Mariko S. Burgin

Assessment of the SMAP Passive Soil Moisture Product

The National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) satellite mission was launched on January 31, 2015. The observatory was developed to provide global mapping of high-resolution soil moisture and freeze-thaw state every two to three days using an L-band (active) radar and an L-band (passive) radiometer. After an irrecoverable hardware failure of the radar on July 7, 2015, the radiometer-only soil moisture product became the only operational soil moisture product for SMAP. The product provides soil moisture estimates posted on a 36 km Earth-fixed grid produced using brightness temperature observations from descending passes. Within months after the commissioning of the SMAP radiometer, the product was assessed to have attained preliminary (beta) science quality, and data were released to the public for evaluation in September 2015. The product is available from the NASA Distributed Active Archive Center at the National Snow and Ice Data Center. This paper provides a summary of the Level 2 Passive Soil Moisture Product (L2_SM_P) and its validation against in situ ground measurements collected from different data sources. Initial in situ comparisons conducted between March 31, 2015 and October 26, 2015, at a limited number of core validation sites (CVSs) and several hundred sparse network points, indicate that the V-pol Single Channel Algorithm (SCA-V) currently delivers the best performance among algorithms considered for L2_SM_P, based on several metrics. The accuracy of the soil moisture retrievals averaged over the CVSs was 0.038 m3/m3 unbiased root-mean-square difference (ubRMSD), which approaches the SMAP mission requirement of 0.040 m3/m3.

Models of L-Band Radar Backscattering Coefficients Over Global Terrain for Soil Moisture Retrieval

Physical models for radar backscattering coefficients are developed for the global land surface at L-band (1.26 GHz) and 40 ° incidence angle to apply to the soil moisture retrieval from the upcoming soil moisture active passive mission data. The simulation of land surface classes includes 12 vegetation types defined by the International Geosphere-Biosphere Programme scheme, and four major crops (wheat, corn, rice, and soybean). Backscattering coefficients for four polarizations (HH/VV/HV/350611873VH) are produced. In the physical models, three terms are considered within the framework of distorted Born approximation: surface scattering, double-bounce volume-surface interaction, and volume scattering. Numerical solutions of Maxwell equations as well as theoretical models are used for surface scattering, double-bounce reflectivity, and volume scattering of a single scatterer. To facilitate fast, real-time, and accurate inversion of soil moisture, the outputs of physical model are provided as lookup tables (with three axes; therefore called datacube). The three axes are the real part of the dielectric constant of soil, soil surface root mean square (RMS) height, and vegetation water content (VWC), each of, which covers the wide range of natural conditions. Datacubes for most of the classes are simulated using input parameters from in situ and airborne observations. This simulation results are found accurate to the co-pol RMS errors of to 3.4 dB (six woody vegetation types), 1.8 dB (grass), and 2.9 dB (corn) when compared with airborne data. Validated with independent spaceborne phased array type L-band synthetic aperture radars and field-based radar data, the datacube errors for the co-pols are within 3.4 dB (woody savanna and shrub) and 1.5 dB (bare surface). Assessed with spaceborne Aquarius scatterometer data, the mean differences range from ~ 1.5 to 2 dB. The datacubes allow direct inversion of sophisticated forward models without empirical parameters or formulae. This capability is evaluated using the time-series inversion algorithm over grass fields.

P-Band Radar Retrieval of Subsurface Soil Moisture Profile as a Second-Order Polynomial: First AirMOSS Results

We propose a new model for estimating subsurface soil moisture using P-band radar data over barren, shrubland, and vegetated terrains. The unknown soil moisture profile is assumed to have a second-order polynomial form as a function of subsurface depth with three unknown coefficients that we estimate using the simulated annealing algorithm. These retrieved coefficients produce the value of soil moisture at any given depth up to a prescribed depth of validity. We use a discrete scattering model to calculate the radar backscattering coefficients of the terrain. The retrieval method is tested and developed with synthetic radar data and is validated with measured radar data and in situ soil moisture measurements. Both forward and inverse models are briefly explained. The radar data used in this paper have been collected during the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) mission flights in September and October of 2012 over a 100 km by 25 km area in Arizona, including the Walnut Gulch Experimental Watershed. The study area and the ancillary data layers used to characterize each radar pixel are explained. The inversion results are presented, and it is shown that the RMSE between the retrieved and measured soil moisture profiles ranges from 0.060 to 0.099 m3/m3, with a Root Mean Squared Error (RMSE) of 0.075 m3/m3 over all sites and all acquisition dates. We show that the accuracy of retrievals decreases as depth increases. The profiles used in validation are from a fairy dry season in Walnut Gulch and so are the accuracy conclusions.

Precipitation estimation using L‐band and C‐band soil moisture retrievals

An established methodology for estimating precipitation amounts from satellite-based soil moisture retrievals is applied to L-band products from the Soil Moisture Active Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) satellite missions and to a C-band product from the Advanced Scatterometer (ASCAT) mission. The precipitation estimates so obtained are evaluated against in situ (gauge-based) precipitation observations from across the globe. The precipitation estimation skill achieved using the L-band SMAP and SMOS datasets is higher than that obtained with the C-band product, as might be expected given that L-band is sensitive to a thicker layer of soil and thereby provides more information on the response of soil moisture to precipitation. The square of the correlation coefficient between the SMAP-based precipitation estimates and the observations (for aggregations to ~100 km and 5 days) is on average about 0.6 in areas of high rain gauge density. Satellite missions specifically designed to monitor soil moisture thus do provide significant information on precipitation variability, information that could contribute to efforts in global precipitation estimation.

A Comparative Study of the SMAP Passive Soil Moisture Product With Existing Satellite-Based Soil Moisture Products

The NASA Soil Moisture Active Passive (SMAP) satellite mission was launched on January 31, 2015 to provide global mapping of high-resolution soil moisture and freeze–thaw state every 2–3 days using an L-band (active) radar and an L-band (passive) radiometer. The Level 2 radiometer-only soil moisture product (L2_SM_P) provides soil moisture estimates posted on a 36-km Earth-fixed grid using brightness temperature observations from descending passes. This paper provides the first comparison of the validated-release L2_SM_P product with soil moisture products provided by the Soil Moisture and Ocean Salinity (SMOS), Aquarius, Advanced Scatterometer (ASCAT), and Advanced Microwave Scanning Radiometer 2 (AMSR2) missions. This comparison was conducted as part of the SMAP calibration and validation efforts. SMAP and SMOS appear most similar among the five soil moisture products considered in this paper, overall exhibiting the smallest unbiased root-mean-square difference and highest correlation. