PROSPECT-4 and 5: Advances in the leaf optical properties model separating photosynthetic pigments

The PROSPECT leaf optical model has, to date, combined the effects of photosynthetic pigments, but a finer discrimination among the key pigments is important for physiological and ecological applications of remote sensing. Here we present a new calibration and validation of PROSPECT that separates plant pigment contributions to the visible spectrum using several comprehensive datasets containing hundreds of leaves collected in a wide range of ecosystem types. These data include leaf biochemical (chlorophyll a, chlorophyll b, carotenoids, water, and dry matter) and optical properties (directional-hemispherical reflectance and transmittance measured from 400 nm to 2450 nm). We first provide distinct in vivo specific absorption coefficients for each biochemical constituent and determine an average refractive index of the leaf interior. Then we invert the model on independent datasets to check the prediction of the biochemical content of intact leaves. The main result of this study is that the new chlorophyll and carotenoid specific absorption coefficients agree well with available in vitro absorption spectra, and that the new refractive index displays interesting spectral features in the visible, in accordance with physical principles. Moreover, we improve the chlorophyll estimation (RMSE = 9 µg/cm²) and obtain very encouraging results with carotenoids (RMSE = 3 µg/cm²). Reconstruction of reflectance and transmittance in the 400-2450 nm wavelength domain using PROSPECT is also excellent, with small errors and low to negligible biases. Improvements are particularly noticeable for leaves with low pigment content.     

A dorsiventral leaf radiative transfer model: Development, validation and improved model inversion techniques

A Dorsiventral Leaf Model (DLM) is presented to simulate leaf radiative transfer. DLM was conceived as a plate model with a stochastic distribution of different groups of layers. Leaf asymmetry was modeled by assigning non-uniform distributions of pigments, water and dry matter to palisade and mesophyll layers and by simulating different amounts of light diffusion for adaxially and abaxially incident light. Surface reflections are based on micro-facets theory enabling the simulation of directional-hemispherical reflectance and a range of bidirectional reflectance factors. Adaxial and abaxial optical properties could be accurately simulated for a variety of leaf types with an overall error in reflectance and transmittance below 1.3%.Sensitivity analysis focused on optimizing model inversion schemes improves parameter estimation accuracy. Different inversion schemes were compared for two independent datasets. Results underpin most of the propositions of the sensitivity analysis: (i) masking the near-infrared wavelengths (band weighting) to account for variability in the dry matter composition consistently increased predicted accuracies for dry matter content, (ii) white reflectance measurements (reflectance with a 100% diffusely reflecting background) provided results superior to other optical measurements, making it a valuable and fast alternative and (iii) combining reflectance and transmittance into absorptance however did not result in improvements. Comparisons of DLM with the PROSPECT 5 model indicate an almost equal performance in content estimations. Improvements were thus not related to differences in model structure but to techniques that reduce the impact of leaf structure and compensate for sampling errors and variations in specific absorption spectra. DLM has important potential in the study of leaf radiative transfer and in the integration with canopy radiative transfer models.     

A method to account for shoot scale clumping in coniferous canopy reflectance models

The three-dimensional structure of a coniferous shoot gives rise to multiple scattering of light between the needles of the shoot, causing the shoot spectral reflectance to differ from that of a flat leaf. Forest reflectance models based on the radiative transfer equation handle shoot level clumping by correcting the radiation attenuation coefficient with a clumping index. The clumping index causes a reduction in the interception of radiation by the canopy at a fixed leaf area index (LAI). In this study, we show how within-shoot multiple scattering is related to shoot scale clumping and derive a similar, but wavelength dependent, correction to the scattering coefficient. The results provide a method for integrating shoot structure into current radiative transfer equation based forest reflectance models. The method was applied to explore the effect of shoot scale clumping on canopy spectral reflectance using simple model canopies with a homogeneous higher level structure. The clumping of needles into shoots caused a wavelength dependent reduction in canopy reflectance, as compared to that of a leaf canopy with similar interception. This is proposed to be one reason why coniferous and broad-leaved canopies occupy different regions in the spectral space and exhibit different dependency of spectral vegetation indices on LAI.     

