Ice, Cloud And Land Elevation Satellite Geoscience Laser Altimeter System Research Articles

Quality assessment of open sourced digital elevation models in southeast coast of India

Digital elevation model (DEM) provides elevation values of the terrain. At present several open source DEMs are available and its vertical accuracy varies from place to place. The present study assessed the vertical accuracy of 1 arc-sec data of Advanced Land Observing Satellite digital surface model (AW3D30) Version-3, Shuttle Radar Topographic Mission (SRTM) DEM (V3), Carto DEM (V3R1) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global DEM (V2) using Ice, Cloud, and land Elevation Satellite/Geoscience Laser Altimeter System (ICESat/GLAS) and Real Time Kinematic Global Positioning System (RTK GPS) data in Southeast coast of India. Root mean square error (RMSE) was calculated for DEMs, showed the elevation accuracy as 2.48 m (AW3D30), 3.02 m (SRTM DEM), 3.88 m (Carto DEM) and 8.02 m (ASTER GDEM) in the Tamil Nadu state while it was 2.29 m (AW3D30), 3.10 m (SRTM DEM), 3.93 m (Carto DEM) and 8.98 m (ASTER GDEM) in the 10 km buffer zone of coastal region and the results are satisfactory than the official product’s specifications. Landform analysis was carried out at selected RTK GPS surveyed sites where the elevation of different coastal landforms is validated with DEMs and it was found the AW3D30 showed least RMSE than others. This paper suggests that AW3D30 is best for representing terrain elevation values followed by SRTM and Carto DEMs which are apt choices for application studies.

Retrieval of Vertical Foliage Profile and Leaf Area Index Using Transmitted Energy Information Derived from ICESat GLAS Data

The vertical foliage profile (VFP) and leaf area index (LAI) are critical descriptors in terrestrial ecosystem modeling. Although light detection and ranging (lidar) observations have been proven to have potential for deriving the VFP and LAI, existing methods depend only on the received waveform information and are sensitive to additional input parameters, such as the ratio of canopy to ground reflectance. In this study, we proposed a new method for retrieving forest VFP and LAI from Ice, Cloud and land Elevation Satellite (ICESat) Geoscience Laser Altimeter System (GLAS) data over two sites similar in their biophysical parameters. Our method utilized the information from not only the interaction between the laser and the forest but also the sensor configuration, which brought the benefit that our method was free from an empirical input parameter. Specifically, we first derived the transmitted energy profile (TEP) through the lidar 1-D radiative transfer model. Then, the obtained TEP was utilized to calculate the vertical gap distribution. Finally, the vertical gap distribution was taken as input to derive the VFP based on the Beer–Lambert law, and the LAI was calculated by integrating the VFP. Extensive validations of our method were carried out based on the discrete anisotropic radiative transfer (DART) simulation data, ground-based measurements, and the Moderate Resolution Imaging Spectroradiometer (MODIS) LAI product. The validation based on the DART simulation data showed that our method could effectively characterize the VFP and LAI under various canopy architecture scenarios, including homogeneous turbid and discrete individual-tree scenes. The ground-based validation also proved the feasibility of our method: the VFP retrieved from the GLAS data showed a similar trend with the foliage distribution density in the GLAS footprints; the GLAS LAI had a high correlation with the field measurements, with a determination coefficient (R2) of 0.79, root mean square error (RMSE) of 0.49, and bias of 0.17. Once the outliers caused by low data quality and large slope were identified and removed, the accuracy was further improved, with R2 = 0.85, RMSE = 0.35, and bias = 0.10. However, the MODIS LAI product did not present a good relationship with the GLAS LAI. Relative to the GLAS LAI, the MODIS LAI showed an overestimation in the low and middle ranges of the LAI and a saturation at high values of approximately LAI = 5.5. Overall, this method has the potential to produce continental- and global-scale VFP and LAI datasets from the spaceborne lidar system.

