Estimation of monthly air-sea CO2 flux in the southern Atlantic and Indian Ocean using in-situ and remotely sensed data

Empirical relationships between the sea surface partial pressure of carbon dioxide (pCO₂), sea surface chlorophyll-a concentration (Chl-a), and sea surface temperature (SST), were derived from shipboard pCO₂ measurements in sea water and atmosphere, in-situ Chl-a, and SST data along cruise tracks between Zhongshan Station in East Antarctica and Changcheng Station on the Antarctic Peninsula in December 1999, January 2000, December 2004 and January 2005 during the CHINARE XVI and XXI campaigns. These relationships were then applied to datasets of remotely sensed Chl-a and SST to estimate the monthly air-sea carbon flux and the uptake of atmospheric CO₂ in the southern Atlantic and Indian Ocean. The results show significant spatial and temporal variability of carbon flux in the southern Atlantic and Indian Ocean. The monthly uptakes of atmospheric CO₂ in the region from 50°S to the ice edge between 60°W and 80°E are − 0.00355 GtC, − 0.00573 GtC in December 1999 and January 2000, and − 0.00361 GtC, − 0.00525 GtC in December 2004 and January 2005, respectively.     

Effect of remote sensing spatial resolution on interpreting tower-based flux observations

Validation comparisons between satellite-based surface energy balance models and tower-based flux measurements over heterogeneous landscapes can be strongly influenced by the spatial resolution of the remote sensing inputs. In this paper, a two-source energy balance model developed to use thermal and visible /near-infrared remotely sensed data is applied to Landsat imagery collected during the 2004 Soil Moisture Experiment (SMEX04) conducted in southern Arizona. Using a two dimensional flux-footprint algorithm, modeled surface fluxes are compared to tower measurements at three locations in the SMEX04 study area: two upland sites, and one riparian site. The effect of pixel resolution on evaluating the performance of the land surface model and interpreting spatial variations of land surface fluxes over these heterogeneous areas is evaluated. Three Landsat scenes were examined, one representing the dry season and the other two representing the relatively wet monsoon season. The model was run at three resolution scales: namely the Landsat visible/near-infrared band resolution (30 m), the Landsat 5 thermal band resolution (120 m), and 960 m, which is nominally the MODIS thermal resolution at near-nadir. Comparisons between modeled and measured fluxes at the three tower sites showed good agreement at the 30 m and 120 m resolutions — pixel scales at which the source area influencing the tower measurement (∼ 100 m) is reasonably resolved. At 960 m, the agreement is relatively poor, especially for the latent heat flux, due to sub-pixel heterogeneity in land surface conditions at scales exceeding the tower footprint. Therefore in this particular landscape, thermal data at 1-km resolution are not useful in assessing the intrinsic accuracy of the land-surface model in comparison with tower fluxes. Furthermore, important spatial patterns in the landscape are lost at this resolution. Currently, there are no definite plans supporting high resolution thermal data with regular global coverage below ∼ 700 m after Landsat 5 and ASTER fail. This will be a serious problem for the application and validation of thermal-based land-surface models over heterogeneous landscapes.     

Assessments of urban growth in the Tampa Bay watershed using remote sensing data

Urban development has expanded rapidly in the Tampa Bay area of west-central Florida over the past century. A major effect associated with this population trend is transformation of the landscape from natural cover types to increasingly impervious urban land. This research utilizes an innovative approach for mapping urban extent and its changes through determining impervious surfaces from Landsat satellite remote sensing data. By 2002, areas with subpixel impervious surface greater than 10% accounted for approximately 1800 km², or 27 percent of the total watershed area. The impervious surface area increases approximately three-fold from 1991 to 2002. The resulting imperviousness data are used with a defined suite of geospatial data sets to simulate historical urban development and predict future urban and suburban extent, density, and growth patterns using SLEUTH model. Also examined is the increasingly important influence that urbanization and its associated imperviousness extent have on the individual drainage basins of the Tampa Bay watershed.     

