Evaluation of AVHRR PAL and GIMMS 10‐day composite NDVI time series products using SPOT‐4 vegetation data for the African continent

Global 8 km resolution AVHRR (advanced very high resolution radiometer) NDVI (normalized difference vegetation index) 10‐day composite data sets have been used for numerous local to global scale vegetation time series studies during recent years. AVHRR Pathfinder (PAL) NDVI was available from 1981 until 2001, and the new AVHRR GIMMS NDVI was available from 1981 to the present time. A number of aspects potentially introduce noise in the NDVI data set due to the AVHRR sensor design and data processing. NDVI from SPOT‐4 VGT data is considered an improvement over AVHRR, and for this reason it is important to examine how and if the differences in sensor design and processing influence continental scale NDVI composite products. In this study, the quality of these AVHRR NDVI time series are evaluated by the continental scale 1 km resolution SPOT‐4 vegetation (VGT) 10‐day composite (S10) NDVI data. Three years of AVHRR PAL (1998–2000) and seven years of GIMMS (1998–2004) have been compared to 8 km resampled SPOT‐4 VGT (1998–2004) data. The dynamic range of SPOT‐4 VGT NDVI tends to be higher than the AVHRR PAL NDVI, whereas there is an exact match between AVHRR GIMMS NDVI and SPOT‐4 VGT NDVI. Ortho‐regression analysis on annually integrated values of AVHRR PAL/GIMMS and SPOT‐4 VGT on a continental scale reveals high correlations amongst the AVHRR and the SPOT data set, with lowest RMSE (root mean square error) on the GIMMS/SPOT‐4 VGT compared to the PAL/SPOT‐4 VGT.

Analyses on decade data likewise show that a linear relation exists between Spot‐4 VGT NDVI and the two AVHRR composite products; GIMMS explaining most of the Spot‐4 VGT NDVI variance compared to PAL. These results show that the AVHRR GIMMS NDVI is more consistent with Spot‐4 VGT NDVI compared to AVHRR PAL versus Spot‐4 VGT NDVI (in terms of RMSE and dynamic range) and can therefore be considered the more accurate long time AVHRR data record. Analyses performed on monthly maximum composites and decade composite data, however, reveal intra‐annual variations in the correlation between SPOT‐4 VGT and the two AVHRR data sets, which are attributed to different cloud masking algorithms. The SPOT‐4 VGT cloud‐screening algorithm is insufficient, thereby suppressing the rainy season NDVI.     

基于GIMMS、VGT和MODIS的中国东部植被指数对比分析

GIMMS NDVI、VGT NDVI和MODIS NDVI/EVI是目前在植被变化有关研究中经常使用的植被遥感数据,它们之间的差异也得到了广泛关注。然而,在分析这些数据之间的差异时,较少有研究注意到植被本身固有的季节循环可能夸大了各数据间的相关关系。应用2000~2006年GIMMS NDVI、VGT NDVI、MODIS NDVI/EVI等不同植被遥感数据,对比了基于这些数据集的中国东部植被年际变化的差异,探讨了植被季节循环对不同遥感数据之间相关性的影响。结果表明:由不同遥感数据提取的植被年际变化特征具有明显的一致性,然而,植被本身固有的季节循环特征掩盖了不同数据集的差异。季节循环去除前,各数据集之间具有显著的相关性;季节循环去除后,各数据集的相关性明显降低,但不同数据集在北部区域依然具有较好的一致性,其差异主要出现在南部区域,差异最明显的是GIMMS与MODIS数据,二者在淮河以南的区域几乎不存在显著相关。     

Evaluation of MODIS and NOAA AVHRR vegetation indices with in situ measurements in a semi‐arid environment

