Vegetation Index Measurements Research Articles

Seasonal and interannual variability of climate and vegetation indices across the Amazon

Drought exerts a strong influence on tropical forest metabolism, carbon stocks, and ultimately the flux of carbon to the atmosphere. Satellite-based studies have suggested that Amazon forests green up during droughts because of increased sunlight, whereas field studies have reported increased tree mortality during severe droughts. In an effort to reconcile these apparently conflicting findings, we conducted an analysis of climate data, field measurements, and improved satellite-based measures of forest photosynthetic activity. Wet-season precipitation and plant-available water (PAW) decreased over the Amazon Basin from 1996-2005, and photosynthetically active radiation (PAR) and air dryness (expressed as vapor pressure deficit, VPD) increased from 2002-2005. Using improved enhanced vegetation index (EVI) measurements (2000-2008), we show that gross primary productivity (expressed as EVI) declined with VPD and PAW in regions of sparse canopy cover across a wide range of environments for each year of the study. In densely forested areas, no climatic variable adequately explained the Basin-wide interannual variability of EVI. Based on a site-specific study, we show that monthly EVI was relatively insensitive to leaf area index (LAI) but correlated positively with leaf flushing and PAR measured in the field. These findings suggest that production of new leaves, even when unaccompanied by associated changes in LAI, could play an important role in Basin-wide interannual EVI variability. Because EVI variability was greatest in regions of lower PAW, we hypothesize that drought could increase EVI by synchronizing leaf flushing via its effects on leaf bud development.

An Empirical Algorithm for Estimating Agricultural and Riparian Evapotranspiration Using MODIS Enhanced Vegetation Index and Ground Measurements of ET. I. Description of Method

We used the Enhanced Vegetation Index (EVI) from MODIS to scale evapotranspiration (ETactual) over agricultural and riparian areas along the Lower Colorado River in the southwestern US. Ground measurements of ETactual by alfalfa, saltcedar, cottonwood and arrowweed were expressed as fraction of potential (reference crop) ETo (EToF) then regressed against EVI scaled between bare soil (0) and full vegetation cover (1.0) (EVI*). EVI* values were calculated based on maximum and minimum EVI values from a large set of riparian values in a previous study. A satisfactory relationship was found between crop and riparian plant EToF and EVI*, with an error or uncertainty of about 20% in the mean estimate (mean ETactual = 6.2 mm d−1, RMSE = 1.2 mm d−1). The equation for ETactual was: ETactual = 1.22 × ETo-BC × EVI*, where ETo-BC is the Blaney Criddle formula for ETo. This single algorithm applies to all the vegetation types in the study, and offers an alternative to ETactual estimates that use crop coefficients set by expert opinion, by using an algorithm based on the actual state of the canopy as determined by time-series satellite images.

An Empirical Algorithm for Estimating Agricultural and Riparian Evapotranspiration Using MODIS Enhanced Vegetation Index and Ground Measurements of ET. II. Application to the Lower Colorado River, U.S.

Large quantities of water are consumed by irrigated crops and riparian vegetation in western U.S. irrigation districts. Remote sensing methods for estimating evaporative water losses by soil and vegetation (evapotranspiration, ET) over wide river stretches are needed to allocate water for agricultural and environmental needs. We used the Enhanced Vegetation Index (EVI) from MODIS sensors on the Terra satellite to scale ET over agricultural and riparian areas along the Lower Colorado River in the southwestern U.S., using a linear regression equation between ET of riparian plants and alfalfa measured on the ground, and meteorological and remote sensing data, with an error or uncertainty of about 20%. The algorithm was applied to irrigation districts and riparian areas from Lake Mead to the U.S./Mexico border. The results for agricultural crops were similar to results produced by crop coefficients developed for the irrigation districts along the river. However, riparian ET was only half as great as crop coefficient estimates set by expert opinion, equal to about 40% of reference crop evapotranspiration. Based on reported acreages in 2007, agricultural crops (146,473 ha) consumed 2.2 × 109 m3 yr−1 of water. All riparian shrubs and trees (47,014 ha) consumed 3.8 × 108 m3 yr−1, of which saltcedar, the dominant riparian shrub (25,044 ha), consumed 1.8 × 108 m3 yr−1, about 1% of the annual flow of the river. This method could supplement existing protocols for estimating ET by providing an estimate based on the actual state of the canopy as determined by frequent-return satellite data.