On the consistency in variations of the South China Sea Warm Pool as revealed by three sea surface temperature datasets

The areal and intensity indices of the South China Sea Warm Pool (SCSWP) derived from three datasets, the Advanced Very High Resolution Radiometer (AVHRR), Tropical Rainfall Measuring Mission's Microwave Imager (TMI) and Optimum Interpolation Version 2 (OI.v2) sea surface temperature (SST), are generally consistent with each other at monthly, seasonal and interannual scales. However, the three records are different in some cases. First, minor differences among the monthly records of intensity index are observed in the period July to September. Secondly, the interannual records of SCSWP intensity derived from AVHRR and OI.v2 are different in autumn during the period 1990-1996. The reason is not yet clear and nor is it clear which record best represents fluctuations in SCSWP intensity. These suggest that various drawbacks of the three datasets, such as low resolution of OI.v2, and cloud and rain contamination on AVHRR and TMI data, would be serious enough to allow deviation from each other to appear. Merging AVHRR and TMI SST data might be the way leading to a more convincing time series of SCSWP. In addition, changes of areal and intensity indices are not always consistent with each other, for example, they have different monthly patterns. Although the three interannual records of intensity index in three seasons all capture the main Multivariate ENSO Index (MEI) signals at a half-year lag, only those which are in the summer significantly correlated with MEI.     

Effects of remote sensing pixel resolution on modeled energy flux variability of croplands in Iowa

With increased availability of satellite data products used in mapping surface energy balance and evapotranspiration (ET), routine ET monitoring at large scales is becoming more feasible. Daily satellite coverage is available, but an essential model input, surface temperature, is at 1 km or greater pixel resolution. At such coarse spatial resolutions, the capability to monitor the impact of land cover change and disturbances on ET or to evaluate ET from different crop covers is severely hampered. The effect of sensor resolution on model output for an agricultural region in central Iowa is examined using Landsat data collected during the Soil Moisture Atmosphere Coupling Experiment (SMACEX). This study was conducted in concert with the Soil Moisture Experiment 2002 (SMEX02). Two images collected during a rapid growth period in soybean and corn crops are used with a two-source (soil+vegetation) energy balance model, which explicitly evaluates soil and vegetation contributions to the radiative temperature and to the net turbulent exchange/surface energy balance. The pixel resolution of the remote sensing inputs are varied from 60 m to 120, 240, and 960 m. Model output at high resolution are first validated with tower and aircraft-based flux measurements to assure reliability of model computations. Histograms of the flux distributions and resulting statistics at the different pixel resolutions are compared and contrasted. Results indicate that when the input resolution is on the order of 1000 m, variation in fluxes, particularly ET, between corn and soybean fields is not feasible. However, results also suggest that thermal sharpening techniques for estimating surface temperature at higher resolutions (∼250 m) using the visible/near infrared waveband resolutions could provide enough spatial detail for discriminating ET from individual corn and soybean fields. Additional support for this nominal resolution requirement is deduced from a geostatistical analysis of the vegetation index and surface temperature images.     

