Using the space-borne NASA scatterometer (NSCAT) to determine the frozen and thawed seasons

We hypothesize that the strong sensitivity of radar backscatter to surface dielectric properties, and hence to the phase (solid or liquid) of any water near the surface should make space-borne radar observations a powerful tool for large-scale spatial monitoring of the freeze/thaw state of the land surface, and thus ecosystem growing season length. We analyzed the NASA scatterometer (NSCAT) backscatter from September 1996 to June 1997, along with temperature and snow depth observations and ecosystem modeling, for three BOREAS sites in central Canada. Because of its short wavelength (2.14 cm), NSCAT was sensitive to canopy and surface water. NSCAT had 25 km spatial resolution and approximately twice-daily temporal coverage at the BOREAS latitude. At the northern site the NSCAT signal showed strong seasonality, with backscatter around −8 dB in winter and −12 dB in early summer and fall. The NSCAT signal for the southern sites had less seasonality. At all three sites there was a strong decrease in backscatter during spring thaw (4–6 dB). At the southern deciduous site, NSCAT backscatter rose from −11 to −9.2 dB during spring leaf-out. All sites showed 1–2 dB backscatter shifts corresponding to changes in landscape water state coincident with brief midwinter thaws, snowfall, and extreme cold (Tmax\u3c−25°C). Freeze/thaw detection algorithms developed for other radar instruments gave reasonable results for the northern site but were not successful at the two southern sites. We developed a change detection algorithm based on first differences of 5-day smoothed NSCAT backscatter measurements. This algorithm had some success in identifying the arrival of freezing conditions in the autumn and the beginning of thaw in the spring. Changes in surface freeze/thaw state generally coincided with the arrival and departure of the seasonal snow cover and with simulated shifts in the directions of net carbon exchange at each of the study sites

Polarimetric-interferometric boreal forest scattering model for BIOMASS end-to-end simulator

A polarimetric-interferometric forward model (FM) for extended covariance matrix modeling is presented. The FM has been designed to be used within the end-to-end simulator for BIOMASS, a new ESA satellite mission aiming at the global mapping of above-ground forest biomass with P-band synthetic aperture radar (SAR). The FM uses linear regression models for prediction of backscatter intensity and HH-VV correlation coefficient, and the random volume over ground (RVoG) model for the prediction of the interferometric correlation coefficients. For boreal forest, parameter values for these sub-models have been derived using polarimetric-interferometric SAR data acquired within the BioSAR 2007 campaign over the Swedish test site Remningstorp. The FM is evaluated qualitatively in a boreal forest scenario through a side-by-side comparison with BioSAR 2007 data. The general agreement is good, although there are regions with structures which cannot be reproduced by the model, probably due to insufficient forest description by the input parameters

Multiband radar characterization of forest biomes

The utility of airborne and orbital SAR in classification, assessment, and monitoring of forest biomes is investigated through analysis of orbital synthetic aperature radar (SAR) and multifrequency and multipolarized airborne SAR imagery relying on image tone and texture. Preliminary airborne SAR experiments and truck-mounted scatterometer observations demonstrated that the three dimensional structural complexity of a forest, and the various scales of temporal dynamics in the microwave dielectric properties of both trees and the underlying substrate would severely limit empirical or semi-empirical approaches. As a consequence, it became necessary to develop a more profound understanding of the electromagnetic properties of a forest scene and their temporal dynamics through controlled experimentation coupled with theoretical development and verification. The concatenation of various models into a physically-based composite model treating the entire forest scene became the major objective of the study as this is the key to development of a series of robust retrieval algorithms for forest biophysical properties. In order to verify the performance of the component elements of the composite model, a series of controlled laboratory and field experiments were undertaken to: (1) develop techniques to measure the microwave dielectric properties of vegetation; (2) relate the microwave dielectric properties of vegetation to more readily measured characteristics such as density and moisture content; (3) calculate the radar cross-section of leaves, and cylinders; (4) improve backscatter models for rough surfaces; and (5) relate attenuation and phase delays during propagation through canopies to canopy properties. These modeling efforts, as validated by the measurements, were incorporated within a larger model known as the Michigan Microwave Canopy Scattering (MIMICS) Model

Extrapolation of Airborne Polarimetric and Interferometric SAR Data for Validation of Bio-Geo-Retrieval Algorithms for Future Spaceborne SAR Missions