Evaluation of Footprint Horizontal Geolocation Accuracy of Spaceborne Full-Waveform LiDAR Based on Digital Surface Model

Spaceborne full-waveform LiDAR has shown unique advantages in measuring global surface elevation. Laser footprints generally have decimeter-level vertical accuracy, meeting the requirement of ground elevation control points. In contrast, the footprint horizontal geolocation accuracy is in the meter to ten-meter levels. Although previous researches attempted to locate the footprint horizontal coordinate based on the digital surface model (DSM), the applicability and performance of the DSM-based positioning method in evaluating the footprint geolocation accuracy should be rigorously assessed before large-scale applications. Therefore, this study practices the DSM-based footprint positioning method over several study sites with various land covers and different laser campaigns. The footprint geolocation accuracy of the ICESat/GLAS (Ice, Cloud, and land Elevation Satellite/Geoscience Laser Altimeter System), the first Earth observation full-waveform LiDAR satellite, is evaluated by the DSM-based method. Results indicate that the DSM-based positioning method is only suitable for areas with significant height features, but not applicable in areas with high spatial correlation. The derived footprint geolocation accuracy (8.19-m horizontal shifting with 4.19-m standard deviation) is relatively reliable in urban site with relatively high spatial heterogeneity. This study helps make better use of the DSM-based footprint positioning method and design calibration experiments of full-waveform LiDAR satellites.

Evaluation of the Newly Released Worldwide AW3D30 DEM Over Typical Landforms of China Using Two Global DEMs and ICESat/GLAS Data

This paper evaluates the accuracy of newly released global 1 arc-second advance land observing satellite (ALOS) world 3D–30 m (AW3D30) digital elevation model (DEM) over five validation samples of typical landforms across China using ICESat/GLAS (ice, cloud, and land elevation satellite/geoscience laser altimeter system) data. In comparison, shuttle radar topography mission (SRTM) 1-arc second (SRTM1) and global DEM version 2 (GDEM2) are also considered. The result reveals that the RMSEs of mean horizontal shifts compared with SRTM1 and GDEM2 are 5.30 and 10.07 m in x -direction, 5.64 and 11.75 m in y -direction, respectively. The vertical accuracy analysis shows that the AW3D30 offers the highest accuracy with RMSE of 4.81 m, followed by SRTM1 (5.86 m), and then by GDEM2 (14.14 m). Our result reveals that the vertical accuracy can be improved by horizontal alignment. The analysis suggests that terrain slopes and land cover types jointly affect the vertical accuracies of AW3D30 and the former is likely to have a greater impact than the latter. The results of hydrological potential assessment reveal that the drainage network derived from AW3D30 is more accurate than that from SRTM1 and GDEM2. However, all the DEMs exhibit river extraction limitations over the flat area due to the unresolved flow direction based on local elevation information alone. This study provides a comprehensive understanding of the AW3D30 and recommends that applications requiring accurate Earth surface depictions should utilize AW3D30 over SRTM1 and GDEM2, as it presents the best accuracy among the freely available DEMs.

Comparison of digital elevation models and relevant derived attributes

The digital elevation model (DEM) and its derivative attributes are important parameters for evaluating any process using digital terrain analysis. Five freely available global DEM products including Advanced Spaceborne Thermal Emission and Reflection Radiometer-Global Digital Elevation Model version 2 (ASTER GDEM2), Shuttle Radar Topographic Mission version 4.1 (SRTM V4.1), Global Multiresolution Terrain Elevation Data 2010 (GMTED2010), EarthEnv-DEM90, and Global 30 Arc-Second Elevation (GTOPO30) were assessed in this study. The objective of this study was to compare the differences of elevations, slopes, and topographic wetness indices (TWIs) derived from these five DEM products. SRTM V4.1 showed a better accuracy [root mean square error (RMSE)=4.87 m] than ASTER GDEM2 (RMSE=7.08 m) based on ICESat/GLAS (the Ice, Cloud, and land Elevation Satellite/Geoscience Laser Altimeter System) laser altimetry points. ICESat/GLAS data were then selected as the benchmark to rectify the SRTM V4.1 data using the simple kriging (SK) interpolation method. The corrected high-accuracy SRTM V4.1 data (RMSE=1.14 m) were then regarded as the reference data. EarthEnv-DEM90 displayed the best accuracy in the DEM and slope, whereas the TWI accuracy of GMTED2010 was best. The accuracy of topographic attributes was sensitive to the roughness of the terrain. DEM and slope displayed a larger error variance as the elevation increased. DEM was sensitive to the data source and slope was sensitive to the data source and spatial resolution. TWI was influenced by data source and spatial resolution. As the spatial resolution decreased, the differences of topographic attributes tended to decrease.