Estimating northern peatland CO2 exchange from MODIS time series data

Studies using satellite sensor-derived data as input to models for CO₂ exchange show promising results for closed forest stands. There is a need for extending this approach to other land cover types, in order to carry out large-scale monitoring of CO₂ exchange. In this study, three years of eddy covariance data from two peatlands in Sweden were averaged for 16-day composite periods and related to data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and modeled photosynthetic photon flux density (PPFD). Noise in the time series of MODIS 250 m vegetation indices was reduced by using double logistic curve fits. Smoothed normalized difference vegetation index (NDVI) showed saturation during summertime, and the enhanced vegetation index (EVI) generally gave better results in explaining gross primary productivity (GPP). The strong linear relationships found between GPP and the product of EVI and modeled PPFD (R² = 0.85 and 0.76) were only slightly stronger than for the product of EVI and MODIS daytime 1 km land surface temperature (LST) (R² = 0.84 and 0.71). One probable reason for these results is that several controls on GPP were related to both modeled PPFD and daytime LST. Since ecosystem respiration (ER) was largely explained by diurnal LST in exponential relationships (R² = 0.89 and 0.83), net ecosystem exchange (NEE) was directly related to diurnal LST in combination with the product of EVI and modeled PPFD in multiple exponential regressions (R² = 0.81 and 0.73). Even though the R² values were somewhat weaker for NEE, compared to GPP and ER, the RMSE values were much lower than if NEE would have been estimated as the sum of GPP and ER. The overall conclusion of this study is that regression models driven by satellite sensor-derived data and modeled PPFD can be used to estimate CO₂ fluxes in peatlands.     

AVHRR satellite remote sensing and shipboard measurements of the thermal plume from the Daya Bay, nuclear power station, China

The 1800 MW Daya Bay Nuclear Power Station (DNPS), China's first nuclear power station, is located on the coast of the South China Sea. DNPS discharges 29 10×10⁵ m³ year⁻¹ of warm water from its cooling system into Daya Bay, which could have ecological consequences. This study examines satellite sea surface temperature data and shipboard water column measurements from Daya Bay. Field observations of water temperature, salinity, and chlorophyll a data were conducted four times per year at 12 sampling stations in Daya Bay during January 1997 to January 1999. Sea surface temperatures were derived from the Advanced Very High Resolution Radiometer (AVHRR) onboard National Oceanic and Atmospheric Administration (NOAA) polar orbiting satellites during November 1997 to February 1999. A total of 2905 images with 1.1×1.1 km resolution were examined; among those images, 342 have sufficient quality for quantitative analysis. The results show a seasonal pattern of thermal plumes in Daya Bay. During the winter months (December to March), the thermal plume is localized to an area within a few km of the power plant, and the temperature difference between the plume and non-plume areas is about 1.5 °C. During the summer and fall months (May to November), there is a larger thermal plume extending 8-10 km south along the coast from DNPS, and the temperature change is about 1.0 °C. Monthly variation of SST in the thermal plume is analyzed. AVHRR SST is higher in daytime than in nighttime in the bay during the whole year. The strong seasonal difference in the thermal plume is related to vertical mixing of the water column in winter and to stratification in summer. Further investigations are needed to determine any other ecological effects of the Daya Bay thermal plume.     

Estimation of regional terrestrial water cycle using multi-sensor remote sensing observations and data assimilation

An integrated data assimilation system is implemented over the Red-Arkansas river basin to estimate the regional scale terrestrial water cycle driven by multiple satellite remote sensing data. These satellite products include the Tropical Rainfall Measurement Mission (TRMM), TRMM Microwave Imager (TMI), and Moderate Resolution Imaging Spectroradiometer (MODIS). Also, a number of previously developed assimilation techniques, including the ensemble Kalman filter (EnKF), the particle filter (PF), the water balance constrainer, and the copula error model, and as well as physically based models, including the Variable Infiltration Capacity (VIC), the Land Surface Microwave Emission Model (LSMEM), and the Surface Energy Balance System (SEBS), are tested in the water budget estimation experiments. This remote sensing based water budget estimation study is evaluated using ground observations driven model simulations. It is found that the land surface model driven by the bias-corrected TRMM rainfall produces reasonable water cycle states and fluxes, and the estimates are moderately improved by assimilating TMI 10.67 GHz microwave brightness temperature measurements that provides information on the surface soil moisture state, while it remains challenging to improve the results by assimilating evapotranspiration estimated from satellite-based measurements.     

