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.     

An assessment of angular variations of red and NIR reflectances in multi-date IRS-1D Wide Field Sensor data

A study has been carried out to assess angular variations in red and near infrared (NIR) reflectance of different features of the Earth's surface in a common overlap area of accumulated four-date Indian Remote Sensing Satellite (IRS-1D) Wide Field Sensor (WiFS) data from the first fortnight of October 2003. An improved dark object subtraction (DOS) method has been used to perform image based atmospheric corrections. Red and NIR reflectance variations of four structurally different classes—dense vegetation (shrub), sparse crop (pearl millet/maize), wasteland and forest with Sun-target-sensor geometry were analysed. A linearly constrained least squares technique was used to estimate red and NIR model coefficients of the linear Ross Thick-Li Sparse (RTLS) semi- empirical Bidirectional Reflectance Distribution Function (BRDF) model and compared with Moderate Resolution Imaging Spectrometer (MODIS) BRDF product coefficients. The relative reflectance difference between two dates as well as anisotropic factors for red and NIR for all classes and dates were also computed. Red and NIR reflectance of the four land cover classes at different locations with different observation geometry were estimated using both WiFS derived and MODIS BRDF product RTLS model coefficients and compared with WiFS observed reflectance. It was found that the mean relative difference in red and NIR reflectances between consecutive dates varied between 4–11% and 6–8%, respectively, while the computed mean anisotropy factors varied between 3–10% in the red and 8–11% in the NIR. A small anisotropy in the Normalized Difference Vegetation Index (NDVI) as a function of the scattering angle was observed for the four land cover classes. This may imply that angular effects in WiFS are relatively small and do not exceed 10–11 % for the land cover classes considered here.     

Assessing the seasonal dynamics of the Brazilian Cerrado vegetation through the use of spectral vegetation indices

In this study, the response of vegetation indices (VIs) to the seasonal patterns and spatial distribution of the major vegetation types encountered in the Brazilian Cerrado was investigated. The Cerrado represents the second largest biome in South America and is the most severely threatened biome as a result of rapid land conversions. Our goal was to assess the capability of VIs to effectively monitor the Cerrado and to discriminate among the major types of Cerrado vegetation. A full hydrologic year (1995) of composited AVHRR, local area coverage (LAC) data was converted to Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) values. Temporal extracts were then made over the major Cerrado vegetation communities. Both the NDVI and SAVI temporal profiles corresponded well to the phenological patterns of the natural and converted vegetation formations and depicted three major categories encompassing the savanna formations and pasture sites, the forested areas, and the agricultural crops. Secondary differences in the NDVI and SAVI temporal responses were found to be related to their unique interactions with sun-sensor viewing geometries. An assessment of the functional behaviour of the VIs confirmed SAVI responds primarily to NIR variations, while the NDVI showed a strong dependence on the red reflectance. Based on these results, we expect operational use of the MODIS Enhanced Vegetation Index (EVI) to provide improved discrimination and monitoring capability of the significant Cerrado vegetation types.     

Retrieving and broadcasting near-real-time biophysical parameters from MODIS and SEVIRI receiving stations at the global change unit of the University of Valencia

We present here the automatic processing chains implemented at the Global Change Unit of the University of Valencia. These allow for a near-real-time retrieval of various biophysical parameters from both Sun-synchronous TERRA/AQUA Moderate Resolution Imaging Spectroradiometer (MODIS) and geostationary Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager (MSG SEVIRI) sensors. Retrieved parameters, namely sea and land surface temperatures (SST and LST, respectively), normalized difference vegetation index (NDVI), and vegetation condition index (VCI), are similar for both sensors, and specific approaches have been developed and implemented for near-real-time parameter retrievals: <2 hours for MODIS and <5 min for MSG SEVIRI. Bidirectional reflectance distribution function (BRDF) correction is still required to be implemented in both processing chains, while more advanced parameters are already retrieved (hot spot detection and MSG SEVIRI phenology), in good agreement with independent ground observations. Validation of the retrieved products is underway and the above-mentioned products are available for downloading at http://ceosspain.lpi.uv.es.     

