Spectral reflectance characterisation of different soils of Deccan plateau

Spectral reflectance of different soil subgroups from different soil orders were measured using a field radiometer. The results showed that, on the basis of spectral signatures, different soils can be delineated. The physico-chemical characteristics including moisture content showed a definite bearing on the spectral reflectance. The plant cover, tillage and crop residue content influenced greatly spectral reflectance as compared to bare soil. The spectral reflectance on soil aggregate size showed that reflectance decreased with increase in the aggregate size. The saline and sodic soils also recorded greater spectral reflectance in relation to normal soils.     

Spectral Reflectance Characteristics of the Soils on Basaltic Terrain of Central Indian Plateau

In-situ spectral reflectance characteristics of soils were studied under field conditions with Multiband Ground Truth Radiometer covering 0.45?C0.52, 0.52?C0.59, 0.62?C0.68, and 0.77?C0.86 ??m spectral bands. Twenty-two surface soil samples were studied in laboratory for their spectral reflectance characteristics using ISCO Model S.R. Spectroradiometer in visible wavelength (450?C725 nm), with 25 nm bandwidth, and in infrared wavelength (750?C1550 nm), with 50 nm bandwidth. The Bidirectional reflectance factor representative of spectral reflectance varied from 3.78 to 11.3???m in band 1, 6.09 to 15.41???m in band 2, 8.05 to 19.41???m in band 3, and 12.18 to 31.2???m in band 4. In-situ spectral reflectance in general increases with the wavelength from visible to infrared bands for all the soils. Black soils have relatively lower reflectance as compared to red soils, which is attributed to the variation in the physicochemical properties of soils. Spectral reflectance, under laboratory conditions, for all the soils increases with wavelength from visible to infrared region except at 950 nm and 1200 nm, where reflectance decreased in all soils, due to weaker water absorption bands and also at 1350 nm, due to strong water absorption at this band. The spectral reflectance of red soils were higher, in-situ as well as under laboratory conditions, as compared to black soils, which is attributed to variation in soil colour, organic matter and clay content of soils. It is observed that the spectral reflectance decrease due to moisture content in soils in all the spectral bands because of darker appearance of soils at moist conditions. Laboratory reflectance measurements serve to define the extent to which intrinsic spectral information is available from soils as a consequence of their composition.     

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.     

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.     

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。     

HJ-1A CCD与TM数据及其估算草地LAI和鲜生物量效果比较分析

基于地面实测和PROSAIL模型模拟数据,研究了新型传感器HJ-1ACCD与TM数据一致性问题,分析了传感器天顶角和光谱相应函数差异的影响,对比两种传感器数据估算草地LAI和鲜生物量的效果,得出以下结论:(1)HJ-1ACCD和TM反射率数据的拟合系数R2在0.7322和0.9205左右,在反射率较小时,两种传感器数据一致性较好;随着反射率增大,HJ-1ACCD数值逐渐高于TM。总体而言,在可见光和近红外波段,两种传感器较为接近,其中红波段最接近。(2)两种传感器的NDVI数据一致性非常高,且受传感器天顶角和光谱响应函数影响作用较小(相对误差约为0.34%—0.53%),而反射率的相对差别在3.34%—9.54%。(3)传感器天顶角较光谱响应函数对反射率影响更大。(4)基于HJ-1ACCD反射率数据估算草地LAI和鲜生物量效果较好,其中以CCD2传感器估算效果最好。     

Spatial distribution of altered minerals in the Gadag Schist Belt (GSB) of Karnataka,Southern India using hyperspectral remote sensing data

