Spectral reflectance characteristics of soils and its correlation with soil properties and surface conditions

In-situ spectral reflectance of soils was measured at various test sites of India in four spectral bands within the visible and near-infrared wavelength comparable to Landsat Multispectral Scanner (MSS). Reflectance behaviour of soils under different field conditions was analysed and the spectral reflectance curves for different soil types were obtained. Soil samples pertaining to each test site were analysed for mechanical composition, physioco-chemical properties to identify their relationship with soil reflectance. These spectral reflectance curves were further examined as to their usefulness in discriminating various soil types. Five distinct soil types namely, Black cotton soils (Typic Pellusterts), Marine Soils (Typic Halaquepts), Lateritic Soils (Plinthic Tropohumults), Alluvial Soils (Typic Ustochrepts), Coastal Sandy Soils (Typic Psammaquents), were discriminated on the basis of significant relationships between the spectral reflectance data and soil properties.     

Study of fractional vegetation cover using high spectral resolution data

A field experiment was conducted to study the effect of vegetation cover on soil spectra and relationship of spectral indices with vegetation cover. Multi-date spectral measurements were carried out on twelve wheat fields. Five sets of measurements were taken during the growth period of wheat crop. Field reflectance data were collected in the range 350 to 1800 nm using ASD spectroradiometer. Analysis of data was done to select narrow spectral bands for estimation of ground cover. The ratio of reflectance from vegetation covered soil and reflectance from bare soil indicated that spectral reflectance at 670 and 710 nm are the most sensitive bands. Two bands in visible (670 and 560 nm), three bands in near infrared (710, 870 and 1100 nm) and three bands in middle infrared (1480, 1700 and 1800 nm) were found highly correlated with fractional cover. Vegetation indices developed using narrow band spectral data have been found to be better than those developed using broad- band data for estimation of ground cover.     

Spectral responses of jojoba under salinity stress

Spectral reflectance characteristics of jojoba (Simmondsia chinensis (Link) Schneid.), a dioecious member of Buxaceae have been studied, especially under salinity stress. Reflectance is minimum at bands 1 and 2 (450–520 nm and 520–590 run) of visible range and maximum at bands 3 and 4 (620–680 nm and 770–860 nm) of near infrared range. At all wavelength intervals, male plants have greater reflectance than females. Reflectance in near infrared range (band 4) decreases with increasing age and leaf area index (LAI). A reverse trend occurs at band 3. Absorptance increases in visible as well as Infrared ranges with increasing salinity from control to 10 PSU of sea water concentration.     

航空遥感数据典型自然下垫面偏振特性分析

对于航空航天大气偏振遥感来说,下垫面偏振辐射噪声影响扣除至关重要。本文基于航空偏振遥感数据,探讨了典型自然下垫面对可见光及短波红外波段偏振敏感性。研究发现,在可见光波段与短波红外(2250 nm)波段,植被下垫面偏振反射率线性拟合斜率都接近1,相关系数大于0.95,表明植被偏振反射率对光谱波段不敏感。比较分析了平静水面和存在耀光水面在670 nm和2250 nm两波段的偏振特性,存在耀光的水面其偏振反射率大约是平静水面的3倍。此外,在实验室测量了红砂土和河沙土的偏振反射率,偏振反射率随波段的改变量很小,其与波段的线性拟合斜率仅为10-5量级,说明两者的偏振反射率对波段很不敏感。因此,利用典型自然下垫面在可见和短波红外波段的偏振反射特性,将能够有效进行地气解耦,提高大气偏振遥感精度。     

地物反射光谱对MODIS近红外波段水汽反演影响的模拟分析

在近红外辐射传输方程的基础上,利用近红外波段水汽的不同吸收属性,在MODTRAN的模拟下,深入分析了基于MODIS近红外数据的可降水汽反演算法,并着重讨论了地物反射光谱非线性在可降水汽反演中的影响。研究结果显示,当波段间反射率之比不等于1时,MODIS近红外波段反演水汽将存在较大偏差。同时,在地物光谱库基础上,计算了不同地物反射率比值,其分布表明,大部分地物波段反射率比值不等于1。研究表明,应用现有MODIS近红外波段水汽反演算法,如果不考虑地表反射率光谱变化的影响,由地表反射光谱造成的误差最大约为反射率比值与1偏差的15倍,同时,这一误差还与大气波段透过率之比有关。     

