MODIS tasselled cap: land cover characteristics expressed through transformed MODIS data

The tasselled cap concept is extended to Moderate Resolution Imaging Spectroradiometer (MODIS) Nadir BRDF‐Adjusted Reflectance (NBAR, MOD43) data. The transformation is based on a rigid rotation of principal component axes (PCAs) derived from a global sample spanning one full year of NBAR 16‐day composites. To provide a standard for MODIS tasselled cap axes, we recommend an orientation in MODIS spectral band space as similar as possible to the orientation of the Landsat Thematic Mapper (TM) tasselled cap axes. To achieve this we first transformed our global sample of MODIS NBAR reflectance values to TM tasselled cap values using the existing TM transformation, then used an existing algorithm (Procrustes) to compute the transformation that minimizes the mean square difference between the TM transformed NBAR values and NBAR PCA values. This transformation can then be used as a standard to rotate the MODIS NBAR PCA axes into a new MODIS Kauth–Thomas (KT) orientation. Global land cover patterns in tasselled cap space are demonstrated graphically by linking the global sample with several other products, including the MODIS Land Cover product (MOD12) and the MODIS Vegetation Continuous Fields product (MOD44). Patterns seen at this global scale agree with previous explorations of TM tasselled cap space, but are shown here in greater detail with a globally representative sample. Temporal trends of eight smaller‐scale BigFoot Project (www.fsl.orst.edu/larse/bigfoot) sites were also examined, confirming the spectral shifts in tasselled cap space related to phenology.     

Multi-temporal MODIS-Landsat data fusion for relative radiometric normalization, gap filling, and prediction of Landsat data

A semi-physical fusion approach that uses the MODIS BRDF/Albedo land surface characterization product and Landsat ETM+ data to predict ETM+ reflectance on the same, an antecedent, or subsequent date is presented. The method may be used for ETM+ cloud/cloud shadow and SLC-off gap filling and for relative radiometric normalization. It is demonstrated over three study sites, one in Africa and two in the U.S. (Oregon and Idaho) that were selected to encompass a range of land cover land use types and temporal variations in solar illumination, land cover, land use, and phenology. Specifically, the 30 m ETM+ spectral reflectance is predicted for a desired date as the product of observed ETM+ reflectance and the ratio of the 500 m surface reflectance modeled using the MODIS BRDF spectral model parameters and the sun-sensor geometry on the predicted and observed Landsat dates. The difference between the predicted and observed ETM+ reflectance (prediction residual) is compared with the difference between the ETM+ reflectance observed on the two dates (temporal residual) and with respect to the MODIS BRDF model parameter quality. For all three scenes, and all but the shortest wavelength band, the mean prediction residual is smaller than the mean temporal residual, by up to a factor of three. The accuracy is typically higher at ETM+ pixel locations where the MODIS BRDF model parameters are derived using the best quality inversions. The method is most accurate for the ETM+ near-infrared (NIR) band; mean NIR prediction residuals are 9%, 12% and 14% of the mean NIR scene reflectance of the African, Oregon and Idaho sites respectively. The developed fusion approach may be applied to any high spatial resolution satellite data, does not require any tuning parameters and so may be automated, is applied on a per-pixel basis and is unaffected by the presence of missing or contaminated neighboring Landsat pixels, accommodates for temporal variations due to surface changes (e.g., phenological, land cover/land use variations) observable at the 500 m MODIS BRDF/Albedo product resolution, and allows for future improvements through BRDF model refinement and error assessment.     

Daily MODIS 500 m reflectance anisotropy direct broadcast (DB) products for monitoring vegetation phenology dynamics