Monitoring and forecasting ecosystem dynamics using the Terrestrial Observation and Prediction System (TOPS)

We present an approach for monitoring and forecasting landscape level indicators of the condition of protected area (PA) ecosystems including changes in snowcover, vegetation phenology and productivity using the Terrestrial Observation and Prediction System (TOPS). TOPS is a modeling framework that integrates operational satellite data, microclimate mapping, and ecosystem simulation models to characterize ecosystem status and trends. We have applied TOPS to investigate trends and patterns in landscape indicators using test cases at both national and park-level scales to demonstrate the potential utility of TOPS for supporting efforts by the National Park Service to develop standardized indicators for protected area monitoring. Our analysis of coarse resolution satellite-derived normalized difference vegetation index (NDVI) measurements for North America from 1982–2006 indicates that all but a few PAs are located in areas that exhibited a sustained decline in vegetation condition. We used Yosemite National Park as our park-level test case, and while no significant trends in NDVI were detected during the same period, evidence of drought-induced vegetation mortality and recovery patterns dominated the 25-year record. In our Yosemite analysis, we show that analyzing MODIS (Moderate Resolution Imaging Spectro-radiometer) products (vegetation indices, absorbed radiation, land surface temperature and gross primary production) in conjunction with ground-based measurements, such as runoff, lends additional utility to satellite-based monitoring of ecosystems indicators, as together they provide a comprehensive view of ecosystem condition. Analyses of MODIS products from 2001–2006 show that year-to-year changes in the onset of spring at Yosemite were as large as 45 days, and this signal in the satellite data record is corroborated by observed changes in spring runoff patterns. Finally, we applied TOPS to assess long-term climate impacts on ecosystem condition at the scale of an individual park. When driven by projected climatic changes at Yosemite of 4–6 °C warming by 2100 with no changes in precipitation patterns, TOPS predicts significantly reduced winter snowpack and an earlier onset of the growing season, resulting in prolonged summer drought and reduced vegetation productivity.

Identifying Climatic Controls on Ring Width: The Timing of Correlations between Tree Rings and NDVI

Abstract The authors examine the effects of latitude and life history on the timing of relationships between satellite measures of normalized difference vegetation index (NDVI) and ground-based measures of tree-ring width in forests at mid- and high latitudes in the Northern Hemisphere. Results indicate a correlation between NDVI and tree rings over the entire growing season for all areas analyzed. For sites south of 40°N, a correlation appears in early spring and late fall while a correlation appears during summer months north of 40°N. For conifers, the correlation appears in summer while deciduous trees show the relationship during early spring and late fall. Of these two correlations, the effect of life history seems to dominate the effect of latitude. The timing of these correlations may help clarify the relationship between climate and tree rings and the effect of canopy duration on carbon uptake by trees.

Assessing Steady-state Fluorescence and PRI from Hyperspectral Proximal Sensing as Early Indicators of Plant Stress: The Case of Ozone Exposure.

High spectral resolution spectrometers were used to detect optical signals ofongoing plant stress in potted white clover canopies subjected to ozone fumigation. Thecase of ozone stress is used in this manuscript as a paradigm of oxidative stress. Steadystatefluorescence (Fs) and the Photochemical Reflectance Index (PRI) were investigatedas advanced hyperspectral remote sensing techniques able to sense variations in the excessenergy dissipation pathways occurring when photosynthesis declines in plants exposed to astress agent. Fs and PRI were monitored in control and ozone fumigated canopies during a21-day experiment together with the traditional Normalized Difference Vegetation Index(NDVI) and physiological measurements commonly employed by physiologists to describestress development (i.e. net CO₂ assimilation, active fluorimetry, chlorophyll concentrationand visible injuries). It is shown that remote detection of an ongoing stress through Fs andPRI can be achieved in an early phase, characterized by the decline of photosynthesis. Onthe contrary, NDVI was able to detect the stress only when damage occurred. These resultsopen up new possibilities for assessment of plant stress by means of hyperspectral remotesensing.