Mapping Lorey’s height over Hyrcanian forests of Iran using synergy of ICESat/GLAS and optical images

ABSTRACTLorey’s height, representative of mean height in uneven-aged forest stands, is a valuable parameter for forest ecosystem management. While in situ measures provide the most precise information, remote-sensing techniques may provide less expensive but denser and more operational alternative of Lorey’s height estimation over highly mountainous areas. This research aims first to evaluate the performances of two nonparametric data mining methods, random forest (RF) and artificial neural network (ANN), for estimation of Lorey’s height using ice, cloud and land elevation satellite/geoscience laser altimeter system (ICESat/GLAS) in Hyrcanian forests of Iran and then to provide Lorey’s height map using a synergy of ICESat/GLAS and optical images (TM and SPOT). RF and ANN GLAS height models were developed using waveform deterministic metrics, principal components (PCs) from principal component analysis (PCA) and terrain index (TI) extracted from a digital elevation model (DEM). The best result was obtained using an ANN combining first three PCs of PCA and waveform extent ʺWextʺ (RMSE = 3.4 m, RMSE% = 12.4). In order to map Lorey’s height, GLAS-estimated heights were regressed against indices derived from optical images and also topographic information. The best model (RF regression with RMSE = 5.5 m and = 0.59) was applied on the entire study area, and a wall-to-wall height map was generated. This map showed relatively good compatibility with in situ measurements collected in part of the study area.

Estimation of Forest Aboveground Biomass in Changbai Mountain Region Using ICESat/GLAS and Landsat/TM Data

Mapping the magnitude and spatial distribution of forest aboveground biomass (AGB, in Mg·ha−1) is crucial to improve our understanding of the terrestrial carbon cycle. Landsat/TM (Thematic Mapper) and ICESat/GLAS (Ice, Cloud, and land Elevation Satellite, Geoscience Laser Altimeter System) data were integrated to estimate the AGB in the Changbai Mountain area. Firstly, four forest types were delineated according to TM data classification. Secondly, different models for prediction of the AGB at the GLAS footprint level were developed from GLAS waveform metrics and the AGB was derived from field observations using multiple stepwise regression. Lastly, GLAS-derived AGB, in combination with vegetation indices, leaf area index (LAI), canopy closure, and digital elevation model (DEM), were used to drive a data fusion model based on the random forest approach for extrapolating the GLAS footprint AGB to a continuous AGB map. The classification result showed that the Changbai Mountain region was characterized as forest-rich in altitudinal vegetation zones. The contribution of remote sensing variables in modeling the AGB was evaluated. Vegetation index metrics account for large amount of contribution in AGB ranges <150 Mg·ha−1, while canopy closure has the largest contribution in AGB ranges ≥150 Mg·ha−1. Our study revealed that spatial information from two sensors and DEM could be combined to estimate the AGB with an R2 of 0.72 and an RMSE of 25.24 Mg·ha−1 in validation at stand level (size varied from ~0.3 ha to ~3 ha).

VERTICAL ACCURACY ASSESSMENT OF ZY-3 DIGITAL SURFACE MODEL USING ICESAT/GLAS LASER ALTIMETER DATA

Abstract. The Ziyuan-3 (ZY-3) satellite, as the first civilian high resolution surveying and mapping satellite in China, has a very important role in national 1 : 50,000 stereo mapping project. High accuracy digital surface Model (DSMs) can be generated from the three line-array images of ZY-3, and ZY-3 DSMs of China can be produced without using any ground control points (GCPs) by selecting SRTM (Shuttle Radar Topography Mission) and ICESat/GLAS (Ice, Cloud, and land Elevation Satellite, Geo-science Laser Altimeter System) as the datum reference in the Satellite Surveying and Mapping Application Center, which is the key institute that manages and distributes ZY-3 products. To conduct the vertical accuracy evaluation of ZY-3 DSMs of China, three representative regions were chosen and the results were compared to ICESat/GLAS data. The experimental results demonstrated that the root mean square error (RMSE) elevation accuracy of the ZY-3 DSMs was better than 5.0 m, and it even reached to less than 2.5 m in the second region of eastern China. While this work presents preliminary results, it is an important reference for expanding the application of ZY-3 satellite imagery to widespread regions. And the satellite laser altimetry data can be used as referenced data for wide-area DSM evaluation.

