A 2005 biomass burning (wildfire, prescribed, and agricultural) emission inventory has been developed for the contiguous United States using a newly developed simplified method of combining information from multiple sources for use in the US EPA's National Emission Inventory (NEI). Our method blends the temporal and spatial resolution of the remote sensing information with the ground based fire size estimate. This method is faster and considerably less expensive than the method used for the 2002 National Emissions Inventory and is more accurate than methods used for 2001 and prior years. In addition, the 2005 fire inventory is the first EPA inventory utilizing remote sensing information. A comparison with the 2002 inventory for wildfire, prescribed, and agricultural fires indicates a large year-to-year variability in wildfire emissions and less variation for prescribed and agricultural fires. Total PM2.5 emissions from wildfires, prescribed burning, and agricultural burning for the contiguous United States were estimated to be 109,000 short tons, 209,000 short tons, and 232,000 short tons, respectively, for 2005. Our total emission estimate for 2005 is 550,000 short tons. Our analysis shows that year-to-year spatial variability accounts for the substantial difference in the wildfire emission estimates.

We combined satellite (Landsat 7 and Moderate Resolution Imaging Spectrometer) and U.S. Department of Agriculture forest inventory and analysis (FIA) data to estimate forest aboveground biomass (AGB) across New England, USA. This is practical for large-scale carbon studies and may reduce uncertainty of AGB estimates. We estimate that total regional forest AGB was 1,867 teragram (10¹², dry weight) in 2001, with a mean AGB density of 120 Mg/ha (Standard deviation = 54 Mg/ha) ranging from 15 to 240 Mg/ha within a 95% percentile. The majority of regional AGB density was in the range of 80 to 160 Mg/ha (58.2%). High AGB densities were observed along the Appalachian Mountains from northwestern Connecticut to the Green Mountains in Vermont and White Mountains in New Hampshire, while low AGB densities were concentrated in the Downeast area of Maine (ME) and the Cape Cod area of Massachusetts (MA). At the state level, the averaged difference in mean AGB densities between simulated and FIA (as reference) was -2.0% ranging from 0% to -4.2% with a standard error of 3.2%. Within the 95% confidence interval the differences between FIA and simulated AGB densities ranged from 0 to 6% (absolute value). Our study may provide useful information for regional fuel-loading estimates.

Plume rise is the height smoke plumes can reach. This information is needed by air quality models such as the Community Multiscale Air Quality (CMAQ) model to simulate physical and chemical processes of point-source fire emissions. This study seeks to understand the importance of plume rise to CMAQ air quality simulation of prescribed burning to plume rise. CMAQ simulations are compared between fire emissions as area and point sources. For point source, Daysmoke is used to calculate plume rise. A burn day in Florida is examined. The results indicate significant sensitivity of simulated PM_2.5 (particulate matter with a size smaller than 2.5 μm) concentrations to plume rise. The air quality effects, measured by PM_2.5 concentrations at the burning area, are more significant by specifying fire emissions as an area source. The implication of the results for the uncertainty in evaluating the contribution of prescribed burning to regional air pollution is discussed.

We compare biomass burning emissions estimates from four different techniques that use satellite based fire products to determine area burned over regional to global domains. Three of the techniques use active fire detections from polar-orbiting MODIS sensors and one uses detections and instantaneous fire size estimates from geostationary GOES sensors. Each technique uses a different approach for estimating trace gas and particulate emissions from active fires. Here we evaluate monthly area burned and CO emission estimates for most of 2006 over the contiguous United States domain common to all four techniques. Two techniques provide global estimates and these are also compared. Overall we find consistency in temporal evolution and spatial patterns but differences in these monthly estimates can be as large as a factor of 10. One set of emission estimates is evaluated by comparing model CO predictions with satellite observations over regions where biomass burning is significant. These emissions are consistent with observations over the US but have a high bias in three out of four regions of large tropical burning. The large-scale evaluations of the magnitudes and characteristics of the differences presented here are a necessary first step toward an ultimate goal of reducing the large uncertainties in biomass burning emission estimates, thereby enhancing environmental monitoring and prediction capabilities.

The wildfires which occurred in April 2007 in southern Georgia lasted for almost two months. Approximately 386,722 acres were burned. In this paper, we explored the strategy to use MODIS products to study fire impacts on vegetation. Vegetation variations caused by the fires are studied using these MODIS products: 8-day composite fire products, 16-day composite vegetation indices, and land cover types. Several tiles of MODIS products from dates immediately before and after the fire were employed to monitor vegetation changes. Unburned control plots were selected to establish new variables QNDVI and QEVI which are used to evaluate the vegetation recovery status. The results show that vegetation indices: NDVI and EVI decreased dramatically after the fires. The variations of QNDVI and QEVI indicate that vegetation status still has a disparity between fire spots and surrounding undisturbed area, even though commonly used vegetation indices of fire spots have attained pre-fire levels. The appropriate distance outside the fire spots for selecting control plots is related to the size of the burned area. The larger the burned area is, the bigger distance we should choose for control plots. EVI and QEVI are better indicators to show vegetation changes due to fires. The method presented in this paper can be employed to monitor vegetation change to fires as well as to indicate different recovery rate. It also can be useful to identify the fire severity and assess the ecological consequence of fires.

Remote sensing (RS) and geographic information systems (GIS) are widely applied in forest mapping to obtain high quality and quantity data. The launch of RS satellites heralded a new beginning in natural resource survey and management. Soon the technique was adopted for mapping the distribution of individual species, such as bamboo. Recognizing the importance of bamboo to livelihoods in rural areas, forest ecosystems, and the economy, this work highlights the importance of RS and GIS in the mapping of bamboo-bearing areas through satellite imagery. The work also analyzes the applicability of RS/GIS to better management and conservation of bamboo resources. Mapping bamboo requires improvement in the traditional classification scheme and thorough ground truthing. Successful classification of the imagery would provide data on the current status of bamboo and assist in modeling future scenarios. In a GIS environment, action plans can be generated for effective bamboo resource planning. Over all, since RS and GIS have tremendous ability to remotely identify and classify bamboo, the methods should be put to better use to utilize the potential offered.

Seasonal and interannual variability of chlorophyll a concentration along the West Coast of India (WCI) was detected spatially by ocean colour satellite remote sensing during 1998 - 2003. Start timing of the summer bloom was different spatially. The summer bloom started Trivendrum in May, along Cochin coast in June, northward of it in July and also showed interannual variability that corresponding with the wind speed in the area. Summer bloom in 1998 and 2002 appeared about two weeks earlier than in 1999, 2000, 2001 and 2003, and it corresponded with weak winds that can lead to an early development of the thermocline. The bloom was late in 1999 and 2001 at Trivendrum and along the Cochin coast, and in the northern are in 2000. It corresponded with stronger wind stress that delayed seasonal thermocline formation. The winter bloom appeared from early October to last week of January, and it did not have a clear temporal transition. The area where chlorophyll a concentration exceeds 5 mg/m3 was wider in northern area than in the southern area of the WCI every year. The magnitude of winter bloom was different between years, but it did not show a relation with average wind speed in winter. Those results indicated that the timing of the seasonal bloom along the WCI is largely affected by the variability of global climate such as El Nino Southern Oscillation (ENSO) events.