Spectral characteristics of sensors onboard IRS-1D and P6 satellites: Estimation and their influence on surface reflectance and NDVI

This paper reports the results of a modeling study carried out with two objectives, (1) to estimate and compare effective spectral characteristics (central wavelength, bandwidth and bandpass exo-atmospheric solar irradiance Eo) of various spectral channels of LISS-III, WiFS, LISS-III*, LISS-IV and AWiFS onboard Indian Remote Sensing Satellites IRS-ID and P6 using moment method based on the laboratory measurements of sensor spectral response, and (2) to quantify the influence of varying sensor spectral response on reflectance and Normalized Difference Vegetation Index (NDVI) measurements using surface reflectance spectra corresponding to different leaf area index conditions of crop target obtained through field experiment. Significant deviation of 4 to 14 nm in central wavelength and 1.6 to 14.07 nm in spectral width was observed for the corresponding channel of IRS sensors. Coefficient of variation of the order of 0.1 to 1.11% was noticed in Eo among various IRS sensors, which could induce a difference of 0.72 to 3.35% in the estimation of top of atmosphere reflectance for crop target. The variation in spectral response of IRS sensors implied a relative difference of the order of 0.91 to 3.38% in surface reflectance and NDVI measurements. Polynomial approximations are also provided for spectral correction that can be utilized for normalizing the artifacts introduced due to differences in spectral characteristics among IRS sensors.

A Simple Reclassification Method for Correcting Uncertainty in Land Use/Land Cover Data Sets Used with Land Surface Models

With increasing computational resources, environmental models are run at finer grid spacing to resolve the land surface characteristics. The land use/land cover (LULC) data sets input into land surface models are used to assign various default parameters from a look-up tables. The objective of this study is to assess the potential uncertainty in the LULC data and to present a reclassification method for improving the accuracy of LULC data sets. The study focuses on the Southern Great Plains and specifically the Walnut River Watershed in southeastern Kansas, USA. The uncertainty analysis is conducted using two data sets: The National Land Cover Dataset 1992 (NLCD 92) and the Gap Analysis Program (GAP) data set, and a reclassification logic tree. A comparison of these data sets showed that they do not agree for approximately 27% of the watershed. Moreover, an accuracy assessment of these two data sets indicated that neither had an overall accuracy as high as 80%. Using the relationships between land-surface characteristics and LULC, a reclassification of the watershed was conducted using a logical model. This model iteratively reclassified the uncertain pixels according to their surface characteristics. The model utilized normalized difference vegetation index (NDVI) measurements during April and July 2003, elevation, and slope. The reclassification yielded a revised LULC dataset that was substantially improved. The overall accuracy of the revised data set was nearly 93%. The study results suggest: (i) as models adopt finer grid spacings, the uncertainty in the LULC data will become significant; (ii) assimilating NDVI into the land-surface models can reduce the uncertainty due to LULC assignment; (iii) the standard LULC data sets must be used with caution when the focus is on local scale; and (iv) reclassification is a valuable means of improving the accuracy of LULC data sets prior to applying them to local issues or phenomena.

By‐Plant Prediction of Corn Forage Biomass and Nitrogen Uptake at Various Growth Stages Using Remote Sensing and Plant Height

As research intensifies on developing precision agricultural practices for corn (Zea mays L.) production, an important component will be to identify the scale at which these practices should be implemented. We hypothesized that optical sensing can be used to measure individual corn plant biomass and N uptake. A 3‐yr study was conducted at three locations in Oklahoma. Optical sensor readings of normalized difference vegetation index (NDVI) and plant height measurements were collected on individual corn plants at various growth stages ranging from V8 (collar of eighth leaf unfolded) to VT (last branch of the tassel is completely visible) and correlated with individual plant biomass, forage yield per unit area occupied by the plant, and N uptake of that plant. Individual plant height measurement, collected before reproductive growth, was a good predictor of plant biomass across the six site years of the study (r2 = 0.81). The index of NDVI × plant height provided the highest correlation with by‐plant forage yield on an area basis. Optical sensor and plant height measurements collected at the V8 to V10 (collar of 10th leaf unfolded) growth stage can distinguish individual plants and provide information as to their biomass accumulation and N uptake. This research demonstrates that by‐plant information can be collected and used to direct high resolution N applications. The index, NDVI × plant height, may be used to refine midseason fertilizer N rates based on expected N removal and by‐plant measurements at or before V10.