An empirical investigation of cross-sensor relationships of NDVI and red/near-infrared reflectance using EO-1 Hyperion data

Long term observations of global vegetation from multiple satellites require much effort to ensure continuity and compatibility due to differences in sensor characteristics and product generation algorithms. In this study, we focused on the bandpass filter differences and empirically investigated cross-sensor relationships of the normalized difference vegetation index (NDVI) and reflectance. The specific objectives were: 1) to understand the systematic trends in cross-sensor relationships of the NDVI and reflectance as a function of spectral bandpasses, 2) to examine/identify the relative importance of the spectral features (i.e., the green peak, red edge, and leaf liquid water absorption regions) in and the mechanism(s) of causing the observed systematic trends, and 3) to evaluate the performance of several empirical cross-calibration methods in modeling the observed systematic trends. A Level 1A Hyperion hyperspectral image acquired over a tropical forest—savanna transitional region in Brazil was processed to simulate atmospherically corrected reflectances and NDVI for various bandpasses, including Terra Moderate Resolution Imaging Spectroradiometer (MODIS), NOAA-14 Advanced Very High Resolution Radiometer (AVHRR), and Landsat-7 Enhanced Thematic Mapper Plus (ETM+). Data were extracted from various land cover types typically found in tropical forest and savanna biomes and used for analyses. Both NDVI and reflectance relationships among the sensors were neither linear nor unique and were found to exhibit complex patterns and bandpass dependencies. The reflectance relationships showed strong land cover dependencies. The NDVI relationships, in contrast, did not show land cover dependencies, but resulted in nonlinear forms. From sensitivity analyses, the green peak (∼550 nm) and red-NIR transitional (680-780 nm) features were identified as the key factors in producing the observed land cover dependencies and nonlinearity in cross-sensor relationships. In particular, differences in the extents to which the red and/or NIR bandpasses included these features significantly influenced the forms and degrees of nonlinearity in the relationships. Translation of MODIS NDVI to “AVHRR-like” NDVI using a weighted average of MODIS green and red bands performed very poorly, resulting in no reduction of overall discrepancy between MODIS and AVHRR NDVI. Cross-calibration of NDVI and reflectance using NDVI-based quadratic functions performed well, reducing their differences to ± .025 units for the NDVI and ± .01 units for the reflectances; however, many of the translation results suffered from bias errors. The present results suggest that distinct translation equations and coefficients need to be developed for every sensor pairs and that land cover-dependency need to be explicitly accounted for to reduce bias errors.     

A generalized split-window algorithm for land surface temperature estimation from MSG-2/SEVIRI data

This paper aims to determine land surface temperature (LST) using data from a spinning enhanced visible and infrared imager (SEVIRI) on board Meteosat Second Generation 2 (MSG-2) by using the generalized split-window (GSW) algorithm. Coefficients in the GSW algorithm are pre-determined for several overlapping sub-ranges of the LST, land surface emissivity (LSE), and atmospheric water vapour content (WVC) using the data simulated with the atmospheric radiative transfer model MODTRAN 4.0 under various surface and atmospheric conditions for 11 view zenith angles (VZAs) ranging from 0° to 67°. The results show that the root mean square error (RMSE) varies with VZA and atmospheric WVC and that the RMSEs are within 1.0 K for the sub-ranges in which the VZA is less than 30° and the atmospheric WVC is less than 4.25 g cm^?2. A sensitivity analysis of LSE uncertainty, atmospheric WVC uncertainty, and instrumental noise (NEΔT) is also performed, and the results demonstrate that LSE uncertainty can result in a larger LST error than other uncertainties and that the total error for the LST is approximately 1.21 and 1.45 K for dry atmosphere and 0.86 and 2.91 K for wet atmosphere at VZA = 0° and at VZA = 67°, respectively, if the uncertainty in the LSE is 1% and that in the WVC is 20%. The GSW algorithm is then applied to the MSG-2 – SEVIRI data with the LSE determined using the temperature-independent spectral indices method and the WVC either determined using the measurements in two split-window channels or interpolated temporally and spatially using European Centre for Medium Range Weather Forecasting (ECMWF) data. Finally, the SEVIRI LST derived in this paper (SEVIRI LST1) is evaluated through comparisons with the SEVIRI LST provided by the land surface analysis satellite applications facility (LSA SAF) (SEVIRI LST2) and the Moderate Resolution Imaging Spectroradiometer (MODIS) LST product (MOD11B1 LST product). The results show that more than 80% of the differences between SEVIRI LST1 and SEVIRI LST2 are within 2 K, and approximately 70% of the differences between SEVIRI LST1 and MODIS LST are within 4 K. Furthermore, compared to MODIS LST, for four specific areas with different land surfaces, our GSW algorithm overestimates the LST by up to 1.0 K for vegetated surfaces and by 1.3 K for bare soil.     

