Planning for a Soil Moisture Satellite Mission: SMAP Algorithms & Cal/Val Workshop; Oxnard, California, 9–11 June 2009

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/94943/1/eost16930.pd

Microwave Breast Imaging System Prototype with Integrated Numerical Characterization

The increasing number of experimental microwave breast imaging systems and the need to properly model them have motivated our development of an integrated numerical characterization technique. We use Ansoft HFSS and a formalism we developed previously to numerically characterize an S-parameter- based breast imaging system and link it to an inverse scattering algorithm. We show successful reconstructions of simple test objects using synthetic and experimental data. We demonstrate the sensitivity of image reconstructions to knowledge of the background dielectric properties and show the limits of the current model

Theoretical Modeling and Analysis of L- and P-band Radar Backscatter Sensitivity to Soil Active Layer Dielectric Variations

Freeze-thaw (FT) and moisture dynamics within the soil active layer are critical elements of boreal, arctic and alpine ecosystems, and environmental change assessments. We evaluated the potential for detecting dielectric changes within different soil layers using combined L- and P-band radar remote sensing as a prerequisite for detecting FT and moisture profile changes within the soil active layer. A two-layer scattering model was developed and validated for simulating radar responses from vertically inhomogeneous soil. The model simulations indicated that inhomogeneity in the soil dielectric profile contributes to both L- and P-band backscatter, but with greater P-band sensitivity at depth. The difference in L- and P-band responses to soil dielectric profile inhomogeneity appears suitable for detecting associated changes in soil active layer conditions. Additional evaluation using collocated airborne radar (AIRSAR) observations and in situ soil moisture measurements over alpine tundra indicates that combined L- and P-band SAR observations are sensitive to soil dielectric profile heterogeneity associated with variations in soil moisture and FT conditions

Radar for Measuring Soil Moisture Under Vegetation

A two-frequency, polarimetric, spaceborne synthetic-aperture radar (SAR) system has been proposed for measuring the moisture content of soil as a function of depth, even in the presence of overlying vegetation. These measurements are needed because data on soil moisture under vegetation canopies are not available now and are necessary for completing mathematical models of global energy and water balance with major implications for global variations in weather and climate

Advancing NASA’s AirMOSS P-Band Radar Root Zone Soil Moisture Retrieval Algorithm via Incorporation of Richards’ Equation

P-band radar remote sensing applied during the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) mission has shown great potential for estimation of root zone soil moisture. When retrieving the soil moisture profile (SMP) from P-band radar observations, a mathematical function describing the vertical moisture distribution is required. Because only a limited number of observations are available, the number of free parameters of the mathematical model must not exceed the number of observed data. For this reason, an empirical quadratic function (second order polynomial) is currently applied in the AirMOSS inversion algorithm to retrieve the SMP. The three free parameters of the polynomial are retrieved for each AirMOSS pixel using three backscatter observations (i.e., one frequency at three polarizations of Horizontal-Horizontal, Vertical-Vertical and Horizontal-Vertical). In this paper, a more realistic, physically-based SMP model containing three free parameters is derived, based on a solution to Richards’ equation for unsaturated flow in soils. Evaluation of the new SMP model based on both numerical simulations and measured data revealed that it exhibits greater flexibility for fitting measured and simulated SMPs than the currently applied polynomial. It is also demonstrated that the new SMP model can be reduced to a second order polynomial at the expense of fitting accuracy

Evaluation of ALOS PALSAR Data for High-Resolution Mapping of Vegetated Wetlands in Alaska

As the largest natural source of methane, wetlands play an important role in the carbon cycle. High-resolution maps of wetland type and extent are required to quantify wetland responses to climate change. Mapping northern wetlands is particularly important because of a disproportionate increase in temperatures at higher latitudes. Synthetic aperture radar data from a spaceborne platform can be used to map wetland types and dynamics over large areas. Following from earlier work by Whitcomb et al. (2009) using Japanese Earth Resources Satellite (JERS-1) data, we applied the “random forests” classification algorithm to variables from L-band ALOS PALSAR data for 2007, topographic data (e.g., slope, elevation) and locational information (latitude, longitude) to derive a map of vegetated wetlands in Alaska, with a spatial resolution of 50 m. We used the National Wetlands Inventory and National Land Cover Database (for upland areas) to select training and validation data and further validated classification results with an independent dataset that we created. A number of improvements were made to the method of Whitcomb et al. (2009): (1) more consistent training data in upland areas; (2) better distribution of training data across all classes by taking a stratified random sample of all available training pixels; and (3) a more efficient implementation, which allowed classification of the entire state as a single entity (rather than in separate tiles), which eliminated discontinuities at tile boundaries. The overall accuracy for discriminating wetland from upland was 95%, and the accuracy at the level of wetland classes was 85%. The total area of wetlands mapped was 0.59 million km2, or 36% of the total land area of the state of Alaska. The map will be made available to download from NASA’s wetland monitoring website