Landsat-7 Long-Term Acquisition Plan

The long-term acquisition plan (LTAP) was developed to fulfill the Landsat-7 (L7) mission of building and seasonally refreshing an archive of global, essentially cloud-free, sunlit, land scenes. The LTAP is considered one of the primary successes of the mission. By incorporating seasonality and cloud avoidance into the decision making used to schedule image acquisitions, the L7 data in the U.S. Landsat archive is more complete and of higher quality than has ever been previously achieved in the Landsat program. Development of the LTAP system evolved over more than a decade, starting in 1995. From 2002 to 2004 most attention has been given to validation of LTAP elements. We find that the original expectations and goals for the LTAP were surpassed for Landsat 7. When the L7 scan line corrector mirror failed, we adjusted the LTAP operations, effectively demonstrating the flexibility of the LTAP concept to address unanticipated needs. During validation, we also identified some seasonal and geographic acquisition shortcomings of the implementation: including how the spectral vegetation index measurements were used and regional/seasonal cloud climatology concerns. Some of these issues have already been at least partially addressed in the L7 LTAP, while others will wait further attention in the development of the LTAP for the Landsat Data Continuity Mission (LDCM). The lessons learned from a decade of work on the L7 LTAP provide a solid foundation upon which to build future mission LTAPs including the LDCM.

Mapping tropical forest structure in southeastern Madagascar using remote sensing and artificial neural networks

Tropical forest condition has important implications for biodiversity, climate change and human needs. Structural features of forests can serve as useful indicators of forest condition and have the potential to be assessed with remotely sensed imagery, which can provide quantitative information on forest ecosystems at high temporal and spatial resolutions. Herein, we investigate the utility of remote sensing for assessing, predicting and mapping two important forest structural features, stem density and basal area, in tropical, littoral forests in southeastern Madagascar. We analysed the relationships of basal area and stem density measurements to the Normalised Difference Vegetation Index (NDVI) and radiance measurements in bands 3, 4, 5 and 7 from the Landsat Enhanced Thematic Mapper Plus (ETM+). Strong relationships were identified among all of the individual bands and field based measurements of basal area ( p<0.01) while there were weak and insignificant relationships among spectral response and stem density measurements. NDVI was not significantly correlated with basal area but was strongly and significantly correlated with stem density ( r=−0.69, p<0.01) when using a subset of the data, which represented extreme values. We used an artificial neural network (ANN) to predict basal area from radiance values in bands 3, 4, 5 and 7 and to produce a predictive map of basal area for the entire forest landscape. The ANNs produced strong and significant relationships between predicted and actual measures of basal area using a jackknife method ( r=0.79, p<0.01) and when using a larger data set ( r=0.82, p<0.01). The map of predicted basal area produced by the ANN was assessed in relation to a pre-existing map of forest condition derived from a semi-quantitative field assessment. The predictive map of basal area provided finer detail on stand structural heterogeneity, captured known climatic influences on forest structure and displayed trends of basal area associated with degree of human accessibility. These findings demonstrate the utility of ANNs for integrating satellite data from the Landsat ETM+ spectral bands 3, 4, 5 and 7 with limited field survey data to assess patterns in basal area at the landscape scale.

Empirical comparison of Landsat 7 and IKONOS multispectral measurements for selected Earth Observation System (EOS) validation sites

The Space Imaging IKONOS observatory may provide an important benefit in terrestrial scientific research. The five-band, 1 m panchromatic and 4 m multispectral measurements have the potential to provide a source of measurements to evaluate subpixel land cover variability in measurements from observatories such as Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Terra MODIS sensor. The IKONOS observations are at a spatial scale equivalent to field measurements typically carried out in ecological and land cover research. As such, the IKONOS observations may serve as a source of “virtual” ground measurements, for the lower spatial resolution, global observatories. In this study we examine how well IKONOS sensor observations replicate Landsat 7 ETM+ visible/near infrared observations for selected Earth Observation System (EOS) validation sites in the United States. The sites examined—Beltsville, MD, Konza Prairie, KS, and Sevilleta, NM—sample the east–west moisture gradient across the United States. Observations for each site were acquired, as nearly time-coincident as possible, from ETM+ and the IKONOS sensor, several times over the growing season. This was done to insure that we compared these two sensors over the widest range of observing conditions possible. We also examined IKONOS imagery from Phoenix, AZ, where Space Imaging had and had not applied a modulation transfer function compensation (MTFC) process. The MTFC is their standard product. We found that this product, at the original 4 m spatial resolution, appears to have minor radiometric artifacts as a result of the process. When the IKONOS observations were aggregated to 30 m, this problem was essentially absent, allowing us to proceed with the remainder of our study. We processed the IKONOS sensor and ETM+ measurements to produce close approximates of each other. Our processing steps included ortho-rectification, calibration to planetary reflectance, pixel alignment and pixel aggregation. We initially found radiometric differences between the two sensors that increased with increasing wavelength. Space Imaging updated their calibration information, based on analyses from NASA Stennis Space Center staff, which removed much of this discrepancy. We now find that the IKONOS red and near infrared measurements differ between the two sensors, with IKONOS generally producing higher reflectance in the red band and lower reflectance in the near infrared band than the Landsat 7 ETM+ sensor. This results in the IKONOS sensor producing lower spectral vegetation index measurements, for the same target, than ETM+, a measurement variation that has been observed between other sensors. We also encountered far more cirrus cloud (and shadow) contamination in these paired observations that we had expected. After careful initial selection, we lost over half of our image pairs from the analysis because of cirrus cloud contamination. We do not know whether this is simply because of the paired, comparative design of this study or whether it relates to the increased spatial and radiometric resolution of the IKONOS sensor. The results of this study not only provide a baseline assessment of IKONOS versus Landsat 7 ETM+ visible and near infrared measurements but also suggest some of the issues that need more attention when comparing other sensor systems as well as developing the design of future land observatories.