Transitioning from MODIS to VIIRS: an analysis of inter-consistency of NDVI data sets for agricultural monitoring

The Visible/Infrared Imager/Radiometer Suite (VIIRS) aboard the Suomi National Polar-orbiting Partnership (S-NPP) satellite was launched in 2011, in part to provide continuity with the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument aboard National Aeronautics and Space Administration’s (NASA) Terra and Aqua remote-sensing satellites. The VIIRS will eventually replace Aqua MODIS for both land science and applications and add to the coarse-resolution, long-term data record. It is, therefore, important to provide the user community with an assessment of the consistency of equivalent products from the two sensors. For this study, we do this in the context of example agricultural monitoring applications. Surface reflectance that is routinely delivered within the M{O,Y}D09 and VNP09 series of products provides critical input for generating downstream products. Given the range of applications utilizing the normalized difference vegetation index (NDVI) generated from the M{O,Y}D09 and VNP09 products and the inherent differences between MODIS and VIIRS sensors in calibration, spatial sampling, and spectral bands, the main objective of this study is to quantify uncertainties associated with transitioning from using MODIS to VIIRS-based NDVIs. In particular, we compare NDVIs derived from two sets of Level 3 MYD09 and VNP09 products with various spatial-temporal characteristics, namely 8-day composites at 500 m spatial resolution and daily climate modelling grid images at 0.05° spatial resolution. Spectral adjustment of VIIRS I1 (red) and I2 (near infra-red – NIR) bands to match MODIS/Aqua b1 (red) and b2 (NIR) bands is performed to remove a bias between MODIS and VIIRS-based red, NIR, and NDVI estimates. Overall, red reflectance, NIR reflectance, and NDVI uncertainties were 0.014, 0.029, and 0.056, respectively, for the 500 m product and 0.013, 0.016, and 0.032 for the 0.05° product. The study shows that MODIS and VIIRS NDVI data can be used interchangeably for applications with an uncertainty of less than 0.02–0.05, depending on the scale of spatial aggregation, which is typically the uncertainty of the individual data sets.     

Cross‐calibration of MSG1‐SEVIRI infrared channels with Terra‐MODIS channels

This paper addresses the cross‐calibration of the infrared channels 4 (3.9 µm), 9 (10.8 µm) and 10 (12.0 µm) of the Spinning Enhanced Visible and Infra‐Red Imager (SEVIRI) onboard the Meteosat Second Generation 1 (MSG1) satellite with the channels of the MODerate resolution Imaging Spectroradiometer (MODIS) onboard Terra. The cross‐calibrations, including the Ray‐Matching (RM) method and the Radiative Transfer Modelling (RTM) method, were developed and implemented over a tropical area using SEVIRI and MODIS measurements of July 2005 and July 2006 with absolute view zenith angle differences (|ΔVZA|)<0.5°, absolute view azimuth angle differences (|ΔVAA|)<0.5° and absolute time differences (|ΔTime|)<10 min. The results obtained by the RM and RTM methods revealed calibration discrepancies between the two sensors. The results obtained by the RM method were consistent with previously published results. The results obtained by the RTM method were consistent with the results obtained by the RM method if the temperature differences caused by the spectral differences between the two sensors were taken into account. From the cross‐calibration results obtained by the two methods, the use of the results obtained by the RTM method to recalibrate the SEVIRI data is recommended. The recalibrations remove the overestimation of the Land Surface Temperature (LST) retrieved from the SEVIRI data by a split‐window method.     