Much effort has been made in recent years to improve the spectral and spatial resolution of satellite sensors to develop improved vegetation indices reflecting surface conditions. In this study satellite vegetation indices from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Advanced Very High Resolution Radiometer (AVHRR) are evaluated against two years of in situ measurements of vegetation indices in Senegal. The in situ measurements are obtained using four masts equipped with self‐registrating multispectral radiometers designed for the same wavelengths as the satellite sensor channels. In situ measurements of the MODIS Normalized Difference Vegetation Index (NDVI) and AVHRR NDVI are equally sensitive to vegetation; however, the MODIS NDVI is consistently higher than the AVHRR NDVI. The MODIS Enhanced Vegetation Index (EVI) proved more sensitive to dense vegetation than both AVHRR NDVI and MODIS NDVI. EVI and NDVI based on the MODIS 16‐day constrained view angle maximum value composite (CV‐MVC) product captured the seasonal dynamics of the field observations satisfactorily but a standard 16‐day MVC product estimated from the daily MODIS surface reflectance data without view angle constraints yielded higher correlations between the satellite indices and field measurements (R ² values ranging from 0.74 to 0.98). The standard MVC regressions furthermore approach a 1?:?1 line with in situ measured values compared to the CV‐MVC regressions. The 16‐day MVC AVHRR data did not satisfactorily reflect the variation in the in situ data. Seasonal variation in the in situ measurements is captured reasonably with R ² values of 0.75 in 2001 and 0.64 in 2002, but the dynamic range of the AVHRR satellite data is very low—about a third to a half of the values from in situ measurements. Consequently the in situ vegetation indices were emulated much better by the MODIS indices than by the AVHRR NDVI.     

Atmospheric conditions for monitoring the long-term vegetation dynamics in the Amazon using normalized difference vegetation index

This study examined the effect of biomass-burning aerosols and clouds on the temporal dynamics of the normalized difference vegetation index (NDVI) exhibited by two widely used, time-series NDVI data products: the Pathfinder AVHRR land (PAL) dataset and the NASA Global Inventory Monitoring and Modeling Studies (GIMMS) dataset. The PAL data are 10-day maximum-value NDVI composites from 1982 to 1999 with corrections for Rayleigh scattering and ozone absorption. The GIMMS data are 15-day maximum-value NDVI composites from 1982 to 1999. In our analysis, monthly maximum-value NDVI was extracted from these datasets. The effects were quantified by comparing time-series of NDVI from PAL and GIMMS with observations from the SPOT/VEGETATION sensor and aerosol index data from the Total Ozone Mapping Spectrometer (TOMS), and results from radiative transfer simulation. Our analysis suggests that the substantial large-scale NDVI seasonality observed in the south and east Amazon forest region with PAL and GIMMS is primarily caused by variations in atmospheric conditions associated with biomass-burning aerosols and cloudiness. Reliable NDVI data can be typically acquired from April to July when such effects are relatively low, whereas there is a few effective NDVI data from September to December. In the central Amazon forest region, where aerosol loads are relatively low throughout the year, large-scale NDVI seasonality results primarily from seasonal variations in cloud cover. Careful treatment of these aerosol and cloud effects is required when using NDVI from PAL and GIMMS (or other source) to determine large-scale seasonal and interannual dynamics of vegetation greenness and ecosystem-atmosphere CO₂ exchange in the Amazon region.     

Using earth observation-based dry season NDVI trends for assessment of changes in tree cover in the Sahel

The co-existence of trees and grasses is a defining feature of savannah ecosystems and landscapes. During recent decades, the combined effect of climate change and increased demographic pressure has led to complex vegetation changes in these ecosystems. A number of recent Earth observation (EO)-based studies reported positive changes in biological productivity in the Sahelian region in relation to increased precipitation, triggering an increased amount of herbaceous vegetation during the rainy season. However, this ‘greening of the Sahel’ may mask changes in the tree–grass composition, with a potential reduction in tree cover having important implications for the Sahelian population. Large-scale EO-based evaluation of changes in Sahelian tree cover is assessed by analysing long-term trends in dry season minimum normalized difference vegetation index (NDVI_min) derived from three different satellite sensors: Système Pour l’Observation de la Terre (SPOT)-VEGETATION (VGT), Terra Moderate Resolution Imaging Spectroradiometer (MODIS), and the Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Modeling and Mapping Studies (GIMMS) dataset. To evaluate the reliability of using NDVI_min as a proxy for tree cover in the Sahel, two factors that could potentially influence dry season NDVI_min estimates were analysed: the total biomass accumulated during the preceding growing season and the percentage of burned area observed during the dry season. Time series of dry season NDVI_min derived from low-resolution satellite time series were found to be uncorrelated to dry grass residues from the preceding growing season and to seasonal fire frequency and timing over most of the Sahel (88%), suggesting that NDVI_min can serve as a proxy for assessing changes in tree cover. Good agreement (R² = 0.79) between significant NDVI_min trends (p < 0.05) derived from VGT and MODIS was found. Significant positive trends in NDVI_min were registered by both MODIS and VGT dry season NDVI_min time series over the Western Sahel, whereas trends based on GIMMS data were negative for the greater part of the Sahel. EO-based trends were generally not confirmed at the local scale based on selected study cases, partly caused by a temporal mismatch between data sets (i.e. different periods of analysis). Analysis of desert area NDVI_min trends indicates less stable values for VGT and GIMMS data as compared with MODIS. This suggests that trends in dry season NDVI_min derived from VGT and GIMMS should be used with caution as an indicator for changes in tree cover, whereas the MODIS data stream shows a better potential for tree-cover change analysis in the Sahel.     

