应用高光谱遥感数据估算土壤表层水分的研究

土壤水分是土壤的重要组成部分，它在陆地表层和大气之间的物质和能量交换方面扮演着重要角色，寻求快速而准确的方法估算土壤水分具有重要意义。通常，从可见光一近红外对土壤表层水分的估计多是建立在土壤水分与反射率的关系之上的。而在土壤水分含量不高时，土壤水分的增加使土壤光谱反射率在整个波长范围内降低，尤其在760nm，970nm，1190nm，1450nm，1940nm和2950nm等水分吸收波段，而在土壤水分含量较高时，土壤水分的增加会使土壤光谱反射率在某些光谱波段升高。而土壤水分的估计往往是基于土壤水分与土壤水分吸收波段的吸收强度之间的线性关系上，虽然这些经验的方法对于估算某些土壤的表层水分含量是有效的，但这些关系应用于其它条件(如不同种类土壤、土壤湿度变化范围很大的情况)时却面临很多困难，这与土壤的光谱反射率是由土壤的组成成分(土壤水分、有机质、氧化铁和粘土矿物等)的含量和它们在土壤中的分布密切相关。微分技术处理“连续”的光谱是遥感中常用的数学方法，微分技术能部分消除低频光谱成分的影响。现在微分光谱已广泛地应用于研究植被的生物物理参数、矿物和有机质等。然而利用微分光谱对土壤水分反演的研究却鲜见报道。本文通过对实验室中多种不同类型的土壤进行光谱与土壤表层水分含量进行观测，探讨了通过土壤反射率与微分光谱对土壤表层水分的反演方法。4种类型的土壤光谱数据(反射率(R)，反射率倒数的对数(log(1／R))，反射率的一阶微分光谱(dR／dλ)，反射率倒数的对数的一阶微分光谱(d(log(1／R))／dλ))与土壤表层水分之间的关系在本文中得到分析，R与log(1／R)对于不同土壤类型与土壤表层水分都很敏感，说明通过R与log(1／R)反演土壤表层水分受土壤类型的影响很大，而dR／dλ，d(log(1／R))／dλ)对土壤类型却不敏感，对土壤表层水分较为敏感，说明dR／dλ和d(log(1／R))／dλ)对于反演不同类型土壤具有很大的潜力，微分光谱与土壤水分在某些波段具有显著的相关性。通过随机对9种土壤(各具有4个土壤水分)的数据建立反演土壤水分的模型，并其他9种土壤(各具有4个土壤水分)的数据进行验证模型，结果表明，dR／dλ和d(log(1／R))／dλ)能够显著提高R与log(1／R)对于不同土壤类型土壤表层水分的反演精度，由于吸收过程是非线性的，在四种类型的土壤光谱数据中，总体来说，d(log(1／R))／dλ)具有最好的能力预测不同类型土壤的表层水分含量。     

利用可见光/近红外反射光谱估算土壤总氮含量的实验研究

利用土壤的室内反射率光谱，探讨土壤氮元素的高光谱机理。利用土壤光谱各吸收带的特征参数与总氮含量进行逐步回归运算，确定与氮元素关系比较密切的几个吸收带。计算出这几个特征吸收带内土壤反射率的变化形式：一阶导数(FDR)、倒数(1/R)、倒数之对数(log(1/R))、波段深度(Depth)，并与总氮含量进行逐步回归分析，得到比较理想的结果：建模样本的Ra^2(修正的判定系数)分别为0．789、0．753、0．736、0．699，验证样本的Ra^2分别为0．759、0．468、0．794、0．725。可见土壤的反射率光谱与氮元素含量之间存在比较明显的相关性，可见光／近红外反射光谱具有快速估算土壤中氮元素含量的潜力。     

Reflectance spectroscopic characterisation of mineral alteration footprints associated with sediment-hosted gold mineralisation at Mt Olympus (Ashburton Basin,Western Australia)

