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NASA NTRS2022

The Mass Change Designated Observable Study: Overview and Results

The 2017–2027 United States National Academy of Sciences Decadal Survey (DS) for Earth Science and Applications from Space identified Mass Change (MC) as one of five Designated Observables (DOs) having the highest priority in terms of Earth observations required to advance Earth system science over the next decade. In response to this designation, NASA initiated several multi-center studies, with the goal of recommending observing system architectures for each DO for implementation within this decade. This paper provides an overview of the Mass Change Designated Observable (MCDO) Study along with key findings. The study process included: (a) generation of a Science and Applications Traceability Matrix (SATM) that maps required measurement parameters to the DS Science and Applications Objectives; (b) identification of three architecture classes relevant for measuring mass change: Precise Orbit Determination (POD), Satellite-Satellite-Tracking (SST) and Gravity Gradiometry (GG), along with variants within each architecture class; and (c) creation of a Value Framework process that considers science value, cost, risk, schedule, and partnership opportunities, to identify and recommend high value observing systems for further in-depth study. The study team recommended the implementation of an SST architecture, and identified variants that simultaneously (a) satisfy the baseline measurement parameters of the SATM; (b) maximize the probability of providing overlap with the Gravity Recovery and Climate Experiment Follow-On (GRACE-FO) mission currently in operation, accelerating science return from both missions; and (c) provide a pathway towards substantial improvements in resolution and accuracy of mass change data products relative to the program of record.

D N Wiese↗

NASA NTRS2012

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

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 (+/-20 Mg/ha or 20%, the greater of the two, for at least 80% of grid cells). A 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 density (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 contiguous areas (>0.5 ha) that are relatively homogenous in remote sensing metrics (forest patches). We used this image-derived data with simulated spaceborne LiDAR derived from orbit and cloud cover simulations and airborne data from NASA's Laser Vegetation Imaging Sensor (LVIS) to compute AGB and estimate LiDAR sampling error for forest patches and 100 m, 250 m, 500 m, and 1 km grid cells. 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. First, 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. Second, a data fusion approach for estimating AGB using simulated spaceborne LiDAR with SAR and passive optical image combinations reduced forest AGB sampling errors 12%-38% from those where LiDAR is used with SAR or passive optical alone. In absolute terms, sampling errors were reduced from 14-40 Mg/ha to 11-28 Mg/ha across all grid scales and prediction methods, where minimum sampling errors were 11, 15, 18, and 22 Mg/ha for 1 km, 500 m, 250 m, and 100 m grid scales, respectively. Third, spaceborne global scale accuracy requirements were achieved whereby at least 80% of the grid cells at 100 m, 250 m, 500 m, and 1 km grid levels met AGB 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 500 m and 250 m grid level, respectively..

LiDAR↗