Land surface vegetation phenology is an efficient bio-indicator for monitoring ecosystem variation in response to changes in climatic factors. The primary objective of the current article is to examine the utility of the daily MODIS 500 m reflectance anisotropy direct broadcast (DB) product for monitoring the evolution of vegetation phenological trends over selected crop, orchard, and forest regions. Although numerous model-fitted satellite data have been widely used to assess the spatio-temporal distribution of land surface phenological patterns to understand phenological process and phenomena, current efforts to investigate the details of phenological trends, especially for natural phenological variations that occur on short time scales, are less well served by remote sensing challenges and lack of anisotropy correction in satellite data sources. The daily MODIS 500 m reflectance anisotropy product is employed to retrieve daily vegetation indices (VI) of a 1 year period for an almond orchard in California and for a winter wheat field in northeast China, as well as a 2 year period for a deciduous forest region in New Hampshire, USA. Compared with the ground records from these regions, the VI trajectories derived from the cloud-free and atmospherically corrected MODIS Nadir BRDF (bidirectional reflectance distribution function) adjusted reflectance (NBAR) capture not only the detailed footprint and principal attributes of the phenological events (such as flowering and blooming) but also the substantial inter-annual variability. This study demonstrates the utility of the daily 500 m MODIS reflectance anisotropy DB product to provide daily VI for monitoring and detecting changes of the natural vegetation phenology as exemplified by study regions comprising winter wheat, almond trees, and deciduous forest.     

Differentiation of semi‐arid vegetation types based on multi‐angular observations from MISR and MODIS

Mapping accurately vegetation type is one of the main challenges for monitoring arid and semi‐arid grasslands with remote sensing. The multi‐angle approach has been demonstrated to be useful for mapping vegetation types in deserts. The current paper presents a study on the use of directional reflectance derived from two sensor systems, using two different models to analyse the data and two different classifiers as a means of mapping vegetation types. The multiangle imaging spectroradiometer (MISR) and the moderate resolution imaging spectroradiometer (MODIS) provide multi‐spectral and angular, off‐nadir observations. In this study, we demonstrate that reflectance from MISR observations and reflectance anisotropy patterns derived from MODIS observations are capable of working together to increase classification accuracy. The patterns are described by parameters of the modified Rahman–Pinty–Verstraete and the RossThin‐LiSparseMODIS bidirectional reflectance distribution function (BRDF) models. The anisotropy patterns derived from MODIS observations are highly complementary to reflectance derived from radiances observed by MISR. Support vector machine algorithms exploit the information carried by the same data sets more effectively than the maximum likelihood classifier.     

Accuracy assessment of the MODIS 16-day albedo product for snow: comparisons with Greenland in situ measurements

The accuracy of the Moderate Resolution Imaging Spectroradiometer (MODIS) 16-day albedo product (MOD43) is assessed using ground-based albedo observations from automatic weather stations (AWS) over spatially homogeneous snow and semihomogeneous ice-covered surfaces on the Greenland ice sheet. Data from 16 AWS locations, spanning the years 2000-2003, were used for this assessment. In situ reflected shortwave data were corrected for a systematic positive spectral sensitivity bias of between 0.01 and 0.09 on a site-by-site basis using precise optical black radiometer data. Results indicate that the MOD43 albedo product retrieves snow albedo with an average root mean square error (RMSE) of ±0.07 as compared to the station measurements, which have ±0.035 RMSE uncertainty. If we eliminate all satellite retrievals that rely on the backup algorithm and consider only the highest quality results from the primary bidirectional reflectance distribution function (BRDF) algorithm, the MODIS albedo RMSE is ±0.04, slightly larger than the in situ measurement uncertainty. There is general agreement between MODIS and in situ observations for albedo <0.7, while near the upper limit, a −0.05 MODIS albedo bias is evident from the scatter of the 16-site composite.     

Validation of the MODIS reflectance product under UK conditions

Surface reflectance obtained from remote-sensing data is the main input to almost all remote-sensing applications. The availability and special characteristics of Moderate Resolution Imaging Spectroradiometer (MODIS) products have led to their use worldwide. Validation of the MODIS reflectance product is then crucial to provid information on uncertainty in the reflectance data, and in other MODIS products and in the applied surface–atmosphere models. Compact Airborne Spectrographic Imager (CASI) and Système Pour l'Observation de la Terre (SPOT) data, collected during the Network for Calibration and Validation in Earth Observation (NCAVEO) 2006 Field Campaign, were applied to validate daily MODIS reflectance data over a site in the southern UK. The difference in the view geometry of at-nadir CASI and SPOT data and off-nadir MODIS data was dealt with using a semi-empirical bidirectional reflectance distribution function (BRDF) model. The validation results showed that for our particular study site, the absolute errors in the MODIS reflectance product were too large to allow the albedo data to be used directly in climate models. The errors were mainly related to the uncertainties in the MODIS atmospheric variables, the BRDF model, and undetected clouds and cloud shadows. More generally, the study highlights the extreme difficulty of achieving pixel-level validation of coarse spatial resolution satellite sensor data in an environment in which the atmosphere is constantly changing, and in which the landscape is characterized by high space–time heterogeneity.     