Colored dissolved organic matter (CDOM) optical properties were studied in the northern Gulf of Mexico coastal waters influenced by the Atchafalaya River in July 2005 during a period of low river discharge using both field and satellite ocean color data. Although a general conservative CDOM absorption-salinity relationship between the riverine and oceanic end members were observed, deviations in surface CDOM absorption from this relationship (CDOM loss) and average higher spectral slope S of the log-linearized absorption spectra in comparison to bottom waters suggested photodegradation of surface CDOM. Excess CDOM absorption in bottom waters at many stations were associated with low oxygen or hypoxic conditions suggesting a potential CDOM source due to remineralization of organic matter in the bottom waters. At a few stations anomalously high CDOM absorption could be linked to biological production associated with algal blooms. Absorption spectra of a surface sample at one such station deviated from standard exponential form in the 300-400m UV range probably due to the presence of photo-protective pigments. CDOM derived from the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) using a regional algorithm provided a synoptic view of surface CDOM variations and the extent of river influences into the northern Gulf of Mexico.

Bio-optical spectral properties were determined on fresh suspensions of Trichodesmium spp. collected in a tropical lagoon and put in seawater tanks (total chlorophyll concentrations range between 0.1 and 3.8 mg m^-3). The spectrum of the backscattering coefficient was a hyperbolic function with a slope of 1.2, often showing troughs at 440, 550 and 676 nm, due to absorption peaks of chlorophyll and phycoerythrin. The absorption spectrum computed with a specific beta correction for Trichodesmium, showed a blue to red ratio (B/R) equivalent to the one of a single colony (B/R=2), and also showed the double peak of mycosporine-like amino acids (MAA's, 330 and 360 nm). The CDOM absorption spectrum showed minor MAA peaks when cyanobacterial concentrations were above 1 mg Chl a m^-3. The chlorophyll a-specific backscattering and absorption coefficients at 442 nm were respectively 0.0126 m² (mg.chl a)^-1 and 0.027 m² (mg chl a)^-1. Suspensions in tanks exhibited a high backscattering ratio at 660 nm (b_b=b_bp/b_p). The above-water reflectance spectrum clearly showed troughs at the wavelength of the pigment absorption peaks. Datasets of Trichodesmium normalized absorption, backscattering and reflectance spectra will allow its detection with future hyperspectral ocean colour sensors.

In this paper we report the use of Fraunhofer Line Radiometry and the Fraunhofer Line Depth (FLD) method to measure the passive fluorescence of select, high quantum yield (> 0.7) fluorophores. The measurements we describe involved direct solar illumination of both a non-fluorescent standard material (NIST, Spectralon), and the fluorescent target materials (in solution) within several relevant Fraunhofer lines: 486 nm (H-beta), 518 nm (Mg), 589 nm (Na), and 656 nm (H-alpha). Of the selected labels, Rhodamine B demonstrated the highest fluorescence coefficient of 2.2, at 589 nm, and possessed a fluorescence intensity of 1.1 × 106 cps at an optical density of 1.0. These results show fluorescent labels can be selectively measured and observed passively in relevant Fraunhofer lines. The implications for such findings indicate the potential daytime measurement of environmental labels and probes as a promising sensing technology.

The first MODerate-resolution Imaging Spectroradiometer (MODIS) was launched on the Terra spacecraft on December 18, 1999. The second MODIS was launched on the Aqua spacecraft on May 4, 2002. As an integral part of NASA's Earth Observing System (EOS), these instruments provide daily observations of nearly the whole Earth with the goal of enhancing scientific characterization of land, ocean, atmosphere, climate change, and natural hazards. MODIS uses a scanning mirror, 490 detectors distributed among 36 spectral bands, and on-board calibrators (a solar diffuser, solar diffuser stability monitor, a blackbody, and a spectro-radiometric calibration assembly) to meet these objectives. The MODIS Level 1B (L1B) algorithms, written in C, input uncalibrated, geo-located observations, convert the instrument response into calibrated data, and generate science data sets. This calibration is performed on a pixel-by-pixel basis for each detector. The instrument characterization needed to run the L1B code is implemented using 96 Terra and 99 Aqua Look-up Tables. This paper describes the current L1B algorithm and discusses the changes made as a consequence of on-orbit analyses and operational considerations.

The Vertical Temperature Profile Radiometer (VTPR) was an operational 8-channel infrared sounding system mounted on the NOAA-2 through NOAA-5 spacecraft. The instrument was a predecessor of the High-Resolution Infrared Radiation Sounder (HIRS) on the continuing NOAA polar orbiting satellite series. The VTPR measurements covered more than six years of data from late 1972 to early 1979. Major work has been done to clear erroneous data records. Theoretical biases between similar channels of VTPR and HIRS are derived using a radiative transfer model to show the potential bias features between the observations of the two instruments. The model simulation shows that for about half of the channels the biases can be in the order of 1 K for certain temperature ranges. Because each spacecraft carried two sets of VTPR instrument, but only one set was turned on at a given time, differences between two sets of channel measurement are expected to exist. It is shown that the differences between two sets of VTPR instrument can range from nearly zero for some channels to about 2 K for other channels. To make the VTPR dataset accessible to the general scientific research community, we have processed the whole VTPR data to common formats and placed the data online along with data quality statistics.

The Moderate Resolution Imaging Spectroradiometer (MODIS) is one of the key instruments for NASA's Earth Observing System (EOS) missions. Its high quality land, ocean, and atmosphere data products heavily rely on accurate on-orbit calibration and characterization. MODIS reflective solar bands (RSB) on-orbit calibration is reflectance based and is provided by an on-board Solar Diffuser (SD). The Bi-directional Reflectance Factor (BRF) of the SD was characterized pre-launch. The earthshine (ES) contamination to the SD signal was estimated to be approximately 0.5%. On-orbit observations and geometric optical modeling results show that this contamination strongly depends on the wavelength, calibration viewing geometry, and the Earth surface type. In this paper, the contribution from the earthshine on MODIS RSB calibration is illustrated and characterized systematically. A simple approach is proposed to reduce its impact on the RSB calibration. This approach has been applied to both Terra and Aqua MODIS RSB calibration and proved to be effective in reducing the earthshine contaminations.

