Sparse and unevenly distributed soil samples across the northern high-latitude region greatly limit the accuracy of soil organic carbon (SOC) mapping. Therefore, substantial discrepancies exist in SOC estimation in this region, which makes it challenging to characterize the SOC spatial variability and its potential responses to climate change and permafrost degradation. To address these challenges, we enhanced a machine learning model for SOC mapping by developing a data resampling approach that accounts for soil samples spatial heterogeneity, using Alaska as a case study. Specifically, in-situ SOC data were resampled with weights proportional to the variance within a 15-km radius, and then fitted using a random forest (RF) regression model. Multiple features, including temporal composites of Sentinel-1 C-band radar backscatter, vegetation indices from Sentinel-2, climate indices including thawing and freezing indices from moderate resolution imaging spectroradiometer (MODIS), and ancillary topography data, were selected as inputs for the RF model after recursive feature elimination to generate top-layer (0-30 cm) SOC content maps in Alaska at a 250-m resolution. The enhanced RF model with data resampling showed improved accuracy compared to the original RF model, with the coefficient of determination (R2) increased from 0.36 to 0.56 and the root mean square error (RMSE) decreased from 16% to 11% for the surface (0-10 cm) SOC content, and slightly improved accuracy for the deeper (10-30 cm) SOC content. Additionally, the enhanced RF model also better captured local-scale variability of SOC than the original RF model and SoilGrids 2.0 dataset, with high-resolution remote sensing indices playing a major role. The improved SOC content estimates were then used to estimate soil bulk density and calculate total SOC stock for Alaska. Our results suggest that Alaskan topsoil (0-30 cm) stores approximately 25.21±17.18 Pg C, with the largest SOC reserves found in shrublands. These findings highlight the importance of accounting for spatial heterogeneity in in-situ samples and leveraging high-resolution remote sensing data for regional soil mapping.

This paper presents a physics-based radar modeling framework that enables joint retrievals of the Arctic tundra permafrost active layer soil organic matter content and soil moisture profile using P-band polarimetric SAR. Initially, an extensive set of field observations are used to model the subsurface soil moisture and organic matter profiles with independent model parameters. Then a new organic soil dielectric model is used to translate the soil profile properties into equivalent dielectric properties that bridge the soil field properties to their manifestation in radar measurements. Finally, a multi-layered dielectric structure is adopted by an electromagnetic scattering computational model that predicts equivalent backscattering coefficients resulting from the soil profile state parameters. These pieces together construct the forward model, which is at the heart of a physics-based radar retrieval algorithm. Finally, we integrate the developed model into a retrieval scheme, in which the soil moisture and organic profile model parameters are estimated using radar backscattering coefficients measured by AirMOSS P-band radar. We show retrieved soil moisture and soil organic matter profiles, derived pixel-wise by the algorithm providing the first airborne-driven soil organic carbon map.

The U.S. Department of Energy Office of Legacy Management (DOE LM) is investigating options for future management of selected uranium mill tailings disposal cell covers as vegetated, evapotranspiration (ET) covers. ET limits drainage of water through the cell cover profile, while soil structure and drying by plants can increase radon diffusion; therefore, soil water content is a key performance parameter. This study used theoretical simulations to analyze the sensitivity of multi-frequency radar backscatter to soil moisture (SM) at different depths of an in-service DOE LM disposal cell. A machine-learning approach was then developed using Google Earth Engine to integrate multi-source observations and estimate SM across six soil layers from depths of 0-2 m. The model predictors included backscatter observations from satellite Synthetic Aperture Radar, vegetation and temperature products from optical-infrared sensors, and accumulated rainfall data from Daymet. The model was trained using in-situ SM measurements from 2019 and validated using data from 2014-2018 and 2020-2021. The approach produced accurate SM estimates for the six soil layers (R-values from 0.75 to 0.94; RMSE from 0.003 to 0.017 cm3/cm3; bias ~0.00 cm3/cm3). Additionally, the approach captured seasonal SM variability and spatial heterogeneity at 30-m resolution. The machine-learning based multi-source data fusion approach may characterize soil moisture dynamics at DOE LM disposal sites better than in situ measurements alone.

Satellite microwave radiometers effectively monitor landscape freeze/thaw (FT) transitions but have difficulty distinguishing soil from other landscape properties, which can lower retrieval accuracy. Here, we applied a deep learning model for soil FT classification driven by daily brightness temperatures (TBs) from AMSR2 and SMAP, and trained on soil (~0-5cm depth) FT observations. The probability of frozen or thawed conditions was derived using a model cost function optimized using observational training data over the Northern Hemisphere (NH) and five year (2016-2020) study period. Results showed favorable accuracy against soil FT observations from ERA5 reanalysis (mean annual accuracy, MAE: 92.7%) and NH weather stations (MAE: 91.0%). Moreover, SMAP L-band (1.41 GHz) TBs provided enhanced soil FT performance over alternative retrievals derived using only AMSR2 inputs. FT accuracy was also consistent across different land covers and seasons. The results provide better soil FT precision to improve understanding of complex seasonal transitions and their influence on ecological processes and climate feedbacks.

Accurate soil carbon data are important for understanding the permafrost response and potential carbon release to future climate change. However, there is a large discrepancy in current soil organic carbon (SOC) estimates in the Arctic, where sparse measurements are unable to capture SOC complexity over the vast and remote region. Polarimetric Synthetic Aperture Radar (SAR) data are sensitive to roughness and moisture conditions of soil and vegetation, and may provide useful information on surface and profile SOC properties ( Yi et al., 2021 , 2022 ). The NASA Arctic Boreal Vulnerability Experiment (ABoVE) airborne campaign acquired an abundance of full-polarimetric P- and L-band SAR data across Alaska and western Canada ( Miller et al., 2019 ), which provides opportunities to test new remote sensing applications. The main objective of this study is to investigate the potential of low-frequency polarimetric SAR data for regional SOC retrieval in the Arctic through data analysis and modeling. We chose the Alaska North Slope as our study area due to more in-situ data available in this area.