A method to convert AVHRR Normalized Difference Vegetation Index time series to a standard viewing and illumination geometry

The bi-directional reflectance distribution function (BRDF) alters the seasonal and inter-annual variations exhibited in Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) data and this hampers the detection and, consequently, the interpretation of temporal variations in land-surface vegetation. The magnitude and sign of bi-directional effects in commonly used AVHRR data sets depend on land-surface properties, atmospheric composition and the type of atmospheric correction that is applied to the data. We develop an approach to estimate BRDF effects in AVHRR NDVI time series using the Moderate Resolution Imaging Spectrometer (MODIS) BRDF kernels and subsequently adjust NDVI time series to a standard illumination and viewing geometry. The approach is tested on NDVI time series that are simulated for representative AVHRR viewing and illumination geometry. These time series are simulated with a canopy radiative transfer model coupled to an atmospheric radiative transfer model for four different land cover types—tropical forest, boreal forest, temperate forest and grassland - and five different atmospheric conditions - turbid and clear top-of-atmosphere, turbid and clear top-of-atmosphere with a correction for ozone absorption and Rayleigh scattering applied (Pathfinder AVHRR Land data) and ground-observations (fully corrected for atmospheric effects). The simulations indicate that the timing of key phenological stages, such as start and end of growing season and time of maximum greenness, is affected by BRDF effects. Moreover, BRDF effects vary with latitude and season and increase over the time of operation of subsequent NOAA satellites because of orbital drift. Application of the MODIS kernels on simulated NVDI data results in a 50% to 85% reduction of BRDF effects. When applied to the global 18-year global Normalized Difference Vegetation Index (NDVI) Pathfinder data we find BRDF effects similar in magnitude to those in the simulations. Our analysis of the global data shows that BRDF effects are especially large in high latitudes; here we find that in at least 20% of the data BRDF errors are too large for accurate detection of seasonal and interannual variability. These large BRDF errors tend to compensate, however, when averaged over latitude.     

Evaluation of the MODIS (MOD10A1) daily snow albedo product over the Greenland ice sheet

This study evaluates the performance of the beta-test MODIS (MOD10A1) daily albedo product using in situ data collected in Greenland during summer 2004. Results indicate the beta-test product tracks the general seasonal variability in albedo but exhibits significant more temporal variability than observed at the stations. This may indicate problems with the cloud detection algorithm, and/or failure of the BRDF model to adequately model the bidirectional reflectance of snow. Comparisons with in situ observations at five automatic weather stations in Greenland indicate an overall RMSE of 0.067 for the Terra instrument and an RMSE of 0.075 on Aqua. The Terra-retrieved-albedo are slightly better correlated with the in situ data than the Aqua retrievals (r = 0.79 versus r = 0.77). Comparisons were also made between the MODIS daily albedo product and the MODIS 16-day albedo product (MOD43B3). Results indicate general correspondence between the two products, with better agreement found using the Terra-retrieved-albedo than the Aqua-retrieved albedo. The reason for the differences in albedo between the Aqua and Terra satellites remains unclear. At the stations examined, both the Terra and Aqua retrievals were made at nearly the same time of the day and therefore the differences in albedo between the satellites cannot be explained by differences in solar illumination. Finally, the albedo derived using MODIS data and the direct estimation algorithm (DEA) was also compared with 2004 Greenland in situ data. Results from this comparison suggest that the DEA performs well as long as the solar zenith angle of the observation is not greater than 70°.     