Hydrothermal ore deposits are typically characterised by footprints of zoned mineral assemblages that extend far beyond the size of the orebody. Understanding the mineral assemblages and spatial extent of these hydrothermal footprints is crucial for successful exploration, but is commonly hindered by the impact of regolith processes on the Earth's surface. Hyperspectral drill core (HyLogger?-3) data were used to characterise alteration mineralogy at the Mt Olympus gold deposit located 35 km southeast of Paraburdoo along the Nanjilgardy Fault within the northern margin of the Ashburton Basin in Western Australia. Mineralogy interpreted from hyperspectral data over the visible to shortwave (400–2500 nm) and thermal (6000–14500 nm) infrared wavelength ranges was validated with X-ray diffraction and geochemical analyses. Spaceborne multispectral (ASTER) and airborne geophysical (airborne electromagnetic, AEM) data were evaluated for mapping mineral footprints at the surface and sub-surface. At the deposit scale, mineral alteration patterns were identified by comparing the most abundant mineral groups detected in the HyLogger data against lithology logging and gold assays. Potential hydrothermal alteration phases included Na/K-alunite, kaolin phases (kaolinite, dickite), pyrophyllite, white mica, chlorite and quartz, representing low-T alteration of earlier greenschist metamorphosed sediments. The respective zoned mineral footprints varied depending on the type of sedimentary host rock. Siltstones were mainly characterised by widespread white-mica alteration with proximal kaolinite alteration or quartz veining. Sandstones showed (1) distal white mica, intermediate dickite, and proximal alunite + kaolinite or (2) widespread white-mica alteration with associated intervals of kaolinite. In both, sandstones and siltstones, chlorite was distal to gold mineralisation. Conglomerates showed distal kaolinite/dickite and proximal white-mica/dickite alteration. Three-dimensional visualisation of the gold distribution and spatially associated alteration patterns around Mt Olympus revealed three distinct categories: (1) several irregular, poddy, SE-plunging zones of >0.5 ppm gold intersected by the Zoe Fault; (2) sulfate alteration proximal to mineralisation, particularly on the northern side of the Mt Olympus open pit; and (3) varying Al^IVAl^VISi^IV_–1(Mg,Fe)^VI_–1 composition of white micas with proximity to gold mineralisation. Chlorite that developed during regional metamorphic or later hydrothermal alteration occurs distal to gold mineralisation. ASTER mineral mapping products, such as the MgOH Group Content used to map chlorite (±white mica) assemblages, showed evidence of correlation to mapped, local structural features and unknown structural or lithological contacts as indicated by inversion modelling of AEM data.     

Determination of the factors governing soil erodibility using hyperspectral visible and near-infrared reflectance spectroscopy

Soil erodibility, which is difficult to estimate and upscaling, was determined in this study using multiple spectral models of soil properties (soil organic matter (SOM), water-stable aggregates (WSA) > 0.25 mm, the geometric mean radius (D_g)). Herein, the soil erodibility indicators were calculated, and soil properties were quantitatively analyzed based on laboratory simulation experiments involving two selected contrasting soils. In addition, continuous wavelet transformation was applied to the reflectance spectra (350–2500 nm) of 65 soil samples from the study area. To build the relationship, the soil properties that control erodibility were identified prior to the spectral analysis. In this study, the SOM, D_g and WSA >0.25 mm were selected to represent the most significant soil properties controlling erodibility and describe the erodibility indicator based on a logarithmic regression model as a function of SOM or WSA > 0.25 mm. Five, six and three wavelet features were observed to calibrate the estimated soil properties model, and the best performance was obtained with a combination feature regression model for SOM (R² = 0.86, p < 0.01), D_g (R² = 0.79, p < 0.01) and WSA >0.25 mm (R² = 0.61, p < 0.01), respectively. One part of the wavelet features captured amplitude variations in the broad shape of the reflectance spectra, and another part captured variations in the shape and depth of the soil dry substances. The wavelet features for the validated dataset used to predict the SOM, WSA >0.25 mm and D_g were not significantly different compared with the calibrated dataset. The synthesized spectral models of soil properties, and the formation of a new equation for soil erodibility transformed from the spectral models of soil properties are presented in this study. These results show that a spectral analytical approach can be applied to complex datasets and provide new insights into emerging dynamic variation with erodibility estimation.     

Re-parameterization of a quasi-analytical algorithm for colored dissolved organic matter dominant inland waters

Quasi-Analytical Algorithms (QAAs) are based on radiative transfer equations and have been used to derive inherent optical properties (IOPs) from the above surface remote sensing reflectance (R_rs) in aquatic systems in which phytoplankton is the dominant optically active constituents (OACs). However, Colored Dissolved Organic Matter (CDOM) and Non Algal Particles (NAP) can also be dominant OACs in water bodies and till now a QAA has not been parametrized for these aquatic systems. In this study, we compared the performance of three widely used QAAs in two CDOM dominated aquatic systems which were unsuccessful in retrieving the spectral shape of IOPS and produced minimum errors of 350% for the total absorption coefficient (a), 39% for colored dissolved matter absorption coefficient (a_CDM) and 7566.33% for phytoplankton absorption coefficient (a_phy). We re-parameterized a QAA for CDOM dominated (hereafter QAA_CDOM) waters which was able to not only achieve the spectral shape of the OACs absorption coefficients but also brought the error magnitude to a reasonable level. The average errors found for the 400–750 nm range were 30.71 and 14.51 for a, 14.89 and 8.95 for a_CDM and 25.90 and 29.76 for a_phy in Funil and Itumbiara Reservoirs, Brazil respectively. Although QAA_CDOM showed significant promise for retrieving IOPs in CDOM dominated waters, results indicated further tuning is needed in the estimation of a(λ) and a_phy(λ). Successful retrieval of the absorption coefficients by QAA_CDOM would be very useful in monitoring the spatio-temporal variability of IOPS in CDOM dominated waters.     