Spaceborne SAR system concepts and mission design is often based on algorithms developed and the experience gathered from airborne SAR experiments and associated dedicated campaigns. However, airborne SAR systems have better performance parameters than their future space-borne counterparts as their design is not impacted by mass, power, and storage constraints. This paper describes a methodology to extrapolate spaceborne quality SAR image products from long wavelength airborne polarimetric SAR data which were acquired especially for the development and validation of bio/geo-retrieval algorithms in forested regions. For this purpose not only system (sensor) related parameters are altered, but also those relating to the propagation path (ionosphere) and to temporal decorrelation

Potential of ALOS2 and NDVI to estimate forest above-ground biomass, and comparison with lidar-derived estimates

Remote sensing supports carbon estimation, allowing the upscaling of field measurements to large extents. Lidar is considered the premier instrument to estimate above ground biomass, but data are expensive and collected on-demand, with limited spatial and temporal coverage. The previous JERS and ALOS SAR satellites data were extensively employed to model forest biomass, with literature suggesting signal saturation at low-moderate biomass values, and an influence of plot size on estimates accuracy. The ALOS2 continuity mission since May 2014 produces data with improved features with respect to the former ALOS, such as increased spatial resolution and reduced revisit time. We used ALOS2 backscatter data, testing also the integration with additional features (SAR textures and NDVI from Landsat 8 data) together with ground truth, to model and map above ground biomass in two mixed forest sites: Tahoe (California) and Asiago (Alps). While texture was useful to improve the model performance, the best model was obtained using joined SAR and NDVI (R2 equal to 0.66). In this model, only a slight saturation was observed, at higher levels than what usually reported in literature for SAR; the trend requires further investigation but the model confirmed the complementarity of optical and SAR datatypes. For comparison purposes, we also generated a biomass map for Asiago using lidar data, and considered a previous lidar-based study for Tahoe; in these areas, the observed R2 were 0.92 for Tahoe and 0.75 for Asiago, respectively. The quantitative comparison of the carbon stocks obtained with the two methods allows discussion of sensor suitability. The range of local variation captured by lidar is higher than those by SAR and NDVI, with the latter showing overestimation. However, this overestimation is very limited for one of the study areas, suggesting that when the purpose is the overall quantification of the stored carbon, especially in areas with high carbon density, satellite data with lower cost and broad coverage can be as effective as lidar

Forest disturbance and recovery: A general review in the context of spaceborne remote sensing of impacts on aboveground biomass and canopy structure

Abrupt forest disturbances generating gaps \u3e0.001 km2 impact roughly 0.4–0.7 million km2a−1. Fire, windstorms, logging, and shifting cultivation are dominant disturbances; minor contributors are land conversion, flooding, landslides, and avalanches. All can have substantial impacts on canopy biomass and structure. Quantifying disturbance location, extent, severity, and the fate of disturbed biomass will improve carbon budget estimates and lead to better initialization, parameterization, and/or testing of forest carbon cycle models. Spaceborne remote sensing maps large-scale forest disturbance occurrence, location, and extent, particularly with moderate- and fine-scale resolution passive optical/near-infrared (NIR) instruments. High-resolution remote sensing (e.g., ∼1 m passive optical/NIR, or small footprint lidar) can map crown geometry and gaps, but has rarely been systematically applied to study small-scale disturbance and natural mortality gap dynamics over large regions. Reducing uncertainty in disturbance and recovery impacts on global forest carbon balance requires quantification of (1) predisturbance forest biomass; (2) disturbance impact on standing biomass and its fate; and (3) rate of biomass accumulation during recovery. Active remote sensing data (e.g., lidar, radar) are more directly indicative of canopy biomass and many structural properties than passive instrument data; a new generation of instruments designed to generate global coverage/sampling of canopy biomass and structure can improve our ability to quantify the carbon balance of Earth\u27s forests. Generating a high-quality quantitative assessment of disturbance impacts on canopy biomass and structure with spaceborne remote sensing requires comprehensive, well designed, and well coordinated field programs collecting high-quality ground-based data and linkages to dynamical models that can use this information