An assessment of photosynthetic light use efficiency from space: Modeling the atmospheric and directional impacts on PRI reflectance

Estimation of photosynthetic light use efficiency (ε) from satellite observations is an important component of climate change research. The photochemical reflectance index, a narrow waveband index based on the reflectance at 531 and 570 nm, allows sampling of the photosynthetic activity of leaves; upscaling of these measurements to landscape and global scales, however, remains challenging. Only a few studies have used spaceborne observations of PRI so far, and research has largely focused on the MODIS sensor. Its daily global coverage and the capacity to detect a narrow reflectance band at 531 nm make it the best available choice for sensing ε from space. Previous results however, have identified a number of key issues with MODIS-based observations of PRI. First, the differences between the footprint of eddy covariance (EC) measurements and the MODIS footprint, which is determined by the sensor's observation geometry make a direct comparison between both data sources challenging and second, the PRI reflectance bands are affected by atmospheric scattering effects confounding the existing physiological signal. In this study we introduce a new approach for upscaling EC based ε measurements to MODIS. First, EC-measured ε values were “translated” into a tower-level optical PRI signal using AMSPEC, an automated multi-angular, tower-based spectroradiometer instrument. AMSPEC enabled us to adjust tower-measured PRI values to the individual viewing geometry of each MODIS overpass. Second, MODIS data were atmospherically corrected using a Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm, which uses a time series approach and an image-based rather than pixel-based processing for simultaneous retrievals of atmospheric aerosol and surface bidirectional reflectance (BRDF). Using this approach, we found a strong relationship between tower-based and spaceborne reflectance measurements (r² = 0.74, p < 0.01) throughout the vegetation period of 2006. Swath (non-gridded) observations yielded stronger correlations than gridded data (r² = 0.58, p < 0.01) both of which included forward and backscatter observations. Spaceborne PRI values were strongly related to canopy shadow fractions and varied with different levels of ε. We conclude that MAIAC-corrected MODIS observations were able to track the site-level physiological changes from space throughout the observation period.     

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。     

MODIS spectral signals at a flux tower site: Relationships with high-resolution data, and CO2 flux and light use efficiency measurements

In this study, spectral indices were calculated from single date HyMap (3 m; 126 bands), Hyperion (30 m; 242 bands), ASTER (15/30 m; 9 bands), and a time series of MODIS nadir BRDF-adjusted reflectance (NBAR; 1 km, 7 bands) for a study area surrounding the Tumbarumba flux tower site in eastern Australia. The study involved: a) the calculation of a range of physiologically-based vegetation indices from ASTER, HyMap, Hyperion and MOD43B NBAR imagery over the flux tower site; b) comparison across scales between HyMap, Hyperion and MODIS for the normalized difference water index (NDWI) and the Red-Green ratio; c) analysis of relationships between tower-based flux and light use efficiency (LUE) measurements and seasonal and climatic constraints on growth; and d) examination of relationships between fluxes, LUE and time series of NDVI, NDWI and Red-Green ratio. Strong seasonal patterns of variation were observed in NDWI and Red/Green ratio from MODIS NBAR. Correlations between fine (3 and 30 m) and coarse (1 km) scale indices for a small region around the flux tower site were moderately good for Red/Green ratio, but poor for NDWI. Hymap NDWI values for the understorey canopy were much lower than values for the tree canopy. MODIS NDWI was negatively correlated with CO₂ fluxes during warm and cool seasons. The correlation indicated that surface reflectance, affected by a spectrally bright grassland understorey canopy, was decoupled from growth of trees with access to deep soil moisture. The application of physiologically-based indices at earth observation scale requires careful attention to applicability of band configurations, contribution of vegetation components to reflectance signals, mechanistic relationships between biochemical processes and spectral indexes, and incorporation of ancillary information into any analysis.     

Daily MODIS products for analyzing early season vegetation dynamics across the North Slope of Alaska