Seasonal to interannual variability of vegetation and fires at SAFARI 2000 sites inferred from advanced very high resolution radiometer time series data

NOAA advanced very high resolution radiometer data are used to place the SAFARI 2000 and SAFARI 1992 intensive campaigns in the context of the interannual variability of vegetation conditions in southern Africa. Normalized difference vegetation index (NDVI) measurements and sea surface temperature (SST) indices of El Niño/Southern Oscillation (ENSO) are compared and the connections explained. The paper shows the vegetation evolution for the 2000 growing season, with unprecedented high and persistent NDVI anomalies associated with a cold phase of ENSO (La Niña) and above average rainfall between November and May, south of 15°S. In contrast, the 1992 season showed marked negative NDVI anomalies associated with an extreme drought in the southern part of the region, associated with the warm phase of ENSO (El Niño). These differences in NDVI patterns resulted in different patterns of fire distribution. More satellite detections of active fires were observed in 2000 than in 1992, especially for Botswana, Namibia, southern Zimbabwe, and southern Mozambique. Comparisons between in situ airborne measurements collected in 2000 and 1992 require an understanding of the extremely different vegetation and fire conditions associated with those years. Generalizations from results of the two SAFARI campaigns should be made with careful consideration of the extreme differences in conditions and the large interannual variability encountered in southern Africa, as depicted by the long‐term record of satellite vegetation measurements.

Evaluating land surface moisture conditions from the remotely sensed temperature/vegetation index measurements: An exploration with the simplified simple biosphere model

Land soil moisture conditions play a critical role in evaluating terrestrial environmental conditions related to ecological, hydrological, and atmospheric processes. Extensive efforts to exploit the potential of remotely sensed observations to help quantify this complex variable are still underway. Among the various methods, several investigators have explored a combination of surface temperatures and spectral vegetation index (SVI) measurements, the TVX method, as a means to account for the variable influence of vegetation cover in soil moisture assessment. Although considerable empirical evidence has been presented exploring the potential of TVX methods to assess regional moisture conditions, less attention has been given to assessing the underlying biophysics of the observed TVX patterns. In this study, the Simplified Simple Biosphere (SSiB) model is exploited to examine the factors that lead to the observed TVX relation. For a range of typical, midlatitude, growing season conditions, the SSiB model produces the expected TVX relationship, surface temperature decreases with increasing SVI values. The most critical factors that cause the TVX relation to vary include near-surface soil moisture (2 cm), incident radiation (IR), and, to a lesser degree, wind speed. Whereas many empirical studies have suggested that the slope of the TVX relation may provide an important diagnostic of soil moisture conditions, in this analysis, the impact of plant stomatal function is shown to confuse this interpretation of the TVX slope. However, other aspects of the TVX metrics, specifically bare soil temperature and canopy temperature, do provide diagnostic near-surface soil moisture information. Growing season variations in TVX metrics were examined for the conditions recorded at the Hydrological and Atmospheric Pilot Experiment—Modelisation du Bilan Hydrique (HAPEX-Mobilhy) study site. The results from this analysis indicate that soil and canopy temperatures vary as a function of soil moisture conditions and, to a lesser degree, as a result of varying solar insolation and wind speed. The results also show that the TVX metrics are able to provide daily soil moisture variation up to 2 cm of soil depth and seasonal trend up to 10 cm. Using the satellite-derived surface temperatures and a SSiB-derived retrieval equation, the retrieved soil moistures at the HAPEX-Mobilhy site generally closely approximate the conditions recorded on the ground.