A Least-Squares Adjusted Grounding Line for the Amery Ice Shelf Using ICESat and Landsat 8 OLI Data

The grounding line (GL) is the transition boundary between a grounded ice sheet and a floating ice shelf. The location of the GL is very important for calculating the ice sheet mass balance and studying the interactions between ice and ocean. In this paper, Landsat 8 optical images and NASA Ice, Cloud, and Land Elevation Satellite/Geoscience Laser Altimeter System (ICESat/GLAS) data are merged together to develop a precise GL for the Amery Ice Shelf (AIS). An improved plane-fitting model is used to remove the slope-induced error in the ICESat GL extraction. A least-squares model is then applied to the manually digitized Landsat 8 GL to adjust the positions where the ICESat GL is detected. The adjusted GL combines the texture from the Landsat 8 data and the elevation information from the ICESat data. The combination of different remote sensing data is used to produce a new adjusted GL for the AIS.

Absolute and relative height-pixel accuracy of SRTM-GL1 over the South American Andean Plateau

Previously available only over the Continental United States (CONUS), the 1 arc-second mesh size (spatial resolution) SRTM-GL1 (Shuttle Radar Topographic Mission – Global 1) product has been freely available worldwide since November 2014. With a relatively small mesh size, this digital elevation model (DEM) provides valuable topographic information over remote regions. SRTM-GL1 is assessed for the first time over the South American Andean Plateau in terms of both the absolute and relative vertical point-to-point accuracies at the regional scale and for different slope classes. For comparison, SRTM-v4 and GDEM-v2 Global DEM version 2 (GDEM-v2) generated by ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) are also considered. A total of approximately 160,000 ICESat/GLAS (Ice, Cloud and Land Elevation Satellite/Geoscience Laser Altimeter System) data are used as ground reference measurements. Relative error is often neglected in DEM assessments due to the lack of reference data. A new methodology is proposed to assess the relative accuracies of SRTM-GL1, SRTM-v4 and GDEM-v2 based on a comparison with ICESat/GLAS measurements. Slope values derived from DEMs and ICESat/GLAS measurements from approximately 265,000 ICESat/GLAS point pairs are compared using quantitative and categorical statistical analysis introducing a new index: the False Slope Ratio (FSR). Additionally, a reference hydrological network is derived from Google Earth and compared with river networks derived from the DEMs to assess each DEM’s potential for hydrological applications over the region. In terms of the absolute vertical accuracy on a global scale, GDEM-v2 is the most accurate DEM, while SRTM-GL1 is more accurate than SRTM-v4. However, a simple bias correction makes SRTM-GL1 the most accurate DEM over the region in terms of vertical accuracy. The relative accuracy results generally did not corroborate the absolute vertical accuracy. GDEM-v2 presents the lowest statistical results based on the relative accuracy, while SRTM-GL1 is the most accurate. Vertical accuracy and relative accuracy are two independent components that must be jointly considered when assessing a DEM’s potential. DEM accuracies increased with slope. In terms of hydrological potential, SRTM products are more accurate than GDEM-v2. However, the DEMs exhibit river extraction limitations over the region due to the low regional slope gradient.

Vertical accuracy assessment of freely available digital elevation models over low-lying coastal plains

The frequency of coastal flood damages is expected to increase significantly during the twenty-first century as sea level rises in the coastal floodplain. Coastal digital elevation model (DEM) data describing coastal topography are essential for assessing future flood-related damages and understanding the impacts of sea-level rise. The Shuttle Radar Topography Mission (SRTM) and Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) are currently the most accurate and freely available DEM data. However, an accuracy assessment specifically targeted at DEMs over low elevation coastal plains is lacking. The present study focuses on these areas to assess the vertical accuracy of SRTM and ASTER GDEM using Ice, Cloud, and land Elevation Satellite, Geoscience Laser Altimeter System (ICESat/GLAS) and Real Time Kinematic (RTK) Global Positioning System (GPS) field survey data. The findings show that DEM accuracy is much better than the mission specifications over coastal plains. In addition, optical remote sensing image analysis further reveals the relationship between DEM vertical accuracy and land cover in these areas. This study provides a systematic approach to assess the accuracy of DEMs in coastal zones, and the results highlight the limitations and potential of these DEMs in coastal applications.

