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NASA's Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI's footprint-level (~25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies, and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI's waveform-to-biomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g. RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AGBD and/or height metrics. There was considerable variability in model performance across geographic strata, and areas with sparse training data and/or high AGBD values had the poorest performance. These models are used to produce global predictions of AGBD, but will be improved in the future as more and better training data become available.

Changes and disturbances to water diversity and quality are complex and multi-scale in space and time. Although in situ methods provide detailed point information on the condition of water bodies, they are of limited use for making area-based monitoring over time, as aquatic ecosystems are extremely dynamic. Remote sensing (RS) provides methods and data for the cost-effective, comprehensive, continuous and standardised monitoring of characteristics and changes in characteristics of water diversity and water quality from local and regional scales to the scale of entire continents. In order to apply and better understand RS techniques and their derived spectral indicators in monitoring water diversity and quality, this study defines five characteristics of water diversity and quality that can be monitored using RS. These are the diversity of water traits, the diversity of water genesis, the structural diversity of water, the taxonomic diversity of water and the functional diversity of water. It is essential to record the diversity of water traits to derive the other four characteristics of water diversity from RS. Furthermore, traits are the only and most important interface between in situ and RS monitoring approaches. The monitoring of these five characteristics of water diversity and water quality using RS technologies is presented in detail and discussed using numerous examples. Finally, current and future developments are presented to advance monitoring using RS and the trait approach in modelling, prediction and assessment as a basis for successful monitoring and management strategies. [ABSTRACT FROM AUTHOR]

High-resolution measurements obtained from NOAA 'best' atmospheric turbulence (BAT) probes mounted on an EGRETT high-altitude research aircraft were used to characterize turbulence in the upper troposphere and lower stratosphere at scales from 2 m to 20 km, focusing on three-dimensional behavior in the sub-kilometer-scale range. Data were analyzed for 129 separate level flight segments representing 41 h of flight time and 12 600 km of wind-relative flight distances. The majority of flights occurred near the tropopause layer of the winter subtropical jet stream in the Southern Hemisphere. Second-order structure functions for velocity and temperature were analyzed for the separate level-flight segments, individually and in various ensembles. A 3D scaling range was observed at scales less than about 100 m, with power-law exponents for the structure functions of the velocity component in the flight direction varying mostly between 0.4 and 0.75 for the separate flight segments, but close to 2/3 for the ensemble-averaged curves for all levels and for various sub- ensembles. Structure functions in the 3D scaling range were decoupled from those at scales greater than 10 km, with the large-scale structure functions showing less variation than those at smaller scales. Weakly anisotropic behavior was observed in the 3D range, with structure parameters for the lateral and vertical velocities on the same order as those in the flight direction but deviating from the expected isotropic value. Anisotropy was correlated with turbulence intensity, with greater anisotropy associated with weaker turbulence. DOI: 10.1175/2009JAS3108.1

