New spectral vegetation indices based on the near-infrared shoulder wavelengths for remote detection of grassland phytomass

This article examines the possibility of exploiting ground reflectance in the near-infrared (NIR) for monitoring grassland phytomass on a temporal basis. Three new spectral vegetation indices (infrared slope index, ISI; normalized infrared difference index, NIDI; and normalized difference structural index, NDSI), which are based on the reflectance values in the H25 (863–881 nm) and the H18 (745–751 nm) Chris Proba (mode 5) bands, are proposed. Ground measurements of hyperspectral reflectance and phytomass were made at six grassland sites in the Italian and Austrian mountains using a hand-held spectroradiometer. At full canopy cover, strong saturation was observed for many traditional vegetation indices (normalized difference vegetation index (NDVI), modified simple ratio (MSR), enhanced vegetation index (EVI), enhanced vegetation index 2 (EVI 2), renormalized difference vegetation index (RDVI), wide dynamic range vegetation index (WDRVI)). Conversely, ISI and NDSI were linearly related to grassland phytomass with negligible inter-annual variability. The relationships between both ISI and NDSI and phytomass were however site specific. The WinSail model indicated that this was mostly due to grassland species composition and background reflectance. Further studies are needed to confirm the usefulness of these indices (e.g. using multispectral specific sensors) for monitoring vegetation structural biophysical variables in other ecosystem types and to test these relationships with aircraft and satellite sensors data. For grassland ecosystems, we conclude that ISI and NDSI hold great promise for non-destructively monitoring the temporal variability of grassland phytomass.     

Retrieval of sub-pixel snow cover information in the Himalayan region using medium and coarse resolution remote sensing data

Numerous constrained and unconstrained algorithms have been used to retrieve sub-pixel snow-cover information quantitatively using medium and coarse spatial resolution multispectral images from the Advanced Wide Field Sensor (AWiFS) and Moderate Resolution Imaging Spectrometer (MODIS) sensors over the Himalayan region. Both the methods give slow convergence rates and inaccurate estimation of sub-pixel components analysed using root mean square (RMS) error and image deviation. Multiplicative iterative algorithms such as the Expectation Maximization Maximum Likelihood Method (EMML) and the Image Space Reconstruction Algorithm (ISRA) based on the minimization of least squares and Kullback–Leibler distances have been attempted to compute the endmembers' abundances in unmixing of satellite data. In this paper we discuss the eigenvalues of minimum noise fraction (MNF) transformation bands, data noise removal using MNF transformation and selection of pure endmembers using satellite images. The normalized difference snow index (NDSI) is also estimated using field spectral reflectance results and satellite images in green and shortwave infrared (SWIR) wavelength regions in order to carry out a comparative analysis for its variations with sub-pixel snow cover fractions. The present analysis shows the advantage of iterative over direct (constrained and unconstrained) methods; constraints are easily handled and allow better regularization of the solution for the ill-conditioned cases. Iterative methods are found to be faster compared to those of direct methods and can be used operationally for all resolution data for accurate estimation of sub-pixel snow cover.     

Mapping dry/wet snow cover in the Indian Himalayas using IRS multispectral imagery

Application of remote sensing data has been made to differentiate between dry/wet snows in a glacierized basin. The present study has been carried out in the Gangotri glacier, Himalayas, using IRS-LISS-III multispectral data for the period March-November 2000 and the digital elevation model. The methodology involves conversion of satellite sensor data into reflectance values, computation of NDSI, determination of the boundary between dry/wet snows from spectral response data, and threshold slicing of the image data. The areas of dry snow cover and wet snow cover for different dates of satellite overpasses have been computed. The dry snow area has been compared with non-melting area obtained from the temperature lapse rate method, and the two are found to be in close mutual correspondence (< 15%). It is observed that there occur four water-bearing zones in the glacierized basin: dry snow zone, wet snow zone, exposed glacial ice and moraine-covered glacial ice, each of which possesses unique hydrological characteristics and can be distinguished and mapped from satellite sensor data. It is suggested that input of data on the position and extent of specifically wet snow and exposed glacial ice, which can be directly derived from remote sensing, should improve hydrological simulation of such basins.     