Spatial distribution of altered minerals in rocks and soils in the Gadag Schist Belt (GSB) is carried out using Hyperion data of March 2013. The entire spectral range is processed with emphasis on VNIR (0.4–1.0 μm) and SWIR regions (2.0–2.4 μm). Processing methodology includes Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes correction, minimum noise fraction transformation, spectral feature fitting (SFF) and spectral angle mapper (SAM) in conjunction with spectra collected, using an analytical spectral device spectroradiometer. A total of 155 bands were analysed to identify and map the major altered minerals by studying the absorption bands between the 0.4–1.0-μm and 2.0–2.3-μm wavelength regions. The most important and diagnostic spectral absorption features occur at 0.6–0.7 μm, 0.86 and at 0.9 μm in the VNIR region due to charge transfer of crystal field effect in the transition elements, whereas absorption near 2.1, 2.2, 2.25 and 2.33 μm in the SWIR region is related to the bending and stretching of the bonds in hydrous minerals (Al-OH, Fe-OH and Mg-OH), particularly in clay minerals. SAM and SFF techniques are implemented to identify the minerals present. A score of 0.33–1 was assigned for both SAM and SFF, where a value of 1 indicates the exact mineral type. However, endmember spectra were compared with United States Geological Survey and John Hopkins University spectral libraries for minerals and soils. Five minerals, i.e. kaolinite-5, kaolinite-2, muscovite, haematite, kaosmec and one soil, i.e. greyish brown loam have been identified. Greyish brown loam and kaosmec have been mapped as the major weathering/altered products present in soils and rocks of the GSB. This was followed by haematite and kaolinite. The SAM classifier was then applied on a Hyperion image to produce a mineral map. The dominant lithology of the area included greywacke, argillite and granite gneiss.     

Estimation of soil colour through spectral reflectance characteristics

Surface samples (Ap horizon) of twenty one soils widely differing in their physical appearance of colour were collected from different parts of India. Air-dried soils, passed through 2 mm sieve, were packed in black round trays for reflectance measurements. Soil colour of air-dry soils was designated using Munsell colour charts. Spectral reflectance patterns were obtained by a portable spectroradiometer using sunlight as the illuminant. For each sample, the XYZ tristimulus values; x,y chromaticity coordinates were obtained from reflectance patterns using the CIE standard methods. Munsell. notations were also converted to tristimulus and chromaticity coordinates using published tables of Wyszecki and Stiles (1982). The RGB colour coordinates obtained from both the systems were compared. The RGB values from spectral data were well correlated with reflectance in TM bands, while those from Munsell notation were correlated to a lesser extent. The tristimulus values were used to designate each soil with specific set of values to locate them in Universal Colour Space.     

Prediction of soil properties using imaging spectroscopy: Considering fractional vegetation cover to improve accuracy

Spectroscopic techniques have become attractive to assess soil properties because they are fast, require little labor and may reduce the amount of laboratory waste produced when compared to conventional methods. Imaging spectroscopy (IS) can have further advantages compared to laboratory or field proximal spectroscopic approaches such as providing spatially continuous information with a high density. However, the accuracy of IS derived predictions decreases when the spectral mixture of soil with other targets occurs. This paper evaluates the use of spectral data obtained by an airborne hyperspectral sensor (ProSpecTIR-VS – Aisa dual sensor) for prediction of physical and chemical properties of Brazilian highly weathered soils (i.e., Oxisols). A methodology to assess the soil spectral mixture is adapted and a progressive spectral dataset selection procedure, based on bare soil fractional cover, is proposed and tested. Satisfactory performances are obtained specially for the quantification of clay, sand and CEC using airborne sensor data (R² of 0.77, 0.79 and 0.54; RPD of 2.14, 2.22 and 1.50, respectively), after spectral data selection is performed; although results obtained for laboratory data are more accurate (R² of 0.92, 0.85 and 0.75; RPD of 3.52, 2.62 and 2.04, for clay, sand and CEC, respectively). Most importantly, predictions based on airborne-derived spectra for which the bare soil fractional cover is not taken into account show considerable lower accuracy, for example for clay, sand and CEC (RPD of 1.52, 1.64 and 1.16, respectively). Therefore, hyperspectral remotely sensed data can be used to predict topsoil properties of highly weathered soils, although spectral mixture of bare soil with vegetation must be considered in order to achieve an improved prediction accuracy.     

Studies on Textural and Compositional Characteristics of Sand and Clay Mixtures Using Hyperspectral Radiometry

This paper examines the hyperspectral signatures (in the Visible Near Infrared (VNIR)-Shortwave Infrared (SWIR) regions) of soil samples with varying colour and minerals. 36 samples of sands (from river and beach) with differing clay contents were examined using a hyperspectral radiometer operating in the 350–2,500 nm range, and the spectral curves were obtained. Analysis of the spectra indicates that there is an overall increase in the reflectance in the VNIR-SWIR region with an increase in the content of kaolinite clay in the sand samples. As regards the red and black clays and sand mixtures, the overall reflectance increases with decreasing clay content. Several spectral parameters such as depth of absorption at 1,400 nm and 1,900 nm regions, radius of curvature of the absorption troughs, slope at a particular wavelength region and the peak reflectance values were derived. There exists a correlation between certain of these spectral parameters (depth, slope, position, peak reflectance, area under the curve and radius of the curve) and the compositional and textural parameters of the soils. Based on these well-defined relations, it is inferred that hyperspectral radiometry in the VNIR and SWIR regions can be used to identify the type of clay and estimate the clay content in a given soil and thus define its geotechnical category.     