Monitoring the growth and distribution of Arctic tundra vegetation is important for understanding changes in early growing season conditions in Arctic ecosystems in response to a warming climate. The primary objective of this study is to examine the utility of computed Daily Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) products relative to 16-day maximum value composite (MVC) datasets for observing early season green-up dynamics of Arctic tundra vegetation across the North Slope of Alaska. Greening in the Arctic typically occurs shortly after snowmelt and can potentially be captured by using satellite observations that are available on a daily basis. Daily MODIS Snow Cover products were employed to retrieve dates of complete snowmelt (DOCS) for 2003-2005 for pixels that were cloud free at the time of complete snowmelt. Given the sparseness of cloud-free observations in both space and time, early season NDVI trajectories for cloud-free pixels were derived using daily MODIS data based on two approaches: a chronosequence (temporally continuous but aspatial) and a pixel trajectory (temporally discontinuous but spatial explicit) approach.On average during the three-year period, 12.5% of the North Slope region was cloud free at the time of complete snowmelt and a majority of these cloud-free pixels (65%) were associated with the Coastal Plain province. In contrast, the Foothills region was relatively less cloudy from the time following complete snowmelt until peak greenness (56%) than the Coastal Plain province (61%). As a result, vegetation communities that lie mostly in the Foothills province such as shrub tundra and moist acidic tundra classes had more cloud-free observations available to characterize NDVI trajectories using the pixel trajectory approach. Complete snowmelt in the North Slope generally occurred between day of year (DOY) 140 and 170 over the three years with areas covered by the shrub tundra vegetation community (Foothills province) experiencing snowmelt first in all three years with mean DOCS ranging from DOY 148 in 2004 to DOY 158 in 2003. For approximately two weeks following complete snowmelt (Phase I, a period of rapid NDVI increase), the Daily NDVI derived trajectories were substantially different from the MVC NDVI trajectories. Early season integrated NDVI (ESINDVI) values computed for Phase I were 7% higher using the Daily NDVI approaches relative to those derived from the MVC MODIS data for the North Slope region. Following this initial period, until peak greenness (Phase 2, a period of gradual NDVI increase), the Daily and MVC trajectories were similar in shape and magnitude. This study demonstrates the utility of the Daily MODIS Snow product for assessing cloud cover and snowmelt patterns and Daily MODIS NDVI data for observing and detecting sharp and rapid changes in early season vegetation phenology as seen during Phase I.     

Assessment of biases in MODIS surface reflectance due to Lambertian approximation

Using MODIS data and the AERONET-based Surface Reflectance Validation Network (ASRVN), this work studies errors of MODIS atmospheric correction caused by the Lambertian approximation. On one hand, this approximation greatly simplifies the radiative transfer model, reduces the size of the look-up tables, and makes operational algorithm faster. On the other hand, uncompensated atmospheric scattering caused by Lambertian model systematically biases the results. For example, for a typical bowl-shaped bidirectional reflectance distribution function (BRDF), the derived reflectance is underestimated at high solar or view zenith angles, where BRDF is high, and is overestimated at low zenith angles where BRDF is low. The magnitude of biases grows with the amount of scattering in the atmosphere, i.e., at shorter wavelengths and at higher aerosol concentration. The slope of regression of Lambertian surface reflectance vs. ASRVN bidirectional reflectance factor (BRF) is about 0.85 in the red and 0.6 in the green bands. This error propagates into the MODIS BRDF/albedo algorithm, slightly reducing the magnitude of overall reflectance and anisotropy of BRDF. This results in a small negative bias of spectral surface albedo. An assessment for the GSFC (Greenbelt, USA) validation site shows the albedo reduction by 0.004 in the near infrared, 0.005 in the red, and 0.008 in the green MODIS bands.     

A multisensor fusion approach to improve LAI time series

High-quality and gap-free satellite time series are required for reliable terrestrial monitoring. Moderate resolution sensors provide continuous observations at global scale for monitoring spatial and temporal variations of land surface characteristics. However, the full potential of remote sensing systems is often hampered by poor quality or missing data caused by clouds, aerosols, snow cover, algorithms and instrumentation problems. A multisensor fusion approach is here proposed to improve the spatio-temporal continuity, consistency and accuracy of current satellite products. It is based on the use of neural networks, gap filling and temporal smoothing techniques. It is applicable to any optical sensor and satellite product. In this study, the potential of this technique was demonstrated for leaf area index (LAI) product based on MODIS and VEGETATION reflectance data. The FUSION product showed an overall good agreement with the original MODIS LAI product but exhibited a reduction of 90% of the missing LAI values with an improved monitoring of vegetation dynamics, temporal smoothness, and better agreement with ground measurements.     