Multi-platform comparisons of MODIS and AVHRR normalized difference vegetation index data

The relationship between AVHRR-derived normalized difference vegetation index (NDVI) values and those of future sensors is critical to continued long-term monitoring of land surface properties. The follow-on operational sensor to the AVHRR, the Visible/Infrared Imager/Radiometer Suite (VIIRS), will be very similar to the NASA Earth Observing System's Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. NDVI data derived from visible and near-infrared data acquired by the MODIS (Terra and Aqua platforms) and AVHRR (NOAA-16 and NOAA-17) sensors were compared over the same time periods and a variety of land cover classes within the conterminous United States. The results indicate that the 16-day composite NDVI values are quite similar over the composite intervals of 2002 and 2003, and linear relationships exist between the NDVI values from the various sensors. The composite AVHRR NDVI data included water and cloud masks and adjustments for water vapor as did the MODIS NDVI data. When analyzed over a variety of land cover types and composite intervals, the AVHRR derived NDVI data were associated with 89% or more of the variation in the MODIS NDVI values. The results suggest that it may be possible to successfully reprocess historical AVHRR data sets to provide continuity of NDVI products through future sensor systems.     

Earth observation of vegetation status in the Sahelian and Sudanian West Africa: comparison of Terra MODIS and NOAA AVHRR satellite data

The first year of Moderate Resolution Imaging Spectroradiometer (MODIS) data are compared with National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) data for derivation of biophysical variables in Senegal, West Africa. The dynamic range of the two MODIS vegetation indices (VIs)—the continuity vegetation index (CVI) and the enhanced vegetation index (EVI)—is generally much larger than for the NOAA AVHRR normalized difference vegetation index (NDVI) data, indicating the importance of the change in near-infrared wavelength configuration from the NOAA AVHRR sensor to the MODIS sensor. Senegal is characterized by a pronounced gradient in the vegetation density covering a range of agro-climatic zones from arid to humid and it is found that the MODIS CVI values saturate for high VI values while the EVI demonstrates improved sensitivity for high biomass. Compared to NOAA AVHRR the MODIS VIs generally correlate better to the MODIS fraction of absorbed photosynthetically active radiation (fAPAR) absorbed by vegetation canopies and the leaf area index (LAI; the one-sided green leaf area per unit ground area). CVI is found to correlate better to both fAPAR and LAI than is the case for EVI because of the larger dynamic range of the CVI data. This suggests that the problem of background contamination on VIs from soil is not as severe in Senegal as has been found in other semi-arid African areas.     

Analysis of leaf area index products from combination of MODIS Terra and Aqua data

A prototype product suite, containing the Terra 8-day, Aqua 8-day, Terra-Aqua combined 8- and 4-day products, was generated as part of testing for the next version (Collection 5) of the MODerate resolution Imaging Spectroradiometer (MODIS) leaf area index (LAI) products. These products were analyzed for consistency between Terra and Aqua retrievals over the following data subsets in North America: single 8-day composite over the whole continent and annual time series over three selected MODIS tiles (1200 × 1200 km). The potential for combining retrievals from the two sensors to derive improved products by reducing the impact of environmental conditions and temporal compositing period was also explored. The results suggest no significant discrepancies between large area (from continent to MODIS tile) averages of the Terra and Aqua 8-day LAI and surface reflectances products. The differences over smaller regions, however, can be large due to the random nature of residual atmospheric effects. High quality retrievals from the radiative transfer based algorithm can be expected in 90-95% of the pixels with mostly herbaceous cover and about 50-75% of the pixels with woody vegetation during the growing season. The quality of retrievals during the growing season is mostly restricted by aerosol contamination of the MODIS data. The Terra-Aqua combined 8-day product helps to minimize this effect and increases the number of high quality retrievals by 10-20% over woody vegetation. The combined 8-day product does not improve the number of high quality retrievals during the winter period because the extent of snow contamination of Terra and Aqua observations is similar. Likewise, cloud contamination in the single-sensor and combined products is also similar. The LAI magnitudes, seasonal profiles and retrieval quality in the combined 4-day product are comparable to those in the single-sensor 8-day products. Thus, the combined 4-day product doubles the temporal resolution of the seasonal cycle, which facilitates phenology monitoring in application studies during vegetation transition periods. Both Terra and Aqua LAI products show anomalous seasonality in boreal needle leaf forests, due to limitations of the radiative transfer algorithm to model seasonal variations of MODIS surface reflectance data with respect to solar zenith angle. Finally, this study suggests that further improvement of the MODIS LAI products is mainly restricted by the accuracy of the MODIS observations.     