Inversion of a radiative transfer model for estimating vegetation LAI and chlorophyll in a heterogeneous grassland

Radiative transfer models have seldom been applied for studying heterogeneous grassland canopies. Here, the potential of radiative transfer modeling to predict LAI and leaf and canopy chlorophyll contents in a heterogeneous Mediterranean grassland is investigated. The widely used PROSAIL model was inverted with canopy spectral reflectance measurements by means of a look-up table (LUT). Canopy spectral measurements were acquired in the field using a GER 3700 spectroradiometer, along with simultaneous in situ measurements of LAI and leaf chlorophyll content. We tested the impact of using multiple solutions, stratification (according to species richness), and spectral subsetting on parameter retrieval. To assess the performance of the model inversion, the normalized RMSE and R² between independent in situ measurements and estimated parameters were used. Of the three investigated plant characteristics, canopy chlorophyll content was estimated with the highest accuracy (R² = 0.70, NRMSE = 0.18). Leaf chlorophyll content, on the other hand, could not be estimated with acceptable accuracy, while LAI was estimated with intermediate accuracy (R² = 0.59, NRMSE = 0.18). When only sample plots with up to two species were considered (n = 107), the estimation accuracy for all investigated variables (LAI, canopy chlorophyll content and leaf chlorophyll content) increased (NRMSE = 0.14, 0.16, 0.19, respectively). This shows the limits of the PROSAIL radiative transfer model in the case of very heterogeneous conditions. We also found that a carefully selected spectral subset contains sufficient information for a successful model inversion. Our results confirm the potential of model inversion for estimating vegetation biophysical parameters at the canopy scale in (moderately) heterogeneous grasslands using hyperspectral measurements.     

Comparison of active and passive water vapor remote sensing from space: An analysis based on the simulated performance of IASI and space borne differential absorption lidar

Two remote sensing systems, which are considered to be operated in space, the Infrared Atmospheric Sounding Interferometer (IASI) and the Water Vapour Lidar Experiment in Space (WALES) are compared with respect to their measurement methodologies and their performance. The focus is the retrieval of water vapor, which is determined by the inversion of the radiative transfer equation in case of IASI and by the differential absorption lidar (DIAL) technique in case of WALES. It is realized that different techniques and definitions for the specification of errors exist which are subject of confusion in the remote sensing community. After a clarification of this issue, a comparison of IASI and WALES water vapor retrievals is performed using the same water vapor climatologies and the same 2-d water vapor fields provided by a global numerical weather prediction (NWP) model.The methodologies and the capabilities of each instrument are compared in regions without clouds. Using end-to-end simulations for both instruments, which are to our knowledge performed for the first time, systematic errors are compared up to 16 km. It is found that the dependence of IASI retrievals on a variety of atmospheric parameters leads to compensating effects. Due to the multiwavelength retrieval, errors in the water vapor spectroscopy can partly cancel. The residual error is quantified by inversion of the radiative transfer equation in dependence of several atmospheric variables. In contrast, errors in water vapor DIAL are very sensitive to laser spectral properties as well as to the accuracy of water vapor spectroscopy, as single water vapor absorption lines are used for each vertical segment of the retrieval. As laser transmitters with excellent spectral specifications are feasible, this can still lead to very low systematic errors under all atmospheric conditions.Noise errors are determined using analytical models and are compared up to 16 km. At the same vertical (1-2 km) and horizontal (100-200 km) resolutions, respectively, the average noise errors in each profile are of the order of 10% for both methods. Depending on the climatology, the vertical range of IASI measurements is always several kilometers lower than that of DIAL. The performance of IASI degrades in dry atmospheres whereas the DIAL performance remains nearly independent of the climatology chosen. Bias errors show a similar behavior. Neglecting bias errors in the spectral measurements, from mid-latitudes to the tropics, IASI biases are <2 % in the vertical range where the noise errors remain <20%. In the sub-arctic winter atmosphere, the bias increases to about −4% close to the ground. Space borne DIAL bias profiles range between −2-1% under all conditions plus an additional height independent bias of about ±2 due to remaining uncertainties in absorption line spectroscopy.Operation in the region of clouds are not a focus of this publication but it is worth to mention that the results demonstrate that space borne DIAL can perform measurements down to cloud tops and often through optically thin clouds. Particularly powerful is the synergistic combinations of both sensors in the future. Iteration between IASI temperature and DIAL water vapor retrievals will increase both accuracies.     

基于数据机理的植被叶面积指数遥感反演研究

定量获取地表植被高精度时序及空间覆盖的叶面积指数（Leaf Area Index, LAI）是生态监测及农业生产应用的重要研究内容。通过使用Moderate Resolution Imaging Spectroradiometer（MODIS）植被冠层多角度观测MOD09GA数据及叶面积指数MOD15A2数据,发展了一种参数化的叶面积指数遥感反演方法并完成了必要的检验分析。研究使用基于辐射传输理论的RossThick LiSparse Reciprocal（RTLSR）核驱动模型及Scattering by Arbitrarily Inclined Leaves with Hotspot（SAILH）模型进行植被冠层辐射特征的提取,使用Anisotropic Index （ANIX）异质性指数作为指示植被冠层二向反射分布Bidirectional Reflectance Distribution Function（BRDF）的辅助特征信息,发展了基于数据机理（Data-Based Mechanistic, DBM）的植被叶面积指数建模和估算方法。通过必要的林地、农作物、草地植被实验区反演及数值分析可得知：①时间序列多角度遥感观测数据结合数据机理的叶面积指数估算方法,可实现模型参数的时序动态更新,改进叶面积指数估算结果的时序完整性及精度。②异质性指数可以用做指示植被冠层二向反射分布特征信息,可降低因观测数据几何条件差异所导致的反演结果不确定情况,同时能够补充植被时序生长过程表现的植被结构变化等动态特征。经研究实践,可将算法应用于时空尺度的叶面积指数估算,并能够为生态、农业应用提供植被的高精度遥感监测指标。     

Assessing the effect of hydrocarbon oil type and thickness on a remote sensing signal: A sensitivity study based on the optical properties of two different oil types and the HYMAP and Quickbird sensors

We measured the light absorption properties of two naturally occurring Australian hydrocarbon oils, a Gippsland light crude oil and a North West Shelf light condensate. Using the results from these measurements in conjunction with estimated sensor environmental noise thresholds, the theoretical minimum limit of detectability of each oil type (as a function of oil thickness) was calculated for both the hyperspectral HYMAP and multispectral Quickbird sensors. The Gippsland crude oil is discernable at layer thickness of 20 µm or more in the Quickbird green channel. The HYMAP sensor was found to be theoretically capable of detecting a layer of Gippsland crude oil with a thickness of 10 µm in approximately six sensor channels. By contrast, the North West Shelf light condensate was not able to be detected by either sensor for any thickness up to 200 µm. Optical remote sensing is therefore not applicable for detecting diagnostic absorption features associated with this light condensate oil type, which is typical of the chemistry of many hydrocarbon oils found in the Australian Northwest Shelf area and condensates world wide. We conclude that oil type is critical to the applicability of optical remote sensing for natural oil slick detection and identification. We recommend that a sensor- and oil-specific sensitivity study should be conducted prior to applying optical remote sensors for oil exploration.The oil optical properties were obtained using two different laboratory methods, a reflectance-based approach and transmittance-based approach. The reflectance-based approach was relatively complex to implement, but was chosen in order to replicate as closely as possible real world remote sensing measurement conditions of an oil film on water. The transmittance-based approach, based upon standard laboratory spectrophotometric measurements was found to generate results in good agreement with the reflectance-based approach. Therefore, for future oil- and sensor-specific sensitivity studies, we recommend the relatively accessible transmittance-based approach, which is detailed in this paper.     

Enhancing a leaf radiative transfer model to estimate concentrations and in vivo specific absorption coefficients of total carotenoids and chlorophylls a and b from single-needle reflectance and transmittance