Combined Radar-Radiometer Surface Soil Moisture and Roughness Estimation

A robust physics-based combined radar-radiometer, or Active-Passive, surface soil moisture and roughness estimation methodology is presented. Soil moisture and roughness retrieval is performed via optimization, i.e., minimization, of a joint objective function which constrains similar resolution radar and radiometer observations simultaneously. A data-driven and noise-dependent regularization term has also been developed to automatically regularize and balance corresponding radar and radiometer contributions to achieve optimal soil moisture retrievals. It is shown that in order to compensate for measurement and observation noise, as well as forward model inaccuracies, in combined radar-radiometer estimation surface roughness can be considered a free parameter. Extensive Monte-Carlo numerical simulations and assessment using field data have been performed to both evaluate the algorithms performance and to demonstrate soil moisture estimation. Unbiased root mean squared errors (RMSE) range from 0.18 to 0.03 cm3cm3 for two different land cover types of corn and soybean. In summary, in the context of soil moisture retrieval, the importance of consistent forward emission and scattering development is discussed and presented

Sensitivity of active-layer freezing process to snow cover in Arctic Alaska

The contribution of cold-season soil respiration to the Arctic–boreal carbon cycle and its potential feedback to the global climate remain poorly quantified, partly due to a poor understanding of changes in the soil thermal regime and liquid water content during the soil-freezing process. Here, we characterized the processes controlling active-layer freezing in Arctic Alaska using an integrated approach combining in situ soil measurements, local-scale (∼50 m) longwave radar retrievals from NASA airborne P-band polarimetric SAR (PolSAR) and a remote-sensing-driven permafrost model. To better capture landscape variability in snow cover and its influence on the soil thermal regime, we downscaled global coarse-resolution (∼0.5∘) MERRA-2 reanalysis snow depth data using finer-scale (500 m) MODIS snow cover extent (SCE) observations. The downscaled 1 km snow depth data were used as key inputs to the permafrost model, capturing finer-scale variability associated with local topography and with favorable accuracy relative to the SNOTEL site measurements in Arctic Alaska (mean RMSE=0.16 m, bias=-0.01 role= presentation \u3ebias=−0.01 m). In situ tundra soil dielectric constant (ε) profile measurements were used for model parameterization of the soil organic layer and unfrozen-water content curve. The resulting model-simulated mean zero-curtain period was generally consistent with in situ observations spanning a 2∘ latitudinal transect along the Alaska North Slope (R: 0.6±0.2; RMSE: 19±6 days), with an estimated mean zero-curtain period ranging from 61±11 to 73±15 days at 0.25 to 0.45 m depths. Along the same transect, both the observed and model-simulated zero-curtain periods were positively correlated (R\u3e0.55, p\u3c0.01) with a MODIS-derived snow cover fraction (SCF) from September to October. We also examined the airborne P-band radar-retrieved ε profile along this transect in 2014 and 2015, which is sensitive to near-surface soil liquid water content and freeze–thaw status. The ε difference in radar retrievals for the surface (∼\u3c0.1 role= presentation \u3e∼\u3c0.1 m) soil between late August and early October was negatively correlated with SCF in September (R=-0.77 role= presentation \u3eR=−0.77, p\u3c0.01); areas with lower SCF generally showed larger ε reductions, indicating earlier surface soil freezing. On regional scales, the simulated zero curtain in the upper (\u3c0.4 m) soils showed large variability and was closely associated with variations in early cold-season snow cover. Areas with earlier snow onset generally showed a longer zero-curtain period; however, the soil freeze onset and zero-curtain period in deeper (\u3e0.5 m) soils were more closely linked to maximum thaw depth. Our findings indicate that a deepening active layer associated with climate warming will lead to persistent unfrozen conditions in deeper soils, promoting greater cold-season soil carbon loss

The role of snow cover affecting boreal-arctic soil freeze–thaw and carbon dynamics

Northern Hemisphere permafrost affected land areas contain about twice as much carbon as the global atmosphere. This vast carbon pool is vulnerable to accelerated losses through mobilization and decomposition under projected global warming. Satellite data records spanning the past 3 decades indicate widespread reductions (~ 0.8–1.3 days decade−1) in the mean annual snow cover extent and frozen-season duration across the pan-Arctic domain, coincident with regional climate warming trends. How the soil carbon pool responds to these changes will have a large impact on regional and global climate. Here, we developed a coupled terrestrial carbon and hydrology model framework with a detailed 1-D soil heat transfer representation to investigate the sensitivity of soil organic carbon stocks and soil decomposition to climate warming and changes in snow cover conditions in the pan-Arctic region over the past 3 decades (1982–2010). Our results indicate widespread soil active layer deepening across the pan-Arctic, with a mean decadal trend of 6.6 ± 12.0 (SD) cm, corresponding to widespread warming. Warming promotes vegetation growth and soil heterotrophic respiration particularly within surface soil layers (≤ 0.2 m). The model simulations also show that seasonal snow cover has a large impact on soil temperatures, whereby increases in snow cover promote deeper (≥ 0.5 m) soil layer warming and soil respiration, while inhibiting soil decomposition from surface (≤ 0.2 m) soil layers, especially in colder climate zones (mean annual T ≤ −10 °C). Our results demonstrate the important control of snow cover on northern soil freeze–thaw and soil carbon decomposition processes and the necessity of considering both warming and a change in precipitation and snow cover regimes in characterizing permafrost soil carbon dynamics