Glacier changes during the past 40 years in the West Kunlun Shan

Recent studies on glaciers in the West Kunlun Shan, northwest Tibetan Plateau, have shown that they may be stable or retreating slightly. Here, we assess changes in the mass of the glaciers in the West Kunlun Shan (WKS) in an attempt to understand the processes that control their behavior. Glaciers over the recent 40 years (1970–2010) have shrunk 3.4±3.1% in area, based on a comparison between two Chinese glacier inventories. Variations of surface elevations, derived from ICESat-GLAS (Ice, Cloud, and Land Elevation Satellite-Geoscience Laser Altimeter System) elevation products (GLA14 data) using the robust linear-fit method, indicate that the glaciers have been gaining mass at a rate of 0.23±0.24 m w.e./a since 2003. The annual mass budget for the whole WKS range from 2003 to 2009 is estimated to be 0.71±0.62 Gt/a. This gain trend is confirmed by MOD10A1 albedo for the WKS region which shows a descent of the mean snowline altitude from 2003 to 2009.

Joint Use of ICESat/GLAS and Landsat Data in Land Cover Classification: A Case Study in Henan Province, China

Lidar waveform features from the Ice, Cloud, and land Elevation Satellite/Geoscience Laser Altimeter System (ICESat/GLAS) and spectral features from Landsat Thematic Mapper (TM)/Enhanced TM Plus (ETM+) were used to discriminate land cover categories for GLAS footprints in Henan Province, China. Fifteen waveform metrics were derived from GLAS data while band ratios and surface spectral reflectance were taken from Landsat TM/ETM+. Random forest (RF) was used in feature selection and classification of footprints along with support vector machines (SVMs). The categories of classification included croplands, forests, shrublands, water bodies, and impervious surfaces. Compared with the use of waveform or spectral features alone in land cover classification, the joint use of waveform and spectral data as inputs improved the classification accuracy of footprints. An overall accuracy (OA) of 91% was achieved by either RF or SVM when features from both GLAS and Landsat sources were used increasing upon an accuracy of 85% if only one source was used. The high accuracy land cover data obtained by the joint use of the two data sources could be used as additional references in large scale land cover mapping when ground truth is hard to obtain. It is believed that the increase in accuracy is largely a result from the inclusion of the additional information of vertical structure offered by waveform lidar.

ICESat-Derived Elevation Changes on the Lena Delta and Laptev Sea, Siberia

We employ elevation data from the Ice, Cloud, and land Elevation Satellite (ICESat) Geoscience Laser Altimeter System (GLAS) to investigate surface changes across the Lena Delta and sea ice of the coastal Laptev Sea, Siberia during winters of 2003 through 2008. We compare ICESat GLAS-derived elevation changes on sea ice and the Bykovskaya and Sardakhskaya Channels with datum-corrected tide gauge height measurements from Danai, Sannikova and Tiksi stations. We find the coastal sea ice and large inland ice covered channels elevation changes are in phase with the tide-height changes on a same month-year and datum-controlled basis. Furthermore, we find elevation change on tundra drained lake basins to be +0.03 ± 0.02 m, on average. These findings indicate that ICESat GLAS is capable of detection of tide fluxes of ice covered coastal rivers, and with a small error range, it is suitable for investigations of active-layer and permafrost dynamics associated with seasonal freezing (heave) and thawing (subsidence) using repeat-location profiles.