The NASA Orbiting Carbon Observatory (OCO) will make space-based measurements of atmospheric carbon dioxide (CO₂) with the precision, resolution, and coverage needed to characterize regional scale CO₂ sources and sinks and quantify their vari¬ability over the seasonal cycle. This mission will be launched in December 2008 and will fly in a 705 km altitude, 1:26 PM sun-synchronous orbit that provides complete coverage of the sunlit hemisphere with a 16-day ground track repeat cycle. OCO carries a single instrument designed to make co-boresighted spectroscopic measurements of reflected sunlight in near-infrared CO₂ and molecular oxygen (O₂) bands. These CO₂ and O₂ measurements will be combined to provide spatially resolved estimates of the column averaged CO₂ dry air mole fraction, X_CO2. The instrument collects 12 to 24 X_CO2 soundings/second over the sunlit portion of the orbit, yielding 200 to 400 soundings per degree of latitude, or 7 to 14 million soundings every 16 days. Existing studies indicate that at least 10% of these soundings will be sufficiently cloud free to yield X_CO2 estimates with accuracies of ~0.3 to 0.5% (1 to 2 ppm) on regional scales every month.

C-band SAR observations show that backscatter varies significantly across small scales (tens of kilometers) in western Dronning Maud Land. Generally, backscatter was found to diminish with altitude reflecting lower accumulation and reduced ice inclusions in the firn of the percolation zone at higher elevations. Reference to (incomplete) mass balance data suggests an anticorrelation between backscatter and net balance, although more data are needed to confirm the trend. Even within the percolation zone, areas of low backscatter (<-12 dB) exist. These are uncorrelated with altitude above sea level. Using a Rayleigh backscatter model, we show that backscatter over such regions may represent differences in grain sizes. The application of a buried-layers model did not accurately estimate backscatter from these regions. We suggest that these regions occupy exposed positions subject to increased wind sublimation which, in turn, results in smaller grain sizes in the firn and therefore reduced backscatter. The Radarsat Antarctica Mapping Mission SAR mosaic indicates such regions occur in Coates Land, near the edge of the Antarctic Plateau, and on exposed promontories elsewhere in East Antarctica. More information regarding the structure of the firn and the mass balance of the region is needed before we can definitively explain controls on backscatter and understand the climatology of the low backscatter zones.

Data from the Coastal Zone Color Scanner (CZCS) was used to study the abundance and variability of chlorophyll a in the southeastern Indian Ocean, an interesting region because it includes the Indonesian through-flow and the anomalous Leeuwin Current. This study is the first to interpret the CZCS observations in terms of the spatial-temporal variability of this large area. The period of data (1979 to 1986) covered the full cycle (pre, during and post) of a major El Nino event, which has yet to be done by the more recent ocean color sensors. The highest seasonal mean chlorophyll a concentrations along the NorthWest Shelf of Australia occurred in Summer (January-March) and in coastal areas off south-western Australia in Autumn (April-June). Concentrations in the offshore oceanic regions were mostly poor. Exceptions to this occurred in proximity to the adjacent Indonesian islands and directly south of Albany (possibly due to northwards flow of subantarctic nutrient-rich waters). A considerable interannual variation was also noted, with the highest mean chlorophyll a concentrations occurring in 1981, 1982 and 1983.

The high level of success of estimating photosynthetic vegetation from multispectral satellite sensors at regional scales has not been repeated for non-photosynthetic vegetation and bare ground. Therefore regional scale estimates of total vegetation from multispectral sensors are largely underestimated with implications for a wide range of agricultural and environmental applications. Recent research using simulated data showed that the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) had the potential to provide reliable estimates of bare ground and total vegetation. This study built on that research and found that estimates of bare ground retrieved from ASTER short-wave infrared imagery using linear spectral unmixing correlated well with field measurements (RMSE < 0.1, r² > 0.7). Image endmember libraries required for spectral unmixing were extracted from the image data using a combination of field knowledge and the lignin and cellulose absorption index. The most reliable results were found by applying a sum-constraint to the unmixing models and tying the signatures at wavebands that corresponded to cellulose or clay-hydroxyl absorption features. The results of this research show that ASTER can improve the estimates of total vegetation extracted from satellite imagery for environmental studies at regional scales.

With the all-weather and day/night imaging capability, synthetic aperture radar (SAR) plays an important role in inundation extent detection. The inundation area detection using SAR will be easy as a result of the dark image tones yielded by specular reflection to the radar wave. Object oriented method (OOM) was applied to detect inundation extent using multi-polarized ENVISAT ASAR data. The traditional pixel-based methods used in information extraction and classification focus on the single pixel, so when they are applied in SAR imagery no perfect results can be achieved because of the speckle of SAR imagery. On the other hand, the pixel-based methods have limitations for detecting inundation extent and flood monitoring because of the neglect of the information of the adjacent pixels. The OOM, which no longer looks at individual pixels, but rather homogeneity areas (image objects), would be much more effective. In this paper, the OOM is applied in the ENVISAT ASAR alternative polarized (VV/VH) images using the software eCognition. The study site is located in Poyang Lake wetland, which has different inundation extent at different time. The images were segmented firstly, then the standard nearest neighbor classifier and the membership function classifier were used to classify the image objects, finally the different inundation areas were detected. The classification accuracies for two classifiers from the OOM are 95.78% and 92.24%, which are higher than that of the maximum likelihood classifier, 86.02%.

This paper demonstrates significant improvement in line detection using hyperspectral data compared to panchromatic data in natural scenes. Improvements in the number of lines detected using a Hough transform vary between 50% and over 400% for two AVIRIS and two HYDICE scenes. This enhanced detection is expected because hyperspectral data preserves gradient information that is lost in the inherent averaging across spectral bands for panchromatic imagery. For the same computational complexity per band, hyperspectral line detection is shown to outperform panchromatic line detection.

Accurate estimation of areas covered by land use land cover classes is central to many resource management and monitoring programs, crop yield forecasting, forest and environmental management. In this paper, various techniques of estimating areas of land cover classes derived from remote sensing image classification have been discussed. A comparative study has been conducted to examine the accuracy and consistency of the area estimated from five error matrix based techniques. The results show that 'direct' and 'additive' estimators produce the most accurate and consistent results. The 'map marginal proportion based estimator' and 'inverse estimator' produce accurate results when the testing sample size is large.

Coastal areas are under great pressure due to increase in human population and industrialization/commercialization and hence these areas are vulnerable to environmental degradation, resource reduction and user conflicts. In the present study an Integrated Coastal Zone Management Plan (ICZMP) has been developed for Udupi Coast in Karnataka, along West Coast of India. The various data products used in the present study includes IRS-1C LISS-III + PAN and IRS-P6 LISS III remotely sensed data, Naval Hydrographic Charts and Survey of India (SOI) toposheets, in addition to ground truth data. Thematic maps such as land use/ land cover map, bathymetry map, shoreline configuration map, transportation and drainage network maps, GPS survey map, CRZ map, contour map, DEM, inundation map, critical erosion area map were prepared. A Coastal Vulnerability Index has also been calculated for the study area to know the resistance of study area to sea level rise and is demarcated into four categories; Very high, High, Moderate and Low vulnerability, and a vulnerability map has been prepared. The results of the present study are encouraging. Some of the specific conclusions of the study are; about 50% study area is prone to erosion, river mouths along study area show shifting tendency towards south, and the beaches along the Udupi Coast are maintaining dynamic equilibrium. Coastal Zone Information System (CZIS) has been developed through V.B.6.0 using results of various data analysis.