Variations in soil spectral reflectance related to soil moisture,organic matter and particle size

Spectral reflectance measurements in the visible and near infrared wavelengths of alluvial, black cotton and lateritic soils under different conditions show that reflectance has negative association withsoil moisture and organic matter in all the three soils. In lateritic soils reflectance increases with decrease of particle size. Variations in reflectance due to changes in concentrations of parameters were generally restricted to certain concentration levels. The generally superior discriminant capability of band 4 (0.8 to 1.1 urn) is indicative of its utility in soil and soil characteristics mapping.     

Discrimination of low-grade magnetite ores using remote sensing techniques

The remote sensing community in geology is widely using the Multispectral Landsat Thematic Mapper (TM) data which has a wider choice of spectral bands (six between 0.45 and 2.35 μm, plus a thermal infrared channel 10.4-12.5 urn). These were evaluated for low-grade magnetite ores mapping over the high-grade granulite region of Kanjamalai area of Tamil Nadu state, India. The Fourier Transform Infrared (FTIR) spectroscopy data (0.4-4.0 μm) for powders of the magnetite ores exposed with granulite rock and published spectral reflectance data were used as guides in selecting TM band reflectance ratios, which maximize discrimination of magnetite ores on the basis of their respective mineralogies. The study shows that the weathering mineralogy of magnetite ores causes absorption features in their reflectance spectra which are particularly characteristic of the near infrared. Comparison of TM data with field and petrographic observations shows the presence of magnetite and aluminosilicate minerals & show strong absorption at 0.7-1 μ.m wavelength spectral region & increase in the product of two TM band ratios: band 5 (1.55-1.75 μm) to band 4 (0.76-0.9 μm) and band 3 (0.63-0.69 μm) to band 4 (0.76-0.9 μm). Various computer image enhancement and data extraction techniques such as interactive digital image classification techniques using color compositing stretched ratio, maximum likelihood and thresholding statistical approaches using Landsat TM data are used to map the low-grade magnetite ores of the granulite region. The field traverses and local verification enhanced to map the other rock types namely granulites and gneisses of the study area.     

Assessment of Aphid Infestation in Mustard by Hyperspectral Remote Sensing

Field experiment was conducted during 2009–10 and 2010–11 rabi season at research farm of IARI, New Delhi for assessing the aphid infestation in mustard. In aphid infested plant the LAI was 67 to 94% lower than healthy plant. Chlorophyll concentration decreased to 50% in infested plant as compared to healthy plant. Infestation was more severe in late sown crop and due to aphid infestation the percentage oil content and yield was reduced significantly. The spectral reflectance of aphid infested canopy and healthy canopy taken in the laboratory had significant difference in NIR region. In the visible region, the reflectance peak occurred in healthy canopy at around 550–560 nm while this peak was lower by 31% in the aphid infested canopy. The reflectance for healthy crop was found to be more in visible as well as NIR region as compared to aphid infested canopy. The most significant spectral bands for the aphid infestation in mustard are in visible (550–560 nm) and near infrared regions (700–1250 nm and 1950–2450 nm). The different level of aphid infestation can be identified in 1950–2450 nm spectral regions. Spectral indices viz NDVI, RVI, AI and SIPI had significant correlation with aphid infestation. Hence these indices could be used for identifying aphid infestation in mustard.     

Assessing Metal-Induced Changes in the Visible and Near-Infrared Spectral Reflectance of Leaves: A Pot Study with Sunflower (<Emphasis Type="Italic">Helianthus annuus</Emphasis> L.)