In-flight measurements of atmospheric methane (CH4(a)) and mass balance flux quantification studies can assist with verification and improvement of UNFCCC National Inventory reported CH4 emissions. However, attributing CH4(a) mole fraction readings to one or more emission sources is difficult where co-located plumes mix rapidly within the convective boundary layer. The stable carbon isotopic signatures of CH4 sources can assist with source attribution and potentially identify bottom-up (BU) inventory knowledge gaps and identify mitigation opportunities. In the Surat Basin, Queensland, Australia, both coal seam gas (CSG) production and cattle farming are increasing sources of CH4 emissions into the atmosphere. The CSG fields cover thousands of square kilometres and many CSG facilities and farms are inaccessible as part of ground-based surveys. The aims of this study were to explore the use of CH4(a) isotopic composition (d13CCH4(a)) of in-flight atmospheric air (IFAA) samples and the application of source endmember mixing models to assess where the BU inventory was well characterised, to identify potential gaps in the BU regional inventory (missing sources, or over- and underestimation source categories), identify 30 mitigation opportunities, and to characterise the isotopic signature of CH4 sources that were inaccessible during ground surveys. Forty-nine useable IFAA samples were collected between 100-350 m above ground level (mAGL) over a region of CSG, coal mining and agricultural production over a 2-week period in September 2018. For each IFAA sample the 2-hour back trajectory footprint area was determined using the NOAA HYSPLIT atmospheric trajectory modelling application. Samples were then assigned to a source category set where over 50 % of the 2-hour upwind BU inventory could be attributed 35 to a single source (CSG, grazing cattle, or feedlots), and further differentiated based on altitude (100-200 mAGL or 250-350 mAGL). A novel multi-dataset (source category) regression method with shared parameters (background air CH4(b) and d13CCH4(b)) was used to fit Keeling models simultaneously to each data set. The determination of a common background endmember (CH4(b) and d13CCH4(b)) for the two-endmember mixing model reduces the uncertainty in the derived isotopic signature for a source (d13CCH4(s)). The estimated d13CCH4(s) signatures for each category were then compared to the database of 40 source d13CCH4(s) signatures established from ground studies. For IFAA samples collected between 250-350 mAGL altitude, the best-fit d13CCH4(s) signatures compare well with the ground observation: CSG d13CCH4(s) -55.5 ‰ (CI 95 % ± 13.4 ‰) versus d13CCH4(s) -56.7 ‰ to -45.6 ‰; grazing cattle d13CCH4(s) -60.5 ‰ (CI 95 % ± 15.1 ‰) versus -61.7 ‰ to -57.5 ‰. For feedlots, the derived d13CCH4(s), -69.5 ‰ (CI 95 % ± 22.1 ‰), was isotopically lighter than the ground-based study (d13CCH4(s) from -65.2 ‰ to -60.3 ‰), but within agreement given the large uncertainty for this source. For IFAA samples collected between 100-200 mAGL the d13CCH4(s) signature for the CSG set, -65.3 ‰ (CI 95 % ±13.1 ‰), was isotopically lighter than expected, suggesting a BU inventory knowledge gap or the need to extend the population statistics for CSG d13CCH4(s) signatures. For the 100-200 mAGL set collected over grazing cattle districts the d13CCH4(s) signature, -52.5 ‰ (CI 95 % ± 18.8 ‰), was much heavier than expected from the BU inventory. This is likely due to CSG CH4 emissions entering the study domain from an adjacent CSG field. Using the multi-Keeling-model regression derived background air values (1.825 ppm (CI 95 % ± 0.037 ppm) and -47.3 ‰ (CI 95 % ± 0.3 ‰)), a Keeling model fitted to the isotopically light set yielded a low d13CCH4(s) signature of -80.5 ‰ (CI 95 % ± 9.2 ‰). A CH4 source with this low d13CCH4(s) signature and a high rate of emissions has not been incorporated into existing BU inventories for the region. Further ground-based studies are required to identify the isotopically light CH4 source, with possible sources including termites and CSG brine ponds. If the excess emissions are from the brine ponds, they can be abated. It is concluded that in-flight atmospheric d13CCH4(a) measurements used in conjunction with endmember mixing modelling of CH4 sources are powerful tools for BU inventory verification and for identifying sources that can be mitigated. The subregions identified for BU inventory review would likely have been overlooked based on CH4(a) measurements alone. [ABSTRACT FROM AUTHOR]