For understanding englacial hydrology and its impact on ice sheet mass balance, observations of the liquid water content (LWC) within the ice sheets are needed. Earlier studies have shown the complementary nature of multi-frequency microwave radiometer measurements to detect subsurface LWC distribution in addition to surface LWC, which is critical for understanding the seasonal melt dynamics of ice sheets. In this study, we used 1.4 GHz brightness temperature (TB) measurements from the NASA Soil Moisture Active Passive (SMAP) satellite, and 6.9, 10.7, 18.9, and 36.5 GHz TB measurements from the JAXA Global Change Observation Mission-Water Shizuku (GCOM-W) satellite to investigate the multi-frequency response at pan-Greenland scale. The melt indications derived at different frequencies show trends consistent with persistent seasonal subsurface melt water and delayed subsurface refreezing of the seasonal melt water. The result suggests that the seasonal subsurface persistent melt water occurrences that are not captured by the high-frequency retrievals are both temporally and spatially very significant.

Due to their larger penetration and sensing depth, low frequency microwave measurements have been recently employed to detect ice sheet melt events. In this paper, the response of NASA's SMAP (Soil Moisture Active Passive) L-band measurements to surface melting of the Greenland ice sheet from 2015 through 2021 is investigated. SMAP covers virtually the entire Greenland ice sheet twice a day with its L-band (1.4 GHz) radiometer. The results show that the ice sheet experienced unusually strong surface melting on August 14,2021, which extended the melt area across much of dry snow zone over a period of two days. Moreover, the observational results agree well with model simulations conducted using Glacier Energy and Mass Balance (GEMB) module within the Ice-sheet and Sea-level System Model (ISSM).

Knowledge of the spatial and temporal distribution of active layer thickness (ALT) throughout northern Alaska would help to understand the effects of climate change in the region, as well as to quantify how much the permafrost degradation manifestly in progress there is contributing to the accumulation of greenhouse gases in the atmosphere. For this reason, we are developing extensive high-resolution maps of ALT in northern Alaska. We use machine learning along with an extensive set of spatial data layers to upscale ALT from thousands of training pixels taken from high resolution swaths of estimated ALT derived from airborne polarimetric P-band synthetic aperture radar (SAR). The resulting maps of up-scaled ALT have been compared to thousands of validation samples set aside from the PolSAR-derived swaths and to in situ ALT measurements. The maps have achieved root-mean-square errors (RMSEs) of 5–7 cm relative to validation samples, and RMSEs of approximately 10–12 cm relative to in situ ALT measurements.

Accurate vegetation information is essential for analyzing above-ground biomass and understanding subsurface characteristics, such as root biomasss, soilorganicmatter and soil moisture profiles. This paper investigates novel mappings of forest species and canopy height in interior Alaska. We employ Random Forests to train a regression model for canopy height mapping and a classification model for forest species mapping utilizing L-band and P-band Uninhabited Aerial Vehicle Synthetic Aperture Radar(UAVSAR). For canopy height, canopy height model (CHM) data derived from Goddard's LiDAR, Hyperspectral, and Thermal Imager (G-LiHT) are treated as ground truth. For forest species prediction, Tanana Valley State Forest (TVSF) Timber Inventory and Forest Inventory and Analysis (FIA) data are used as reference. The experimental results show the proposed method yields a root-mean-square error of 1.90 m for forest height estimation and overall accuracy of 79.54% for forest species classification. They also demonstrate the feasibility of obtaining precise vegetation information by data-driven methods, which can be further used to enhance forest radar scattering forward models.

The freeze-thaw (FT) status of soil regulates ecological and hydrologic processes and is therefore a vital component of land surface models. This study utilizes a Hidden Markov Model (HMM) to retrieve surface FT status from L-band (SMAP) and Ka-band (AMSR) satellite microwave brightness temperatures in cold-constrained lands north of 45 °N. The HMM, parameterized on a per-gridcell basis such that there are two possible states and emissions probabilities are assumed to be a two-component Gaussian Mixture, produces the posterior probability (a continuous variable between 0 and 1) that the surface is frozen given the radiometer input. HMM classification accuracy, averaged over five core validation networks, is acceptable (Ap > 80 %) when judged against in situ air and soil temperature measurements from individual validation sites and is comparable to that of current FT products that produce a discrete state. Patterns in performance across variable land class, open water fraction, and elevation are assessed from 91 sparse network weather stations within 87 gridcells in the domain. The resulting satellite data record provides a continuous variable estimate of the daily probability of frozen conditions over northern land areas experiencing widespread thawing of permafrost and a shrinking frozen season due to global warming.

We used full-polarimetric L-band and P-band synthetic aperture radar (SAR) data collected from the recent NASA Arctic Boreal Vulnerability Experiment (ABoVE) airborne campaign and Sentinel-1 C-band dual-polarization data to understand the sensitivity of radar backscatter intensity and phase to fire-induced changes in the surface and subsurface soil processes in Arctic tundra underlain by permafrost. The 2007 Anaktuvuk River fire on the Alaska North Slope was used as a case study. At ~10-year postfire, we observed a strong increase (>~3–4 dB) in the low-frequency radar backscatter in severely burned areas during the thaw season, in contrast to limited (1 dB) in burned areas than the adjacent unburned areas. Polarimetric decomposition analysis indicated a general trend toward more random surface scattering, and strong increases in double-bounce scattering and volume scattering power at both P- and L-band in the burned areas. The ice-rich yedoma region shows the largest backscatter increases in burned areas and the highest correlation with burn severity and microtopography changes. The above backscatter changes are attributed to increasing surface roughness and microtopography due to ice-wedge degradation and thermokarst development and increasing subsurface scattering due to an overall drier and deeper active layer in burned areas. Among all frequencies, P-band shows consistently larger contrast in backscatter power and phase between burned and unburned areas, which makes it potentially more useful to study fire–permafrost interactions in the Arctic over decadal time scales.