The aim of this study was to monitor changes in leaf spectral reflectance due to phytoaccumulation of trace elements (Cd, Pb, and As) in sunflower mutant (M5 mutant line 38/R4-R6/15-35-190-04-M5) grown in spiked and in situ metal-contaminated potted soils. Reflectance spectra (350–2500 nm) of leaves were collected using portable ASD spectroradiometer, and respective leaves sample were analyzed for total metal contents. The spectral changes were quite noticeable and showed increased visible and decreased NIR reflectance for sunflower grown in soil spiked with 900 mg As kg^?1, and in in situ metal-contaminated soils. These changes also involved a blue-shift feature of red-edge position in the first derivatives spectra, studied vegetation indices and continuum removed absorption features at 495, 680, 970, 1165, 1435, 1780, and 1925 nm wavelength. Correlograms of leaf-metal concentration and reflectance values show highest degrees of overall correlation for visible, near-infrared, and water-sensitive wavelengths. Partial least square and multiple linear regression statistical models (cross-validated), respectively, based on Savitzky–Golay filter first-order derivative spectra and combination of spectral feature such as vegetation indices and band depths yielded good prediction of leaf-metal concentrations.     

Spectral studies of lunar equivalent rocks — A prelude to lunar mineral mapping

Hyperspectral remote sensing technique is widely applied for geological studies including the study of extra-terrestrial rocks. Since it has many spectral bands, discrimination between rocks and minerals can be done more precisely. To perform chemical and mineralogical mapping and to study the rocks on the lunar surface, India has proposed to launch its first lunar remote sensing satellite Chandrayaan-1 in the year 2008. For mineralogical mapping, the mission will carry a Hyperspectral Imager (HySI) instrument, which operates in the VNIR region. This paper presents-an attempt to study the spectral response of lunar-akin terrestrial rocks, in the VNIR region (as in the case of the proposed HySI on-board Chandrayaan-1). For this purpose, rocks similar to those present on the lunar surface were collected and their spectral response in the 64 simulated bands of HySI sensor were studied using a spectro-radiometer. Petrographic studies and modal analysis were carried out using thin sections of the rock samples. On studying the spectral response of the lunar-like rock samples in the 64 HySI bands, it is seen that there are distinct absorption features in bands 58 (923.75nm-927.5nm) and 63 (942.5nm-946.25nm) of the NIR wavelength ranges, for basalt rocks; distinct reflectance features in band 20 (590nm to 600nm) for ganmbbro: distinct reflectance features in band 19 (580nm to 590nm) and absorption in band 18 (570-580nm) for gabbroic anorthosite and distinct reflection features in band 63 (942.5nm to 946.25nm) for anorthosite. Thus, this study demonstrates the possibility of identifying the minerals and rocks on lunar surface using the hyperspectral approach and the spectral signatures of lunar-like rocks present on Earth.     

Spectral characteristics of sensors onboard IRS-1D and P6 satellites: Estimation and their influence on surface reflectance and NDVI

This paper reports the results of a modeling study carried out with two objectives, (1) to estimate and compare effective spectral characteristics (central wavelength, bandwidth and bandpass exo-atmospheric solar irradiance E_o) of various spectral channels of LISS-III, WiFS, LISS-III*, LISS-IV and AWiFS onboard Indian Remote Sensing Satellites IRS-ID and P6 using moment method based on the laboratory measurements of sensor spectral response, and (2) to quantify the influence of varying sensor spectral response on reflectance and Normalized Difference Vegetation Index (NDVI) measurements using surface reflectance spectra corresponding to different leaf area index conditions of crop target obtained through field experiment. Significant deviation of 4 to 14 nm in central wavelength and 1.6 to 14.07 nm in spectral width was observed for the corresponding channel of IRS sensors. Coefficient of variation of the order of 0.1 to 1.11% was noticed in E_o among various IRS sensors, which could induce a difference of 0.72 to 3.35% in the estimation of top of atmosphere reflectance for crop target. The variation in spectral response of IRS sensors implied a relative difference of the order of 0.91 to 3.38% in surface reflectance and NDVI measurements. Polynomial approximations are also provided for spectral correction that can be utilized for normalizing the artifacts introduced due to differences in spectral characteristics among IRS sensors.     