Quality assessment of Landsat surface reflectance products using MODIS data

Surface reflectance adjusted for atmospheric effects is a primary input for land cover change detection and for developing many higher level surface geophysical parameters. With the development of automated atmospheric correction algorithms, it is now feasible to produce large quantities of surface reflectance products using Landsat images. Validation of these products requires in situ measurements, which either do not exist or are difficult to obtain for most Landsat images. The surface reflectance products derived using data acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS), however, have been validated more comprehensively. Because the MODIS on the Terra platform and the Landsat 7 are only half an hour apart following the same orbit, and each of the 6 Landsat spectral bands overlaps with a MODIS band, good agreements between MODIS and Landsat surface reflectance values can be considered indicators of the reliability of the Landsat products, while disagreements may suggest potential quality problems that need to be further investigated. Here we develop a system called Landsat-MODIS Consistency Checking System (LMCCS). This system automatically matches Landsat data with MODIS observations acquired on the same date over the same locations and uses them to calculate a set of agreement metrics. To maximize its portability, Java and open-source libraries were used in developing this system, and object-oriented programming (OOP) principles were followed to make it more flexible for future expansion. As a highly automated system designed to run as a stand-alone package or as a component of other Landsat data processing systems, this system can be used to assess the quality of essentially every Landsat surface reflectance image where spatially and temporally matching MODIS data are available. The effectiveness of this system was demonstrated using it to assess preliminary surface reflectance products derived using the Global Land Survey (GLS) Landsat images for the 2000 epoch. As surface reflectance likely will be a standard product for future Landsat missions, the approach developed in this study can be adapted as an operational quality assessment system for those missions.     

MODIS NDVI与MODIS EVI的比较分析

MODIS NDVI与MODIS EVI是目前应用比较广泛的植被指数,MODIS EVI是对NDVI的发展和延续,从植被指数计算公式和合成方法两方面做了改进。具体表现在:避免了MODIS NDVI在植被高覆盖区易饱和的问题,考虑了土壤背景对植被指数的影响,对气溶胶等残留做了进一步校正,采用BRDF/CV-MVC合成方法保证了合成采用最佳像元。EVI时间序列相较于NDVI时间序列季节性更明显,能够更好地反映高植被覆盖区的季节性变化特征,并且很少有突降现象,时间序列曲线较平滑。EVI的这些优势为高覆盖植被物候特征的季节性变化监测提供了新的思路。     

The importance of data type,laser spot density and modelling method for vegetation height mapping in continental China

Vegetation height not only has great significance in the field of ecology but also offers a useful contribution to detailed land cover classification. The first vegetation height map was acquired in this study using the ice, cloud, and land elevation satellite /geosciences laser altimeter system (ICESat/GLAS) and other multisource remote sensing data, such as moderate-resolution imaging spectroradiometer (MODIS) tree cover products, leaf area index (LAI) products, Nadir bidirectional reflectance distribution function (BRDF)-adjusted reflectance (NBAR), climatic variables, and topographic indices. We mainly discuss the importance of data type, density of laser spot and modelling method in the generation of this vegetation height map in continental China. It was found that (1) a higher density of laser spot could improve the reliability of modelling in mountainous areas covered by a wide range of forest and shrub land; (2) in terms of the importance of input variables, in the random forest regression modelling, the most important ones are elevation, slope, mean air temperature, temperature variance, precipitation, precipitation variance, and NBAR; (3) when modelling using 50 ecozones covering the whole of continental China, the model showed a good performance with an accuracy of root mean square error (RMSE), correlation coefficient (r), index of agreement (d), and mean absolute error (MAE) at 5.7, 0.7, 0.8, and 3.8 m, respectively. A visual comparison suggests that the spatial pattern of vegetation height is consistent with that of land cover in China. It is very necessary in evaluating the importance of data type, laser spot density, and modelling method in vegetation height mapping in continental China.     

基于ART模型的MODIS积雪反照率反演研究

积雪反照率是研究局地或全球的能量收支平衡和气候变化中的重要参数,遥感反演为积雪反照率的获取提供了便利的手段。积雪反照率大小主要取决于积雪的自身物理属性(雪粒径、形状和污染物等因子)以及天气状况,遥感反演反照率大多基于双向反射模型(BRDF),积雪BRDF模型常使用积雪辐射传输模型获得。采用考虑了雪粒径、粒子形状以及污染物影响的渐进辐射传输理论(ART)模型,建立了MODIS积雪反照率反演算法,得到了MODIS 8d合成积雪反照率产品。将此算法应用于具有均一积雪地表的格陵兰岛地区,并使用GC-Net实测数据进行了验证,反演的总均方根误差(RMSE)为0.018,相关系数(r)为0.83,结果表明考虑了积雪特性的ART模型能够较好地反演积雪反照率,而且反演需要的参数较少。     

