By Yonghong Yi, John S. Kimball, Richard H. Chen, Mahta Moghaddam, Rolf H. Reichle, Umakant Mishra, Donatella Zona, Walter C. Oechel
An important feature of the Arctic is large spatial heterogeneity in active layer conditions, which is generally poorly represented by global models and can lead to large uncertainties in predicting regional ecosystem responses and climate feedbacks. In this study, we developed a spatially integrated modeling and analysis framework combining field observations, local-scale ( ∼ 50m resolution) active layer thickness (ALT) and soil moisture maps derived from low-frequency (L+P-band) airborne radar measurements, and global satellite environmental observations to investigate the ALT sensitivity to recent climate trends and landscape heterogeneity in Alaska. Modeled ALT results show good correspondence with in situ measurements in higher-permafrost-probability (PP ≥ 70%) areas (n = 33; R = 0.60; mean bias = 1.58cm; RMSE = 20.32cm), but with larger uncertainty in sporadic and discontinuous permafrost areas. The model results also reveal widespread ALT deepening since 2001, with smaller ALT increases in northern Alaska (mean trend = 0.32±1.18cmyr−1) and much larger increases (> 3cmyr−1) across interior and southern Alaska. The positive ALT trend coincides with regional warming and a longer snow-free season (R = 0.60±0.32). A spatially integrated analysis of the radar retrievals and model sensitivity simulations demonstrated that uncertainty in the spatial and vertical distribution of soil organic carbon (SOC) was the largest factor affecting modeled ALT accuracy, while soil moisture played a secondary role. Potential improvements in characterizing SOC heterogeneity, including better spatial sampling of soil conditions and advances in remote sensing of SOC and soil moisture, will enable more accurate predictions of active layer conditions and refinement of the modeling framework across a larger domain.
By Seyed Hamed Alemohammad, Alexandra G. Konings, Thomas Jagdhuber, Mahta Moghaddam, Dara Entekhabi
Remote Sensing of Environment
Understanding the scattering mechanisms from the ground surface in the presence of different vegetation densities is necessary for the interpretation of P-band Synthetic Aperture Radar (SAR) observations and for the design of geophysical retrieval algorithms. In this study, a quantitative analysis of vegetation and soil scattering mechanisms estimated from the observations of an airborne P-band SAR instrument across nine different biomes in North America is presented. The goal is to apply a hybrid (model- and eigen-based) three component decomposition approach to separate the contributions of surface, double-bounce and vegetation volume scattering across a wide range of biome conditions. The decomposition makes no prior assumptions about vegetation structure. We characterize the dynamics of the decomposition across different North American biomes and assess their characteristic range. Impacts of vegetation coverseasonality and soil surface roughness on the contributions of each scattering mechanism are also investigated. Observations used here are part of the NASA Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) mission and data have been collected between 2013 and 2015.
By Mahta Moghaddam et al.
Organic matter (OM) content and a shallow water table are two key variables that govern the physical properties of the subsurface within the active layer of arctic soils underlain by permafrost, where the majority of biogeochemical activities take place. A detailed understanding of the soil moisture and OM profile behavior over short vertical distances through the active layer is needed to adequately model the subsurface physical processes. To observe and characterize the profiles of soil properties in the active layer, we conducted detailed soil sampling at five sites along Dalton Highway on Alaska’s North Slope. These data were used to derive a generalized logistics function to characterize the total OM and water saturation fraction behavior through the profile. Furthermore, a new pedotransfer function was developed to estimate the soil bulk density and porosity—information that is largely missing from existing soil datasets—within each layer, solely from the soil texture (organic and mineral properties). Given the currently sparse soil database of the Alaskan Arctic, these profile models can be highly beneficial for radar remote sensing models to study active layer dynamics.
By Mahta Moghaddam et al.
Forecasting the values of essential climate variables like land surface temperature and soil moisture can play a paramount role in understanding and predicting the impact of climate change. This work concerns the development of a deep learning model for analyzing and predicting spatial time series, considering both satellite derived and model-based data assimilation processes. To that end, we propose the Embedded Temporal Convolutional Network (E-TCN) architecture, which integrates three different networks, namely an encoder network, a temporal convolutional network, and a decoder network. The model accepts as input satellite or assimilation model derived values, such as land surface temperature and soil moisture, with monthly periodicity, going back more than fifteen years. We use our model and compare its results with the state-of-the-art model for spatiotemporal data, the ConvLSTM model. To quantify performance, we explore different cases of spatial resolution, spatial region extension, number of training examples and prediction windows, among others. The proposed approach achieves better performance in terms of prediction accuracy, while using a smaller number of parameters compared to the ConvLSTM model. Although we focus on two specific environmental variables, the method can be readily applied to other variables of interest.
By Mahta Moghaddam et al.
Small Satellite Conference
The NASA Cyclone Global Navigation Satellite System (CYGNSS) mission consists of a constellation of eight microsatellites launched on 15 December 2016 into a common circular orbit at ~525 km altitude and 35 deg inclination. Each observatory carries a four channel bistatic radar receiver to measure GPS signals scattered by the Earth surface. Over ocean, near-surface wind speed, air-sea latent and sensible heat flux, and ocean microplastic concentration are derived from the measurements. Over land, near-surface soil moisture and inland water bodies extent are derived. The measurements penetrate through all levels of precipitation and most vegetation due to the 19 cm wavelength of GPS L1 signals. The sampling produced by the constellation makes possible the reliable detection of short time scale weather events such as flood inundation dynamics immediately after a tropical cyclone landfall and rapid soil moisture dry down immediately after major precipitation events. The sun-asynchronous nature of the CYGNSS orbit also supports full sampling of the diurnal cycle of hydrological dynamics within a short period of time. Summaries are presented of engineering and science highlights of the CYGNSS mission, with particular emphasis on those aspects most directly enabled by the use of a constellation of SmallSats.
By Mahta Moghaddam et al.
Water Resources Research
Surface organic carbon content and soil moisture (SM) represent first-order controls on permafrost thaw and vulnerability, yet remain challenging to map accurately. Here we explored the links between surface organic soil properties and SM dynamics in the Alaska North Slope through data analysis and process-based modeling. Our analysis, based on in situ SM and brightness temperature data from the Soil Moisture Active Passive (SMAP) mission, indicated that the SM drydown process in Arctic tundra is closely related to surface soil organic carbon (SOC) properties. More rapid drydown was generally observed in areas with high SOC concentration (SOCC) or low bulk density. The drydown timescale derived from the SMAP polarization ratio (PR) was significantly correlated with SoilGrids surface (0–5 cm) SOCC data (R = −0.54 ∼ −0.68, p < 0.01) at regional scale. To understand the process, we used a coupled permafrost hydrology and microwave emission model to simulate changes in the L-band PR during the thaw season. The model accounts for the variations in organic soil hydraulic and dielectric properties with SOC content and decomposition state. Model sensitivity runs showed larger L-band PR decreases during the early thaw season in soils with higher SOCC consistent with the above analysis, whereby highly organic soils (SOCC > 34.8%) drain water more easily with a larger amount of water discharged or lost (through evapotranspiration) relative to soils with less carbon concentration (SOCC < 17.4%). Our findings indicate that satellite L-band observations are sensitive to tundra SM and carbon properties, and may provide critical constraints on predictions of Arctic permafrost thaw and vulnerability.