Multiscale geostatistical analysis of AVHRR, SPOT-VGT, and MODIS global NDVI products

Global NDVI data are routinely derived from the AVHRR, SPOT-VGT, and MODIS/Terra earth observation records for a range of applications from terrestrial vegetation monitoring to climate change modeling. This has led to a substantial interest in the harmonization of multisensor records. Most evaluations of the internal consistency and continuity of global multisensor NDVI products have focused on time-series harmonization in the spectral domain, often neglecting the spatial domain. We fill this void by applying variogram modeling (a) to evaluate the differences in spatial variability between 8-km AVHRR, 1-km SPOT-VGT, and 1-km, 500-m, and 250-m MODIS NDVI products over eight EOS (Earth Observing System) validation sites, and (b) to characterize the decay of spatial variability as a function of pixel size (i.e. data regularization) for spatially aggregated Landsat ETM+ NDVI products and a real multisensor dataset. First, we demonstrate that the conjunctive analysis of two variogram properties - the sill and the mean length scale metric - provides a robust assessment of the differences in spatial variability between multiscale NDVI products that are due to spatial (nominal pixel size, point spread function, and view angle) and non-spatial (sensor calibration, cloud clearing, atmospheric corrections, and length of multi-day compositing period) factors. Next, we show that as the nominal pixel size increases, the decay of spatial information content follows a logarithmic relationship with stronger fit value for the spatially aggregated NDVI products (R² = 0.9321) than for the native-resolution AVHRR, SPOT-VGT, and MODIS NDVI products (R² = 0.5064). This relationship serves as a reference for evaluation of the differences in spatial variability and length scales in multiscale datasets at native or aggregated spatial resolutions. The outcomes of this study suggest that multisensor NDVI records cannot be integrated into a long-term data record without proper consideration of all factors affecting their spatial consistency. Hence, we propose an approach for selecting the spatial resolution, at which differences in spatial variability between NDVI products from multiple sensors are minimized. This approach provides practical guidance for the harmonization of long-term multisensor datasets.     

Development of a two-band enhanced vegetation index without a blue band

The enhanced vegetation index (EVI) was developed as a standard satellite vegetation product for the Terra and Aqua Moderate Resolution Imaging Spectroradiometers (MODIS). EVI provides improved sensitivity in high biomass regions while minimizing soil and atmosphere influences, however, is limited to sensor systems designed with a blue band, in addition to the red and near-infrared bands, making it difficult to generate long-term EVI time series as the normalized difference vegetation index (NDVI) counterpart. The purpose of this study is to develop and evaluate a 2-band EVI (EVI2), without a blue band, which has the best similarity with the 3-band EVI, particularly when atmospheric effects are insignificant and data quality is good. A linearity-adjustment factor β is proposed and coupled with the soil-adjustment factor L used in the soil-adjusted vegetation index (SAVI) to develop EVI2. A global land cover dataset of Terra MODIS data extracted over land community validation and FLUXNET test sites is used to develop the optimal parameter (L, β and G) values in EVI2 equation and achieve the best similarity between EVI and EVI2. The similarity between the two indices is evaluated and demonstrated with temporal profiles of vegetation dynamics at local and global scales. Our results demonstrate that the differences between EVI and EVI2 are insignificant (within ± 0.02) over a very large sample of snow/ice-free land cover types, phenologies, and scales when atmospheric influences are insignificant, enabling EVI2 as an acceptable and accurate substitute of EVI. EVI2 can be used for sensors without a blue band, such as the Advanced Very High Resolution Radiometer (AVHRR), and may reveal different vegetation dynamics in comparison with the current AVHRR NDVI dataset. However, cross-sensor continuity relationships for EVI2 remain to be studied.     