Estimating fractional cover of photosynthetic vegetation, non-photosynthetic vegetation and bare soil in the Australian tropical savanna region upscaling the EO-1 Hyperion and MODIS sensors

Quantitative estimation of fractional cover of photosynthetic vegetation (f_PV), non-photosynthetic vegetation (f_NPV) and bare soil (f_BS) is critical for natural resource management and for modeling carbon dynamics. Accurate estimation of fractional cover is especially important for monitoring and modeling savanna systems, subject to highly seasonal rainfall and drought, grazing by domestic and native animals, and frequent burning. This paper describes a method for resolving f_PV, f_NPV and f_BS across the ~ 2 million km² Australian tropical savanna zone with hyperspectral and multispectral imagery. A spectral library compiled from field campaigns in 2005 and 2006, together with three EO-1 Hyperion scenes acquired during the 2005 growing season were used to explore the spectral response space for f_PV, f_NPV and f_BS. A linear unmixing approach was developed using the Normalized Difference Vegetation Index (NDVI) and the Cellulose Absorption Index (CAI). Translation of this approach to MODerate resolution Imaging Spectroradiometer (MODIS) scale was assessed by comparing multiple linear regression models of NDVI and CAI with a range of indices based on the seven MODIS bands in the visible and shortwave infrared region (SWIR) using synthesized MODIS surface reflectance data on the same dates as the Hyperion acquisitions. The best resulting model, which used NDVI and the simple ratio of MODIS bands 7 and 6 (SWIR3/SWIR2), was used to generate a time series of fractional cover from 16 day MODIS nadir bidirectional reflectance distribution function-adjusted reflectance (NBAR) data from 2000-2006. The results obtained with MODIS NBAR were validated against grass curing measurement at ten sites with good agreement at six sites, but some underestimation of f_NPV proportions at four other sites due to substantial sub-pixel heterogeneity. The model was also compared with remote sensing measurements of fire scars and showed a good matching in the spatio-temporal patterns of grass senescence and posterior burning. The fractional cover profiles for major grassland cover types showed significant differences in relative proportions of f_PV, f_NPV and f_BS, as well as large intra-annual seasonal variation in response to monsoonal rainfall gradients and soil type. The methodology proposed here can be applied to other mixed tree-grass ecosystems across the world.     

Assessment of MODIS NDVI time series data products for detecting forest defoliation by gypsy moth outbreaks

This paper discusses an assessment of Moderate Resolution Imaging Spectroradiometer (MODIS) time-series data products for detecting forest defoliation from European gypsy moth (Lymantria dispar). This paper describes an effort to aid the United States Department of Agriculture (USDA) Forest Service in developing and assessing MODIS-based gypsy moth defoliation detection products and methods that could be applied in near real time without intensive field survey data collection as a precursor. In our study, MODIS data for 2000-2006 were processed for the mid-Appalachian highland region of the United States. Gypsy moth defoliation maps showing defoliated forests versus non-defoliated areas were produced from temporally filtered and composited MOD02 and MOD13 data using unsupervised classification and image thresholding of maximum value normalized difference vegetation index (NDVI) datasets computed for the defoliation period (June 10-July 27) of 2001 and of the entire time series. These products were validated by comparing stratified random sample locations to relevant Landsat and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) reference data sets. Composites of 250 m daily MOD02 outperformed 16-day MOD13 data in terms of classifying forest defoliation, showing a lower omission error rate (0.09 versus 0.56), a similar Kappa (0.67 versus 0.79), a comparable commission error rate (0.22 versus 0.14), and higher overall classification agreement (88 versus 79%). Results suggest that temporally processed MODIS time-series data can detect with good agreement to available reference data the extent and location of historical regional gypsy moth defoliation patches of 0.25 km² or more for 250-meter products. The temporal processing techniques used in this study enabled effective broad regional, “wall to wall” gypsy moth defoliation detection products for a 6.2 million ha region that were not produced previously with either MODIS or other satellite data. This study provides new, previously unavailable information on the relative agreement of temporally processed, gypsy moth defoliation detection products from MODIS NDVI time series data with respect to higher spatial resolution Landsat and ASTER data. These results also provided needed timely information on the potential of MODIS data for contributing near real time defoliation products to a USDA Forest Service Forest Threat Early Warning System.