Spectral reflectance of some typical Indian soils as affected by tillage and cover types

Measuring spectral reflectance of soils in situ which simulates measurements made from aircraft and by satellite scanner system has become an integral part of soil mapping using remote sensing techniques. A preliminary study has been conducted to measure the spectral reflectance of some typical red and black soils of India using a field radiometer (EXOTECH-100-A) and to study the changes in specrtral reflectance patterns due to tillage and crop and non-crop cover, The spectral reflectance were measured in four different pands of electromagnetic spectrum—two in visible (0.5-0.6μ and 0.6-0.7μ) and two in infrared (0.7-0.8μ and 0.8-1.0μ) region. Spectral reflectance curves were drawn from these values which helped in understanding the spectral separability and mixing of various red and black soil types. Black soils having grass cover showed maximum reflectance value followed by ploughed one and bare counterparts whereas, the order of decrease in spectral reflectance of red soils was bare soils> ploughed soil> soils with grass cover.     

Remote Sensing Study of Sittampundi Anorthosite Complex,India

In the present study, The Landsat 7 ETM satellite data was collected for the Sittampundi anorthosites complex and digital image analysis was carried out. The anorthositic rocks available at Sittampundi complex is considered as an equivalent of lunar highland rocks. Hence, a remote sensing study comprises of image analysis and spectral profile analysis was carried out. The satellite data was digitally processed and generated various outputs like band combinations, color composites, stretched outputs, and PCA. The suitable processed outputs were identified for delineating the anorthosite complex. The diagnostic absorption features of reflectance spectra are the sensitive indicators of mineralogy and chemical composition of rocks, which are interest to the planetary scientists. The spectral profile of Landsat ETM plotted for pure and mixed anorthosite pixels and compared with the field and lab reflectance spectra. The percentages of image spectra vary from 30% to 60% for Sittampundi anorthosite. The spectral bands 2, 4 and 6 have low reflectance and bands 3 and 5 have high reflectance. The spectral range of bands 2,3,4,5 and 6 are 525 nm–605 nm, 630 nm–690 nm, 750 nm–900 nm, 1550 nm–1750 nm and 10400 nm–12500 nm respectively. The field spectral curve has weak absorptions at 650 nm and 1000 nm due to the iron transition absorption and low ca- pyroxene respectively available in the anorthosite, matching with the image spectra. However, hyperspectal image with narrow bandwidth could be more useful in selecting the suitable spectrum for remotely mapping the anorthosite region, as equivalent test site for lunar highland region.     

Assessment of crop growth parameters of wheat under stress condition through ground based spectral data

A field experiment was conducted on wheat crop during rabi seasons of 1995–96, 1996–97 and 1997–98 to study the spectral response of wheat crop (between 490 to 1080 nm) under water and nutrient stress condition. An indigenously developed ground truth radiometer having narrow band in visible and near infrared region (490 – 1080 nm) was used. Vegetation indices derived using different band combinations and related to crop growth parameters. The near infrared spectral region of 710 – 1025 nm was found most important for monitoring stress condition. Relationship has been developed between crop growth parameters and vegetation indices. Leaf Area Index (LAI) and chlorophyll could be predicted by knowing different reflectance ratios at milking stage of crop with R² value of 0.78 and 0.89, respectively. Dry biomass (DBM), Plant Water Content (PWC) and grain yield are also significantly related with reflectance ratios at flowering stage of crop with R² value of 0.90, 0.98 and 0.74, respectively.     

A band selection technique for spectral classification

In hyperspectral remote sensing, sensors acquire reflectance values at many different wavelength bands, to cover a complete spectral interval. These measurements are strongly correlated, and no new information might be added when increasing the spectral resolution. Moreover, the higher number of spectral bands increases the complexity of a classification task. Therefore, feature reduction is a crucial step. An alternative would be to choose the required sensor bands settings a priori. In this letter, we introduce a statistical procedure to provide band settings for a specific classification task. The proposed procedure selects wavelength band settings which optimize the separation between the different spectral classes. The method is applicable as a band reduction technique, but it can as well serve the purpose of data interpretation or be an aid in sensor design. Results on a vegetation classification task show an improvement in classification performance over feature selection and other band selection techniques.     