Seven scenes of ASAR images acquired by ENVISAT satellite during the time period April 2004 - June 2005 have been analyzed to assess the geo-coding accuracy of the data. Eighty ground control points (GCPs) spread all over Kuwait were measured using Trimble 5700 GPS which are mainly the road intersections. The same road intersections were identified in the ASAR image and its geo-locations were measured using BEST software package provided by the European Space Agency. The GPS and BEST results were compared to estimate the geo-location accuracy of ASAR data. The average accuracy in geo-coding is estimated to be 54 m in both azimuth and range directions. These results are in agreement with the results reported in the literature. Twenty well-defined targets (built-up areas, grass lands, airports, agricultural plots, desert soils, etc.) were considered for the backscattering signature study. The size of each target varies from 50,000 to 100,000 Single Look Complex (SLC) pixels for good statistical accuracy. It has been estimated that the Standard Deviation (Std) of backscatter signature is 1.3% (after speckle filtering) of the mean value. The temporal / spatial variability of the signatures are within the Std. During the study period, the soil moisture varied from 6% to 10% as estimated from AMSR-E on board Acqua satellite.

An airborne DIfferential Absorption Lidar (DIAL) system was used to detect methane, propane, and light crude gas vapors during real-world collections. A series of overflights were conducted at two separate test locations (Kingsville/Corpus Christi, TX and Spencerport, NY). DIAL, digital orthophotography, and video data were collected, processed and analyzed at both sites. A new Hydrocarbon Detection Algorithm (HHDA) was developed to detect gasses other than methane. The HHDA shows promise for detecting multiple species in a local area with uniform surface reflectance or known variability.

Direct estimation of aboveground biomass with spectral reflectance data has proven challenging for high biomass forests of the Pacific Northwestern United States. We present an alternative modeling strategy which uses Landsat's spatial, spectral and temporal characteristics to predict live forest carbon through integration of stand age and site index maps and locally calibrated Chapman-Richards curves. Predictions from the curve-fit model were evaluated at the local and landscape scales using two periods of field inventory data. At the pixel-level, the curve-fit model had large positive bias statistics and at the landscape scale over-predicted study area carbon for both inventory periods. Despite the over-estimation, the change in forest carbon estimated by the curve-fit model was well within the standard error of the inventory estimates. In addition to validating the curve-fit models carbon predictions we used Landsat data to evaluate the degree to which the field inventory plots captured the forest conditions of the study area. Landsat-based frequency histograms revealed the systematic sample of inventory plots effectively captured the broad range of forest conditions found inthe study area, whereas stand age trajectories revealed a temporally punctuated shift in landuse which was not spectrally detected by the inventory sample.

To assess the canopy water content of summer barley from spectroradiometer and HyMap image data, spectral indices and a canopy reflectance model (PROSPECT + SAIL) were applied. In addition to traditional indices (WI, NDWI), a Simple Ratio index (SR*) combining SWIR (1355 nm) and red edge (710 nm) reflectances was found to provide highly reliable estimates for the calibration data. After the adaption to HyMap resolution, the calibrated WI and SR* estimation models were applied to the image data. Here, limitations of the purely empirical index approach became apparent as the retrieved spatial patterns differed distinctly. The physically based approach of PROSPECT + SAIL provided estimates clearly too low but also highly correlated with the measured canopy water contents; the underestimation is due to the fact that some of the canopy water is partly unseeable for the remote sensor which can be traced back to erectophile canopy elements and thick or clumped tissues. Thus, PROSPECT + SAIL estimates were much better in line with the water content of the leaf fraction. In total, the PROSPECT + SAIL results kept stable when applied to synthetic and real HyMap data. This point gives confidence to the spatial pattern of water content as retrieved by PROSPECT + SAIL that differed clearly from the results obtained from the spectral indices.

Research on urban forest carbon storage requires new allometric equations that relate easily measured aspects of tree form to total tree volume. Existing methods for creating these equations are time consuming and can require destructive sampling. In this study we used a terrestrial lidar sensor; data produced by this system consists of the three-dimensional locations of points that lay on object surfaces (in this case the trunk, branches, twigs, etc.). The challenge in calculating tree volume was the aggregation of these individual points into objects whose dimensions could be reliably estimated. We converted the lidar observations into 3-dimensional voxels, identified narrow (< 20 cm) sections along the stems, and merged sections on the basis of their connectivity. The distribution of returns from small branches was analyzed on the basis of branch size and the sampling pattern of the sensor. Two hundred and fifty measurements of stem diameter were made on 14 trees of two species using both lidar and field measurements; the estimates were highly correlated (r² > 98%) and unbiased. The processing algorithm was applied to lidar data from 179 trees of 11 species; the accuracy of results was dependant on tree volume, species and tree form.

The use of disparate data sources within a pixel level image fusion procedure has been well documented for pan-sharpening studies. The present paper explores various image fusion procedures for the fusion of multi-spectral ASTER data and a RadarSAT-1 SAR scene. The research sought to determine which fusion procedure merged the largest amount of SAR texture into the ASTER scenes, while also preserving the spectral content. An additional application based maximum likelihood classification assessment was also undertaken. Three SAR scenes were tested namely, one backscatter scene and two textural measures calculated using grey level co-occurrence matrices (GLCM). Each of these were fused to the ASTER data using the following established approaches; Brovey transformation, Intensity Hue and Saturation, Principal Component Substitution, Discrete wavelet transformation, and a modified discrete wavelet transformation using the IHS approach. Resulting data sets were assessed using qualitative and quantitative (entropy, universal image quality index, maximum likelihood classification) approaches. Results from the study indicated that while all post fusion data sets contained more information (entropy analysis), only the frequency-based fusion approaches managed to preserve the spectral quality of the original imagery. Furthermore results also indicated that the textural (mean, contrast) SAR scenes did not add any significant amount of information to the post-fusion imagery. Classification accuracy was not improved when comparing ASTER optical data and pseudo optical bands generated from the fusion analysis. Accuracies range from 68.4% for the ASTER data to well below 50% for the component substitution methods. Frequency based approaches also returned lower accuracies when compared to the unfused optical data. The present study essentially replicated (pan-sharpening) studies using the high resolution SAR scene as a pseudo panchromatic band.