The relative concentrations of different pigments within a leaf have significant physiological and spectral consequences. Photosynthesis, light use efficiency, mass and energy exchange, and stress response are dependent on relationships among an ensemble of pigments. This ensemble also determines the visible characteristics of a leaf, which can be measured remotely and used to quantify leaf biochemistry and structure. But current remote sensing approaches are limited in their ability to resolve individual pigments. This paper focuses on the incorporation of three pigments—chlorophyll a, chlorophyll b, and total carotenoids—into the LIBERTY leaf radiative transfer model to better understand relationships between leaf biochemical, biophysical, and spectral properties.Pinus ponderosa and Pinus jeffreyi needles were collected from three sites in the California Sierra Nevada. Hemispheric single-leaf visible reflectance and transmittance and concentrations of chlorophylls a and b and total carotenoids of fresh needles were measured. These data were input to the enhanced LIBERTY model to estimate optical and biochemical properties of pine needles. The enhanced model successfully estimated reflectance (RMSE = 0.0255, BIAS = 0.00477, RMS%E = 16.7%), had variable success estimating transmittance (RMSE = 0.0442, BIAS = 0.0294, RMS%E = 181%), and generated very good estimates of carotenoid concentrations (RMSE = 2.48 µg/cm², BIAS = 0.143 µg/cm², RMS%E = 20.4%), good estimates of chlorophyll a concentrations (RMSE = 10.7 µg/cm², BIAS = − 0.992 µg/cm², RMS%E = 21.1%), and fair estimates of chlorophyll b concentrations (RMSE = 7.49 µg/cm², BIAS = − 2.12 µg/cm², RMS%E = 43.7%). Overall root mean squared errors of reflectance, transmittance, and pigment concentration estimates were lower for the three-pigment model than for the single-pigment model. The algorithm to estimate three in vivo specific absorption coefficients is robust, although estimated values are distorted by inconsistencies in model biophysics. The capacity to invert the model from single-leaf reflectance and transmittance was added to the model so it could be coupled with vegetation canopy models to estimate canopy biochemistry from remotely sensed data.     

基于大疆精灵4无人机多光谱影像的西沙植被监测

西沙群岛位于热带,常年多云,在光学卫星数据获取时易受天气影响导致缺失,使得地表动态监测困难。为解决这一问题,探讨无人机低空平台对西沙群岛植被的监测能力,选取大疆精灵4多光谱无人机,通过5个多光谱波段提取4项植被指数,包括归一化差值植被指数（NDVI）、叶绿素指数（GCI）、绿色归一化植被指数（GNDVI）以及归一化绿红差值指数（NGRDI）,评估了2020年5月西沙群岛北岛的植被生长状况,并结合关键气象参数以及Worldview2卫星光学影像对比分析了2020年5月和2018年5月北岛植被生长变化及其潜在归因。研究结果表明：2020年5月北岛平均NDVI、GCI、GNDVI和NGRDI别为0.30、0.84、0.26和0.05,反映出植被覆盖度较低,可能存在枯黄现象,与地面监测结果一致;2020年人工管理植被区和自然生长植被区各项指数差异由2018年的-23%—15%增加到15%—40%,表明2020年自然生长植被长势显著差于人工管理植被,反映出较强的环境胁迫;气象数据显示2020年4月—5月该地区日平均温度较常年同期升高、累计降水量减少、平均风速增大同时增加了土壤水分亏缺,可能是引起...     

Leaf area index estimation with MODIS reflectance time series and model inversion during full rotations of Eucalyptus plantations

The leaf area index (LAI) of fast-growing Eucalyptus plantations is highly dynamic both seasonally and inter-annually, and is spatially variable depending on pedo-climatic conditions. LAI is very important in determining the carbon and water balance of a stand, but is difficult to measure during a complete stand rotation and at large scales. Remote-sensing methods allowing the retrieval of LAI time series with accuracy and precision are therefore necessary. Here, we tested two methods for LAI estimation from MODIS 250m resolution red and near-infrared (NIR) reflectance time series. The first method involved the inversion of a coupled model of leaf reflectance and transmittance (PROSPECT4), soil reflectance (SOILSPECT) and canopy radiative transfer (4SAIL2). Model parameters other than the LAI were either fixed to measured constant values, or allowed to vary seasonally and/or with stand age according to trends observed in field measurements. The LAI was assumed to vary throughout the rotation following a series of alternately increasing and decreasing sigmoid curves. The parameters of each sigmoid curve that allowed the best fit of simulated canopy reflectance to MODIS red and NIR reflectance data were obtained by minimization techniques. The second method was based on a linear relationship between the LAI and values of the GEneralized Soil Adjusted Vegetation Index (GESAVI), which was calibrated using destructive LAI measurements made at two seasons, on Eucalyptus stands of different ages and productivity levels. The ability of each approach to reproduce field-measured LAI values was assessed, and uncertainty on results and parameter sensitivities were examined. Both methods offered a good fit between measured and estimated LAI (R² = 0.80 and R² = 0.62 for model inversion and GESAVI-based methods, respectively), but the GESAVI-based method overestimated the LAI at young ages.     

