DESDynI Lidar for Solid Earth Applications

As part of the NASA's DESDynI mission, global elevation profiles from contiguous 25 m footprint Lidar measurements will be made. Here we present results of a performance simulation of a single pass of the multi-beam Lidar instrument over uplifted marine terraces in southern Alaska. The significance of the Lidar simulations is that surface topography would be captured at sufficient resolution for mapping uplifted terraces features but it will be hard to discern I-2m topographic change over features less than tens of meters in width. Since Lidar would penetrate most vegetation, the accurate bald Earth elevation profiles will give new elevation information beyond the standard 30-m OEM

GSFC OSTM, Jason-l and TOPEX POD Update

The OSTM (Jason-2) has been in orbit for three years (since June 2008), and the full suite of altimeter data from TOPEX/Poseidon, Jason-I and Jason-2 now span nearly twenty years since the launch of TOPEX in 1992. Issues that affect the stability of the orbits through time and the orbit accuracy include the reference frame, the radiation pressure models for the altimeter satellites and the fidelity of the dynamic force model, including time-variable gravity, as well as the performance of the individual tracking systems. We have conducted detailed analyses of the new ITRF2008 reference frame and find only a small effect on global mean sea level compared to ITRF2005, although we note an improvement in POD quality over the most recent time periods for Jason-2. In the past year we have developed a new time series of orbits for TOPEX/Poseidon, Jason-I, and Jason-2 based on the ITRF2008 reference frame using SLR and DORIS data and for Jason-2 using GPS data. In addition, we have continued to experiment with improvements to the radiation pressure model for the altimeter satellites in particular the Jason satellites since these nonconservative force model errors now rank as the most outstanding source of error on altimeter satellite POD. In the previous (ITRF2005-based) and current (ITRF2008-based) orbits we have relied on a simplified time-variable gravity (TVG) model, derived from GRACE solutions. We have recently experimented with improvements using higher fidelity TVG models (both temporally and spatially) and report on the results. We have computed a time series of GPS-only reduced-dynamic orbits at GSFC, and used these in conjunction with the SLR-DORIS dynamic and reduced-dynamic orbits to assess reference fiame stability with respect to the different tracking systems for both ITRF2005 and ITRF2008. We show through internal (GSFConly) and external comparisons (with other analysis centers) that the radial orbit accuracy for Jason-2 remains at I cm

Mass balance of the Greenland ice sheet from 1958 to 2007

We combine estimates of the surface mass balance, SMB, of the Greenland ice sheet for years 1958 to 2007 with measurements of the temporal variability in ice discharge, D, to deduce the total ice sheet mass balance. During that time period, we find a robust correlation (R2 = 0.83) between anomalies in SMB and in D, which we use to reconstruct a continuous series of total ice sheet mass balance. We. find that the ice sheet was losing 110 ± 70 Gt/yr in the 1960s, 30 ± 50 Gt/yr or near balance in the 1970s-1980s, and 97 ± 47 Gt/yr in 1996 increasing rapidly to 267 ± 38 Gt/yr in 2007. Multi-year variations in ice discharge, themselves related to variations in SMB, cause 60 ± 20 more variation in total mass balance SMB, and therefore dominate the ice sheet mass budget. Copyright 2008 by the American Geophysical Union

Achieving Accuracy Requirements for Forest Biomass Mapping: A Data Fusion Method for Estimating Forest Biomass and LiDAR Sampling Error with Spaceborne Data

The synergistic use of active and passive remote sensing (i.e., data fusion) demonstrates the ability of spaceborne light detection and ranging (LiDAR), synthetic aperture radar (SAR) and multispectral imagery for achieving the accuracy requirements of a global forest biomass mapping mission. This data fusion approach also provides a means to extend 3D information from discrete spaceborne LiDAR measurements of forest structure across scales much larger than that of the LiDAR footprint. For estimating biomass, these measurements mix a number of errors including those associated with LiDAR footprint sampling over regional - global extents. A general framework for mapping above ground live forest biomass (AGB) with a data fusion approach is presented and verified using data from NASA field campaigns near Howland, ME, USA, to assess AGB and LiDAR sampling errors across a regionally representative landscape. We combined SAR and Landsat-derived optical (passive optical) image data to identify forest patches, and used image and simulated spaceborne LiDAR data to compute AGB and estimate LiDAR sampling error for forest patches and 100m, 250m, 500m, and 1km grid cells. Forest patches were delineated with Landsat-derived data and airborne SAR imagery, and simulated spaceborne LiDAR (SSL) data were derived from orbit and cloud cover simulations and airborne data from NASA's Laser Vegetation Imaging Sensor (L VIS). At both the patch and grid scales, we evaluated differences in AGB estimation and sampling error from the combined use of LiDAR with both SAR and passive optical and with either SAR or passive optical alone. This data fusion approach demonstrates that incorporating forest patches into the AGB mapping framework can provide sub-grid forest information for coarser grid-level AGB reporting, and that combining simulated spaceborne LiDAR with SAR and passive optical data are most useful for estimating AGB when measurements from LiDAR are limited because they minimized forest AGB sampling errors by 15 - 38%. Furthermore, spaceborne global scale accuracy requirements were achieved. At least 80% of the grid cells at 100m, 250m, 500m, and 1km grid levels met AGB density accuracy requirements using a combination of passive optical and SAR along with machine learning methods to predict vegetation structure metrics for forested areas without LiDAR samples. Finally, using either passive optical or SAR, accuracy requirements were met at the 500m and 250m grid level, respectively

Fusing simulated GEDI, ICESat-2 and NISAR data for regional aboveground biomass mapping

Accurate mapping of forest aboveground biomass (AGB) is critical for better understanding the role of forests in the global carbon cycle. NASA's current GEDI and ICESat-2 missions as well as the upcoming NISAR mission will collect synergistic data with different coverage and sensitivity to AGB. In this study, we present a multi-sensor data fusion approach leveraging the strength of each mission to produce wall-to-wall AGB maps that are more accurate and spatially comprehensive than what is achievable with any one sensor alone. Specifically, we calibrate a regional L-band radar AGB model using the sparse, simulated spaceborne lidar AGB estimates. We assess our data fusion framework using simulations of GEDI, ICESat-2 and NISAR data from airborne laser scanning (ALS) and UAVSAR data acquired over the temperate high AGB forest and complex terrain in Sonoma County, California, USA. For ICESat-2 and GEDI missions, we simulate two years of data coverage and AGB at footprint level are estimated using realistic AGB models. We compare the performance of our fusion framework when different combinations of the sparse simulated GEDI and ICEsat-2 AGB estimates are used to calibrate our regional L-band AGB models. In addition, we test our framework at Sonoma using (a) 1-ha square grid cells and (b) similarly sized irregularly shaped objects. We demonstrate that the estimated mean AGB across Sonoma is more accurately estimated using our fusion framework than using GEDI or ICESat-2 mission data alone, either with a regular grid or with irregular segments as mapping units. This research highlights methodological opportunities for fusing new and upcoming active remote sensing data streams toward improved AGB mapping through data fusion.</p

ICESat Laser Altimeter Pointing, Ranging and Timing Calibration from Integrated Residual Analysis: A Summary of Early Mission Results

On January 12, 2003 the Ice, Cloud and land Elevation Satellite (ICESat) was successfUlly placed into orbit. The ICESat mission carries the Geoscience Laser Altimeter System (GLAS), which consists of three near-infrared lasers that operate at 40 short pulses per second. The instrument has collected precise elevation measurements of the ice sheets, sea ice roughness and thickness, ocean and land surface elevations and surface reflectivity. The accurate geolocation of GLAS's surface returns, the spots from which the laser energy reflects on the Earth's surface, is a critical issue in the scientific application of these data Pointing, ranging, timing and orbit errors must be compensated to accurately geolocate the laser altimeter surface returns. Towards this end, the laser range observations can be fully exploited in an integrated residual analysis to accurately calibrate these geolocation/instrument parameters. Early mission ICESat data have been simultaneously processed as direct altimetry from ocean sweeps along with dynamic crossovers resulting in a preliminary calibration of laser pointing, ranging and timing. The calibration methodology and early mission analysis results are summarized in this paper along with future calibration activitie

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