CARTOSAT-1, launched in May 2005, is basically a stereo mission, as it is equipped with two cameras onboard, namely Aft and Fore, tilted along track by -5° and +26° respectively, allowing the acquisition of stereo data with a resolution of 2.5 m. The Institute "Area di Geodesia e Geomatica" at Sapienza Universita di Roma, Eurimage S.p.A. and the former CyberCity AG participated at the ISPRS-ISRO Cartosat-1 Scientific Assessment Programme (C-SAP), in order to investigate the generation of Digital Surface Models (DSMs) from Cartosat-1 stereo scenes. The paper describes the work carried out during the summer of 2006, on the stereopair belonging to the test field called TS-8, acquired on 8th June 2005 over the city of Rome, Italy. The processing included the sensor orientation, Digital Surface Model (DSM) generation, orthoimage production and 3D features extraction. For the evaluation of the DSM, orthophoto and 3D feature accuracy, a large scale 3D map (1:2′000) was used.

The principal intent of this research is to: (a) investigate the potential of passive microwave data from Advanced Microwave Sounding Unit (AMSU) in detecting snowfall events and in measuring their intensity, and (b) evaluate the effect of both land cover and atmospheric conditions on the retrieval accuracy. A neural-network-based model has been developed and has shown a great potential in detecting snowfall events and classifying their intensity into light, moderate or heavy. This algorithm has been applied for different snow storms which occurred in four winter seasons in the Northeastern United States. Additional information such as cloud cover and air temperature were added to the process to reduce misidentified snowfall pixels. Only pixels with cloud cover and falling within a specific range of temperature are presented to the snowfall detection model. Surface temperature collected from ground station-based observations and archived by the National Climatic Data Center (NCDC) were used for this test. Different heavy storm events and non-snowfall observations that occurred at the same time as AMSU acquisition were selected. Hourly snow accumulation data collected by the NCDC were used as truth data to train and validate the model. The preliminary results indicate that the neural-network-based model provides a significant improvement in snowfall detection accuracy over existing satellite-based methods.

The Moderate Resolution Imaging Spectroradiometer (MODIS) is currently flying on both the Terra and Aqua satellite platforms. The Ocean Biology Processing Group (OBPG) at NASA Goddard Space Flight Center is producing operational ocean color products from the MODIS-Aqua sensor; however, documented uncertainties and instabilities in the prelaunch and on-orbit characterization have inhibited the production of similar products from MODIS-Terra. In particular, the radiometric response of the 412-nm band has degraded by more than 40% over the 7-year mission lifespan, with similar though less extreme changes in the longer wavelengths. While such variability may be fully correctable through the on-board calibration system, it suggests that the optical properties of the scan mirror have changed significantly since launch. Furthermore, the degradation trends are substantially different between the two mirror sides, which is likely a result of asymmetric damage done to the mirror during prelaunch testing. These effects contribute to uncertainty in our knowledge of instrument response versus incidence angle on the mirror and sensitivity with respect to polarization of the observed radiance. In this paper, we examine the impact of apparent MODIS-Terra instrument characterization errors on the derived ocean color products and show that residual errors in the current operational calibration give rise to significant cross-scan artifacts, mirror-side differences, and detector-to-detector striping in the retrieved water-leaving radiances. In addition, we describe OBPG efforts to reduce these artifacts through statistical and vicarious instrument characterization, and show the quality of the resulting water-leaving radiance retrievals relative to those derived from MODIS-Aqua.

This work deals with unsupervised change detection in bi-date Synthetic Aperture Radar (SAR) images. Whatever the indicator of change used to compute the criterion image, e.g. log-ratio or Kullback-Leibler divergence between images, we have observed poor quality change maps for some events when using the Hidden Markov Chain (HMC) model we focus on in this work. The main reason comes from the stationary assumption involved in this model --and in most Markovian models such as Hidden Markov Random Fields--, which can not be justified in most observed scenes: changed areas are not necessarily stationary in the image. Besides the non-stationary Markov models proposed in the literature, the aim of this paper is to describe a pragmatic solution to tackle change detection stationarity by evaluating and comparing a 1D and a 2D window approaches. By moving the window through the criterion image, the process is able to produce a change map which can better exhibit non-stationary changes than the classical HMC applied directly on the whole criterion image. Special care is devoted to the estimation of the number of classes in each window, which can vary from one (no change) to three (positive change, negative change and no change) by using the corrected Akaike Information Criterion suited to small samples. The quality assessment of the proposed approaches is achieved with a pair of RADARSAT images bracketing the Mount Nyiragongo volcano eruption event in January 2002. The available ground truth confirms the effectiveness of the proposed approach compared to a classical HMC-based strategy.

An optical parametric amplifier has been developed to generate tunable 1570 nm radiation from a 1064 nm pump at high efficiency. A micropulse Nd:YAG with two amplifiers generating an average power of 2 watts (10 kHz) is used to pump a periodically poled lithium niobate crystal injection seeded by two CW distributed feedback lasers: one at 1570.824 nm and the second at 1570.973 nm. A conversion efficiency of ~28% from the pump into the signal wavelength has been demonstrated. The 1-nanosecond signal has a measured time-averaged jitter of <0.3 pm

The MERTIS instrument is a state of the art imaging spectrometer in the TIR range onboard ESA's Bepi Colombo mission to the planet Mercury. MERTIS has four science goals: the study of Mercury's surface composition, identification of rock-forming minerals, mapping of the surface mineralogy, and the study of surface temperature variations and of the thermal inertia. The instrument is designed to achieve a signal-to-noise-ratio above 100 in the 7-14 μm range with a spectral channel width of 90 nm and a nominal spatial ground resolution of 500 m within the complex thermal and radiation environment of Mercury.

Hyperspectral data acquired using airborne push-broom sensors frequently contain geometric distortion caused by aircraft motion. This paper presents a simple single reference line algorithm to correct push-broom imagery for geometric distortions resulting from aircraft roll during the scan period. If a straight line is not available throughout the entire image, multiple lines may be drawn to correct the entire image. The algorithm was applied to two hyperspectral urban images (Terre Haute, Indiana, USA and Provo, Utah, USA) acquired during summer 2006. The results demonstrate that this method is an effective way to correct many of the errors caused by the effect of aircraft roll on hyperspectral push-broom sensors. After performing the reference line correction one of the images was georeferenced using a DOQQ, and a RMSE value of 1.5 meters was achieved. The correction program used to implement the method was programmed in Python, and the program can be downloaded from the project's website.

The advanced technology in site-specific and spaceborne determination of grain crude protein content (CP) by remote sensing can help optimize the strategies for buyers in aiding purchasing decisions, and help farmers to maximize the grain output by adjusting field nitrogen (N) fertilizer inputs. We performed field experiments to study the relationship between grain quality indicators and foliar nitrogen concentration (FNC). FNC at anthesis stage was significantly correlated with CP, while spectral vegetation index was significantly correlated to FNC. Based on the relationships among nitrogen reflectance index (NRI), FNC and CP, a model for CP prediction was developed. NRI was able to evaluate FNC with a higher coefficient of determination of R²= 0.7302 in Experiment A. The relationship between laboratory measured and remotely sensed FNC had a coefficient of determination of R²=0.7279 in Experiment B. The method developed in this study could contribute towards developing optimal procedures for evaluating wheat grain quality by in situ canopy-reflected spectrum and ASTER image at anthesis stage. CP derived from both in situ spectrum and the ASTER image exhibited high accuracy and the precision in Experiment C. The RMSE were 0.893% for in situ spectrum model and 1.654% for ASTER image model, and the R² were 0.7661 and 0.7194 for both, respectively. It is thus feasible to forecast grain quality by NRI derived from in situ canopy-reflected spectrum and ASTER image. Our results indicated that the inversion of FNC and the evaluation of CP by NRI were surprisingly good.