The National Airborne Field Experiment 2005 (NAFE'05) and the Campaign for validating the Operation of Soil Moisture and Ocean Salinity (COSMOS) were undertaken in November 2005 in the Goulburn River catchment, which is located in southeastern Australia. The objective of the joint campaign was to provide simulated Soil Moisture and Ocean Salinity (SMOS) observations using airborne L-band radiometers supported by soil moisture and other relevant ground data for the following: 1) the development of SMOS soil moisture retrieval algorithms; 2) developing approaches for downscaling the low-resolution data from SMOS; and 3) testing its assimilation into land surface models for root zone soil moisture retrieval. This paper describes the NAFE'05 and COSMOS airborne data sets together with the ground data collected in support of both aircraft campaigns. The airborne L-band acquisitions included 40 km x 40 km coverage flights at 500-m and 1-km resolution for the simulation of a SMOS pixel, multiresolution flights with ground resolution ranging from 1 km to 62.5 m, multiangle observations, and specific flights that targeted the vegetation dew and sun glint effect on L-band soil moisture retrieval. The L-band data were accompanied by airborne thermal infrared and optical measurements. The ground data consisted of continuous soil moisture profile measurements at 18 monitoring sites throughout the 40 km x 40 km study area and extensive spatial near-surface soil moisture measurements concurrent with airborne monitoring. Additionally, data were collected on rock coverage and temperature, surface roughness, skin and soil temperatures, dew amount, and vegetation water content and biomass. These data are available at www.nafe.unimelb.edu.au. Index Terms--Microwave radiometry, National Airborne Field Experiment (NAFE), passive microwave, soil moisture, Soil Moisture and Ocean Salinity (SMOS).

Over the last ten years, some operational airborne remote sensing systems have become available for mapping surface salinity over large areas in near real time. A new dual-polarized Polarimetric L-band Multibeam Radiometer (PLMR) has been developed to improve accuracy and precision when compared with previous instrument generations. This paper reports on the first field evaluation of the performance of the PLMR by measuring salinity gradients in the central Great Barrier Reef. Before calibration, the raw salinity values of the PLMR and conductivity-temperature-depth (CTD) differed by 3-6 psu. The calibration, which uses in situ salinity data to remove long-term drifts in the PLMR as well as environmental effects such as surface roughness and radiation from the sky and atmosphere, was carried out by equating the means of the PLMR and CTD salinity data over a subsection of the transect, after which 85% of the salinity values between the PLMR and CTD are within 0.1 psu along the complete transect. From offshore to inshore across the shelf, the PLMR shows an average cross-shelf salinity increase of about 0.4 psu and a decrease of 2 psu over the inshore 20 km at -19 [degrees] S (around Townsville) and -18 [degrees] S (around Lucinda), respectively. The average cross-shelf salinity increase was 0.3 psu for the offshore 100 km over all transects. These results are consistent with the in situ CTD results. This survey shows that PLMR provided an effective method of rapidly measuring the surface salinity in near real time when a calibration could be made. Index Terms--Great Barrier Reef (GBR), microwave radiometer, remote sensing, sea surface salinity.

Cliff-ramp patterns (CR) are a common feature of scalar turbulence, characterized by a sharp temperature increase (cliff) followed by a more gradual temperature decrease (ramp). Aircraft measurements obtained from NOAA best aircraft turbulence probes (BAT) were used to characterize and compare CR patterns observed under stably stratified conditions in the upper troposphere, a region for which there are few such studies. Experimental data were analyzed for three locations, one over Wales and two over southern Australia, the latter in correspondence with the Southern Hemisphere winter subtropical jet stream. Comparison of observed CR patterns with published direct numerical simulations (DNS) revealed that they were likely signatures of Kelvin-Helmholtz (KH) billows, with the ramps associated with the well-mixed billows and the cliffs marking the highly stretched braids. Strong correlation between potential temperature and horizontal velocity supported the KH link, though expected correlations with vertical velocity were not observed. The temperature fronts associated with the cliffs were oriented in a direction approximately normal to the mean wind direction. Locally high values of temperature structure constant near these fronts were associated with steep temperature gradients across the fronts; this may be misleading in the context of electromagnetic propagation, suggesting a false positive indication of high levels of small-scale turbulence that would not correspond to scintillation effects. Billow aspect ratios, braid angles, and length scales were estimated from the data and comparisons with published DNS provided a means for assessing the stage of evolution of the KH billows and the initial Richardson number of the layer.