Evapotranspiration on western U.S. rivers estimated using the Enhanced Vegetation Index from MODIS and data from eddy covariance and Bowen ratio flux towers

We combined remote sensing and in-situ measurements to estimate evapotranspiration (ET) from riparian vegetation over large reaches of western U.S. rivers and ET by individual plant types. ET measured from nine flux towers (eddy covariance and Bowen ratio) established in plant communities dominated by five major plant types on the Middle Rio Grande, Upper San Pedro River, and Lower Colorado River was strongly correlated with Enhanced Vegetation Index (EVI) values from the Moderate Resolution Imaging Spectrometer (MODIS) sensor on the NASA Terra satellite. The inclusion of maximum daily air temperatures (T_a) measured at the tower sites further improved this relationship. Sixteen-day composite values of EVI and T_a were combined to predict ET across species and tower sites (r² = 0.74); the regression equation was used to scale ET for 2000-2004 over large river reaches with T_a from meteorological stations. Measured and estimated ET values for these river segments were moderate when compared to historical, and often indirect, estimates and ranged from 851-874 mm yr^− 1. ET of individual plant communities ranged more widely. Cottonwood (Populus spp.) and willow (Salix spp.) stands generally had the highest annual ET rates (1100-1300 mm yr^− 1), while mesquite (Prosopis velutina) (400-1100 mm yr^− 1) and saltcedar (Tamarix ramosissima) (300-1300 mm yr^− 1) were intermediate, and giant sacaton (Sporobolus wrightii) (500-800 mm yr^− 1) and arrowweed (Pluchea sericea) (300-700 mm yr^− 1) were the lowest. ET rates estimated from the flux towers and by remote sensing in this study were much lower than values estimated for riparian water budgets using crop coefficient methods for the Middle Rio Grande and Lower Colorado River.     

Room-temperature CO2 sensing using metal-insulator-semiconductor capacitors comprising atomic-layer-deposited La2O3 thin films

Room temperature detection of CO₂ using metal-insulator-silicon (MIS) devices is reported. These devices comprise atomic layer deposited La₂O₃ thin films as the gas-sensitive dielectric layer and Pt, Pt/Ta and Al as the electrodes. Physical mechanisms that lead to the detection of CO₂ at room temperature are discussed.     

Hydrological consistency using multi-sensor remote sensing data for water and energy cycle studies

A multi-sensor/multi-platform approach to water and energy cycle prediction is demonstrated in an effort to understand the variability and feedback of land surface and atmospheric processes over large space and time scales. Remote sensing-based variables including soil moisture (from AMSR-E), surface heat fluxes (from MODIS) and precipitation rates (from TRMM) are combined with North American Regional Reanalysis derived atmospheric components to examine the degree of hydrological consistency throughout these diverse and independent hydrologic data sets. The study focuses on the influence of the North American Monsoon System (NAMS) over the southwestern United States, and is timed to coincide with the SMEX04 North American Monsoon Experiment (NAME). The study is focused over the Arizona portion of the NAME domain to assist in better characterizing the hydrometeorological processes occurring across Arizona during the summer monsoon period. Results demonstrate that this multi-sensor approach, in combination with available atmospheric observations, can be used to obtain a comprehensive and hydrometeorologically consistent characterization of the land surface water cycle, leading to an improved understanding of water and energy cycles within the NAME region and providing a novel framework for future remote observation and analysis of the coupled land surface-atmosphere system.     

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

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

Near-surface thermal structure and surface diurnal warming in the Adriatic Sea using satellite and drifter data