Height and biomass of mangroves in Africa from ICESat/GLAS and SRTM

The accurate quantification of the three-dimensional (3-D) structure of mangrove forests is of great importance, particularly in Africa where deforestation rates are high and the lack of background data is a major problem. The objectives of this study are to estimate (1) the total area, (2) canopy height distributions, and (3) above-ground biomass (AGB) of mangrove forests in Africa. To derive the 3-D structure and biomass maps of mangroves, we used a combination of mangrove maps derived from Landsat Enhanced Thematic Mapper Plus (ETM+), lidar canopy height estimates from ICESat/GLAS (Ice, Cloud, and land Elevation Satellite/Geoscience Laser Altimeter System), and elevation data from SRTM (Shuttle Radar Topography Mission) for the African continent. The lidar measurements from the large footprint GLAS sensor were used to derive local estimates of canopy height and calibrate the interferometric synthetic aperture radar (InSAR) data from SRTM. We then applied allometric equations relating canopy height to biomass in order to estimate AGB from the canopy height product. The total mangrove area of Africa was estimated to be 25,960 km2 with 83% accuracy. The largest mangrove areas and the greatest total biomass were found in Nigeria covering 8573 km2 with 132 × 106 Mg AGB. Canopy height across Africa was estimated with an overall root mean square error of 3.55 m. This error includes the impact of using sensors with different resolutions and geolocation error. This study provides the first systematic estimates of mangrove area, height, and biomass in Africa.

Vegetation height and cover fraction between 60° S and 60° N from ICESat GLAS data

Abstract. We present new coarse resolution (0.5° × 0.5°) vegetation height and vegetation-cover fraction data sets between 60° S and 60° N for use in climate models and ecological models. The data sets are derived from 2003–2009 measurements collected by the Geoscience Laser Altimeter System (GLAS) on the Ice, Cloud and land Elevation Satellite (ICESat), the only LiDAR instrument that provides close to global coverage. Initial vegetation height is calculated from GLAS data using a development of the model of Rosette et al. (2008) with with further calibration on desert sites. Filters are developed to identify and eliminate spurious observations in the GLAS data, e.g. data that are affected by clouds, atmosphere and terrain and as such result in erroneous estimates of vegetation height or vegetation cover. Filtered GLAS vegetation height estimates are aggregated in histograms from 0 to 70 m in 0.5 m intervals for each 0.5° × 0.5°. The GLAS vegetation height product is evaluated in four ways. Firstly, the Vegetation height data and data filters are evaluated using aircraft LiDAR measurements of the same for ten sites in the Americas, Europe, and Australia. Application of filters to the GLAS vegetation height estimates increases the correlation with aircraft data from r = 0.33 to r = 0.78, decreases the root-mean-square error by a factor 3 to about 6 m (RMSE) or 4.5 m (68% error distribution) and decreases the bias from 5.7 m to −1.3 m. Secondly, the global aggregated GLAS vegetation height product is tested for sensitivity towards the choice of data quality filters; areas with frequent cloud cover and areas with steep terrain are the most sensitive to the choice of thresholds for the filters. The changes in height estimates by applying different filters are, for the main part, smaller than the overall uncertainty of 4.5–6 m established from the site measurements. Thirdly, the GLAS global vegetation height product is compared with a global vegetation height product typically used in a climate model, a recent global tree height product, and a vegetation greenness product and is shown to produce realistic estimates of vegetation height. Finally, the GLAS bare soil cover fraction is compared globally with the MODIS bare soil fraction (r = 0.65) and with bare soil cover fraction estimates derived from AVHRR NDVI data (r = 0.67); the GLAS tree-cover fraction is compared with the MODIS tree-cover fraction (r = 0.79). The evaluation indicates that filters applied to the GLAS data are conservative and eliminate a large proportion of spurious data, while only in a minority of cases at the cost of removing reliable data as well. The new GLAS vegetation height product appears more realistic than previous data sets used in climate models and ecological models and hence should significantly improve simulations that involve the land surface.