Snow optical properties for different particle shapes with application to snow grain size retrieval and MODIS/CERES radiance comparison over Antarctica

We investigated the single scattering optical properties of snow for different ice particle shapes and degrees of microscopic scale roughness. These optical properties were implemented and tested in a coupled atmosphere-snow radiative transfer model. The modeled surface spectral albedo and radiance distribution were compared with surface measurements. The results show that the reflected radiance and irradiance over snow are sensitive to the snow grain size and its vertical profile. When inhomogeneity of the particle size distribution in the vertical is taken into account, the measured spectral albedo can be matched, regardless of the particle shapes. But this is not true for the modeled radiance distribution, which depends a lot on the particle shape. The usual “equivalent spheres” assumption significantly overestimates forward reflected radiances, and underestimates backscattering radiances, around the principal plane. On average, the aggregate shape assumption has the best agreement with the measured radiances to a mean bias within 2%.The snow optical properties with the aggregate assumption were applied to the retrieval of snow grain size over the Antarctic plateau. The retrieved grain sizes of the top layer showed similar and large seasonal variation in all years, but only small year to year variation. Using the retrieved snow grain sizes, the modeled spectral and broadband radiances showed good agreements with MODIS and CERES measurements over the Antarctic plateau. Except for the MODIS 2.13 μm channel, the mean relative model-observation differences are within few percent. The modeled MODIS radiances using measured surface reflectance at Dome C also showed good agreement in visible channels, where radiation is not sensitive to snow grain size and the measured surface bidirectional reflectance is applicable over the Antarctic plateau. But modeled radiances using local, surface-measured reflectance in the near infrared yielded large errors because of the high sensitivity to the snow grain size, which varies spatially and temporally. The CERES broadband shortwave radiance is moderately sensitive to the snow grain size, comparable to the MODIS 0.86 μm channel. The variation of broadband snow reflectance due to the seasonal variation in snow grain size is about 5% in a year over the Antarctic plateau. CERES broadband radiances simulated with grain sizes retrieved using MODIS are about 2% larger than those observed.     

Estimating leaf nitrogen concentration in heterogeneous crop plants from hyperspectral reflectance

The estimation of leaf nitrogen concentration (LNC) in crop plants is an effective way to optimize nitrogen fertilizer management and to improve crop yield. The objectives of this study were to (1) analyse the spectral features, (2) explore the spectral indices, and (3) investigate a suitable modelling strategy for estimating the LNC of five species of crop plants (rice (Oryza sativa L.), corn (Zea mays L.), tea (Camellia sinensis), gingili (Sesamum indicum), and soybean (Glycine max)) with laboratory-based visible and near-infrared reflectance spectra (300–2500 nm). A total of 61 leaf samples were collected from five species of crop plant, and their LNC and reflectance spectra were measured in laboratories. The reflectance spectra of plants were reduced to 400–2400 and smoothed using the Savitzky–Golay (SG) smoothing method. The normalized band depth (NBD) values of all bands were calculated from SG-smoothed reflectance spectra, and a successive projections algorithm-based multiple linear regression (SPA-MLR) method was then employed to select the spectral features for five species. The SG-smoothed reflectance spectra were resampled using a spacing interval of 10 nm, and normalized difference spectral index (NDSI) and three-band spectral index (TBSI) were calculated for all wavelength combinations between 400 and 2400 nm. The NDSI and TBSI values were employed to calibrate univariate regression models for each crop species. The leave-one-out cross-validation procedure was used to validate the calibrated regression models. Study results showed that the spectral features for LNC estimation varied among different crop species. TBSI performed better than NDSI in estimating LNC in crop plants. The study results indicated that there was no common optimal TBSI and NDSI for different crop species. Therefore, we suggest that, when monitoring LNC in heterogeneous crop plants with hyperspectral reflectance, it might be appropriate to first classify the data set considering different crop species and then calibrate the model for each species. The method proposed in this study requires further testing with the canopy reflectance and hyperspectral images of heterogeneous crop plants.     