Comparison and validation of MODIS and VEGETATION global LAI products over four BigFoot sites in North America

A new set of recently developed leaf area index (LAI) algorithms has been employed for producing a global LAI dataset at 1 km resolution and in time-steps of 10 days, using data from the Satellite pour l'observation de la terre (SPOT) VEGETATION (VGT) sensor. In this paper, this new LAI product is compared with the global MODIS Collection 4 LAI product over four validation sites in North America. The accuracy of both LAI products is assessed against seven high resolution ETM+ LAI maps derived from field measurements in 2000, 2001, and 2003. Both products were closely matched outside growing season. The MODIS product tended to be more variable than the VGT product during the summer period when the LAI was maximum. VGT and ETM+ LAI maps agreed well at three out of the four sites. The median relative absolute error of the VGT LAI product varied from 24% to 75% at 1 km scale and it ranged from 34% to 88% for the MODIS LAI product. The importance of correcting field measurements for the clumping effect is illustrated at the deciduous broadleaf forest site (HARV). Inclusion of the sub-pixel land cover information improved the quality of LAI estimates for the prairie grassland KONZ site. Further improvement of the global VGT LAI product is suggested by production and inclusion of pixel-specific global foliage clumping index and forest background reflectance maps that would serve as an input into the VGT LAI algorithms.     

On the relationship of NDVI with leaf area index in a deciduous forest site

Numerous studies have reported on the relationship between the normalized difference vegetation index (NDVI) and leaf area index (LAI), but the seasonal and annual variability of this relationship has been less explored. This paper reports a study of the NDVI-LAI relationship through the years from 1996 to 2001 at a deciduous forest site. Six years of LAI patterns from the forest were estimated using a radiative transfer model with input of above and below canopy measurements of global radiation, while NDVI data sets were retrieved from composite NDVI time series of various remote sensing sources, namely NOAA Advanced Very High Resolution Radiometer (AVHRR; 1996, 1997, 1998 and 2000), SPOT VEGETATION (1998-2001), and Terra MODIS (2001). Composite NDVI was first used to remove the residual noise based on an adjusted Fourier transform and to obtain the NDVI time-series for each day during each year.The results suggest that the NDVI-LAI relationship can vary both seasonally and inter-annually in tune with the variations in phenological development of the trees and in response to temporal variations of environmental conditions. Strong linear relationships are obtained during the leaf production and leaf senescence periods for all years, but the relationship is poor during periods of maximum LAI, apparently due to the saturation of NDVI at high values of LAI. The NDVI-LAI relationship was found to be poor (R² varied from 0.39 to 0.46 for different sources of NDVI) when all the data were pooled across the years, apparently due to different leaf area development patterns in the different years. The relationship is also affected by background NDVI, but this could be minimized by applying relative NDVI.Comparisons between AVHRR and VEGETATION NDVI revealed that these two had good linear relationships (R²=0.74 for 1998 and 0.63 for 2000). However, VEGETATION NDVI data series had some unreasonably high values during beginning and end of each year period, which must be discarded before adjusted Fourier transform processing. MODIS NDVI had values greater than 0.62 through the entire year in 2001, however, MODIS NDVI still showed an “M-shaped” pattern as observed for VEGETATION NDVI in 2001. MODIS enhanced vegetation index (EVI) was the only index that exhibited a poor linear relationship with LAI during the leaf senescence period in year 2001. The results suggest that a relationship established between the LAI and NDVI in a particular year may not be applicable in other years, so attention must be paid to the temporal scale when applying NDVI-LAI relationships.     

Agreement evaluation of AVHRR and MODIS 16‐day composite NDVI data sets

Satellite‐derived normalized difference vegetation index (NDVI) data have been used extensively to detect and monitor vegetation conditions at regional and global levels. A combination of NDVI data sets derived from AVHRR and MODIS can be used to construct a long NDVI time series that may also be extended to VIIRS. Comparative analysis of NDVI data derived from AVHRR and MODIS is critical to understanding the data continuity through the time series. In this study, the AVHRR and MODIS 16‐day composite NDVI products were compared using regression and agreement analysis methods. The analysis shows a high agreement between the AVHRR‐NDVI and MODIS‐NDVI observed from 2002 and 2003 for the conterminous United States, but the difference between the two data sets is appreciable. Twenty per cent of the total difference between the two data sets is due to systematic difference, with the remainder due to unsystematic difference. The systematic difference can be eliminated with a linear regression‐based transformation between two data sets, and the unsystematic difference can be reduced partially by applying spatial filters to the data. We conclude that the continuity of NDVI time series from AVHRR to MODIS is satisfactory, but a linear transformation between the two sets is recommended.     