Image classification has become one of the most common applications in remote sensing yielding to the creation of a variety of operational thematic maps at multiple spatio-temporal scales. The information contained in these maps summarizes key characteristics related with the physical environment and provides fundamental information of the Earth for vegetation monitoring or land use status over time. However, high spatial resolution land cover maps are usually only produced for specific small regions or in an image tile. We present a general methodology to obtain a high spatial resolution land cover maps using Landsat spectral information, the powerful Google Earth Engine platform, and operational coarse classification schemes such as the MODIS (MOD12) land cover. After the experimental analysis for different regions, we conclude that the method allows to successfully learn the MODIS Plant Functional Type classification scheme at 500 m pixel resolution which greatly improves the level of spatial detail when the machine learning model is applied to Landsat pixel resolution (30 m) reflectance data.

More than half of the global land area undergoes seasonal freeze/thaw (FT) transitions in spring. Spatial patterns and timing of spring thawing influence eco-hydrological processes and landscape moisture availability over arctic and boreal ecosystems. The seasonal progression of spring thawing coincides with warmer temperatures, snowmelt, and a rapid increase in soil moisture, which initiates the growing season for ecosystem productivity. In this study, we utilize complementary satellite observations to determine the pattern and order of occurrence in landscape thawing, soil moisture increase, and ecosystem productivity that collectively define the eco-hydrological spring onset across Alaska and Northern Canada. Satellite data utilized include landscape FT status from SMAP and AMSR-2, OCO-2 derived solar-induced chlorophyll fluorescence (GOSIF), and gross primary production (GPP) and soil moisture from SMAP. The resulting spring onset maps showed spring thawing as the precursor to growing season onset, indicated by a rapid rise in available soil moisture and GPP. Our results indicated an average spring transition period of$3\pm 2$(SD) weeks between initial landscape thawing and growing season onset. A rapid increase in soil moisture generally followed landscape thawing but occurred before the subsequent seasonal rise in GPP. Spring onset generally occurred earlier in boreal forest (DOY$102\pm 14$) than arctic tundra (DOY$124\pm 22$).

Low frequency microwave measurements have been used to gain insight into what happens deep inside ice sheets for some time now. In this paper, we used a deep neural network to classify each pixel within SMAP radiometer footprints over the Antarctica ice sheet as melt or no-melt. NASA's SMAP mission offers a valuable additional set of observations. The SMAP L-band (1.4 GHz) radiometer retrievals also cover virtually the entire Antarctica ice sheet twice a day. Consistent morning and evening sampling are provided by 6 AM/PM equator-crossings of the satellite ascending and descending polar orbits. The spatial resolution of the instrument is about 40 km. The cross-entropy loss function is used in our network. To make training and test sets, we used air temperature records from available weather stations to distinguish melt and no-melt ice sheet conditions. Our results show that the ice sheet experienced extensive surface melting during the 2015–2016 melt season, and also intensive melting in 2019–2020, particularity on the West Antarctic Ice Sheet.

Low frequency microwave measurements have been used to gain insight into what happens deep inside ice sheets for some time. In this paper, the response of SMAP (Soil Moisture Active Passive) L-band measurements to surface melting of the ice sheet from 2015 through 2019 is investigated. SMAP covers virtually the entire Antarctica ice sheet twice a day with its L-band (1.4 GHz) radiometer. The overpasses center on morning and evening hours as the satellite is on a 6 AM/6PM equator-crossing orbit. The spatial resolution of the instrument is about 40 km. We applied a newly developed geophysical model-based algorithm to detect snow wetness, which can be used as an indicator of melt extent and intensity. It is shown that the ice sheet experienced extensive surface melting during the 2015-2016 melt season (~10% melt extent), and also underwent intensive melting in the 2019-2020 season (median of 0.3% snow wetness), particularity on the West Antarctic Ice Sheet.

Detailed information on the spatial and temporal distribution of active layer thickness (ALT) throughout the North Slope of Alaska, were it available, could offer valuable insights into the effects of climate change throughout the region and facilitate the estimation of greenhouse gas emissions resulting from permafrost degradation. We are, therefore, developing extensive high-resolution maps of ALT on the North Slope of Alaska. To do this, we use a machine learning algorithm to extrapolate ALT from high resolution strips of estimated ALT derived from airborne P-band synthetic aperture radar (SAR) acquired over two sets of flights in each of three different years. Our results indicate upscaling root-mean-square error (RMSE) of about 4 cm relative to thousands of randomly-selected SAR-derived ALT validation samples, and RMSE of approximately 10 cm relative to a small number of in-situ ALT measurements.

We used the full polarimetric L-band and P-band SAR data collected from recent NASA Arctic Boreal Vulnerability Experiment (ABoVE) airborne campaign to understand the sensitivity of longwave radar backscatter intensity and phase to the post-fire recovery process of Arctic tundra. The 2007 Anaktuvuk River fire was used as a case study. At 10-years post-fire, we observed a strong increase (>∼4 dB) in both the P- and L-band radar backscatter in the severely burned areas, in contrast to limited backscatter differences (VV, VH) between burned and unburned areas at C-band. The polarimetric target decomposition analysis indicated a general trend towards more random surface scattering, and strong increases of the double-bounce and volumetric scattering power at both P- and L-band in the burned areas. Large differences were also observed in the Pauli phase angle and the dominant-scattering-type Touzi phase angle between burned and adjacent unburned areas. The above changes are likely caused by increasing surface roughness and microtopography due to thermokarst development and ice degradation, and increasing subsurface scattering due to an overall drier and deeper active layer in the burned areas.

The capability of synergistic satellite flood monitoring and forecasts is crucial for improving disaster preparedness and mitigation. In this study, the Soil Moisture Active Passive (SMAP) fractional water (FW) data sets were used for flood mapping over southeast Africa during the Cyclone Idai event. We then developed a machine-learning approach with the support of Google Earth Engine (GEE) for 24-hour flood forecasting and 30-m inundation mapping using observations from SMAP and Landsat coupled with rainfall forecasts from Global Forecast System (GFS) 384-Hour Predicted Atmosphere Data. The forecast results for the Idai event captured the flood dynamics at 30-m resolution and showed inundation patterns consistent with independent satellite Synthetic Aperture Radar (SAR) observations. The approach provides new capacity for flood monitoring and forecasts from synergistic satellite observations and is particularly valuable for data sparse regions.