Evaluation of a useful method to identify snow‐covered areas under vegetation – comparisons among a newly proposed snow index,normalized difference snow index,and visible reflectance

Objective methods of monitoring snow‐covered areas by optical remote sensing were evaluated using synchronous observations conducted with the passage of the Landsat‐7 satellite over the plains of Niigata prefecture, one of the snowiest regions in Japan. The observations were conducted in the springs of 2002 and 2003. Snow‐covered areas were identified using three methods: (1) visible (red) reflectance, (2) Normalized Difference Snow Index (NDSI) which uses visible and shortwave‐infrared reflectances, and (3) a newly proposed snow index called S3 which uses visible, near‐infrared and shortwave‐infrared reflectances. The Snow‐Cover Ratio (SCR) was defined as the ratio of the number of pixels in snow‐covered areas to the total number of pixels in an image. The threshold value for the three indices used to identify snow‐covered areas was defined as 50% of SCR, which converged to nearly the same value regardless of the images analysed. Under clear conditions, visible (red) reflectance can identify snow‐covered areas accurately if no vegetation is present. NDSI can distinguish snow‐covered areas from mixels (mixed pixels) of snow and vegetation by referring to the Normalized Difference Vegetation Index (NDVI). S3 can distinguish snow‐covered areas from mixels of snow and vegetation without any reference data. S3 is, therefore, more useful than NDSI because it automatically distinguishes snow‐covered areas from mixels of snow and vegetation.     

A satellite snow depth multi-year average derived from SSM/I for the high latitude regions

The hydrological cycle for high latitude regions is inherently linked with the seasonal snowpack. Thus, accurately monitoring the snow depth and the associated aerial coverage are critical issues for monitoring the global climate system. Passive microwave satellite measurements provide an optimal means to monitor the snowpack over the arctic region. While the temporal evolution of snow extent can be observed globally from microwave radiometers, the determination of the corresponding snow depth is more difficult. A dynamic algorithm that accounts for the dependence of the microwave scattering on the snow grain size has been developed to estimate snow depth from Special Sensor Microwave/Imager (SSM/I) brightness temperatures and was validated over the U.S. Great Plains and Western Siberia.

The purpose of this study is to assess the dynamic algorithm performance over the entire high latitude (land) region by computing a snow depth multi-year field for the time period 1987–1995. This multi-year average is compared to the Global Soil Wetness Project-Phase2 (GSWP2) snow depth computed from several state-of-the-art land surface schemes and averaged over the same time period. The multi-year average obtained by the dynamic algorithm is in good agreement with the GSWP2 snow depth field (the correlation coefficient for January is 0.55). The static algorithm, which assumes a constant snow grain size in space and time does not correlate with the GSWP2 snow depth field (the correlation coefficient with GSWP2 data for January is − 0.03), but exhibits a very high anti-correlation with the NCEP average January air temperature field (correlation coefficient − 0.77), the deepest satellite snow pack being located in the coldest regions, where the snow grain size may be significantly larger than the average value used in the static algorithm. The dynamic algorithm performs better over Eurasia (with a correlation coefficient with GSWP2 snow depth equal to 0.65) than over North America (where the correlation coefficient decreases to 0.29).     

吸光性污染物对积雪光谱反射率的影响研究

积雪中存在的吸光性污染物对积雪反射率具有显著的降低作用,进而对能量平衡和气候变化有重要影响。但是,污染浓度变化如何影响积雪反射特征仍然缺乏定量描述和深入探讨。选择新疆富蕴作为典型干旱与半干旱积雪实验区,通过人工控制试验在自然积雪状态下生成不同浓度的污染雪样方,并对积雪及污染物自身的反射率进行测量。在实测数据基础上通过构建线性混合模型定量分析不同浓度条件下污染物对积雪反射率的影响力。研究结果表明:积雪反射率降低与污染浓度呈非线性关系,随着污染浓度增大,单位浓度影响力降低,在350~450 nm波段范围1 813 ppm浓度的单位影响力甚至是9 507 ppm浓度的1.5倍以上;同时发现除了污染浓度,积雪与污染物自身物理特性也是影响反射率变化的重要参数。
     

A new water-resistant snow index for the detection and mapping of snow cover on a global scale