A decision support system for mapping the site-specific potassium requirement of wheat in the field

The intensely weathered nature of Western Australian cropping soils and the long history of potassium depletion by the farming system has resulted in increased incidence of potassium deficiency in wheat. There is currently no scientifically based method for potassium recommendation in Western Australia. This paper describes the use of site-specific plot-scale field trials carried out in 1995–98 and a crop response model to develop a generally applicable potassium recommendation system. Geographic information system technology was used to extend the uniform potassium recommendation system into a system for mapping spatially variable potassium requirement that takes account of crop demand and soil available potassium. The field trials were carried out on a range of soil types and showed that wheat response to potassium can be described by the Mitscherlich equation. The size of the response was dependent on the soil test value for plant available potassium and the yield of the crop. The latter is mainly dependent on rainfall in the water-limited Mediterranean environment of Western Australia. The relationships between the maximum achievable yield, crop response and soil available potassium values were quantified in order to allow the decision support system to be developed for uniform whole-paddock fertiliser recommendation. Both soil available potassium and yield are very spatially variable in Western Australia and for wheat, the coefficient of variation of yield within the paddock is often of the order of 30%. Soil property variation can be of a similar order. Maps of soil available potassium values and of spatially variable target yield determined either from (i) farmer’s estimate, (ii) yield monitors and (iii) remotely sensed normalised difference vegetation index measurements allow this decision system to map spatially variable potassium requirement. Comparison of the map of potassium requirement with measured spatially variable response to potassium showed that the decision support system performed satisfactorily.

Using Tidal Salt Marsh Mesocosms to Aid Wetland Restoration

AbstractTidal wetland mesocosms at Tijuana River National Estuarine Research Reserve failed to elucidate effects of hydrologic treatments (excluded, impounded, and fully tidal systems) for most parameters measuring Salicornia virginica (pickleweed). Although soil salinity increased where tidal flushing was excluded for 10 months (salinities rose ∼20 to 50%), pickleweed cover and algal chlorophyll did not differ among treatments. Effects were seen only in pickleweed growth rates (∼30% decrease where tides were excluded) and normalized difference vegetation index (NDVI) measurements. We failed to show any differences between impounded and fully tidal conditions, because the mesocosms had coarse sediments, and impounded water drained easily via subsurface flow. However, the problems that we encountered with the mesocosms led to the following advice for future wetland restoration projects: (1) Mesocosms are useful for testing restoration techniques before an actual restoration project takes place. (2) Mesocosms should be used to test factors that may lead to more successful restoration in the future, including planting techniques, substrate conditions, and hydrology. (3) Mesocosms should be used to develop new assessment methods for monitoring wetland ecosystems. Because of the ability to control some environmental parameters while maintaining seminatural conditions, mesocosms offer great potential for the future evaluation of experimental restoration techniques.

Strategy for direct and indirect methods for correcting the aerosol effect on remote sensing: From AVHRR to EOS-MODIS

Aerosol scatters solar radiation before it reaches the surface and scatters and absorbs it again after it is reflected from the surface and before it reaches the satellite sensor. The effect is spectrally and spatially dependent. Therefore, atmospheric aerosol (dust, smoke, and air pollution particles) has a significant effect on remote sensing. Correction for the aerosol effect on remote sensing of land areas was never achieved on an operational basis though several case studies were demonstrated. We distinguish between direct correction, in which the aerosol loading is derived from the image itself (or supplied from external sources) followed by correction of the image using an appropriate radiative transfer model, and indirect correction, achieved by defining remote sensing functions that are less dependent on the aerosol loading. To some degree this was already achieved in global remote sensing of vegetation where a composite of several days of the normalized difference vegetation index (NDVI) measurements, choosing the maximal value, was used instead of a single cloud-screened value. The atmospheric resistant vegetation index (ARVI) introduced recently for the Earth Observing System (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS) is the most appropriate example of indirect correction, where the index is defined in such a way that the atmospheric effect in a blue spectral channel cancels to a large degree the atmospheric effect in the red channel in computations of the vegetation index. Atmospheric corrections can also use aerosol climatology or simultaneous measurements with ground-based instrumentation. These aspects of aerosol studies and remote sensing are reviewed in this article. New advances in ground-based instrumentation and future satellite systems (including measurements of polarization) are discussed. In the conclusions a strategy is introduced for a combination of ground-based measurements, with direct and indirect corrections that is implemented for the EOS-MODIS and recommended for other similar platforms. Such strategy, planned in advance, is a first step to face the challenge and take advantage of the opportunities that the remote sensing community will face with the launch of EOS and ADEOS in the next several years.