局部自适应混合模型的遥感图像去噪算法

遥感图像的噪声分析和去除作为经典问题一直受到关注并成为遥感图像处理的一个重要研究领域。传统的去噪方法在一定程度上可以去除图像中的噪声,但往往在去噪的同时会使图像的边缘和细节信息模糊化。针对P-M模型在去除遥感图像高斯噪声时所存在的对图像强边缘和细节附近的噪声难以去除,以及ROF模型通常会导致平坦区域出现“假边缘”,甚至会产生块状效应等问题,提出一种基于局部自适应的混合模型。该模型针对图像局部区域所包含纹理信息的不同,自适应地调整约束权函数,使模型在平滑局部区域能更多地发挥P-M模型的特点,而在纹理丰富或边缘区域则更多地发挥ROF模型的特性,使模型在有效地去除高斯噪声的同时,很好地保护了遥感图像中的边缘特征和细节纹理信息。实验结果表明,对相同的高斯噪声所提出的混合模型去噪后图像的SNR较P-M和ROF模型分别提高了3dB和2dB。     

A novel logistic multi-class supervised classification model based on multi-fractal spectrum parameters for hyperspectral data

A novel logistic multi-class supervised classification model based on multi-fractal spectrum parameters is proposed to avoid the error that is caused by the difference between the real data distribution and the hypothetic Gaussian distribution and avoid the computational burden working in the logistic regression classification directly for hyperspectral data. The multi-fractal spectra and parameters are calculated firstly with training samples along the spectral dimension of hyperspectral data. Secondly, the logistic regression model is employed in our work because the logistic regression classification model is a distribution-free nonlinear model which is based on the conditional probability without the Gaussian distribution assumption of the random variables, and the obtained multi-fractal parameters are applied to establish the multi-class logistic regression classification model. Finally, the Newton–Raphson method is applied to estimate the model parameters via the maximum likelihood algorithm. The classification results of the proposed model are compared with the logistic regression classification model based on an adaptive bands selection method by using the Airborne Visible/Infrared Imaging Spectrometer and airborne Push Hyperspectral Imager data. The results illuminate that the proposed approach achieves better accuracy with lower computational cost simultaneously.     

Reflectance seasonality and its relation to the canopy leaf area index in an eastern Siberian larch forest: Multi-satellite data and radiative transfer analyses

Reliable monitoring of seasonality in the forest canopy leaf area index (LAI) in Siberian forests is required to advance the understanding of climate-forest interactions under global environmental change and to develop a forest phenology model within ecosystem modeling. Here, we compare multi-satellite (AVHRR, MODIS, and SPOT/VEGETATION) reflectance, normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and LAI with aircraft-based spectral reflectance data and field-measured forest data acquired from April to June in 2000 in a larch forest near Yakutsk, Russia. Field data in a 30 × 30-m study site and aircraft data observed around the field site were used. Larch is a dominant forest type in eastern Siberia, but comparison studies that consider multi-satellite data, aircraft-based reflectance, and field-based measurement data are rarely conducted. Three-dimensional canopy radiative transfer calculations, which are based on Antyufeev and Marshak's [Antyufeev, V.S., & Marshak, A.L. (1990). Monte Carlo method and transport equation in plant canopies, Remote Sensing of Environment, 31, 183-191] Monte Carlo photon transport method combined with North's [North, P.R. (1996). Three-dimensional forest light interaction model using a Monte Carlo method, IEEE Transactions on Geoscience and Remote Sensing, 34(4), 946-956] geometric-optical hybrid forest canopy scene, helped elucidate the relationship between canopy reflectance and forest structural parameters, including several forest floor conditions. Aircraft-based spectral measurements and the spectral response functions of all satellite sensors confirmed that biases in reflectance seasonality caused by differences in spectral response functions among sensors were small. However, some reflectance biases occur among the near infrared (NIR) reflectance data from satellite products; these biases were potentially caused by absolute calibration errors or cloud/cloud shadow contamination. In addition, reflectance seasonality in AVHRR-based NIR data was very small compared to other datasets, which was partially due to the spring-to-summer increase in the amount of atmospheric water vapor. Radiative transfer simulations suggest that bi-directional reflectance effects were small for the study site and observation period; however, changes in tree density and forest floor conditions affect the absolute value of NIR reflectance, even if the canopy leaf area condition does not change. Reliable monitoring of canopy LAI is achieved by minimizing these effects through the use of NIR reflectance difference, i.e., the difference in reflectance on the observation day from the reflectance on a snow-free/pre-foliation day. This may yield useful and robust parameters for multi-satellite monitoring of the larch canopy LAI with less error from intersensor biases and forest structure/floor differences. Further validation with field data and combined use of other index (e.g. normalized difference water index, NDWI) data will enable an extension of these findings to all Siberian deciduous forests.     