Assessing vegetation condition in the presence of biomass burning smoke by applying the Aerosol‐free Vegetation Index (AFRI) on MODIS images

Vegetation indices (VIs) such as the Normalized Difference Vegetation Index (NDVI) are widely used for assessing vegetation cover and condition. One of the NDVI's significant disadvantages is its sensitivity to aerosols in the atmosphere, hence several atmospherically resistant VIs were formulated using the difference in the radiance between the blue and the red spectral bands. The state‐of‐the‐art atmospherically resistant VI, which is a standard Moderate Resolution Imaging Spectroradiometer (MODIS) product, together with the NDVI, is the Enhanced Vegetation Index (EVI). A different approach introduced the Aerosol‐free Vegetation Index (AFRI) that is based on the correlation between the shortwave infrared (SWIR) and the visible red bands. The AFRI main advantage is in penetrating an opaque atmosphere influenced by biomass burning smoke, without the need for explicit correction for the aerosol effect. The objective of this research was to compare the performance of these three VIs under smoke conditions. The AFRI was applied to the 2.1 µm SWIR channel of the MODIS sensor onboard the Earth Observing System (EOS) Terra and Aqua satellites in order to assess its functionality on these imaging platforms. The AFRI performance was compared with those of NDVI and EVI. All VIs were calculated on images with and without present smoke, using the surface‐reflectance MODIS product, for three case studies of fires in Arizona, California, and Zambia. The MODIS Fire Product was embedded on the images in order to identify the exact location of the active fires. Although good correlations were observed between all VIs in the absence of smoke (in the Arizona case R ² = 0.86, 0.77, 0.88 for the NDVI–EVI, AFRI–EVI, and AFRI–NDVI, respectively) under smoke conditions a high correlation was maintained between the NDVI and the EVI, while low correlations were found for the AFRI–EVI and AFRI–NDVI (0.21 and 0.16, for the Arizona case, respectively). A time series of MODIS images recorded over Zambia during the summer of 2000 was tested and showed high NDVI fluctuations during the study period due to oscillations in aerosol optical thickness values despite application of aerosol corrections on the images. In contrast, the AFRI showed smoother variations and managed to better assess the vegetation condition. It is concluded that, beneath the biomass burning smoke, the AFRI is more effective than the EVI in observing the vegetation conditions.     

Moments from space captured by MSG SEVIRI

A method for the generation of full disk MSG (METEOSAT Second Generation) SEVIRI (Scanning-Enhanced Visible and Infrared Imager) true colour composite images is presented. The algorithm mainly uses the SEVIRI channels VIS006 (0.6 μm), NIR008 (0.8 μm) and NIR016 (1.6 μm). Only one of the full disk SEVIRI channels is located in the visible spectral region. This is channel VIS006 which covers mainly the yellow to red parts of the visible spectrum. The lack of information in the blue and green parts of the visible spectrum is compensated by using data from NASA's (National Aeronautics and Space Administration's) Blue Marble next generation (BMNG) project to fill a look-up table (LUT) transforming RGB (red/green/blue) false colour composite images of VIS006/NIR008/NIR016 into true colour images. Tabulated radiative transfer calculations of a pure Rayleigh atmosphere are used to add an impression of Rayleigh scattering towards the sunlit horizon. The resulting images satisfy naive expectations: clouds are white or transparent, vegetated surfaces are greenish, deserts are sandy-coloured, the ocean is dark blue to black and a narrow halo due to Rayleigh scattering is visible at the sunlit horizon. Therefore, such images are easily interpretable also for inexperienced users not familiar with the characteristics of typical MSG false colour composite images.     