An up-to-date spatio-temporal change analysis of global snow cover is essential for better understanding of climate–hydrological interactions. The normalized difference snow index (NDSI) is a widely used algorithm for the detection and estimation of snow cover. However, NDSI cannot discriminate between snow cover and water bodies without use of an external water mask. A stand-alone methodology for robust detection and mapping of global snow cover is presented by avoiding external dependency on the water mask. A new spectral index called water-resistant snow index (WSI) with the capability of exhibiting significant contrast between snow cover and other cover types, including water bodies, was developed. WSI uses the normalized difference between the value and hue obtained by transforming red, green, and blue, (RGB) colour composite images comprising red, green, and near-infrared bands into a hue, saturation, and value (HSV) colour model. The superiority of WSI over NDSI is confirmed by case studies conducted in major snow regions globally. Snow cover was mapped by considering monthly variation in snow cover and availability of satellite data at the global scale. A snow cover map for the year 2013 was produced at the global scale by applying the random walker algorithm in the WSI image supported by the reference data collected from permanent snow-covered and non-snow-covered areas. The resultant snow-cover map was compared to snow cover estimated by existing maps: MODIS Land Cover Type Product (MCD12Q1 v5.1, 2012), Global Land Cover by National Mapping Organizations (GLCNMO v2.0, 2008), and European Space Agency’s GlobCover 2009. A significant variation in snow cover as estimated by different maps was noted, and was was attributed to methodological differences rather than annual variation in snow cover. The resultant map was also validated with reference data, with 89.46% overall accuracy obtained. The WSI proposed in the research is expected to be suitable for seasonal and annual change analysis of global snow cover.     

Spectroscopy of sediments in the Ganges-Brahmaputra delta: Spectral effects of moisture, grain size and lithology

The lithologic composition and grain size distribution of sediments are primary determinants of their inherent reflectance properties. However, moisture content is also known to have a strong influence on reflectances of soils and sediments. If the effects of sediment composition, grain size and moisture content could be distinguished spectrally, it might be possible to map these properties at synoptic scales using hyperspectral, or perhaps even broadband, remote sensing. Mapping the spatiotemporal distribution of sediment composition and moisture content could provide unique constraints on both the processes by which the sediments are deposited as well as the constraints they may impose on subsequent water flow and sediment transport. The Ganges-Brahmaputra delta (GBD) is formed by the convergence of these two great rivers and is superlative in both size and geologic activity. Sediment redistribution and channel migration associated with the annual floods disrupt the lives of hundreds of thousands of people living on the GBD but is also critical for maintaining the delta area fertile and above sea level. The 30+ year archive of Landsat imagery could provide a basis for spatiotemporal analysis of these fluvial dynamics if sediment properties could be inferred or measured from reflectance spectra. However, before confronting the challenge of broadband detection we must understand the spectral properties of the sediments under more controlled laboratory conditions. Bidirectional reflectance spectroscopy of 109 sediment samples from the GBD yields a spectral mixing space that appears to be structured by variations in moisture content, grain size and possibly lithology. Although the individual Empirical Orthogonal Functions of the Principal Components do not correspond to unique absorption features, clustering within the mixing space is clearly influenced by moisture content and grain size. Laboratory spectra of sediment reflectance measured under varying moisture content yield distinct trajectories through the spectral mixing space for different grain size distributions of sieved sediments. These variations in moisture content account for > 98% of spectral variance observed in these samples. Drying trajectories of coarse, fine and mixed sediments are distinct and suggest that moisture and grain size might be spectrally distinguishable. These results are consistent with Angstrom's hypothesis of moisture-driven spectral absorption but more controlled experiments are necessary to test the hypothesis rigorously.     

雪盖指数法提取积雪范围信息的不确定性研究-以玛纳斯上游地区为例

基于2006年9月10日空间分辨率为30 m的TM影像与DEM数据,通过雪盖指数法自动提取积雪范围与目视解译结果进行对比,以粗糙度为度量,定性、定量分析影响其不确定性的地表覆被和地形因素。结果表明:①当NDSI阈值取0.57~0.72和0.4~0.8时,结果有明显差异,取0.57~0.72时漏分像元比0.4~0.8稍多,但是误分像元大幅减少;②同处于阴影区裸地的光谱曲线与积雪的光谱曲线相似,造成阴影区的积雪与裸地不能正确区分,此外处于阴影区域的植被由于反射率较低,使其NDSI刚好在阈值范围内,被误分为积雪;③半阴坡雪盖指数法提取积雪的不确定性最小,而阳坡、半阳坡雪盖指数法提取积雪的不确定性最大;④雪盖指数法提取积雪的不确定性随着坡度的增加呈下降趋势,即坡度越大不确定性越小。     