Using coarse remote sensing radar observations to control the trajectory of a simple Sahelian land surface model

In the Sahel, land surface processes are significantly interacting with climate dynamics. In this paper, we present an original method to control a simple Sahelian land surface model coupled to a radiative transfer model (RTM) on the basis of ERS wind scatterometer (WSC) observations. In a first step, a sensitivity study is implemented to identify those parameters of the land surface model that can be estimated through the assimilation of WSC data. The assimilation scheme relies on evolution strategies (ES) algorithm that aims at solving the parameter evaluation problem. These algorithms are particularly well suited for complex (nonlinear) inverse problems. The assimilation scheme is applied to several study sites located in the Sahelian mesoscale site of the African Monsoon Multidisciplinary Analysis Project (Gourma region, Mali). The results are compared with ground observations of herbaceous mass. After the WSC data assimilation, the simulated herbaceous mass curves compare well with observations [187 kilogram of dry matter per hectare (kg DM/ha) of average error]. The simulated water fluxes exhibit a behaviour in agreement with ground measurements performed over similar ecosystems during the Hapex Sahel experiment. The accuracy of estimated herbaceous mass and water fluxes resulting from uncertainties on climatic forcing variable is evaluated using a stochastic approach. The average error on the herbaceous mass values mainly depends on the rainfall estimate accuracy and ranges from 139 to 268 kg DM/ha that compares well with a previous study based on the sole inversion of the radiative transfer model. Finally, this study underlines the need for a multispectral assimilation approach to get a better constraint on water fluxes estimation.     

Advances in estimation methods of vegetation water content based on optical remote sensing techniques

Quantitative estimation of vegetation water content(VWC) using optical remote sensing techniques is helpful in forest fire as-sessment,agricultural drought monitoring and crop yield estimation.This paper reviews the research advances of VWC retrieval using spectral reflectance,spectral water index and radiative transfer model(RTM) methods.It also evaluates the reli-ability of VWC estimation using spectral water index from the observation data and the RTM.Focusing on two main definitions of VWC—the fuel mois...     

线性模型的遥感图像时空融合

目的 时空融合是解决当前传感器无法兼顾遥感图像的空间分辨率和时间分辨率的有效方法。在只有一对精细-粗略图像作为先验的条件下,当前的时空融合算法在预测地物变化时并不能取得令人满意的结果。针对这个问题,本文提出一种基于线性模型的遥感图像时空融合算法。方法 使用线性关系表示图像间的时间模型,并假设时间模型与传感器无关。通过分析图像时间变化的客观规律,对模型进行全局和局部约束。此外引入一种多时相的相似像素搜寻策略,更灵活地选取相似像素,消除了传统算法存在的模块效应。结果 在两个数据集上与STARFM（spatial and temporal adaptive reflectance fusion model）算法和FSDAF（flexible spatiotemporal data fusion）算法进行比较,实验结果表明,在主要发生物候变化的第1个数据集,本文方法的相关系数CC（correlation coefficient）分别提升了0.25%和0.28%,峰值信噪比PSNR（peak signal-to-noise ratio）分别提升了0.153 1 dB和1.379 dB,均方根误差RMSE（root mean squared error）分别降低了0.05%和0.69%,结构相似性SSIM（structural similarity）分别提升了0.79%和2.3%。在发生剧烈地物变化的第2个数据集,本文方法的相关系数分别提升了6.64%和3.26%,峰值信噪比分别提升了2.086 0 dB和2.510 7 dB,均方根误差分别降低了1.45%和2.08%,结构相似性分别提升了11.76%和11.2%。结论 本文方法根据时间变化的特点,对时间模型进行优化,同时采用更加灵活的相似像素搜寻策略,收到了很好的效果,提升了融合结果的准确性。     

