系数R2为0.9883,是一种良好的植被叶绿素含量高光谱反演模式。     

Detection of stress in tomatoes induced by late blight disease in California, USA, using hyperspectral remote sensing

Large-scale farming of agricultural crops requires on-time detection of diseases for pest management. Hyperspectral remote sensing data taken from low-altitude flights usually have high spectral and spatial resolutions, which can be very useful in detecting stress in green vegetation. In this study, we used late blight in tomatoes to illustrate the capability of applying hyperspectral remote sensing to monitor crop disease in the field scale and to develop the methodologies for the purpose. A series of field experiments was conducted to collect the canopy spectral reflectance of tomato plants in a diseased tomato field in Salinas Valley of California. The disease severity varied from stage 1 (the light symptom), to stage 4 (the sever damage). The economic damage of the crop caused by the disease is around the disease stage 3. An airborne visible infrared imaging spectrometer (AVIRIS) image with 224 bands within the wavelength range of 0.4–2.5 μm was acquired during the growing season when the field data were collected. The spectral reflectance of the field samples indicated that the near infrared (NIR) region, especially 0.7–1.3 μm, was much more valuable than the visible range to detect crop disease. The difference of spectral reflectance in visible range between health plants and the infected ones at stage 3 was only 1.19%, while the difference in the NIR region was high, 10%. We developed an approach including the minimum noise fraction (MNF) transformation, multi-dimensional visualization, pure pixels endmember selection and spectral angle mapping (SAM) to process the hyperspectral image for identification of diseased tomato plants. The results of MNF transformation indicated that the first 28 eigenimages contain useful information for classification of the pixels and the rest were mainly noise-dominated due to their low eigenvalues that had few signals. Therefore, the 28 signal eigenimages were used to generate a multi-dimensional visualization space for endmember spectra selection and SAM. Classification with the SAM technique of plants’ spectra showed that the late blight diseased tomatoes at stage 3 or above could be separated from the healthy plants while the less infected plants (at stage 1 or 2) were difficult to separate from the healthy plants. The results of the image analysis were consistent with the field spectra. The mapped disease distribution at stage 3 or above from the image showed an accurate conformation of late blight occurrence in the field. This result not only confirmed the capability of hyperspectral remote sensing in detecting crop disease for precision disease management in the real world, but also demonstrated that the spectra-based classification approach is an applicable method to crop disease identification.     

Estimation of chlorophyll fluorescence under natural illumination from hyperspectral data

This paper reports a series of laboratory and field measurements of spectral reflectance under artificial and natural light conditions which demonstrate that effects of natural chlorophyll fluorescence are observable in the reflectance red edge spectral region. These are results from the progress made to link physiologically-based indicators to optical indices from hyperspectral remote sensing in the Bioindicators of Forest Sustainability Project. This study is carried out on twelve sites of Acer saccharum M. in the Algoma Region, Ontario (Canada), where field measurements, laboratory-simulation experiments, and hyperspectral CASI imagery have been carried out in 1997, 1998, 1999 and 2000 campaigns. Leaf samples from the study sites have been used for reflectance and transmittance measurements with the Li-Cor Model 1800 integrating sphere apparatus coupled to an Ocean Optics Model ST1000 fibre spectrometer in which the same leaves are illuminated alternatively with and without fluorescence-exciting radiation. A study of the diurnal change in leaf reflectance spectra, combined with fluorescence measurements with the PAM-2000 Fluorometer show that the difference spectra are consistent with observed diurnal changes in steady-state fluorescence. Small canopies of Acer saccharum M. have been used for laboratory measurements with the CASI hyperspectral sensor, and under natural light conditions with a fibre spectrometer in diurnal trials, in which the variation of measured reflectance is shown experimentally to be consistent with a fluorescence signature imposed on the inherent leaf reflectance signature. Such reflectance changes due to CF are measurable under natural illumination conditions, although airborne experiments with the CASI hyperspectral sensor produced promising but less convincing results in two diurnal experiments carried out in 1999 and 2000, where small variations of reflectance due to the effect of CF were observed.     