Effect of WiFS viewing geometry on crop reflectance: A simulation study using SAIL model

The Wide-Field Sensor (WiFS) on board Indian Remote Sensing Satellites (IRS-1C, IRS-P3 and IRS-1D) has a wide swath (～810 km) and is thus able to provide high repetivity (5/3 days) data acquired from different orbits. The impact of variable viewing geometry from WiFS on crop reflectance was investigated for Delhi (77.2°E and 28.58°N) for all 122 possible acquisitions by IRS-1C during 1997. Using the SAIL simulation model, top-of-canopy red and near-infrared reflectances were simulated for partial and closed crop canopies over soils of different brightness. Results show significant variations in red and near-infrared reflectances. The effects are higher in partial canopies, and they are observed in Normalized Difference Vegetation Index (NDVI) also. For partial canopy, the seasonal variations with solar zenith angle are also significant. For closed canopy, these variations are less, and computed NDVI is relatively insensitive to viewing geometry.     

View angle effects on MODIS snow mapping in forests

Binary snow maps and fractional snow cover data are provided routinely from MODIS (Moderate Resolution Imaging Spectroradiometer). This paper investigates how the wide observation angles of MODIS influence the current snow mapping algorithm in forested areas. Theoretical modeling results indicate that large view zenith angles (VZA) can lead to underestimation of fractional snow cover (FSC) by reducing the amount of the ground surface that is viewable through forest canopies, and by increasing uncertainties during the gridding of MODIS data. At the end of the MODIS scan line, the total modeled error can be as much as 50% for FSC. Empirical analysis of MODIS/Terra snow products in four forest sites shows high fluctuation in FSC estimates on consecutive days. In addition, the normalized difference snow index (NDSI) values, which are the primary input to the MODIS snow mapping algorithms, decrease as VZA increases at the site level. At the pixel level, NDSI values have higher variances, and are correlated with the normalized difference vegetation index (NDVI) in snow covered forests. These findings are consistent with our modeled results, and imply that consideration of view angle effects could improve MODIS snow monitoring in forested areas.     

MSG/SEVIRI cloud mask and type from SAFNWC

Data from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board the first Meteosat Second Generation (MSG) satellite have been available since February 2003. Four MSG satellites are planned to ensure an operational service until at least 2018. A software package, which derives from MSG/SEVIRI imagery a set of 12 products useful for nowcasting purposes, has been developed cooperatively by the Satellite Application Facility for supporting NoWCasting and very short range forecasting (SAFNWC) and is distributed by EUMETSAT.

This paper describes the cloud mask (CMa) and type (CT) algorithms implemented in this SAFNWC/MSG software package. A multispectral thresholding technique has been used: the test sequence depends on illumination conditions and geographical location whereas most thresholds are dynamically computed from ancillary data (atlas, climatology maps, numerical weather prediction (NWP) model forecast fields) using radiative transfer models. These algorithms have been prototyped using GOES‐8 and MODIS imagery before being applied to MSG‐1/SEVIRI. The cloud mask and type can be extracted in any area inside the MSG full disk. Preliminary validation results obtained from a comparison with surface observations using a few months of MSG‐1/SEVIRI data show good performances.     

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.     

Evaluating the species energy relationship with the newest measures of ecosystem energy: NDVI versus MODIS primary production

Ecosystem energy has been shown to be a strong correlate with biological diversity at continental scales. Early efforts to characterize this association used the normalized difference vegetation index (NDVI) to represent ecosystem energy. While this spectral vegetation index covaries with measures of ecosystem energy such as net primary production, the covariation is known to degrade in areas of very low vegetation or in areas of dense forest. Two of the new vegetation products from the MODIS sensor, derived by integrating spectral reflectance, climate data, and land cover, are thought to better approximate primary productivity than NDVI. In this study, we determine if the new MODIS derived measures of primary production, gross primary productivity (GPP) and net primary productivity (NPP) better explain variation in bird richness than historically used NDVI. Moreover, we evaluate if the two productivity measures covary more strongly with bird diversity in those vegetation conditions where limitations of NDVI are well recognized.Biodiversity was represented as native landbird species richness derived from the North American Breeding Bird Survey. Analyses included correlation analyses among predictor variables, and univariate regression analyses between each predictor variable and bird species richness. Analyses were done at two levels: for all BBS routes across natural landscapes in North America; and for routes in 10 vegetation classes stratified by vegetated cover along a gradient from bare ground to herbaceous cover to tree cover. We found that NDVI, GPP and NPP were highly correlated and explained similar variation in bird species richness when analyzed for all samples across North America. However, when samples were stratified by vegetated cover, strength of correlation between NDVI and both productivity measures was low for samples with bare ground and for dense forest. The NDVI also explained substantially less variation in bird species richness than the primary production in areas with more bare ground and in areas of dense forest. We conclude that MODIS productivity measures have higher utility in studies of the relationship of species richness and productivity and that MODIS GPP and NPP improve on NDVI, especially for studies with large variation in vegetated cover and density.     