A feasible method for fractional snow cover mapping in boreal zone based on a reflectance model

A feasible method for mapping the fraction of Snow Covered Area (SCA) in the boreal zone is presented. The method (SCAmod) is based on a semi-empirical model, where three reflectance contributors (wet snow, snow-free ground and forest canopy), interconnected by an effective canopy transmissivity and SCA, constitute the observed reflectance from the target area. Given the reflectance observation, SCA is solved from the model. The predetermined values for the reflectance contributors can be adjusted to an optional wavelength region, which makes SCAmod adaptable to various optical sensors. The effective forest canopy transmissivity specifies the effect of forests on the local reflectance observation; it is estimated using Earth observation data similar to that employed in the actual SCA estimation. This approach enables operational snow mapping for extensive areas, as auxiliary forest data are not needed.Our study area covers 404 000 km², comprising all drainage basins of Finland (with an average area of 60 km²) and some transboundary drainage basins common with Russia, Norway and Sweden. Applying SCAmod to NOAA/AVHRR cloud-free data acquired during melting periods 2001-2003, we estimated the areal fraction of snow cover for all the 5845 basins. The validation against in situ SCA from the Finnish snow course network indicates that with SCAmod, 15% (absolute SCA-units) accuracy for SCA is gained. Good results were also obtained from the validation against snow cover information provided by the Finnish weather station network, for example, 94% of snow-free and fully snow-covered basins were recognized. A general formula for deriving the statistical accuracy of SCA estimates provided by SCAmod is presented, complemented by the results when the AVHRR data are employed.Snow melting in Finland has been operatively monitored with SCAmod in Finnish Environment Institute (SYKE) since year 2001. The SCA estimates have been assimilated to the Finnish national hydrological modelling and forecasting system since 2003, showing a substantial improvement in forecasts.     

黑河综合遥感联合试验研究进展:水文与生态参量遥感反演与估算

介绍了“黑河综合遥感联合试验”在水文和生态变量与参数反演、估算和模型应用方面取得的进展。在水文变量遥感方面,利用车载双偏振多普勒雷达在黑河上游和中游分别开展了高精度降水观测,获取了后向散射系数和极化信息与降水强度之间的定量关系。在综合利用多源观测信息,改进和发展蒸散发估算模型方面取得了实质性的进展。发展了利用K和Ka波段机载微波辐射计数据反演山区积雪深度的方法。针对SAR观测数据反演土壤水分中地表粗糙度的显著干扰,发展了消除粗糙度影响的反演方法。在生态过程遥感参量估算方面,提出了一种基于机载激光雷达和高分辨率光学影像的高精度地物信息分类方法。发展了从高光谱航空遥感提取植被自然光照下的荧光,并与NDVI结合的C3/C4植被分类方法。发展和改进了使用多角度、多光谱观测反演叶面积指数的方法,挖掘了激光雷达在植被垂直结构探测上的潜力,探索了叶面积指数遥感中的尺度转换规律。发展了利用高光谱数据中的荧光信息反演光能利用率的新方法;建立了考虑土壤反射率、冠层结构等因素的光合作用有效辐射比率反演模型;改进了利用遥感估计生态系统生产力的模型。发展了利用高光谱遥感数据提取叶绿素含量和叶绿素荧光强度的方法。     

A coarse-to-fine image registration method based on visual attention model

Image registration is fundamental and crucial to remote sensing. However getting highly accurate registration performance automatically and fast for large-field images consistently is a challenge. As a work around to this problem, we propose a new image registration concept based on visual attention in this paper. This concept employs the advantages of feature-based or area-based methods to improve the precision and efficiency of image registration. The key concept of proposed integrated scheme is to make optimum use of the highly prominent details in the full scene by means of visual attention computational mechanism. To testify the validation, comparisons with other classical methods are carried out on real-world images. The experimental results show that the proposed method can effectively perform on multi-view/multi-temporal remote sensing images with outstanding precision and time saving performance.