Optical properties of the red tide in Isahaya Bay,southwestern Japan: Influence of chlorophyll a concentration

Remote sensing reflectance [R _rs(λ)] and absorption coefficients of red tides were measured in Isahaya Bay, southwestern Japan, to investigate differences in the optical properties of red tide and non-red tide waters. We defined colored areas of the sea surface, visualized from shipboard, as “red tides”. Peaks of the R _rs(λ) spectra of non-red tide waters were at 565 nm, while those of red tides shifted to longer wavelengths (589 nm). The spectral shape of R _rs(λ) was close to that of the reciprocal of the total absorption coefficient [1/a(λ)], implying that the R _rs(λ) peak is determined by absorption. Absorption coefficients of phytoplankton [a _ph(λ)], non-pigment particles and colored dissolved organic matter increased with increasing chlorophyll a concentration (Chl a), and those coefficients were correlated with Chl a for both red tide and non-red tide waters. Using these relationships between absorption coefficients and Chl a, variation in the spectrum of 1/a(λ) as a function of Chl a was calculated. The peak of 1/a(λ) shifted to longer wavelengths with increasing Chl a. Furthermore, the relative contribution of a _ph(λ) to the total absorption in red tide water was significantly higher than in non-red tide water in the wavelength range 550–600 nm, including the peak. Our results show that the variation of a _ph(λ) with Chl a dominates the behavior of the R _rs(λ) peak, and utilization of R _rs(λ) peaks at 589 and 565 nm may be useful to discriminate between red tide and non-red tide waters by remote sensing.     

南黄海陆架中部沉积物反射率光谱的影响因素分析

利用Minolta CM-2002光谱光度计对南黄海陆架136个短柱样品20~25cm段的沉积物进行了颜色反射率数据测量,通过化学元素、粒度、磁化率等来确定影响沉积物颜色反射率变化的成分和因素,介绍了反射率光谱的一阶导数和因子分析的方法。分析结果表明,南黄海陆架沉积物颜色反射率受控于沉积物中的铁氧化物、有机质以及粘土矿物和钙质生物碎屑含量。主因子F1指示氧化环境,并与陆源物质相关,F2指示弱氧化环境,F3指示弱还原环境,主因子的波长范围分别是405~445 nm和495~595 nm,605~695 nm,445~485 nm。     

小球藻和铜绿微囊藻的高浓度Chl-a高光谱定量模型

本研究将地物高光谱遥感技术应用于室内实验,从而得到小球藻和铜绿微囊藻的高光谱特征.通过多种半经验方法,如单波段、波段比值和微分法,建立了两藻种最优的Chl-a高光谱定量模型,并与室外情况进行了对比.结果表明:小球藻的最优定量模型为Chl-a=174.6 1138292(R703) 2.3(109[(R703)]2(p＜0.01),相应的方法适宜性为:一阶微分法＞单波段法＞波段比值法;铜绿微囊藻的最优定量模型为Chl-a=5299164(R757)1.9773(p＜0.01),相应的方法适宜性为:单波段法＞波段比值法＞一阶微分法;从高光谱特征来看,小球藻在540 nm和700 nm附近存在明显的特征波峰,其位置随Chl-a浓度增大而向长波方向偏离,铜绿微囊藻在530 nm、660 nm和700 nm附近存在3个较强的特征波峰,在610 nm和680 nm附近存在明显的波谷;与以往室外研究不同的是铜绿微囊藻的反射率在400-500 nm之间的R值并不低,是因为没有非藻类颗粒物的影响,总吸收明显降低.     

Landsat5图像的增益、偏置取值及其对行星反射率计算分析

在对比不同时相的数据或建立遥感反演模型时,往往需要将图像的灰度值转换为反射率。增益和偏置是计算反射率时使用的两个基本参数。如果这两个参数发生偏差,反射率的计算结果就有问题,大气校正和其他相关工作的结果也是不可靠的。根据USGS文档和1995年以来的Landsat5遥感图像,对图像头文件中的增益和偏置参数的取值进行了分析,并按照标准单位将增益和偏置进行了转换。为了分析不同增益和偏置取值对反射率计算结果的影响,对比计算了植被和水体的行星反射率值。对2004年7月26日的太湖地区的TM图像。取值分为4种方案: (1)使用遥感软件ENV14．0计算;(2)使用图像头文件中的参数;(3)使用USGS提供的同期参数;(4)使用USGS提供的2003年前的参数。结果表明,不同增益和偏置取值导致计算结果变化较大。如果以方案3的USGS参数计算的结果为标准,那么,其他方案的相对误差值可从0．2％到20％以上。从各个波段的误差分布看,以第3波段的相对误差最小。从工作成果的可比性角度出发,建议统一使用USGS的增益和偏置参数计算Landsat5图像的反射率。     

遥感影像时—空融合的“点”—“线”—“面”质量评价

遥感影像时—空融合可集成多源数据高空间分辨率和高时间分辨率互补优势,生成时间连续的高空间分辨率影像,在遥感影像的动态监测与时序分析等方面具有重要应用价值.然而,现有多数研究往往基于单一数据产品对时—空融合算法进行评价,而在实际生产应用中,需要验证算法在多种遥感产品数据的融合表现;此外,目前研究大多基于“单点时刻”进行评...