Ground validation of an algorithm for estimating surface suspended sediment concentrations from multi-spectral reflectance data

Pollution of water resources by sediments eroded from degraded watersheds is a critical concern around the world. Current methods for locating these eroding areas and off-site damage to water resources through visual observations and field sampling with subsequent laboratory analysis are time consuming and expensive. There is thus, a justified interest in developing algorithms for quick estimation of suspended sediment concentrations in large water-bodies from remotely sensed data. This paper presents the results of a ground validation study on characterization and quantification of surface suspended sediment concentrations (SSC) in sediment laden water bodies through an n-waveband specific numerical index, total information content. A comparison of SSC-predictive potential of the proposed new index, derived from four broad (100–300 nm) Landsat MSS, five broad (40–300 nm) Landsat TM and eight narrow (20–40 nm) IRS-P4 OCM spectral bands, with that of the conventional (NIR-Red and NIR+Red) indices, computed from the same spectral band data, is also presented. The study reveaied that at SSCs 250 mg/1, the proposed index (derived from either broad / narrow landsat MSS/TM or IRS-P4 OCM spectral data) could lead to SSC predictions (with mean errors within 20%) comparable with those obtained with the conventional indices (derived from the same spectral band data). It could further be observed that, in general, lower sediment concentrations (i.e. SSCs 150 mg/1) were associated with higher prediction inaccuracies. A comparison of the mean errors of predictions associated with the proposed and the conventional (NIR-Red and NIR+Red) indices computed from broad and narrow band data for SSCs 150 mg/I, revealed that an increase in number of wavebands (from 4 MSS to 5 TM or 8 OCM bands) and a decrease in the bandwidth of these wavebands (from broad MSS/ TM bands to narrow OCM bands) led to a significant increase in the prediction accuracy of the proposed new index. These prediction accuracies were observed to be the highest with the proposed index calculated from narrow OCM-P4 spectral data. However this could not be observed with the conventional indices at any of the SSC ranges and with the proposed index at SSCs 250 mg/l. This shows that the lower SSC-predictive potential of proposed index was a significant function of both the number and the bandwidth of spectral bands used for its computation. In fact in one of the cases, lower SSC (150 mg/l) -predictive accuracy of the proposed index was found to be significantly higher than that of the conventional (NIR+R) index. The proposed algorithm could thus compress the information contained in the entire reflectance spectrum of the sediment laden water bodies to their sediment type and concentration specific characteristic values. This characteristic of the proposed index was not shared by any of the conventional indices, based on only two waveband data. In fact the proposed index appears to be the only mean of completely compressing and quantifying the information contained in all the information channels of a narrow band spectrometer (consisting of 200 wavebands) to be shortly launched by ISRO for satellite based inventory of natural resources.     

Comparative spatio-spectral heterogeneity analysis using multispectral and hyperspectral airborne images

Knowledge of spatio-spectral heterogeneity within multisensor remote sensing images across visible, near-infrared and short wave infrared spectra is important. Till now, little comparative research on spatio-spectral heterogeneity has been conducted on real multisensor images, especially on both multispectral and hyperspectral airborne images. In this study, four airborne images, Airborne Thematic Mapper, Compact Airborne Spectrographic Imager, Specim AISA Eagle and AISI Hawk hyperspectral airborne images of woodland and heath landscapes at Harwood, UK, were applied to quantify and evaluate the differences in spatial heterogeneity through semivariogram modelling. Results revealed that spatial heterogeneity of multisensor airborne images has a close relationship with spatial and spectral resolution and wavelength. Within the visible, near-infrared spectra and short wave infrared spectra, greater spatial heterogeneity is generally observed from the relatively longer wavelength in short wave infrared spectra. There are dramatic changes across the red and red edge spectra, and the peak value is generally examined in the red middle or red edge wavelength across the visible and near-infrared spectra for vegetation or non-vegetation landscape respectively. In all, for real multisensor airborne images, the change in spatial heterogeneity with spatial resolution will accord with the change of support theory depending on whether dramatic change exists across the corresponding wavelength. Besides, if with close spatial resolution, the spatial heterogeneity of multispectral images might be far from the overall integration of these bands from the hyperspectral images involved. A comparative assessment of spatio-spectral heterogeneity using real hyperspectral and multispectral airborne images provides practical guidance for designing the placement and width of a spectral band for different applications and also makes a contribution to the understanding of how to reconcile spatial patterns generated by multisensors.     