An extensive set of in situ temperature data collected by surface drifters is combined with satellite-derived sea surface temperature images to study the difference between the pseudo-bulk and bulk temperatures (ΔT_pb-b) in the Adriatic Sea for the period 21 September 2002-31 December 2003. The variations of this temperature difference are described as a function of local wind speed and incoming solar radiation provided by a local area atmospheric model. The daily sea surface temperature variability is also assessed by computing the temperature difference between the daily maximal and minimal values (ΔT_day-night). The data show that the smaller the wind speed and the larger the solar radiation, the larger ΔT_pb-b. The temperature difference reached the highest value (∼5 °C) on a hot day (more than 600 W/m²) of May 2003 in weak wind condition (around 3 m/s). For strong winds (speed > 6 m/s) the dependence on both the wind and solar radiations vanishes as the temperature difference approaches zero because the near-surface water becomes thermally homogenous due to the wind-induced vertical mixing. Strong diurnal warming of the sea surface, as derived by the pseudo-bulk estimates, and a strong near-surface stratification were found during the spring/summer season. Monthly mean statistics show that the diurnal cycle of the pseudo-bulk and bulk temperature starts to become significant already in February and March. Subsequently (from April to August) both the diurnal warming and the stratification are maximal (monthly means of ΔT_day-night ∼1-2 °C and of ΔT_pb-b ∼0.5 °C ), while in fall and early winter the ΔT_pb-b values are quite small (monthly means near 0 °C) and the ΔT_day-night monthly means are bounded by 0.5-1.5 °C. Maximal amplitudes of the diurnal cycle can exceed 4 °C (mostly in spring-summer) for both the pseudo-bulk and bulk temperatures. However, the monthly means of ΔT_day-night is generally twice as large for the pseudo-bulk estimates (∼2 °C) with respect to the bulk layer (∼1 °C). The diurnal warming of the sea surface, as derived by the pseudo-bulk temperature, occurs at about 14:30 local time, that is more than 2 h after the maximal sun elevation and an hour earlier than the bulk temperature maximum at 20-40 cm depth.     

Remote sensing of the urban heat island effect across biomes in the continental USA

Impervious surface area (ISA) from the Landsat TM-based NLCD 2001 dataset and land surface temperature (LST) from MODIS averaged over three annual cycles (2003-2005) are used in a spatial analysis to assess the urban heat island (UHI) skin temperature amplitude and its relationship to development intensity, size, and ecological setting for 38 of the most populous cities in the continental United States. Development intensity zones based on %ISA are defined for each urban area emanating outward from the urban core to the non-urban rural areas nearby and used to stratify sampling for land surface temperatures and NDVI. Sampling is further constrained by biome and elevation to insure objective intercomparisons between zones and between cities in different biomes permitting the definition of hierarchically ordered zones that are consistent across urban areas in different ecological setting and across scales.We find that ecological context significantly influences the amplitude of summer daytime UHI (urban-rural temperature difference) the largest (8 °C average) observed for cities built in biomes dominated by temperate broadleaf and mixed forest. For all cities combined, ISA is the primary driver for increase in temperature explaining 70% of the total variance in LST. On a yearly average, urban areas are substantially warmer than the non-urban fringe by 2.9 °C, except for urban areas in biomes with arid and semiarid climates. The average amplitude of the UHI is remarkably asymmetric with a 4.3 °C temperature difference in summer and only 1.3 °C in winter. In desert environments, the LST's response to ISA presents an uncharacteristic “U-shaped” horizontal gradient decreasing from the urban core to the outskirts of the city and then increasing again in the suburban to the rural zones. UHI's calculated for these cities point to a possible heat sink effect. These observational results show that the urban heat island amplitude both increases with city size and is seasonally asymmetric for a large number of cities across most biomes. The implications are that for urban areas developed within forested ecosystems the summertime UHI can be quite high relative to the wintertime UHI suggesting that the residential energy consumption required for summer cooling is likely to increase with urban growth within those biomes.     