Evapotranspiration (ET) is a key link between the global carbon, water and energy cycles. ET generally occurs from soil, vegetation and intercepted precipitation. ET components are commonly estimated using a combination of variables, including meteorology, vegetation, and soil moisture conditions. Although vegetation transpiration has a major effect on global ET variations, soil evaporation can also contribute significant water loss to the atmosphere. This study utilizes satellite derived soil moisture data from the NASA SMAP mission to produce global ET estimates using a modified Penman Monteith algorithm. The global ET results were assessed using other available ET benchmarks. In addition, the ET estimates were evaluated for monitoring spring onset in relation to other complementary satellite observations of vegetation phenology and landscape freeze/thaw metrics. The comparisons between global ET products showed similar latitudinal variation, but with larger differences in tropical rainforests. The spring onset results showed landscape thawing related to rising temperature facilitating the new release of plant-available soil moisture that accompanies a dramatic seasonal rise in both vegetation photosynthesis and ET.

High spatial resolution vegetation products are fundamental in different fields, such as improving the understanding of crop seasonality at regional scales. Here, two new vegetation products such as the Leaf Area Index (LAI) and the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) are downscaled at continental scales. A novel HIghly Scalable Temporal Adaptive Reflectance Fusion Model (HIS-TARFM) is used to generate the gap-free time series of Landsat surface reflectance data by fusing MODIS and Landsat reflectance for the contiguous United States. An artificial neural network is trained to capture the relationship between the gap free Landsat surface reflectance and the MODIS LAI/FAPAR products and allows to predict both biophysical variables at 30 meters spatial resolution. The results confirm that both vegetation products largely agree with the test dataset, providing low error and high explained variance.

Microwave measurements have been previously used to detect melt events due to their sensitivity to the presence of liquid water in snow. Since NASA's SMAP mission offers a valuable set of low frequency radiometer measurements, SMAP measurements have been used as a tool to detect melt events. SMAP's L-band radiometer also covers virtually the entire Greenland ice sheet twice daily. The overpasses center on morning and evening hours as the satellite is on a 6AM/6PM equator-crossing orbit, and the spatial resolution of the instrument is about 40 km. In this paper, the response of L-band measurements to surface melting of the ice sheet from 2015 through 2019 melt seasons is investigated. It is shown that the Greenland ice sheet experienced an unusually strong melt event at the end of July 2019, which extended the melt area across much of dry snow zone of the ice sheet over a period of two days.

A rapidly developing "flash drought" occurred over the US Northern Plains in the summer of 2017, spurred by unusually high temperatures and strong evaporative demand. The impacts of the drought included widespread reductions in rangeland and agricultural productivity that cascaded into significant economic losses. Here, we used satellite information from the NASA Soil Moisture Active Passive (SMAP) mission to clarify the nature and impact of the drought on regional vegetation growth. The model enhanced SMAP Level 4 Soil Moisture (L4SM) and Carbon (L4C) products were used with other ancillary data to examine spatial and seasonal anomalies in surface to root zone soil moisture and vegetation productivity (GPP). We find that the flash drought was triggered by a mid-July heat wave, conditioned by exceptionally low spring rainfall. The drought resulted in anomalous low soil moisture levels and regional GPP collapse, coinciding with severe (D3) to exceptional (D4) drought conditions indicated from the US Drought Monitor. The SMAP L4C GPP anomalies closely tracked reported county-level crop production anomalies for the major regional crop types, indicating generally larger productivity decline in managed croplands than surrounding natural areas. The SMAP L4 global products provide an effective indicator of vegetation growth changes and moisture-related restrictions on ecosystem productivity that are complementary with more traditional drought assessment tools.

Global, 3-hourly, 9-km resolution soil moisture estimates are available with a mean latency of ~2.5 days from the NASA Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture (L4_SM) product. These estimates are based on the assimilation of SMAP radiometer brightness temperature (Tb) observations into the NASA Catchment land surface model using a spatially distributed ensemble Kalman filter. Routine monitoring of the L4_SM system's assimilation diagnostics revealed occasionally large observation-minus-forecast Tb differences across eastern central Australia that resulted in large analysis increments (or adjustments) of the model forecast soil moisture. Because this region lacks in situ soil moisture measurements, we developed an alternative approach to assess the veracity of the soil moisture analysis increments in the L4_SM system. Using regional gauge-based precipitation data, we demonstrate that the L4_SM soil moisture increments are correlated with errors in the L4_SM precipitation forcing, suggesting that the SMAP Tb observations contribute valuable information to the L4_SM soil moisture estimates.

Currently, the community lacks capabilities to assess and monitor landscape scale permafrost active layer dynamics over large extents. To address this need, we developed a concept of a remote sensing based Soil Inversion Model for regional Permafrost (SIM-P) monitoring. The current SIM-P framework includes a satellite-based soil process model and a soil dielectric model. We are also working on incorporating a radar scattering model for Arctic tundra into the SIM-P framework. A unified soil parameterization scheme was developed to harmonize key soil thermal, hydraulic and dielectric parameters in the soil process and radar models that can be used in the joint soil-radar inversion framework. The soil parameter retrievals of the SIM-P framework include soil organic content (SOC) and active layer thickness (ALT). Initial tests of SIM-P using in-situ soil permittivity observations showed reasonable accuracy in predicting site-level SOC and soil temperature profiles at an Alaska tundra site and ALT in Arctic Alaska. SIM-P will be further tested using airborne P- and L-band radar data collected during NASA's Arctic Boreal Vulnerability Experiment (ABoVE) to evaluate the sensitivity of longwave radar to active layer properties.

The NASA Soil Moisture Active Passive (SMAP) mission has been successfully operated for almost three years. The SMAP freeze/thaw algorithm is based on a seasonal threshold approach. It's important to have a stable and self-consistent freeze and thaw reference that can be applied for multi-year dataset. The three-year long radiometer datasets allow us to reassess the criteria of reference setup and evaluate its stability. In this paper, we first refined the freeze reference requirements and compare three different methods of setting up the thaw reference to minimize the false flags. The original freeze/thaw products is in the polar grid and only cover the region north of 45° N latitude. The limitation is due to lack of enough freezing days in the lower latitude, where the freezing reference cannot be generated. To extend the freeze/thaw product to global region, we combine the single channel algorithm in the lower latitude and southern atmosphere. The global results have been validated through WMO air temperature.