This research study presents a novel global index based on harmonic mean theory to jointly evaluate the performance of pan-sharpening algorithms without using a reference image. The harmonic mean of relative spatial information gain and relative spectral information preservation provides a unique global index to compare the performance of different methods. The presented index also facilitates in assigning relevance to either the spectral or spatial quality of an image. The information divergence between the multispectral (MS) bands at lower resolutions and the pan-sharpened image provides a measure of the spectral fidelity and mean-shift. Mutual information between the original pan and the synthetic pan images generated from the MS and pan-sharpened images is used to calculate the relative gain. The relative gain helps to quantify the amount of spatial information injected by the method. A trend comparison of the presented approach with other quality indices using well-known pan-sharpening methods on high resolution and medium resolution datasets reveals that the new index can be used to evaluate the quality of pan-sharpened images at the resolution of the pan image without the availability of a reference image.

Traditional approaches to hyperspectral target detection involve the application of detection algorithms to atmospherically compensated imagery. Rather than compensate the imagery, a more recent approach uses physical models to generate target subspaces. These radiance subspaces can then be used in an appropriate detection scheme to identify potential targets. The generation of these subspaces involves some a priori knowledge of data acquisition parameters, scene and atmospheric conditions, and possible calibration errors. Variation is allowed in the model since some parameters are difficult to know accurately. Each vector in the subspace is the result of a MODTRAN simulation coupled with a physical model. Generation of large target spaces can be computationally burdensome. This paper explores the use of statistical methods to describe such target signature spaces. The statistically modeled spaces can then be used to generate arbitrary radiance vectors to form a sub-space with potential "real-time" applications. Statistically modeled target subspaces, using limited training samples, were found to accurately resemble MODTRAN derived radiance vectors.

Noise is one of the basic factors that degrade remote sensing (RS) data and prevent accurate and reliable retrieval of useful information. Availability of a priori information on noise type and properties allows applying more effective methods for image processing, namely, filtering, edge detection, feature extraction, etc. However, noise statistics are often unknown and are to be estimated for an image at hand. Thus one needs blind methods for the evaluation of the noise variance, especially if the number of images or sub-band images of multichannel RS data is large enough. In this paper, we consider several approaches to blind evaluation of noise variance. An important item is that we consider both i.i.d. and spatially correlated noise. It is demonstrated that some techniques that perform well enough for i.i.d. noise fail if the image is corrupted by spatially correlated noise. We show how segmentation-based methods for blind evaluation of noise variance that operate in the spatial domain can be modified in order to provide better accuracy for wide ranges of noise variance and spatial correlation parameters. Numerical simulation results comparing the performance of several techniques are presented. Real RS data processing examples are also given.

Insufficient high vertical and temporal resolution data have limited the precipitation forecast skill of convective initiation. The Geosynchronous Imaging Fourier Transform Spectrometer (GIFTS), a new hyperspectral geostationary satellite measurement system, could provide top-of-atmosphere (TOA) radiances across two broad spectral regions with high-resolutions in spectral, horizontal and temporal spaces within a fixed domain. A set of modeled and observed vertical profiles of atmospheric moisture and temperature during a convective initiation (CI) event within the observing period of the International H2O Project (IHOP_2002) are used to assess the potential values of GIFTS measurements to convective precipitation forecast. First, it is shown that the model simulation captures reasonably well the movement of the precipitation bands and the gradient structures of temperature and water vapor of the convectively initiated storm. Second, the observed vertical and temporal variability of water vapor during the CI period is shown to be quite significant in the lower troposphere. The differences between observations and model simulation are also noticed. Using both the observed and the model-predicted profiles as input to GIFTS radiative transfer model (RTM), it is finally shown that the simulated GIFTS radiance could capture the high vertical and temporal variability of the real and modeled atmosphere prior to the CI, as well as the differences between observations and model forecasts. The study suggests the potential for GIFTS to make important contributions to the improvement of the forecast skill of convective precipitation.

The Hiroshima Institute of Technology (HIT) manages direct downlinks for microwave and optical earth observation satellite data in Japan. This study focuses on validating rice monitoring using ground truth data from ENIVISAT-1/ASAR, such as the height of rice crop, vegetation cover, and leaf area index in test sites in the Hiroshima district in Japan. ENVISAT-1/ASAR data can monitor the rice-crop growing cycle using alternating polarization (AP) mode images. However, ASAR data is influenced by several parameters, such as land-cover structure, and the direction and alignment of rice fields in the test sites. To investigate these parameters, in this study the validation was combined with microwave image data and ground truth data for rice-crop fields. Multitemporal, multidirection (descending and ascending), and multiangle ASAR AP-mode images were used to investigate the rice-crop growing cycle. Finally, the extraction of rice-planted areas was attempted using multitemporal ASAR AP mode data, such as VV/VH and HH/HV. This study clarifies that the estimated rice-planted area agrees with the existing statistical data for areas within the rice field. In addition, HH/HV is more effective than VV/VH in extracting the rice-planted area.

Optical satellite images observed in shallow water represent a mixture of information concerning bottom type, water quality, and water depth. In this study, we extracted such information by estimating the abundance using the mixed pixel classification technique. The method is based on the Orthogonal Subspace Projection algorithm which can first eliminate unwanted information, and then match for the information in which we are interested in. Our results indicated that using the subpixel classification approach, information correlated with water depth can be extracted from optical multi-spectral images. To further test this approach, we used satellite images of Itu-Aba Island in the southern part of the South China Sea as a test image and compared with truth depth data from echo sounder sonar system for verification. The results were promising and showed that the information extracted from the satellite image corresponding to bathymetry was highly correlated to the true water depth.

There has been substantial effort dedicated to the issue of how to incorporate spatial information to improve the classification accuracy in past decades and some excellent methods have been developed. Each method has its own advantages and disadvantages for different images and user requirements. This paper proposes a new classification method, which introduces multiple-point simulation to improve the classification of remotely sensed imagery data by incorporating structural information through a training image. This new method named CCSSM is the derivation of two classifications and based on spectral and spatial information, which then are fused. For validation purpose, a real-life example of road extraction from Landsat TM is used to substantiate the conceptual arguments. An assessment of the accuracy of the proposed method compared with results using a maximum likelihood classifier shows the overall accuracy improves from 48.9% to 82.6%, and the kappa coefficient improves from 0.12 to 0.55 and therefore, the new method has superior overall performance on the classification of remotely sensed data.