A forestry GIS-based study on evaluating the potential of imaging spectroscopy in mapping forest land fertility

Fertility, or the availability of nutrients and water, controls forest productivity. It affects its carbon sequestration, and thus the forest's effect on climate, as well as its commercial value. Although the availability of nutrients cannot be measured directly using remote sensing methods, fertility alters several vegetation traits detectable from the reflectance spectra of the forest stand, including its pigment content and water stress. However, forest reflectance is also influenced by other factors, such as species composition and stand age. Here, we present a case study demonstrating how data obtained using imaging spectroscopy is correlated with site fertility. The study was carried out in Hyytiälä, Finland, in the southern boreal forest zone. We used a database of state-owned forest stands including basic forestry variables and a site fertility index. To test the suitability of imaging spectroscopy with different spatial and spectral resolutions for site fertility mapping, we performed two airborne acquisitions using different sensor configurations. First, the sensor was flown at a high altitude with high spectral resolution resulting in a pixel size in the order of a tree crown. Next, the same area was flown to provide reflectance data with sub-meter spatial resolution. However, to maintain usable signal-to-noise ratios, several spectral channels inside the sensor were combined, thus reducing spectral resolution. We correlated a number of narrowband vegetation indices (describing canopy biochemical composition, structure, and photosynthetic activity) on site fertility. Overall, site fertility had a significant influence on the vegetation indices but the strength of the correlation depended on dominant species. We found that high spatial resolution data calculated from the spectra of sunlit parts of tree crowns had the strongest correlation with site fertility.     

Non-destructive estimation of potato leaf chlorophyll from canopy hyperspectral reflectance using the inverted PROSAIL model

Optimizing nitrogen (N) fertilization in crop production by in-season measurements of crop N status may improve fertilizer N use efficiency. Hyperspectral measurements may be used to assess crop N status by estimating leaf chlorophyll content. This study evaluated the ability of the PROSAIL canopy-level reflectance model to predict leaf chlorophyll content. Trials were conducted with two potato cultivars under different N fertility rates (0–300 kg N ha⁻¹). Canopy reflectance, leaf area index (LAI) and leaf chlorophyll and N contents were measured. The PROSAIL model was able to predict leaf chlorophyll content with reasonable accuracy later in the growing season. The low estimation accuracy earlier in the growing season could be due to model sensitivity to non-homogenous canopy architecture and soil background interference before full canopy closure. Canopy chlorophyll content (leaf chlorophyll content × LAI) was predicted less accurately than leaf chlrophyll content due to the low estimation accuracy of LAI for values higher than 4.5.     

大米草室内叶片光谱特征参数与叶绿素浓度关系研究

本文通过分析大米草叶片反射光谱特征,并提取红边位置、红边斜率和红边面积三个红边参数以及叶面叶绿素指数(LCI)、水分指数(WI)、三角植被指数(TVI)、结构相关色素指数(SIPI)四个高光谱植被指数,利用线性、对数、倒数、二次函数和三次函数曲线模拟算法得到大米草叶片叶绿素a浓度的高光谱估算模型。研究结果表明:叶绿素a浓度与红边斜率和红边面积在0.01水平上显著负相关,与LCI、WI和TVI在0.01水平上显著正相关。基于红边斜率、红边面积、TVI三个参数,选用倒数法构建叶绿素a浓度的估算模型精度明显高于其他算法。基于LCI和WI参数,应用三次函数法构建的叶绿素a浓度的高光谱检测模型精度明显高于其他算法。比较R2和模型估算误差,利用WI水分指数应用三次函数构建叶绿素a浓度的高光谱检测模型精度在所有模型中最高。因此,利用叶片光谱技术可以较高精度地估算叶绿素a浓度。     