Estimating fractional snow cover from MODIS using the normalized difference snow index

Snow-cover information is important for a wide variety of scientific studies, water supply and management applications. The NASA Earth Observing System (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS) provides improved capabilities to observe snow cover from space and has been successfully using a normalized difference snow index (NDSI), along with threshold tests, to provide global, automated binary maps of snow cover. The NDSI is a spectral band ratio that takes advantage of the spectral differences of snow in short-wave infrared and visible MODIS spectral bands to identify snow versus other features in a scene. This study has evaluated whether there is a “signal” in the NDSI that could be used to estimate the fraction of snow within a 500 m MODIS pixel and thereby enhance the use of the NDSI approach in monitoring snow cover. Using Landsat 30-m observations as “ground truth,” the percentage of snow cover was calculated for 500-m cells. Then a regression relationship between 500-m NDSI observations and fractional snow cover was developed over three different snow-covered regions and tested over other areas. The overall results indicate that the relationship between fractional snow cover and NDSI is reasonably robust when applied locally and over large areas like North America. The relationship offers advantages relative to other published fractional snow cover algorithms developed for global-scale use with MODIS. This study indicates that the fraction of snow cover within a MODIS pixel using this approach can be provided with a mean absolute error less than 0.1 over the range from 0.0 to 1.0 in fractional snow cover.     

Fractional snow cover mapping through artificial neural network analysis of MODIS surface reflectance

Accurate areal measurements of snow cover extent are important for hydrological and climate modeling. The traditional method of mapping snow cover is binary where a pixel is considered either snow-covered or snow-free. Fractional snow cover (FSC) mapping can achieve a more precise estimate of areal snow cover extent by estimating the fraction of a pixel that is snow-covered. The most common snow fraction methods applied to Moderate Resolution Imaging Spectroradiometer (MODIS) images have been spectral unmixing and an empirical Normalized Difference Snow Index (NDSI). Machine learning is an alternative for estimating FSC as artificial neural networks (ANNs) have been successfully used for estimating the subpixel abundances of other surfaces. The advantages of ANNs are that they can easily incorporate auxiliary information such as land cover type and are capable of learning nonlinear relationships between surface reflectance and snow fraction. ANNs are especially applicable to mapping snow cover extent in forested areas where spatial mixing of surface components is nonlinear. This study developed a multilayer feed-forward ANN trained through backpropagation to estimate FSC using MODIS surface reflectance, NDSI, Normalized Difference Vegetation Index (NDVI) and land cover as inputs. The ANN was trained and validated with higher spatial-resolution FSC maps derived from Landsat Enhanced Thematic Mapper Plus (ETM+) binary snow cover maps. Testing of the network was accomplished over training and independent test areas. The developed network performed adequately with RMSE of 12% over training areas and slightly less accurately over the independent test scenes with RMSE of 14%. The developed ANN also compared favorably to the standard MODIS FSC product. The study also presents a comprehensive validation of the standard MODIS snow fraction product whose performance was found to be similar to that of the ANN.     

Normalized difference spectral indices for estimating photosynthetic efficiency and capacity at a canopy scale derived from hyperspectral and CO2 flux measurements in rice