Radar polarimetry and interferometry for remote sensing of boreal forest

Forest biomass is a key parameter of the global biosphere which is linked to many fields of research. Modeling addressing climate, ecology, and economics as well as many other prediction frameworks require an accurate assessment of global forest biomass. Methods for producing forest information are rapidly developing and traditional forest inventory by visual estimation has been gradually replaced by the use of airborne and spaceborne instruments. Nevertheless, the estimation of biomass on a global basis including boreal, temperate, and tropical forests, is still a major challenge. Among other spaceborne sensors, synthetic aperture radar (SAR) is one of the most suitable tools for large scale mapping and it has also been often used for forest mapping. However, commonly used backscattering intensity based methods do not provide a satisfactory accuracy for biomass estimation; hence, the scientific radar community has been developing more accurate means based on advanced SAR imaging and analyzing techniques, such as SAR polarimetry and interferometry. The work within this thesis contributes to this effort specifically in the field of remote sensing with the emphasis on SAR polarimetry and interferometry for boreal forest applications. The study concentrates on three main topics: polarimetric SAR image analysis, retrieval of forest height by means of SAR interferometry, and modeling of radar backscattering from trees. The main contributions of this work include a new effective approach in polarimetric target decomposition, novel polarimetric visualization schemes, an improved interferometric tree height estimation method suitable for boreal forest, interferometric tree height estimation capability demonstration for X-band, a novel method for relating SAR measurements to single tree scattering modeling, and taking the scattering modeling from a pine tree to the single needle level with accurate field models. Furthermore, the forest height estimation scheme proposed in this work potentially enables tree height estimation with existing spaceborne interferometric X-band SAR systems. The proposed method uses an interferometric coherence model and a ground elevation model to produce accurate tree height maps from single polarization interferometric SAR data. The method is demonstrated with airborne SAR measurements and will be tested in the near future with satellite data. Since tree height is related to forest biomass through tree allometry, tree height measurements from space would enable more accurate global forest biomass maps

Upper tropospheric ozone production from lightning NOx-impacted convection: Smoke ingestion case study from the DC3 campaign

©2015. American Geophysical Union. All Rights Reserved. As part of the Deep Convective Cloud and Chemistry (DC3) experiment, the National Science Foundation/National Center for Atmospheric Research (NCAR) Gulfstream-V (GV) and NASA DC-8 research aircraft probed the chemical composition of the inflow and outflow of two convective storms (north storm, NS, south storm, SS) originating in the Colorado region on 22 June 2012, a time when the High Park wildfire was active in the area. A wide range of trace species were measured on board both aircraft including biomass burning (BB) tracers hydrogen cyanide (HCN) and acetonitrile (ACN). Acrolein, a much shorter lived tracer for BB, was also quantified on the GV. The data demonstrated that the NS had ingested fresh smoke from the High Park fire and as a consequence had a higher VOC OH reactivity than the SS. The SS lofted aged fire tracers along with other boundary layer ozone precursors and was more impacted by lightning NOx (LNOx) than the NS. The NCAR master mechanism box model was initialized with measurements made in the outflow of the two storms. The NS and SS were predicted to produce 11 and 14ppbv of O3, respectively, downwind of the storm over 2days. Sensitivity tests revealed that the ozone production potential of the SS was highly dependent on LNOx. Normalized excess mixing ratios, ΔX/ΔCO, for HCN and ACN were determined in both the fire plume and the storm outflow and found to be 7.0±0.5 and 2.3±0.5pptvppbv-1, respectively, and 1.4±0.3pptvppbv-1 for acrolein in the outflow only

First results of a GNSS-R experiment from a stratospheric balloon over boreal forests

The empirical results of a global navigation satellite systems reflectometry (GNSS-R) experiment onboard the Balloon EXperiments for University Students (BEXUS) 17 stratospheric balloon performed north of Sweden over boreal forests show that the power of the reflected signals is nearly independent of the platform height for a high coherent integration time T-c = 20 ms. This experimental evidence shows a strong coherent component in the forward scattered signal, as compared with the incoherent component, that allows to be tracked. The bistatic coherent reflectivity is also evaluated as a function of the elevation angle, showing a decrease of similar to 6 dB when the elevation angle increases from 35. to 70 degrees. The received power presents a clearly multimodal behavior, which also suggests that the coherent scattering component may be taking place in different forest elements, i.e., soil, canopy, and through multiple reflections canopy-soil and soil-trunk. This experiment has provided the first GNSS-R data set over boreal forests. The evaluation of these results can be useful for the feasibility study of this technique to perform biomass monitoring that is a key factor to analyze the carbon cycle.Peer ReviewedPostprint (author's final draft