Leaf area index mapping in northern Canada

Leaf area index (LAI) is an important structural vegetation parameter that is commonly derived from remotely sensed data. It has been used as a reliable indicator for vegetation's cover, status, health and productivity. In the past two decades, various Canada-wide LAI maps have been generated by the Canada Centre for Remote Sensing (CCRS). These products have been produced using a variety of very coarse satellite data such as those from SPOT VGT and NOAA AVHRR satellite data. However, in these LAI products, the mapping of the Canadian northern vegetation has not been performed with field LAI measurements due in large part to scarce in situ measurements over northern biomes. The coarse resolution maps have been extensively used in Canada, but finer resolution LAI maps are needed over the northern Canadian ecozones, in particular for studying caribou habitats and feeding grounds.

In this study, a new LAI algorithm was developed with particular emphasis over northern Canada using a much finer resolution of remotely sensed data and in situ measurements collected over a wide range of northern arctic vegetation. A statistical relationship was developed between the in situ LAI measurements collected over vegetation plots in northern Canada and their corresponding pixel spectral information from Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) data. Furthermore, all Landsat TM and ETM+ data have been pre-normalized to NOAA AVHRR and SPOT VGT data from the growing season of 2005 to reduce any seasonal or temporal variations. Various spectral vegetation indices developed from the Landsat TM and ETM?+?data were analysed in this study. The reduced simple ratio index (RSR) was found to be the most robust and an accurate estimator of LAI for northern arctic vegetation. An exponential relationship developed using the Theil–Sen regression technique showed an R ² of 0.51 between field LAI measurement and the RSR. The developed statistical relationship was applied to a pre-existing Landsat TM 250 m resolution mosaic for northern Canada to produce the final LAI map for northern Canada ecological zones. Furthermore, the 250 m resolution LAI estimates, per ecological zone, were almost generally lower than those of the CCRS Canada-wide VGT LAI maps for the same ecozones. Validation of the map with LAI field data from the 2008 season, not used in the derivation of the algorithm, shows strong agreement between the in situ LAI measurement values and the map-estimated LAI values.     

基于AVHRR和MODIS数据的全球植被物候比较分析

AVHRR和MODIS卫星数据在全球和区域尺度植物物候对气候变化响应研究中起着重要的作用,然而两种传感器在全球尺度物候监测的一致性有待验证。首先利用时间序列谐波分析法(HANTS)对2005年全球GIMMS AVHRR NDVI和MODIS 13A2 数据进行滤波处理;然后基于改进的动态阈值方法,提取全球植被的返青期(SOS)、枯黄期(EOS)和生长季长度(DOS);最后分区域比较和评估两种传感器提取物候参数的潜力。研究结果表明：2005年全球大部分地区植被在第100～140 d开始生长,到第260～300 d逐渐停止生长,生长季长度集中在130～180 d,并且和区域研究结果具有一致性;两种传感器提取的植被关键物候期的空间变化趋势是一致的,随着纬度升高,返青期呈现推迟趋势,枯黄期呈现提早趋势,生长季长度呈现缩短趋势;AVHRR和MODIS提取落叶林和草地的SOS、EOS和DOS在欧亚大陆和北美洲区域的相关系数大部分达到0.9以上。     

Impact of orthorectification and spatial sampling on maximum NDVI composite data in mountain regions