Tidal breathing model describing end-tidal, alveolar, arterial and mixed venous CO2 and O2

Thermodilution is the current standard for determination of cardiac output. The method is invasive and constitutes a risk for the patient. As an alternative CO₂ rebreathing allows non-invasive cardiac output estimation using Ficks principle. The method relies on estimation of arterial CO₂ partial pressure from end-tidal CO₂ pressure and estimation of mixed venous CO₂ partial pressure from end-tidal CO₂ during rebreathing. Presumably the oxygenation of blood in the lung capillaries increases lung capillary CO₂ pressure due to the Haldane effect, which during rebreathing may result in overestimation of the mixed venous CO₂ pressure. However, the Haldane effect is not discussed in the current literature describing cardiac output estimation using CO₂ rebreathing. The purpose of this study is to construct and verify a compartmental tidal breathing lung model to investigate the physiological mechanisms that influence the CO₂ rebreathing technique. The model simulations show agreement with previous studies describing end-tidal to arterial differences in CO₂ pressure and rebreathing with high and low O₂ fractions in the rebreathing bag. In conclusion the simulations show that caution has to be taken when using end-tidal measurements to estimate CO₂ pressures, especially during rebreathing where the Haldane effect causes mixed venous CO₂ partial pressure to be substantially overestimated.     

An evaluation of MODIS and SeaWiFS bio-optical algorithms in the Baltic Sea

An extensive bio-optical data set from field measurements was used to evaluate the performance of standard Moderate Resolution Imaging Spectroradiometer (MODIS) and Sea-viewing Wide Field-of-view Sensor (SeaWiFS) ocean color (in-water) algorithms in the Baltic Sea, which represents an example of optically complex Case 2 waters with high concentration of colored dissolved organic matter (CDOM). The data set includes coincident measurements of radiometric quantities, chlorophyll a concentration (Chl a), and absorption coefficient of CDOM, which were taken on 25 cruises between 1993 and 2001. The data cover a wide range of variability with Chl a in surface waters from about 0.3 to 100 mg m⁻³. All the MODIS pigment algorithms examined as well as the SeaWiFS OC4v4 algorithm showed a systematic and large overestimation in chlorophyll retrievals. The mean systematic and random errors based on our entire data set exceeded 150% or even 200% in some cases, making these standard algorithms inadequate for pigment determinations in the Baltic. Although new parameterization of the standard pigment algorithms based on our field measurements in the Baltic resulted in a significant reduction of errors, the overall performance of such regionally tuned algorithms remained unsatisfactory. For example, the mean normalized bias (MNB) for the regionally tuned MODIS chlor_a_2 algorithm was reduced to 26% (from over 200% for the standard algorithm), but the root mean square (RMS) error was still large (>100%). The MODIS K_490 algorithm for estimating the diffuse attenuation coefficient of downwelling irradiance showed the best performance among all the algorithms examined. With the new coefficients based on our field data, the regional version of this algorithm showed an acceptable level of errors, MNB=4% and RMS=30%. In addition to the apparent problems of the standard in-water bio-optical algorithms, we found that the atmospheric correction currently in use for MODIS and SeaWiFS imagery usually fails to retrieve upwelling radiances emerging from the Baltic Sea. The match-up comparisons of the coincident in situ and satellite determinations of normalized water-leaving radiances showed generally poor agreement, especially in the blue spectral region. It appears that new approaches for ocean color algorithms are required in the Baltic Sea.     

A comparison of multi-spectral, multi-angular, and multi-temporal remote sensing datasets for fractional shrub canopy mapping in Arctic Alaska