黑土区田块尺度遥感精准管理分区

基于格网采样与空间插值的精准管理分区方法精度高,但时效性差、成本高。本文以东北农垦地区红星农场农田为研究对象,提出一种基于遥感影像的精准管理分区方法:以裸土高空间分辨率遥感影像作为数据源,结合田间格网采样数据,基于裸土反射光谱特征与黑土主要理化性质的显著相关关系,运用面向对象分割、空间统计分析方法,对典型黑土区田块进行精准管理分区研究,并利用土壤理化性质和农作物生理参数,对分区结果进行评价。得出如下结论:(1)典型黑土区田块内部土壤养分含量空间变异显著;(2)基于裸土影像与面向对象的精准管理分区方法精度高,增强了分区之间的土壤养分与归一化植被指数(NDVI)差异性、分区内部各属性的一致性;(3)基于2015年4月1日和2015年5月20日单期影像分区和两期影像波段叠加(Layer stacking)分区,区间变异系数与区内变异系数之比分别为1.42、1.39和7.63,基于两期影像综合信息的分区结果显著优于基于单期影像分区;(4)基于裸土影像面向对象分割的精准管理分区方法时效性强、成本低、精度高。研究成果为田间变量施肥、发展精准农业、实现农业可持续发展提供依据。     

Is There a Physical Linkage Between Surface Emissive and Reflective Variables Over Non-Vegetated Surfaces?

For a satellite sensor with only one or two thermal infrared channels, it is difficult to retrieve the surface emissivity from the received emissive signal. Empirical linear relationship between surface emissivity and red reflectance are already established for deriving emissivity, but the inner physical mechanism remains unclear. The optical constants of various minerals that cover the spectral range from 0.44 to 13.5 μm in conjunction with modern radiative transfer models were used to produce corresponding surface reflectance and emissivity spectra. Compared to the commonly used empirical linear relationship, a more accurate multiple linear relationship between Landsat TM5 emissivity and optical reflectances was derived using the simulated data, which indicated the necessity of replacing the empirical relationship with the new one for improving surface emissivity estimate in the single channel algorithm. The significant multiple linear relationship between broadband emissivity (BBE, 8–13.5 μm) and MODIS spectral albedos was also derived using the same data. This paper demonstrates that there is a physical linkage between surface emissive and reflective variables, and provides a theoretical perspective on estimating surface emissivity for sensors with only one or two thermal infrared channels.     

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.     

Topographic and spectral data resolve land cover misclassification to distinguish and monitor wetlands in western Uganda

Wetlands provide vital wildlife habitat and ecosystem services, but changes in human land use has made them one of the world’s most threatened ecosystems. Although wetlands are generally protected by law, growing human populations increasingly drain and clear them to provide agricultural land, especially in tropical Africa. Managing and conserving wetlands requires accurately monitoring their spatial and temporal extent, often using remote sensing, but distinguishing wetlands from other land covers can be difficult. Here, we report on a method to separate wetlands dominated by papyrus (Cyperus papyrus L.) from spectrally similar grasslands dominated by elephant grass (Pennisetum purpureum Schumach.). We tested whether topographic, spectral, and temperature data improved land cover classification within and around Kibale National Park, a priority conservation area in densely populated western Uganda. Slope and reflectance in the mid-IR range best separated the combined papyrus/elephant grass pixels (average accuracy: 86%). Using a time series of satellite images, we quantified changes in six land covers across the landscape from 1984 to 2008 (papyrus, elephant grass, forest, mixed agriculture/bare soil/short grass, mixed tea/shrub, and water). We found stark differences in how land cover changed inside versus outside the park, with particularly sharp changes next to the park boundary. Inside the park, changes in land cover varied with location and management history: elephant grass areas decreased by 52% through forest regeneration but there was no net difference in papyrus areas. Outside the park, elephant grass and papyrus areas decreased by 61% and 39%, mostly converted to agriculture. Our method and findings are particularly relevant in light of social, biotic, and abiotic changes in western Uganda, as interactions between climate change, infectious disease, and changing human population demographics and distribution are predicted to intensify existing agricultural pressure on natural areas.     