Thermal Remote Sensing of Surface Soil Water Content with Partial Vegetation Cover for Incorporation into Climate Models

This study outlines a method for the estimation of regional patterns of surface moisture availability (M(sub 0)) and fractional vegetation (Fr) in the presence of spatially variable vegetation cover. The method requires relating variations in satellite-derived (NOAA, Advanced Very High Resolution Radiometer (AVHRR)) surface radiant temperature to a vegetation index (computed from satellite visible and near-infrared data) while coupling this association to an inverse modeling scheme. More than merely furnishing surface soil moisture values, the method constitues a new conceptual and practical approach for combining thermal infrared and vegetation index measurements for incorporating the derived values of M(sub 0) into hydrologic and atmospheric prediction models. Application of the technique is demonstrated for a region in and around the city of Newcastle upon Tyne situated in the northeast of England. A regional estimate of M(sub 0) is derived and is probabbly good for fractional vegetation cover up to 80% before errors in the estimated soil water content become unacceptably large. Moreover, a normalization scheme is suggested from which a nomogram, `universal triangle,' is constructed and is seen to fit the observed data well. The universal triangle also simplifies the inclusion of remotely derived M(sub 0) in hydrology and meteorological models and is perhaps a practicable step toward integrating derived data from satellite measurements in weather forecasting.

Normalized difference vegetation index measurements from the advanced very high resolution radiometer

The potential of computing normalized difference vegetation index (NDVI) measurements from the global, daily observations collected by the NOAA Advanced Very High Resolution Radiometer (AVHRR) has created great interest in the use of these data sets to study global biospheric dynamics. Initial qualitative studies demonstrated this potential but the move to quantitative assessments is hindered by limited understanding of the performance characteristics of the AVHRR observations relative to surface vegetation conditions. Factors related to instrument precision and calibration, atmospheric attenuation and off-nadir viewing create deviations of the NDVI observations unrelated to vegetation dynamics. Deviations in excess of 50% between the satellite and equivalent ground observations are possible if no effort is made to account for these effects. In addition, off-nadir viewing causes spatio-temporal variations in the measurements and cloud occurrence reduces temporal resolution below the AVHRR's daily repeat cycle. It appears possible to reduce these errors to approximately ± 10% (± 0.1 NDVI) with at least a monthly time resolution if all of the observation attributes are addressed adequately. Much of the remaining error resides in atmospheric variability, uncertainties in off-nadir views and loss of sensor precision at large solar zenith angles. A global measure of vegetation green foliage dynamics with a measurement precision of ± 10% and a monthly time resolution may not yet meet the exacting needs of some biospheric modelers, but it is, in fact, remarkable, considering that this possibility was not even conceived when the AVHRR was designed. Further refinement of global remotely sensed vegetation foliage measurement precision appears possible and should be the primary focus for terrestrial remote sensing research in the next decade.

Diurnal Radiance and Shadow Fluntuations in a Cold Desert Shrub Plant C Community

Remote sensing data can be acquired during different times of the year and different times of the day using satellite or aircraft systems. Radiance values of pixels vary with ground target, latitude, time of year, and the time of day for both aerial and ground-measured data. An understanding of the resulting fluctuations in spectra provides information necessary to improve the interpretation of airborne and satellite data. Aerial and field measured spectra v vary for a typical desert shrub plant community with low vegetation cover (27%) and considerable shade differences and bare ground. Low shadow percentages were found from 1000 to 1650 h Pacific Daylight Time (PDT) in July and between 1100 and 1500 PDT in September. Both ground-measured and 5 × 5 m aerial pixels had low radiance values (corrected for a 45° sun-angle) at 0800 PDT and higher radiance values throughout the day until solar zenith. September data had a greater range of values compared to July data. MSS7 / MSS5 and MSS6 / MSS5 ratiobased indices were higher in July than September, indicating a higher photosynthetically active biomass in the shrub component in July. Vegetation index measurements varied throughout the day and from season to season and were influenced by solar altitude and the amount of bare soil, plant biomass, and shadow present.