Shrub cover appears to be increasing across many areas of the Arctic tundra biome, and increasing shrub cover in the Arctic has the potential to significantly impact global carbon budgets and the global climate system. For most of the Arctic, however, there is no existing baseline inventory of shrub canopy cover, as existing maps of Arctic vegetation provide little information about the density of shrub cover at a moderate spatial resolution across the region. Remotely-sensed fractional shrub canopy maps can provide this necessary baseline inventory of shrub cover. In this study, we compare the accuracy of fractional shrub canopy (> 0.5 m tall) maps derived from multi-spectral, multi-angular, and multi-temporal datasets from Landsat imagery at 30 m spatial resolution, Moderate Resolution Imaging SpectroRadiometer (MODIS) imagery at 250 m and 500 m spatial resolution, and MultiAngle Imaging Spectroradiometer (MISR) imagery at 275 m spatial resolution for a 1067 km² study area in Arctic Alaska. The study area is centered at 69 °N, ranges in elevation from 130 to 770 m, is composed primarily of rolling topography with gentle slopes less than 10°, and is free of glaciers and perennial snow cover. Shrubs > 0.5 m in height cover 2.9% of the study area and are primarily confined to patches associated with specific landscape features. Reference fractional shrub canopy is determined from in situ shrub canopy measurements and a high spatial resolution IKONOS image swath. Regression tree models are constructed to estimate fractional canopy cover at 250 m using different combinations of input data from Landsat, MODIS, and MISR. Results indicate that multi-spectral data provide substantially more accurate estimates of fractional shrub canopy cover than multi-angular or multi-temporal data. Higher spatial resolution datasets also provide more accurate estimates of fractional shrub canopy cover (aggregated to moderate spatial resolutions) than lower spatial resolution datasets, an expected result for a study area where most shrub cover is concentrated in narrow patches associated with rivers, drainages, and slopes. Including the middle infrared bands available from Landsat and MODIS in the regression tree models (in addition to the four standard visible and near-infrared spectral bands) typically results in a slight boost in accuracy. Including the multi-angular red band data available from MISR in the regression tree models, however, typically boosts accuracy more substantially, resulting in moderate resolution fractional shrub canopy estimates approaching the accuracy of estimates derived from the much higher spatial resolution Landsat sensor. Given the poor availability of snow and cloud-free Landsat scenes in many areas of the Arctic and the promising results demonstrated here by the MISR sensor, MISR may be the best choice for large area fractional shrub canopy mapping in the Alaskan Arctic for the period 2000-2009.     

Floodplain water storage in the Negro River basin estimated from microwave remote sensing of inundation area and water levels

The objective of this study is to determine spatio-temporal variations of water volume over inundated areas located in large river basins using combined observations from the Synthetic Aperture Radar (SAR) onboard the Japanese Earth Resources Satellite (JERS-1), the Topex/Poseidon (T/P) altimetry satellite, and in-situ hydrographic stations. Ultimately, the goal is to quantify the role of floodplains for partitioning water and sediment fluxes over the great fluvial basins of the world. SAR images are used to identify the type of surface (open water, inundated areas, forest) and, hence, the areas covered with water. Both radar altimetry data and in-situ hydrographic measurements yield water level time series. The basin of the Negro River, the tributary which carries the largest discharge to the Amazon River, was selected as a test site. By combining area estimates derived from radar images classification with changes in water level, variations of water volume (focusing on a seasonal cycle) have been obtained. The absence of relationship between water volume and inundated area, reflecting the diverse and widely dispersed floodplains of the basin, is one of the main result of this study.     

An analysis of urban thermal characteristics and associated land cover in Tampa Bay and Las Vegas using Landsat satellite data

Remote sensing data from both Landsat 5 and Landsat 7 systems were utilized to assess urban area thermal characteristics in Tampa Bay watershed of west-central Florida, and the Las Vegas valley of southern Nevada. To quantitatively determine urban land use extents and development densities, sub-pixel impervious surface areas were mapped for both areas. The urban-rural boundaries and urban development densities were defined by selecting certain imperviousness threshold values and Landsat thermal bands were used to investigate urban surface thermal patterns. Analysis results suggest that urban surface thermal characteristics and patterns can be identified through qualitatively based urban land use and development density data. Results show the urban area of the Tampa Bay watershed has a daytime heating effect (heat-source), whereas the urban surface in Las Vegas has a daytime cooling effect (heat-sink). These thermal effects strongly correlated with urban development densities where higher percent imperviousness is usually associated with higher surface temperature. Using vegetation canopy coverage information, the spatial and temporal distributions of urban impervious surface and associated thermal characteristics are demonstrated to be very useful sources in quantifying urban land use, development intensity, and urban thermal patterns.