We explored simple and useful spectral indices for estimating photosynthetic variables (radiation use efficiency and photosynthetic capacity) at a canopy scale based on seasonal measurements of hyperspectral reflectance, ecosystem CO₂ flux, and plant and micrometeorological variables. An experimental study was conducted over the simple and homogenous ecosystem of an irrigated rice field. Photosynthetically active radiation absorbed by the canopy (APAR), the canopy absorptivity of APAR (fAPAR), net ecosystem exchange of CO₂ (NEE_CO2) gross primary productivity (GPP), photosynthetic capacity at the saturating APAR (P_max), and three parameters of radiation use efficiency (ε_N: NEE_CO2/APAR; ε_G: GPP/APAR; φ: quantum efficiency) were derived from the data set. Based on the statistical analysis of relationships between these ecophysiological variables and reflectance indicators such as normalized difference spectral indices (NDSI[i,j]) using all combinations of two wavelengths (i and j nm), we found several new indices that would were more effective than conventional spectral indices such as photochemical reflectance index (PRI) and normalized difference vegetation index (NDVI = NDSI[near-infrared, red]). ε_G was correlated well with NDSI[710, 410], NDSI[710, 520], and NDSI[530, 550] derived from nadir measurements. φ was best correlated with NDSI[450, 1330]. NDSI[550, 410] and NDSI[720, 420] had a consistent linear relationships with fAPAR throughout the growing season, whereas conventional indices such as NDVI showed very different relationships before and after heading. Off-nadir measurements were more closely related to the efficiency parameters than nadir measurements. Our results provide useful insights for assessing plant productivity and ecosystem CO₂ exchange, using a wide range of available spectral data as well as useful information for designing future sensors for ecosystem observations.     

Sub‐pixel reflectance unmixing in estimating vegetation water content and dry biomass of corn and soybeans cropland using normalized difference water index (NDWI) from satellites

Estimating vegetation cover, water content, and dry biomass from space plays a significant role in a variety of scientific fields including drought monitoring, climate modelling, and agricultural prediction. However, getting accurate and consistent measurements of vegetation is complicated very often by the contamination of the remote sensing signal by the atmosphere and soil reflectance variations at the surface. This study used Landsat TM/ETM+ and MODIS data to investigate how sub‐pixel atmospheric and soil reflectance contamination can be removed from the remotely sensed vegetation growth signals. The sensitivity of spectral bands and vegetation indices to such contamination was evaluated. Combining the strengths of atmospheric models and empirical approaches, a hybrid atmospheric correction scheme was proposed. With simplicity, it can achieve reasonable accuracy in comparison with the 6S model. Insufficient vegetation coverage information and poor evaluation of fractional sub‐pixel bare soil reflectance are major difficulties in sub‐pixel soil reflectance unmixing. Vegetation coverage was estimated by the Normalized Difference Water Index (NDWI). Sub‐pixel soil reflectance was approximated from the nearest bare soil pixel. A linear reflectance mixture model was employed to unmix sub‐pixel soil reflectance from vegetation reflectance. Without sub‐pixel reflectance contamination, results demonstrate the true linkage between the growth of sub‐pixel vegetation and the corresponding change in satellite spectral signals. Results suggest that the sub‐pixel soil reflectance contamination is particularly high when vegetation coverage is low. After unmixing, the visible and shortwave infrared reflectances decrease and the near‐infrared reflectances increase. Vegetation water content and dry biomass were estimated using the unmixed vegetation indices. Superior to the NDVI and the other NDWIs, the SWIR (1650 nm) band‐based NDWI showed the best overall performance. The use of the NIR (1240 nm), which is a unique band of MODIS, was also discussed.     

Semi-empirical approach for estimating broadband albedo of snow

Snow is a medium that exhibits highly anisotropic reflectance throughout the solar spectrum. The anisotropic nature of snow shows more variability in snow metamorphic processes for wavelengths beyond 1.0 μm than in the visible spectrum. This behavior poses challenges for the development of a model that can retrieve broadband albedo from reflectance measurements throughout the snow season. In this paper, a semi-empirical model is presented to estimate near infrared (0.8-2.5 μm) albedo of snow. This model estimates spectral albedo at a wavelength of 1.240 μm using only three variables: solar zenith angle, scattering angle and measured reflectance, which is used to retrieve near infrared albedo. To form a base for such a model, quantification of reflectance patterns and variability in varying snow condition, i.e. snow grain size, and sun-sensor geometry are prerequisite. In this study the DIScrete Ordinate Radiative Transfer (DISORT) model is used to simulate bi-directional reflectance. The performance of the developed model is evaluated by using DISORT simulated spectral albedo for various snow grain sizes and solar zenith angles, as well as the Moderate Resolution Imaging Spectroradiometer (MODIS) and in-situ measurements. The developed model is shown to be capable of estimating spectral albedo at 1.240 μm with acceptable accuracy. The mean error (ME), mean absolute error (MAE), and root mean squared error (RMSE) in the estimates are found to be 0.053, 0.055 and 0.064, respectively, for a wide range of sun-sensor geometries and snow grain sizes. The model shows better accuracy for spectral albedo estimates than for those computed using the Lambertian reflectance assumption for snow, reducing the error in the range and standard deviation by 75% and 65%, respectively. Applying the model to MODIS, the retrieved albedo is found to be in good quantitative agreement (r = 0.82) with in-situ measurements. These improvements in albedo estimation should allow more accurate use of remote sensing measurements in climate and hydrological models.     