夏季太湖叶绿素a浓度的高光谱数据监测模型

本文依据2004年7月的实测数据构建了太湖夏季叶绿素a浓度的实测光谱数据估计模型,并使用2004年8月的数据对模型进行了验证。调查样点覆盖了太湖内的典型水域,水样数据由无锡太湖环境监测站采集。样点的光谱数据用ASDFieldSpec野外光谱仪获取,每个样点测量10次,测量结果被转换为遥感反射率。对不同的波段组合进行比较分析后,从可解释性出发,最终选择了归一化指数表达式作为最佳波段组合,所建立的模型为：Chla（μg/L）=EXP（2.478 ＋16.378＊N66）,其中,N66为（R696 -R661） /（R696 ＋R661）。模型的R^2为0.9051,显著性p〈0.0001。与其他模型相比,本文的模型比较稳健,用于估计8月的叶绿素a浓度具有较小的绝对误差。本文的工作同时表明,在太湖的夏季相邻月份,可以使用实测光谱数据模型进行水体叶绿素a浓度的估计。     

Estimating chlorophyll content of crops from hyperspectral data using a normalized area over reflectance curve (NAOC)

The Normalized Area Over reflectance Curve (NAOC) is proposed as a new index for remote sensing estimation of the leaf chlorophyll content of heterogeneous areas with different crops, different canopies and different types of bare soil. This index is based on the calculation of the area over the reflectance curve obtained by high spectral resolution reflectance measurements, determined, from the integral of the red–near-infrared interval, divided by the maximum reflectance in that spectral region. For this, use has been made of the experimental data of the SPARC campaigns, where in situ measurements were made of leaf chlorophyll content, LAI and fCOVER of 9 different crops – thus, yielding 300 different values with broad variability of these biophysical parameters. In addition, Proba/CHRIS hyperspectral images were obtained simultaneously to the ground measurements. By comparing the spectra of each pixel with its experimental leaf chlorophyll value, the NAOC was proven to exhibit a linear correlation to chlorophyll content. Calculating the correlation between these variables in the 600–800 nm interval, the best correlation was obtained by computing the integral of the spectral reflectance curve between 643 and 795 nm, which practically covers the spectral range of maximum chlorophyll absorption (at around 670 nm) and maximum leaf reflectance in the infrared (750–800 nm). Based on a Proba/CHRIS image, a chlorophyll map was generated using NAOC and compared with the land-use (crops classification) map. The method yielded a leaf chlorophyll content map of the study area, comprising a large heterogeneous zone. An analysis was made to determine whether the method also serves to estimate the total chlorophyll content of a canopy, multiplying the leaf chlorophyll content by the LAI. To validate the method, use was made of the data from another campaign ((SEN2FLEX), in which measurements were made of different biophysical parameters of 7 crops, and hyperspectral images were obtained with the CASI imaging radiometer from an aircraft. Applying the method to a CASI image, a map of leaf chlorophyll content was obtained, which on, establishing comparisons with the experimental data allowed us to estimate chlorophyll with a root mean square error of 4.2 μg/cm², similar or smaller than other methods but with the improvement of applicability to a large set of different crop types.     

叶片化学组分成像光谱遥感探测机理分析

利用地面光谱仪的测量数据 ,进行了成像光谱遥感探测叶片化学组分的机理性研究。采用多元逐步回归方法 ,分析了鲜叶片 7种化学组分含量与其光谱特性的统计关系 ,分别建立了反射率 ρ及其变化式 1/ρ、logρ和ρ的一阶导数Kρ 与化学组分含量的统计方程 ,并对这 4个指标的性能进行了比较和评价。结果表明 ,在 95 %的置信水平下 ,可以由叶片的精细光谱特征较好地反映出化学组分含量 ;特别是利用Kρ 作为因子 ,使置信水平提高到 99% ,尤以对粗蛋白质、N、K含量反映最好 ,R2 均达到 0 8以上 ,粗蛋白质可达 0 95 6 4,从而为进一步探讨在中国利用成像光谱遥感探测叶片化学组分奠定了基础