Uncertainty within satellite LiDAR estimations of vegetation and topography

This paper demonstrates the ability to identify representative ground elevation and vegetation height estimates within the Ice, Cloud and land Elevation Satellite/Geoscience Laser Altimeter System (ICESat/GLAS) waveforms for an area of mixed vegetation and varied topography. Estimating vegetation height within large-footprint Light Detection and Ranging (LiDAR) waveforms relies on the ability to estimate the uppermost canopy surface (signal beginning) and an elevation representing the ground surface, both of which are influenced by vegetation properties and topographic slope. We examined sources of uncertainty for vegetation height estimation from ICESat/GLAS data using airborne LiDAR data, field measurements and the FLIGHT radiative transfer model. In comparison with an independent 10-m resolution digital terrain model (DTM), a method using Gaussian decomposition of the satellite waveform produced a mean bias of −0.10 m when estimating ground elevation. A second method of estimating vegetation height using waveform extent and a terrain index effectively removed slope as an error source but produced a greater ground surface offset (−0.83 m). The two methods of estimating vegetation height compared well with airborne LiDAR estimates (correlation coefficient (R 2) = 0.68, root mean square error (RMSE) = 4.4 m and R 2 = 0.61, RMSE = 4.9 m, respectively). However, the complex interplay of the structural and optical properties of the intercepted vegetation and slope requires further understanding. A tool such as FLIGHT provides a useful means to explore the sensitivity of the waveform to both vegetation properties and topographic slope.

Geographical and seasonal distribution of tropical tropopause thin clouds and their relation to deep convection and water vapor viewed from satellite measurements

The climatologies of deep convection, thin clouds, and water vapor measurements from four independent satellites are compared in this study. Deep convection reaching the tropical tropopause layer (TTL) is indicated by the area of the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) 20 dBZ reflectivity reaching 14 km and the area of the TRMM visible and infrared scanner (VIRS) brightness temperature at 10.8 μm (TB11) < 210 K. TTL clouds are identified by 17 years of Stratospheric Aerosol and Gas Experiment (SAGE) II measurements and by 1 year of Earth Observing System (EOS) Microwave Limb Sounder (MLS) ice water content in 2005. TTL water vapor is estimated from 2005 EOS MLS retrievals. Results suggest that TTL clouds are geographically and seasonally correlated with deep convection as inferred from the area of TB11 < 210 K. Patterns of regional differences of seasonal variations of deep convection are also related to the regional differences of seasonal variations of TTL clouds. By grouping the Ice Cloud and Land Elevation Satellite/Geoscience Laser Altimeter System (ICESat/GLAS) layer cloud products, we find that there is a general spatial correlation between the largest, thickest TTL layer clouds with the highest optical depth and the regions with the most intense deep convection. MLS CO and water vapor at 146 hPa have similar geographical and seasonal variations as deep convection and TTL clouds. EOS MLS 100 hPa water vapor is low over the west Pacific Ocean at the same location and time as more TTL clouds are observed.

Precision and Accuracy of Satellite Radar and Laser Altimeter Data Over the Continental Ice Sheets

The unprecedented accuracy of elevations retrieved from the Ice Cloud and Land Elevation Satellite (ICESat) laser altimeter is investigated and used to characterize the range errors in the Environmental Satellite (Envisat) and European Remote Sensing 2 Satellite (ERS-2) radar altimeters over the continental ice sheets. Cross-mission crossover analysis between time-coincident ERS-2-, Envisat-, and ICESat-retrieved elevations and comparisons to an ICESat-derived digital elevation map are used to quantify the radar elevation error budget as a function of surface slope and to investigate the effectiveness of a method to account for the radar altimeter slope-induced error. The precision and accuracy of the elevations retrieved from the ICESat Geoscience Laser Altimeter System and the European Space Agency radar altimeters on ERS-2 and Envisat are calculated over the Greenland and Antarctic ice sheets using a crossover analysis. As a result of this work, the laser precision is found to vary as a function of surface slope from 14 to 59 cm, and the radar precision varies from 59 cm to 3.7 m for ERS-2 and from 28 cm to 2.06 m for Envisat. Envisat elevation retrievals when compared with ICESat results over regions with less than 0.1deg surface slopes show a mean difference of 9plusmn5 cm for Greenland and -40plusmn98 cm over Antarctica. ERS-2 elevation retrievals over these same low surface slope regions differ from ICESat results by -56plusmn72 cm over Greenland and 1.12plusmn1.16 m over Antarctica. At higher surface slopes of 0.7deg to 0.8deg, the Envisat/ICESat differences increase to -2.27plusmn23 m over Greenland and to 0.05plusmn26 m over Antarctica