利用多时相的高光谱航空图像监测冬小麦条锈病

冬小麦发生锈病 ,叶绿素被大量破坏 ,水分蒸滕量大大增加 ,叶片细胞大小、形态、叶片结构发生了改变 ,从而改变了叶片和冠层的光学特性 ,使得遥感探测与评价成为可能。利用多时相的高光谱航空飞行图像数据 ,了解、分析和发现条锈病病害对作物光谱的影响及其光谱特征 ;设计了病害光谱指数 ,成功地监测了冬小麦条锈病病害程度与范围。对比 3个生育期的条锈病与正常生长冬小麦的PHI图像光谱及光谱特征 ,发现 :5 6 0— 6 70nm黄边、红谷波段 ,条锈病病害冬小麦的冠层反射率高于正常生长的冬小麦光谱反射率 ;近红外波段 ,条锈病病害的冠层反射率低于正常生长的冬小麦光谱反射率 ;条锈病冬小麦冠层光谱红谷吸收深度和绿峰的反射峰高度都会减小     

面向土壤分类的高光谱反射特征参数模型

提出了一种无损、快速、成本低的土壤分类方法,选取松嫩平原4种典型土壤(黑土、黑钙土、风砂土和草甸土)耕层(0—20 cm)土样的实验室反射光谱数据作为研究对象,采用重采样、包络线消除法处理光谱数据,提取反映反射光谱特征的光谱特征参数,利用K均值聚类(K-means clustering)和决策树(decision tree)分别进行聚类分析和分类模型构建,实现土壤的快速分类。结果表明,利用表层土壤反射光谱特征参数构建的决策树分类模型可以对研究区土壤进行分类。研究成果有望加快土壤制图,为土壤理化性质的时空变化研究提供技术支持。     

Wavelength selection and spectral discrimination for paddy rice, with laboratory measurements of hyperspectral leaf reflectance

The objective of this research is to select the most sensitive wavelengths for the discrimination of the imperceptible spectral variations of paddy rice under different cultivation conditions. The paddy rice was cultivated under four different nitrogen cultivation levels and three water irrigation levels. There are 2151 hyperspectral wavelengths available, both in hyperspectral reflectance and energy space transformed spectral data. Based on these two data sets, the principal component analysis (PCA) and band-band correlation methods were used to select significant wavelengths with no reference to leaf biochemical properties, while the partial least squares (PLS) method assessed the contribution of each narrow band to leaf biochemical content associated with each loading weight across the nitrogen and water stresses. Moreover, several significant narrow bands and other broad bands were selected to establish eight kinds of wavelength (broad-band) combinations, focusing on comparing the performance of the narrow-band combinations instead of broad-band combinations for rice supervising applications. Finally, to investigate the capability of the selected wavelengths to diagnose the stress conditions across the different cultivation levels, four selected narrow bands (552, 675, 705 and 776 nm) were calculated and compared between nitrogen-stressed and non-stressed rice leaves using linear discriminant analysis (LDA). Also, wavelengths of 1158, 1378 and 1965 nm were identified as the most useful bands to diagnose the stress condition across three irrigation levels. Results indicated that good discrimination was achieved. Overall, the narrow bands based on hyperspectral reflectance data appear to have great potential for discriminating rice of differing cultivation conditions and for detecting stress in rice vegetation; these selected wavelengths also have great potential use for the designing of future sensors.