Several image processing techniques have been separately and simultaneously applied to data obtained from Landsat ETM+, ERS-2 SAR, and Digital Elevation Models (DEM) to evaluate the performance of different satellite sensors for extracting and mapping geological lineaments in the Eljufra area of northwest Libya. Herein, hundreds of new lineaments in various trends have been identified in comparison to previous results obtained from geological maps. Visible and near infrared color composite of Landsat ETM+ overlaid on shaded relief maps enhanced the ability of extracting and mapping geological lineaments. Analysis and interpretation of the extracted lineaments provided information about the tectonic evolution of the investigated area. A gravity survey was performed to explore the subsurface structure of the study area. Euler deconvolution was applied to the gravity data to estimate the depths and locations of gravity source anomalies. A comparison of results obtained from remote sensing and gravity data indicates that the origin of most lineaments is structural.

Target detection in 3-dimensional (3D), irregular point cloud data sets is an emerging field of study among the remote sensing community. Airborne topographic light detection and ranging (i.e., Lidar) systems are capable of scanning areas with single-pass post spacings on the order 0.2 m. Unfortunately, many of the current spatial search algorithms require higher spatial resolutions on a target object in order to achieve robust detection performance with low false alarm levels. This paper explores the application of Johnson's spin-image surface matching algorithm to low density point clouds for the purpose of providing a preliminary spatial cue to a secondary sensor. In the event that this sensor is an imaging device, a method is presented for transforming 3D points into a fixed, gridded coordinate system relative to the second sensor.

A summary of different approaches for laser beam wander statistics estimation is presented. The principles of an imaging LIDAR technique for remote measurements of refractive turbulence vertical profile based on image motion analysis of a secondary source created by a laser beam at a given altitude are described. The turbulence-induced beam displacement statistics and angle-of-arrival variance of backscattered wave-front are analyzed for different sensing configurations. This study has implications for airborne reconnaissance image restoration, optical satellite communication, adaptive optics, and atmospheric effects on laser weaponry.

Long-term data sets are important in the fields of ecology, hydrology, rangeland science, and geography. Remote sensing is an especially important component of such studies when spatial and temporal capabilities are important considerations. In many cases, satellite remote sensing is not adequate because of resolution or length of observation drawbacks. However, aerial photography, which extends back into the mid 1930s, is often overlooked. In order to find relevant imagery, considerable effort needs to be expended because the aerial photos over a particular study area can be scattered in a large number of archives across the country. Once the photos are assembled, digital scanning, proper documentation, storage, and a searchable data base are necessary to make easy and effective use of the aerial photos. The aerial photo data are of immense value to researchers, natural resource managers, students, and the general public.

This paper deals with landslide hazard analysis using Geographic Information System (GIS) and remote sensing data for Cameron Highland, Malaysia. Landslide locations were identified in the study area from interpretation of aerial photographs and field surveys. Topographical/geological data and satellite images were collected and processed using GIS and image processing tools. There are ten landslide inducing parameters which are considered for the landslide hazards. These parameters are topographic slope, aspect, curvature and distance from drainage, all derived from the topographic database; geology and distance from lineament, derived from the geologic database; landuse from Landsat satellite images; soil from the soil database; precipitation amount, derived from the rainfall database; and the vegetation index value from SPOT satellite images. Landslide hazard was analyzed using landslide-occurrence factors employing the logistic regression model. The results of the analysis were verified using the landslide location data and compared with logistic regression model. The accuracy of hazard map observed was 85.73%. The qualitative landslide hazard analysis was carried out using the logistic regression model by doing map overlay analysis in GIS environment. This information could be used to estimate the risk to population, property and existing infrastructure like transportation network.

Seasonal and interannual variability in satellite-derived estimates of near-surface chlorophyll-a concentrations were examined in the four regions off the southwest coast of India from 1998-2003. Wind-induced upwelling predominates in late spring and summer, coinciding with the maximum in solar radiation, leading to increased accumulations of phytoplankton biomass. Chlorophyll concentrations varied from 2 to 10 mg/m3 and were generally lower in January-April and maximal in May-September. Chlorophyll concentrations along the coast followed a similar seasonal pattern (ranging from 0.5 to 4 mg/m3); however, concentrations were always greater on the Trivendrum and Cochin coast compared to the Calicut and Mangalore coast. The southwest coast is often cloud covered; data density maps provide an index of confidence in the 'regional' applicability of the summary statistics. One consequence of the 1997-1998 El Nino appears to be a reduction in chlorophyll concentration off Trivendrum and Cochin in 1998. An increased frequency of El Nino events may lead to a reduction in offshore carbon in this dynamic upwelling region. Six-month median chlorophyll concentrations were maximal in 2002 off Trivendrum and Cochin.

The goal of this paper was to develop methods and protocols for water productivity mapping (WPM) using remote sensing data at multiple resolutions and scales in conjunction with field-plot data. The methods and protocols involved three broad categories: (a) Crop Productivity Mapping (CPM) (kg/m2); (b) Water Use (evapotranspiration) Mapping (WUM) (m3/m2); and (c) Water Productivity Mapping (WPM) (kg/m3). First, the CPMs were determined using remote sensing by: (i) Mapping crop types; (ii) modeling crop yield; and (iii) extrapolating models to larger areas. Second, WUM were derived using the Simplified Surface Energy Balance (SSEB) model. Finally, WPMs were produced by dividing CPMs and WUMs. The paper used data from Quickbird 2.44m, Indian Remote Sensing (IRS) Resoursesat-1 23.5m, Landsat-7 30m, and Moderate Resolution Imaging Spectroradiometer (MODIS) 250m and 500m, to demonstrate the methods for mapping water productivity (WP). In terms of physical water productivity (kilogram of yield produced per unit of water delivered), wheat crop had highest water productivity of 0.60 kg/m3 (WP), followed by rice with 0.5 kg/m3, and cotton with 0.42 kg/m3. In terms of economic value (dollar per unit of water delivered), cotton ranked highest at $ 0.5/m3 followed by wheat with $ 0.33/m3 and rice at $ 0.10/m3. The study successfully delineated the areas of low and high WP. An overwhelming proportion (50+%) of the irrigated areas were under low WP for all crops with only about 10% area in high WP.

In late 2005 the NASA Earth Science Technology Office convened a working group to review decadal-term technology needs for Earth science active optical remote sensing objectives. The outcome from this effort is intended to guide future NASA investments in laser remote sensing technologies. This paper summarizes the working group findings and places them in context with the conclusions of the National Research Council assessment of future Earth science and applications requirements, completed in 2007.