Topography and accuracy of image geometric registration significantly affect the quality of satellite data, since pixels are displaced depending on surface elevation and viewing geometry. This effect should be corrected for through the process of accurate image navigation and orthorectification in order to meet the geolocation accuracy for systematic observations specified by the Global Climate Observing System (GCOS) requirements for satellite climate data records. We investigated the impact of orthorectification on the accuracy of maximum Normalized Difference Vegetation Index (NDVI) composite data for a mountain region in north-western Canada at various spatial resolutions (1 km, 4 km, 5 km, and 8 km). Data from AVHRR on board NOAA-11 (1989 and 1990) and NOAA-16 (2001, 2002, and 2003) processed using a system called CAPS (Canadian AVHRR Processing System) for the month of August were considered. Results demonstrate the significant impact of orthorectification on the quality of composite NDVI data in mountainous terrain. Differences between orthorectified and non-orthorectified NDVI composites (ΔNDVI) adopted both large positive and negative values, with the 1% and 99% percentiles of ΔNDVI at 1 km resolution spanning values between − 0.16 < ΔNDVI < 0.09. Differences were generally reduced to smaller numbers for coarser resolution data, but systematic positive biases for non-orthorectified composites were obtained at all spatial resolutions, ranging from 0.02 (1 km) to 0.004 (8 km). Analyzing the power spectra of maximum NDVI composites at 1 km resolution, large differences between orthorectified and non-orthorectified AVHRR data were identified at spatial scales between 4 km and 10 km. Validation of NOAA-16 AVHRR NDVI with MODIS NDVI composites revealed higher correlation coefficients (by up to 0.1) for orthorectified composites relative to the non-orthorectified case. Uncertainties due to the AVHRR Global Area Coverage (GAC) sampling scheme introduce an average positive bias of 0.02 ± 0.03 at maximum NDVI composite level that translates into an average relative bias of 10.6% ± 19.1 for sparsely vegetated mountain regions. This can at least partially explain the systematic average positive biases we observed relative to our results in AVHRR GAC-based composites from the Global Inventory Modeling and Mapping Studies (GIMMS) and Polar Pathfinder (PPF) datasets (0.19 and 0.05, respectively). With regard to the generation of AVHRR long-term climate data records, results suggest that orthorectification should be an integral part of AVHRR pre-processing, since neglecting the terrain displacement effect may lead to important biases and additional noise in time series at various spatial scales.     

Analysing NDVI for the African continent using the geostationary meteosat second generation SEVIRI sensor

This study presents first results on Normalized Difference Vegetation Index (NDVI), from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) sensor onboard the geostationary satellite Meteosat Second Generation (MSG) covering the African continent. With a temporal resolution of 15 min MSG offers complementary information for NDVI monitoring compared to vegetation monitoring based on polar orbiting satellites. The improved temporal resolution has potential implications for accurate NDVI assessment of the African continent; e.g. the increased amount of available scenes are expected to help overcome problems related to cloud cover which makes the MSG data particularly well suited for early warning systems. Time series of 2004 MSG NDVI was compared to MODIS (Moderate Resolution Imaging Spectroradiometer) Terra and Aqua NDVI for the Dahra site in the Senegalese Sahel, West Africa. It was found that NDVI was available for 82 days with multiple cloud free acquisitions per day during the growing season as compared to 47 days with information from either MODIS Terra or Aqua for that particular site. Differences in MSG SEVIRI and MODIS BRDF on a seasonal scale were found to influence the time series of NDVI for the test site; MSG NDVI being higher than MODIS in July-August and lower in October-November. Preliminary composite analysis suggests that the period of compositing to produce continent scale cloud free products can be reduced to ∼5 days using MSG NDVI as compared to polar orbiting data. With the availability of diurnal reflectance information the significance of differences between the red and near-infrared wavelengths due to anisotropy become evident, causing diurnal variations in observed NDVI. Diurnal MSG NDVI was compared to in situ measured MSG NDVI at the test site in Senegal and the same “bowl-shaped” diurnal curve was found for a medium dense cover of annual grasses. The range in observed NDVI and time of diurnal minimum was different due to different viewing geometry. Daily minimum of in situ measured NDVI was around solar noon whereas minimum MSG NDVI occurs one hour prior to noon due to the test site location 12° west of the satellite sensor. Diurnal variation in observed NDVI was studied for a number of pixels characterized by different sensor view zenith angles and vegetation types. This analysis illustrated the diurnal NDVI dependency of illumination conditions, view angle and vegetation intensity and pinpoints the importance of proper BRDF modeling to produce daily values of MSG NDVI normalized for acquisition time, which will be the subject of a forthcoming paper.     