The recent two years have been characterized by contrasting climatic variability across the globe driven by the onset of a strong El Niño event and relaxation back to ENSO neutral conditions. We investigated the global pattern and seasonal cycle of net ecosystem-atmosphere CO2exchange (NEE) for 2015 and 2016 using satellite observation based estimates from the NASA Soil Moisture Active Passive (SMAP) mission. The SMAP Level 4 Carbon product (L4C) was previously validated using globally-distributed eddy covariance flux tower observations and other independent observations. For this study, we investigated L4C seasonal and annual anomalies for vegetation productivity and NEE relative to baseline carbon flux estimates determined from the L4C model-based historical (2001-2012) climatology. Our results reveal large global carbon flux anomalies associated with ENSO related events. Australia transitioned from slightly anomalous CO2release under dry conditions in 2015 to a strong CO2sink in response to record precipitation in 2016. We also find contrasting seasonally-dependent CO2source/sink anomalies between the borealand temperate-northern latitudes which began in 2015 and persisted through 2016, associated with early spring onset, hot and dry summers, and an El Niño-enhanced temperate monsoon. These results highlight the capability of the SMAP L4C product for continued global monitoring of terrestrial ecosystems, including environmental and drought related impacts on vegetation growth, carbon sink strength and associated ecosystem goods and services.

The baseline science objective of the NASA Soil Moisture Active Passive (SMAP) mission is to produce a daily landscape freeze/thaw state for the region north of 45° N latitude with a mean spatial classification accuracy of 80% and 2-3 day average intervals separated by AM and PM overpasses [1]. Following the loss of the SMAP radar in July 2015, radiometer inputs were used to develop a standard freeze/thaw product (L3-FT-P) with relaxed spatial resolution from 3km to 36km. A 9km gridded product (L3-FT-P-E) has been developed by applying enhanced resolution radiometer inputs to the same algorithm. This paper provides an overview of the algorithm development as well as the validation and calibration using in situ observations from both selected core sites and sparse ground station networks.

The National Aeronautics and Space Administration’s Soil Moisture Active Passive (SMAP) mission Level 4 Carbon (L4C) product provides model estimates of the Net Ecosystem CO2exchange (NEE) incorporating SMAP soil moisture information. The L4C product includes NEE, computed as total ecosystem respiration less gross photosynthesis, at a daily time step posted to a 9-km global grid by plant functional type. Component carbon fluxes, surface soil organic carbon stocks, underlying environmental constraints, and detailed uncertainty metrics are also included. The L4C model is driven by the SMAP Level 4 Soil Moisture data assimilation product, with additional inputs from the Goddard Earth Observing System, Version 5 weather analysis, and Moderate Resolution Imaging Spectroradiometer satellite vegetation data. The L4C data record extends from March 31, 2015 to present with ongoing production and 8–12 day latency. Comparisons against concurrent global CO2eddy flux tower measurements, satellite solar-induced canopy florescence, and other independent observation benchmarks show favorable L4C performance and accuracy, capturing the dynamic biosphere response to recent weather anomalies. Model experiments and L4C spatiotemporal variability were analyzed to understand the independent value of soil moisture and SMAP observations relative to other sources of input information. This analysis highlights the potential for microwave observations to inform models where soil moisture strongly controls land CO2flux variability; however, skill improvement relative to flux towers is not yet discernable within the relatively short validation period. These results indicate that SMAP provides a unique and promising capability for monitoring the linked global terrestrial water and carbon cycles.

The NASA Soil Moisture Active Passive (SMAP) mission Level 4 Carbon (L4_C) product provides model estimates of Net Ecosystem CO2exchange (NEE) incorporating SMAP soil moisture information as a primary driver. The L4_C product provides NEE, computed as total respiration less gross photosynthesis, at a daily time step and approximate 14-day latency posted to a 9-km global grid summarized by plant functional type. The L4_C product includes component carbon fluxes, surface soil organic carbon stocks, underlying environmental constraints, and detailed uncertainty metrics. The L4_C model is driven by the SMAP Level 4 Soil Moisture (L4_SM) data assimilation product, with additional inputs from the Goddard Earth Observing System, Version 5 (GEOS-5) weather analysis and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data. The L4_C data record extends from March 2015 to present with ongoing production. Initial comparisons against global CO2eddy flux tower measurements, satellite Solar Induced Canopy Florescence (SIF) and other independent observation benchmarks show favorable L4_C performance and accuracy, capturing the dynamic biosphere response to recent weather anomalies and demonstrating the value of SMAP observations for monitoring of global terrestrial water and carbon cycle linkages.

The SMAP Level 4 soil moisture (L4_SM) product provides global estimates of surface and root zone soil moisture, along with other land surface variables and their error estimates. These estimates are obtained through assimilation of SMAP brightness temperature observations into the Goddard Earth Observing System (GEOS-5) land surface model. The L4_SM product is provided at 9 km spatial and 3-hourly temporal resolution and with about 2.5 day latency. The soil moisture and temperature estimates in the L4_SM product are validated against in situ observations. The L4_SM product meets the required target uncertainty of 0.04 m3m-3, measured in terms of unbiased root-mean-square-error, for both surface and root zone soil moisture.

The NASA Soil Moisture Active Passive (SMAP) mission produced a daily landscape freeze/thaw product (L3_FT_A) at 3-km spatial resolution derived from ascending and descending orbits of SMAP high-resolution L-band (1.4 GHz) radar measurements. Following the loss of the SMAP radar in July 2015, coarser (36-km) footprint passive microwave retrievals from the SMAP radiometer were used to derive an alternative daily freeze/thaw product (L3_FT_P). This presentation will provide an overview of the development of both L3_FT products. Validation using in situ observations from core validation sites is used to illustrate differences in the sensitivity of the 3 km radar versus the 36 km radiometer measurements to the landscape freeze/thaw state.