Evaluating the species energy relationship with the newest measures of ecosystem energy: NDVI versus MODIS primary production

Ecosystem energy has been shown to be a strong correlate with biological diversity at continental scales. Early efforts to characterize this association used the normalized difference vegetation index (NDVI) to represent ecosystem energy. While this spectral vegetation index covaries with measures of ecosystem energy such as net primary production, the covariation is known to degrade in areas of very low vegetation or in areas of dense forest. Two of the new vegetation products from the MODIS sensor, derived by integrating spectral reflectance, climate data, and land cover, are thought to better approximate primary productivity than NDVI. In this study, we determine if the new MODIS derived measures of primary production, gross primary productivity (GPP) and net primary productivity (NPP) better explain variation in bird richness than historically used NDVI. Moreover, we evaluate if the two productivity measures covary more strongly with bird diversity in those vegetation conditions where limitations of NDVI are well recognized.Biodiversity was represented as native landbird species richness derived from the North American Breeding Bird Survey. Analyses included correlation analyses among predictor variables, and univariate regression analyses between each predictor variable and bird species richness. Analyses were done at two levels: for all BBS routes across natural landscapes in North America; and for routes in 10 vegetation classes stratified by vegetated cover along a gradient from bare ground to herbaceous cover to tree cover. We found that NDVI, GPP and NPP were highly correlated and explained similar variation in bird species richness when analyzed for all samples across North America. However, when samples were stratified by vegetated cover, strength of correlation between NDVI and both productivity measures was low for samples with bare ground and for dense forest. The NDVI also explained substantially less variation in bird species richness than the primary production in areas with more bare ground and in areas of dense forest. We conclude that MODIS productivity measures have higher utility in studies of the relationship of species richness and productivity and that MODIS GPP and NPP improve on NDVI, especially for studies with large variation in vegetated cover and density.     

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.     

Neural networks as a tool for constructing continuous NDVI time series from AVHRR and MODIS

The long term Advanced Very High Resolution Radiometer (AVHRR)‐Normalized Difference Vegetation Index (NDVI) record provides a critical historical perspective on vegetation dynamics necessary for global change research. Despite the proliferation of new sources of global, moderate resolution vegetation datasets, the remote sensing community is still struggling to create datasets derived from multiple sensors that allow the simultaneous use of spectral vegetation for time series analysis. To overcome the non‐stationary aspect of NDVI, we use an artificial neural network (ANN) to map the NDVI indices from AVHRR to those from MODIS using atmospheric, surface type and sensor‐specific inputs to account for the differences between the sensors. The NDVI dynamics and range of MODIS NDVI data at 1° is matched and extended through the AVHRR record. Four years of overlap between the two sensors is used to train a neural network to remove atmospheric and sensor specific effects on the AVHRR NDVI. In this paper, we present the resulting continuous dataset, its relationship to MODIS data, and a validation of the product.     

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.     

Smoke effects on NDVI determination of savannah vegetation types

Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data collected on 25 and 27 August and 1 September 1995, in central Brazil, were used to describe smoke effects on Normalized Difference Vegetation Index (NDVI) determination of savannah vegetation types. Image-derived empirical relationships were used to predict the response of the red band (sensitive to smoke) from the shortwave infrared (SWIR) bands (insensitive to smoke), so as to minimize smoke effects on NDVI. Results showed that smoke effects were target dependent and produced NDVI differences up to 0.35 between the three dates for dense vegetation covers such as savannah woodland and riparian forest. Although this approach did not consider the smoke influence on the near-infrared (NIR) interval, the small SWIR-derived NDVI differences observed between the dates for a given land cover, under distinct smoke conditions, indicated its applicability in the Brazilian cerrado.