The fifth North American Intercomparison of Ultraviolet Monitoring Spectroradiometers was held June 13 to 21, 2003 at Table Mountain outside of Boulder, Colorado, USA. The main purpose of the Intercomparison was to assess the ability of spectroradiometers to accurately measure solar ultraviolet irradiance, and to compare the results between instruments of different monitoring networks. This Intercomparison was coordinated by NOAA and included participants from six national and international agencies. The UV measuring instruments included scanning spectroradiometers, spectrographs, and multi-filter radiometers. Synchronized spectral scans of the solar irradiance were performed between June 16 and 20, 2003. The spectral responsivities were determined for each instrument using the participants' lamps and calibration procedures and with NOAA/CUCF standard lamps. This paper covers the scanning spectroradiometers and the one spectrograph. The solar irradiance measurements from the different instruments were deconvolved using a high resolution extraterrestrial solar irradiance and reconvolved with a 1-nm triangular band-pass to account for differences in the bandwidths of the instruments. The measured solar irradiance from the spectroradiometers using the rivmSHIC algorithm on a clear-sky day on DOY 172 at 17.0 UTC (SZA = 30°) had a relative 1-σ standard deviation of +/-2.6 to 3.4% for 300- to 360-nm using the participants' calibration.

The International Satellite Cloud Climatology Project (ISCCP) B1 data was recently rescued by NOAA's National Climatic Data Center (NCDC). ISCCP B1 data are geostationary imagery from satellites worldwide which are subsampled to 10 km and 3 hourly resolution. These data were unusable given the disarray of format documentation and lack of software for reading the data files. After developing access software, assessing data quality, and removing infrared window calibration biases, the data have been used for research in studying tropical cyclones and is available for other topics, such as rainfall and cloud cover. This resulted not only in valuable scientific data for weather and climate research but also in important lessons learned for future archiving of scientific data records. The effort also exemplifies principles of scientific data stewardship.

Remotely sensed time series of landscape biophysical states and radar estimates of precipitation are assessed in a statistical streamflow estimation model in four regionally proximate south-central Texas watersheds. Sandies Creek watershed (1420 km2) served as calibration of a streamflow estimation model based on 8-day composited time series of land surface (radiant) temperature (LST) and a vegetation moisture stress index (MSI) from MODIS (Moderate Resolution Imaging Spectroradiometer) satellite imagery products. These time series were hypothesized to serve as proxies for soil moisture condition antecedent to streamflow-generating precipitation events as estimated by NEXRAD (Next Generation Weather Radar) precipitation products. A linear multiple regression statistical model yielding estimation equation for observed flows at Sandies Creek was validated in 3 additional proximal watersheds of varying spatial dimension (860 km2 - 2940 km2), soils, land cover, and climatology. The validation yielded encouraging results as assessed with Pearson's r, Nash-Sutcliffe's E, and relative volume error or bias. Equivalent performance of the calibrated model was seen in the watershed mostly adjacent to Sandies Creek, the mostly similar one in terms of size, land cover, precipitation, and soils. Performance of the model diminished slightly in the more distal, and climatologically-environmentally dissimilar watersheds. The estimation model was re-calibrated for all three validation watersheds, with positive results. The contribution of LST uncertainty and NEXRAD precipitation bias to model discrepancies were evaluated along with discussion of known sources of model error.

A retrieval of tropospheric volcanic ash from Mt Etna has been carried out, using measurements from the Moderate Resolution Imaging Spectroradiometer (MODIS). The NASA-MODIS satellite instrument acquires images in the 0.4 to 14 μm spectral range with a spatial resolution of 1 km at nadir. The eruption which occurred on 24 November 2006 is considered as a test case in this work. In order to derive the ash plume optical thickness, the particle effective radius and the total mass, the Brightness Temperature Difference procedure has been applied to MODIS channels 31 (centered at 11 μm) and 32 (centered at 12 μm). Channel 5 (centered at 1.24 μm) has been used to refine the cloud discrimination, exploiting the distinct reflectivity of meteorological and volcanic clouds in the near infrared spectral range. The detection of volcanic ash pixels has been significantly improved by applying an atmospheric water vapor correction to MODIS data. This procedure doubles the number of pixels identified as containing volcanic ash compared to the original method. The retrieved mean ash optical thickness at 0.55 μm, mean particle effective radius and the total ash mass in the plume are 0.4, 3.5 μm and 3620 tons, respectively. A detailed sensitivity analysis has been carried out to investigate errors in the retrieval caused by the uncertainty in various parameters: surface temperature and emissivity, plume geometry (altitude and thickness), ash type and atmospheric water vapor. Results show that the largest contributions to retrieval errors are from uncertainty in surface parameters, aerosol type and atmospheric water vapor. The total tropospheric volcanic ash retrieval errors are estimated to be 30%, 30% and 40% for mean AOT, mean effective radius and total mass retrieval, respectively.

Current information on land cover, forest type and forest structure for the Virgin Islands is critical to land managers and researchers for accurate forest inventory and ecological monitoring. In this study, we use cloud free image mosaics of panchromatic sharpened Landsat ETM+ images and decision tree classification software to map land cover and forest type for the Virgin Islands, illustrating a low cost, repeatable mapping approach. Also, we test if coarse-resolution discrete lidar data that are often collected in conjunction with digital orthophotos are useful for mapping forest structural attributes. This approach addresses the factors that affect vegetation distribution and structure by testing if environmental variables can improve regression models of forest height and biomass derived from lidar data. The overall accuracy of the 29 forest and non-forest classes is 72%, while most the forest types are classified with greater than 70% accuracy. Due to the large point spacing of this lidar dataset, it is most appropriate for height measurements of dominant and co-dominant trees (R² = 72%) due to its inability to accurately represent forest understory. Above ground biomass per hectare is estimated by its direct relationship with plot canopy height (R² = 0.72%).

Surface soil moisture information is needed for monitoring and modeling surface processes at various spatial scales. While many reflectance based soil moisture quantification models have been developed and validated in laboratories, only few were applied from remote sensing platforms and thoroughly validated in the field. This paper addresses the issues of a) quantifying surface soil moisture with very high resolution spectral measurements from remote sensors in a landscape with sandy substrates and low vegetation cover as well as b) comprehensively validating these results in the field. For this purpose, the recently developed Normalized Soil Moisture Index (NSMI) has been analyzed for its applicability to airborne hyperspectral remote sensing data. Three HyMap scenes from 2004 and 2005 were collected from a lignite mining area in southern Brandenburg, Germany. An NSMI model was calibrated (R²=0.92) and surface soil moisture maps were calculated based on this model. An in-situ surface soil moisture map based on a combination of Frequency Domain Reflectometry (FDR) and gravimetric data allowed for validating each image pixel (R²=0.82). In addition, a qualitative multitemporal comparison between two consecutive NSMI datasets from 2004 was performed and validated, showing an increase in estimated surface soil moisture corresponding with field measurements and precipitation data. The study shows that the NSMI is appropriate for modeling surface soil moisture from high spectral-resolution remote sensing data. The index leads to valid estimations of soil moisture values below field capacity in an area with sandy substrates and low vegetation cover (NDVI < 0.3). Further studies will analyze the validity of the NSMI for surface soil moisture estimation from spaceborne hyperspectral sensors like the Environmental Mapping and Analysis Program (EnMap) in different landscapes.