Accurate mapping of long-term global soil moisture is of great importance to earth science studies and a variety of applications. An approach for deriving volumetric soil moisture using satellite passive microwave radiometry from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) was developed in this study. Unlike the major AMSR-E retrieval algorithms that assume fixed scattering albedo values over the globe, the proposed algorithm adopts a weighted averaging strategy for soil moisture estimation based on a dynamic selection of albedo values that are empirically determined. The resulting soil moisture retrievals demonstrate more realistic global patterns and seasonal dynamics relative to the baseline University of Montana soil moisture product. Quantitative analysis of the new approach against in situ soil moisture measurements over four study regions also indicates improvements over the baseline algorithm, with coefficients of determination (R2) between the retrievals and in situ measurements increasing by approximately 16.9% and 41.5% and bias-corrected root-mean-square errors decreasing by about 25.0% and 38.2% for ascending and descending orbital data records, respectively. The resulting algorithm is readily applied to similar microwave sensors, including the Advanced Microwave Scanning Radiometer 2, and its retrieval strategy is also applicable to other passive microwave sensors, including lower frequency (L-band) observations from the National Aeronautics and Space Administration Soil Moisture Active Passive mission.

Spatial and temporal variability in landscape freeze- thaw (FT) status at higher latitudes and elevations significantly impacts land surface water mobility and surface energy partitioning, with major consequences for regional climate, hydrological, ecological, and biogeochemical processes. With the development of new-generation spaceborne remote sensing instruments, future L-band missions, including the NASA Soil Moisture Active and Passive mission, will provide new operational retrievals of landscape FT state dynamics at moderate (~3 km) spatial resolution. We applied theoretical simulations of L-band radar backscatter using first-order radiative transfer models with two and three-layer modeling schemes to develop a modified seasonal threshold algorithm (STA) and FT classification study over Alaska using 100-m-resolution satellite Phased Array L-band Synthetic Aperture Radar (PALSAR) observations. The backscatter threshold distinguishes between frozen and nonfrozen states, and it is used to classify the predominant frozen or thawed status of a grid cell. An Alaska FT map for April 2007 was generated from PALSAR (ScanSAR) observations and showed a regionally consistent but finer FT spatial pattern than an alternative surface air temperature-based classification derived from global reanalysis data. Validation of the STA-based FT classification against regional soil climate stations indicated approximately 80% and 75% spatial classification accuracy values in relation to respective station air temperature and soil temperature measurement-based FT estimates. An investigation of relative spatial scale effects on FT classification accuracy indicates that the relationship between grid cell size and classified frozen or thawed area follows a general logarithmic function.

An approach for deriving atmosphere total precipitable water vapor (PWV) and surface air temperature over land using satellite passive microwave radiometry from the Advanced Microwave Scanning Radiometer 2 (AMSR2) was developed in this study. The PWV algorithm is based on theoretical analysis and comparisons against similar retrievals from the Atmospheric Infrared Sounder (AIRS). The AMSR2 PWV retrievals compare favorably with AIRS operational PWV products ($R^{2}\geqslant 0.80$and rmse: 4.4–5.6 mm) and independent PWV observations from the SuomiNet North American Global Positioning System station network, with an overall mean rmse of 4.7 mm and more than 78% of absolute retrieval errors below 5 mm. The PWV retrievals were then applied within an AMSR2 multifrequency brightness temperature algorithm for deriving atmosphere-corrected surface air temperatures. The estimated temperatures agree favorably ($R^{2}>0.80$and$\hbox{rmse}<3.5\ \hbox{K}$) with independent weather station daily air temperature measurements spanning global climate and land cover variability. The resulting PWV estimates increase surface air temperature retrieval accuracy in our algorithm scheme. The AMSR2 algorithm is readily applied to similar microwave sensors including the AMSR for EOS and provides suitable performance and accuracy to support hydrologic, ecosystem, and climate change studies.

The annual freeze/thaw (FT) cycle determines the potential growing season in boreal landscapes and is a major factor determining ecosystem productivity and associated exchange of trace gases (CO2, H2O) with the atmosphere. Accurate characterization of these processes can improve regional assessment of seasonal carbon dynamics and climate feedbacks. FT process variations are spatially and temporally complex due to topography, snow depth and wetness, land cover, or local climatic conditions. In this paper, we perform a landscape analysis of multifrequency and multitemporal satellite microwave remote sensing measurements at L-band (JERS-1), C-band (ERS), and Ku-band (QuikSCAT) for characterizing FT dynamics. We first analyze backscatter sensitivity of the three frequencies to FT conditions over selected Alaska temperature sites. We then apply an FT classifier over two study areas (wetland complex and moderate topography) and examine differences in FT timing according to vegetation, elevation, and north/south facing slope. Results show that L-, C-, and Ku-band backscatter are sensitive to landscape FT state transitions, with higher backscatter for nonfrozen than frozen conditions at C- and L-bands but the opposite response at Ku-band. We applied a change detection algorithm to the C-band and L-band data over both study areas and analyzed the FT classifications with land cover information. These results resolve characteristic patterns of earlier spring thawing for south facing slopes, lower elevations, and coniferous vegetation. Our results also inform similar FT algorithm development for the NASA Soil Moisture Active Passive mission by documenting L-band FT sensitivity and heterogeneity over a boreal landscape.

The mapping of the predominant freeze/thaw state of the landscape is one of the main objectives of the National Aeronautics and Space Administration's proposed Soil Moisture Active Passive (SMAP) mission. This study applies Alaska Ecological Transect (ALECTRA) biophysical network temperature measurements and satellite radar scatterometer data from the Quick Scatterometer (QuikSCAT) to evaluate some of the validation issues regarding the planned SMAP freeze/thaw measurements. Although the QuikSCAT data are acquired at Ku-band frequency, rather than at the L-band frequency of the proposed SMAP instrument, QuikSCAT data do provide a high temporal fidelity over the ALECTRA sites, similar to SMAP. The results of this study show that multiple temperature measurements representative of individual landscape components (soil, snow cover, vegetation, and atmosphere) covering different types of terrain within the satellite field of view are important for understanding the freeze/thaw process and the aggregate radar backscatter response to that process. The backscatter temporal dynamics and relative contribution of the freeze/thaw state of these landscape elements to radar signal vary with land cover, seasonal weather, and climate conditions.