东北地区MODIS亚像元积雪覆盖率反演及验证

以中巴资源卫星数据作为地面“真值”影像,根据东北地区地理环境与气候特点对Salomoson亚像元积雪覆盖率模型参数进行修正,反演东北地区MODIS像元积雪覆盖率,并用不同方案对模型的稳定性和精度进行分析。研究结果表明,经修正后的Salomoson亚像元积雪覆盖率反演模型对不同地貌--景观单元具有稳定性,其中较小的波动源于积雪物理性质差异、大气效应、积雪影像分类误差及影像配准误差。在东北平原区,NDSI值在0.52~0.65时,模型反演精度高,但反演雪盖率总体偏低,主要是由NDSI基于对波段反射率的非线性转换引起的;雪盖率高估的像元主要分布在城区外围以及农村居民点,而覆盖城区、乡、镇以及居民点之间道路的像元雪盖率误差小,其原因是人类活动频率影响像元内积雪组分与非积雪组分的光谱特性的差异程度。与MODIS雪产品进行对比分析,积雪覆盖率提供较传统雪盖制图更加丰富的信息,然而对林区冠层下积雪覆盖二者均未给出准确估计。     

Reflectance characteristics of major land surfaces in slash‐and‐burn ecosystems in Laos

The in situ reflectance spectra in the 400–2500 nm wavelength region were obtained using a portable radiometer over a range of land surfaces including burnt fields, crop canopies, and fallow vegetation at different community ages in slash‐and‐burn ecosystems in Laos. Normalized difference spectral indices (NDSI[i,j] = [R_j ?R_i ]/[R_j +R_i ]) were derived using reflectance R_i and R_j at i and j nm wavelengths for a thorough combination (14 706 pairs) of 172 wavebands (10‐nm resolution). The separability of burnt fields from dry/senescent vegetation was highest at NDSI[1090, 2390], whereas it was highly discriminated from fallow and crop vegetation by NDSI[760, 1970]. NDSIs using 730–760 nm with 1970–1990 nm showed the largest differences between dry/senescent vegetation and fallow or crop vegetation. None of the NDSIs was useful in discriminating between fallow and crop vegetations or between slashed/senescent vegetation and crop residue/abandoned field. Community age and biomass of fallow vegetation could not be inferred directly from spectral information, since no NDSIs showed any significant differences among crop and fallow vegetation that had a large variability in the amount of green vegetation. Results would provide useful information for various applications of optical satellite sensor images especially in assessments of land use or post‐fire regeneration of vegetation.     

Retrieval of subpixel snow-covered area and grain size from imaging spectrometer data

We describe and validate an automated model that retrieves subpixel snow-covered area and effective grain size from Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data. The model analyzes multiple endmember spectral mixtures with a spectral library of snow, vegetation, rock, and soil. We derive snow spectral endmembers of varying grain size from a radiative transfer model; spectra for vegetation, rock, and soil were collected in the field and laboratory. For three AVIRIS images of Mammoth Mountain, California that span common snow conditions for winter through spring, we validate the estimates of snow-covered area with fine-resolution aerial photographs and validate the estimates of grain size with stereological analysis of snow samples collected within 2 h of the AVIRIS overpasses. The RMS error for snow-covered area retrieved from AVIRIS for the combined set of three images was 4%. The RMS error for snow grain size retrieved from a 3×3 window of AVIRIS data for the combined set of three images is 48 μm, and the RMS error for reflectance integrated over the solar spectrum and over all hemispherical reflectance angles is 0.018.