Interannual covariability between actual evapotranspiration and PAL and GIMMS NDVIs of northern Asia

This study examined the covariability between interannual changes in the normalized difference vegetation index (NDVI) and actual evapotranspiration (ET). To reduce possible uncertainty in the NDVI time series, two NDVI datasets derived from Pathfinder AVHRR Land (PAL) data and the Global Inventory Monitoring and Modeling Studies (GIMMS) group were used. Analyses were conducted using data over northern Asia from 1982 to 2000. Interannual changes over 19 years in the PAL-NDVI and GIMMS-NDVI were compared with interannual changes in ET estimated from model-assimilated atmospheric data and gridded precipitation data. For both NDVI datasets, the annual maximum correlation with ET occurred in June, which is the beginning of the vegetation growing season. The PAL and GIMMS datasets showed a significant, positive correlation between interannual changes in the NDVI and ET over most of the vegetated land area in June. These results suggest that interannual changes in vegetation activity predominantly control interannual changes in ET in June. Based on analyses of interannual changes in temperature, precipitation, and the NDVI in June, the study area can be roughly divided into two regions, the warmth-dominated northernmost region and the wetness-dominated southern region, indicating that interannual changes in vegetation and the resultant interannual changes in ET are controlled by warmth and wetness in these two regions, respectively.     

A global 1° by 1° NDVI data set for climate studies derived from the GIMMS continental NDVI data

A nine-year (1982–1990) global normalized difference vegetation index (NDVI) data set with a spatial resolution of 1° by 1° and a temporal resolution of one month was compiled for use in climate studies. This data set was derived from higher resolution (5–8 km) monthly continental NDVI data sets that have been processed and archived by the Global Inventory Monitoring and Modelling Studies (GIMMS) group at NASA/Goddard Space Flight Center. The continental GIMMS NDVI data sets were calculated from Global Area Coverage (GAC) data collected at daily intervals by the Advanced Very High Resolution Radiometer (AVHRR) onboard the NOAA-7, -9 and -11 satellites

The global 1° by 1° NDVI data set was produced to calculate land surface parameters for use within general circulation model J of the atmosphere (GCM). In view of this quantitative application, an evaluation is given of the representation by the NDVI data of the spectral properties of vegetation at the landsurface. Errors are defined as deviations from measurements obtained under standard conditions, i.e., conditions at the-top-of-the-atmosphere with no clouds, clear atmosphere, near-nadir viewing angles, overhead Sun, and invariant soil background. The discussion includes an assessment of (1) the data collected and processed onboard the AVHRR; (2) processing of the AVHRR data into the continental GIMMS NDVI data sets; (3) resampling of the continental data sets to a 1° by 1° data set; and (4) propagation of inconsistencies and biases from (1), (2) and (3) into the 1° by 1° global NDVI data. Examples are shown of the temporal and spatial variations in spectral properties of vegetation contained in the 1° by 1° NDVI data, and these are compared with the dynamics of biophysical parameters derived from land cover classes that were used in previous climate studies.     

Differences in monitoring vegetation dynamics between Moderate Resolution Imaging Spectroradiometer Collection 5 and Collection 6 vegetation index products on the Loess Plateau,China

Accurately monitoring vegetation dynamics on the Loess Plateau (LP) is critical for evaluating the benefits of ecological restoration projects. The Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation index (VI) product has been a primary data source for monitoring vegetation dynamics. However, MODIS Collection 5 (C5) VI products are known to be affected by sensor degradation, which has been addressed in the newly released MODIS Collection 6 (C6) VI products. Herein, we compared the spatiotemporal differences in vegetation dynamics between the Terra MODIS C5 and C6 data products and among different annual value retrieval methods for the LP during 2001–2016. Our results indicated a lower magnitude but a greener trend in the normalized difference vegetation index (NDVI), and areas with significant greening (p < 0.050) were found to increase by about 13%–16% from C5 to C6, depending on the retrieval method. Regions with either no particular trend or a downward trend in vegetation derived from the Terra-C5 NDVI mostly showed significant increasing trends based on the Terra-C6 NDVI. Moreover, the different retrieval methods also exhibited differences in the evaluation of vegetation dynamics, with the largest differences in terms of both magnitude and trend being identified with the annual maximum value method. This highlighted a compelling need to choose suitable methods in different regions for the retrieval of annual VI values, in order to facilitate more robust and comparable conclusions. Additionally, discrepancies also existed in the response of vegetation to climate variations between the Terra-C5 and C6 products for all three annual VI retrieval methods. Our findings, based on multiple products and analysis methods, may lead to improved understanding of both vegetation dynamics and their linkage to climate variables. The results suggest that caution be utilized when using only MODIS Terra-C5 products to evaluate vegetation dynamics and calibrate ecosystem models.