The calibration and validation of the freeze/thaw product of NASA's proposed L-band SMAP (Soil Moisture Active and Passive) radar and radiometer mission requires execution of a strategy for characterization of thermal regime of the relevant landscape elements in terms of freeze/thaw state and the associated relationship to the microwave remote sensing signature. The goal of this study is to improve the understanding of the L-band radar backscatter processes over boreal landscapes by comparing ALOS PALSAR high resolution L-band backscatter images with Ku-band backscatter from the SeaWinds QuikSCAT scatterometer and C-, Xand Ka-band brightness temperatures from the Aqua AMSR-E radiometer. The results show that landscape elements driving the L-band backscatter are different from those at higher (Ku-band) frequencies and establishment of an optimal validation strategy for SMAP requires investigation of L-band measurements at spatial scales and temporal fidelity commensurate with landscape freeze/thaw variability.

The landscape freeze-thaw (F/T) state parameter derived from satellite microwave remote sensing is closely linked to the surface energy budget, hydrological activity, vegetation growing season dynamics, terrestrial carbon budgets, and land-atmosphere trace gas exchange. Satellite microwave remote sensing is well suited for global F/T monitoring due to its insensitivity to atmospheric contamination and solar illumination effects, and its strong sensitivity to the relationship between landscape dielectric properties and predominantly frozen and thawed conditions. We investigated the utility of multifrequency and dual polarization brightness temperature$(T_{b})$measurements from the Special Sensor Microwave Imager (SSM/I) to map global patterns and daily variations in terrestrial F/T cycles. We defined a global F/T classification domain by examining biophysical cold temperature constraints to vegetation growing seasons. We applied a temporal change classification algorithm based on a seasonal thresholding scheme to classify daily F/T states from time series$T_{b}$measurements. The SSM/I F/T classification accuracy was assessed using in situ air temperature measurements from the global WMO weather station network. A single-channel classification of 37 GHz, V-polarization$T_{b}$time series provided generally improved performance over other SSM/I frequencies, polarizations and channel combinations. Mean annual F/T classification accuracies were 92.2$\pm$0.8 [SD] % and 85.0$\pm$0.7 [SD] % for respective SSM/I time series of p.m. and a.m. orbital nodes over the global domain and a 20-year (1988–2007) satellite record. The resulting database provides a continuous and relatively long-term record of daily F/T dynamics for the global biosphere with well-defined accuracy.

The mapping of freeze/thaw state of the landscape is one of the main objectives of NASA's upcoming SMAP (Soil Moisture Active and Passive) mission. This study applies ALECTRA (Alaska Ecological Transect) biophysical network and QuikSCAT scatterometer data to evaluate some of the validation issues regarding the SMAP freeze/thaw measurements. Although the QuikSCAT data is at Ku-band frequency, rather than the L-band of the SMAP instrument, the data is utilized due to its uniquely high temporal resolution over the ALECTRA sites. The results show that multiple temperature measurements representative of individual landscape (soil, snow cover, vegetation and atmosphere) elements and spatial heterogeneity within the satellite field-of-view are important for understanding the radar backscatter process and aggregate freeze/thaw signal. The backscatter temporal dynamics and relative contribution of these landscape elements to the freeze-thaw signal varies with land cover type, seasonal weather and climate conditions.

The Soil Moisture Active Passive (SMAP) mission is one of the first Earth observation satellites being developed by NASA in response to the National Research Council's Decadal Survey. SMAP will make global measurements of the soil moisture present at the Earth's land surface and will distinguish frozen from thawed land surfaces. Direct observations of soil moisture and freeze/thaw state from space will allow significantly improved estimates of water, energy, and carbon transfers between the land and the atmosphere. The accuracy of numerical models of the atmosphere used in weather prediction and climate projections are critically dependent on the correct characterization of these transfers. Soil moisture measurements are also directly applicable to flood assessment and drought monitoring. SMAP observations can help monitor these natural hazards, resulting in potentially great economic and social benefits. SMAP observations of soil moisture and freeze/thaw timing will also reduce a major uncertainty in quantifying the global carbon balance by helping to resolve an apparent missing carbon sink on land over the boreal latitudes. The SMAP mission concept will utilize L-band radar and radiometer instruments sharing a rotating 6-m mesh reflector antenna to provide high-resolution and high-accuracy global maps of soil moisture and freeze/thaw state every two to three days. In addition, the SMAP project will use these observations with advanced modeling and data assimilation to provide deeper root-zone soil moisture and net ecosystem exchange of carbon. SMAP is scheduled for launch in the 2014-2015 time frame.

We developed an algorithm to estimate surface soil moisture, vegetation optical depth and fractional open water cover using satellite microwave radiometry. Soil moisture results compare favorably with a simple antecedent site precipitation index, and respond rapidly to precipitation events indicated by TRMM. High optical depth reduces soil moisture sensitivity in forests and croplands during peak biomass, although tundra locations maintain soil moisture sensitivity despite high optical depth. Optical depth varies with characteristic seasonality across vegetation cover types and tracks measures of vegetation canopy cover from MODIS. The algorithm developed in this study is able to monitor the daily variability of several important land surface state variables.

Northern ecosystems are a major sink for atmospheric$\hbox{CO}_{2}$and contain much of the world's soil organic carbon (SOC) that is potentially reactive to near-term climate change. We introduce a simple terrestrial carbon flux (TCF) model driven by satellite remote sensing inputs from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) to estimate surface ($≪ 10$-cm depth) SOC stocks, daily respiration, and net ecosystem carbon exchange (NEE). Soil temperature and moisture information from AMSR-E provide environmental constraints to soil heterotrophic respiration$(R_{h})$, while gross primary production (GPP) information from MODIS provides estimates of the total photosynthesis and autotrophic respiration. The model results were evaluated across a North American network of boreal forest, grassland, and tundra monitoring sites using alternative carbon measures derived from tower$\hbox{CO}_{2}$flux measurements and BIOME-BGC model simulations. Root-mean-square-error (rmse) differences between TCF model estimates and tower observations were 1.2, 0.7, and 1.2$\hbox{g} \cdot \hbox{C} \cdot \hbox{m}^{-2} \cdot \hbox{day}^{-1}$for GPP, ecosystem respiration$({\rm R}_{\rm tot})$and NEE, while mean residual differences were 43% of the rmse. Similar accuracies were observed for both TCF and BIOME-BGC model simulations relative to tower results. TCF-model-derived SOC was in general agreement with soil inventory data and indicates that the dominant SOC source for$R_{h}$has a mean residence time of less than five years, while$R_{h}$is approximately 43% and 55% of$R_{\rm tot}$for respective summer and annual fluxes. An error sensitivity analysis determined that meaningful flux estimates could be derived under prevailing climatic conditions at the study locations, given documented error levels in the remote sensing inputs.