水分含量与红土野外光谱的定量分析

土壤水分含量与土壤光谱关系密切,本文利用实测粤西红土野外光谱数据及其水分含量来分析两者之间的关系。得出以下结论:在0.46-2.5μm波段水分含量与红土光谱反射率呈显著负相关关系,并且随波长的增加其相关性有所增强,在2.22-2.29μm波段两者相关性最强;利用野外红土光谱反射率的倒数与水分含量进行回归分析,得到建模样本修正后的确定系数R2为0.781。这将为利用遥感技术监测土壤水分提供依据。     

盐沼植被光谱特征的间接排序识别分析

运用ASD地物光谱仪,采用12个小型机载成像光谱仪(CASI)默认的植被波段组,以上海崇明东滩自然保护区的盐沼植物群落为对象,应用主成分分析法和相关分析研究了不同群落光谱特征与生态环境因子之间的关系。结果表明:运用PCA间接排序法能够识别盐沼植被中光滩、海三棱藨草群落、芦苇群落和互花米草群落等光谱特征;绝大多数盐沼植物的群落组成与所选波段的光谱特征之间有显著的相关关系;可见光和近红外波段数据可以分别识别低盖度的海三棱藨草群落和高盖度的互花米草和芦苇群落;对光谱反射率影响最大的生态环境因子是植物群落的高度和盖度,而高程和其它环境因子的影响次之。     

Retrieval of soil physicochemical properties towards assessing salt-affected soils using Hyperspectral Data

The relationship between soil salinity parameters and their influence on soil spectral characteristics were analyzed using both satellite data (Hyperion) and reflectance data of soil samples collected from parts of Ahmedabad district of Gujarat, India. The soil spectral reflectance curves were assessed using absorption feature parameters by DISPEC software to identify suitable spectral band for salinity characterization. The Hyperion data of the study area were processed and classified into different classes by spectral angle mapper algorithm using spectral library generated from soil spectra. The results showed that among all the observed soil parameters Electrical Conductivity, Exchangeable Sodium Percentage, Cation Exchange Capacity and Mg⁺⁺ predictions can be made accurately based on partial least square regression models developed from selected wavelengths. Out of the total study area moderately saline-sodic, severely saline-sodic, severely saline and slightly saline soils occupy 23.5, 12.6, 10.9 and 0.04%, respectively.     

RESULTS OF COMBINED ANALYSIS OF GROUND AND REMOTE SENSING INFORMATION IN THE STUDY OF MAJOR EARTHQUAKE CENTERS

Soil salinity is one of the most important problems affecting Egyptian soils. It is caused by: (1) a rising water table, or (2) the misuse of the irrigation water. Two Landsat images acquired in 1987 and 1999 were used to detect and monitor soil salinity over the Siwa Oasis, Western Desert, Egypt. DN values of these images were converted to percent reflectance. Inspection of Landsat images revealed that saline soils had an overall higher spectral reflectance in all spectral bands except the two MIR bands. The reflectance curves of saline soils show a strong relationship between the existence of salts in the soil and the difference between bands 4 and 5. A salinity index (SI) was calculated for both images. The majority of pixels in the 1987 image have salinity index values ranging between 0 and 0.2, whereas the values in the 1999 image histogram ranged between 0 and 0.4. These values indicate that soil salinity has increased twofold during the 12 years spanning the imagery. These values show a strong correlation with vegetation index images, in which the 1999 vegetation index image reveals the appearance of surface water lakes formed due to a rising water table. This study presents a model for the identification of soil salinity using remote sensing measurements in conjunction with piezometer readings taken during the time of image acquisition.     

Spectral reflectance characteristics of salt-affected arid soils of Rajasthan

The spectral reflectance characteristics of different types of natural and anthropogenic salt-affected soils have been studied under field conditions. The spectral reflectance value for non-saline and all types of salt-affected soils was maximum in near infra red region (800–1000 nm). The natural salt-affected soils having surface salt encrustation showed highest reflectance value followed by the sodic soils (formed due to high residual sodium carbonate water irrigation) natural saline soils and saline soils due to saline water irrigation. Soil texture, pH, CaC0₃ and organic matter together accounted for 29.6% variation in the maximum reflectance percentage value out of which only pH accounted for more than half (14.2% variation).