Methods are developed and evaluated to retrieve surface soil temperature information for the advanced microwave scanning radiometer on earth observing system for seven boreal forest and Arctic tundra biophysical monitoring sites across Alaska and Northern Canada. A multiple-band iterative radiative transfer process-based method producing dynamic vegetation and snow cover correction quantities and an empirical multiple regression method using several frequencies are employed. The seasonal pattern of microwave emission and relative accuracy of the soil temperature retrievals are influenced strongly by landscape properties, including the presence of open water, vegetation type and seasonal phenology, snow cover, and freeze-thaw transitions. The retrieval of soil temperature is similar for the two methods with an overall root-mean-square error of 3.1-3.9 K during summer thawed conditions, with a larger error occurring in winter during periods of dynamic snow cover and freeze-thaw state. These results indicate that at high latitudes, the influence of the atmosphere may be less important than that of surface conditions in determining the relative accuracy of the estimated soil temperature. Impacts of surface conditions on surface emissivity, observed brightness temperature, and estimated soil temperature are discussed.

The Moderate Resolution Spectroradiometer (MODIS) sensor has provided near real-time estimates of gross primary production (GPP) since March 2000. We compare four years (2000 to 2003) of satellite-based calculations of GPP with tower eddy CO2flux-based estimates across diverse land cover types and climate regimes. We examine the potential error contributions from meteorology, leaf area index (LAI)/fPAR, and land cover. The error between annual GPP computed from NASA's Data Assimilation Office's (DAO) and tower-based meteorology is 28%, indicating that NASA's DAO global meteorology plays an important role in the accuracy of the GPP algorithm. Approximately 62% of MOD15-based estimates of LAI were within the estimates based on field optical measurements, although remaining values overestimated site values. Land cover presented the fewest errors, with most errors within the forest classes, reducing potential error. Tower-based and MODIS estimates of annual GPP compare favorably for most biomes, although MODIS GPP overestimates tower-based calculations by 20%-30%. Seasonally, summer estimates of MODIS GPP are closest to tower data, and spring estimates are the worst, most likely the result of the relatively rapid onset of leaf-out. The results of this study indicate, however, that the current MODIS GPP algorithm shows reasonable spatial patterns and temporal variability across a diverse range of biomes and climate regimes. So, while continued efforts are needed to isolate particular problems in specific biomes, we are optimistic about the general quality of these data, and continuation of the MOD17 GPP product will likely provide a key component of global terrestrial ecosystem analysis, providing continuous weekly measurements of global vegetation production

The Hydrosphere State Mission (Hydros) is a pathfinder mission in the National Aeronautics and Space Administration (NASA) Earth System Science Pathfinder Program (ESSP). The objective of the mission is to provide exploratory global measurements of the earth's soil moisture at 10-km resolution with two- to three-days revisit and land-surface freeze/thaw conditions at 3-km resolution with one- to two-days revisit. The mission builds on the heritage of ground-based and airborne passive and active low-frequency microwave measurements that have demonstrated and validated the effectiveness of the measurements and associated algorithms for estimating the amount and phase (frozen or thawed) of surface soil moisture. The mission data will enable advances in weather and climate prediction and in mapping processes that link the water, energy, and carbon cycles. The Hydros instrument is a combined radar and radiometer system operating at 1.26 GHz (with VV, HH, and HV polarizations) and 1.41 GHz (with H, V, and U polarizations), respectively. The radar and the radiometer share the aperture of a 6-m antenna with a look-angle of 39/spl deg/ with respect to nadir. The lightweight deployable mesh antenna is rotated at 14.6 rpm to provide a constant look-angle scan across a swath width of 1000 km. The wide swath provides global coverage that meet the revisit requirements. The radiometer measurements allow retrieval of soil moisture in diverse (nonforested) landscapes with a resolution of 40 km. The radar measurements allow the retrieval of soil moisture at relatively high resolution (3 km). The mission includes combined radar/radiometer data products that will use the synergy of the two sensors to deliver enhanced-quality 10-km resolution soil moisture estimates. In this paper, the science requirements and their traceability to the instrument design are outlined. A review of the underlying measurement physics and key instrument performance parameters are also presented.

Spatial and temporal variations in vegetation dielectric properties strongly influence the microwave backscatter characteristics of forested landscapes. This paper examines the relationship between xylem tissue dielectric constant, xylem sap flux density, and xylem sap chemical composition as measured in the stems of two Norway Spruce (Picea abies [L.] Karst.) trees in the Fichtelgebirge region of Northern Bavaria, Germany. Dielectric constant and xylem sap flux were monitored continuously from June through October 1995, at several heights along the tree trunks. At the end of the measurement series, each tree was harvested, and its xylem sap extracted and analyzed to determine the concentrations of amino acids and cations. Results show that the sap flux density was correlated with vapor pressure deficit (VPD) at all heights in the stem. In contrast, the xylem tissue dielectric constant is influenced by VPD but can exhibit a significant temporal lag relative to changes in VPD. This lag varies with position along the tree trunk. The temporal variability of the dielectric constant is compared with both trees at several positions along the tree trunks. Results of xylem sap chemical analysis are presented. We show that spatial and temporal variability in the xylem tissue dielectric constant is influenced not only by water content, but by variations in xylem sap chemistry as well. This has important implications for microwave remote sensing of forested landscapes, as useful information may be acquired regarding stand physiology and water relations and where variations in dielectric properties within individual trees and across geographic areas can be significant error sources for forest inventory mapping.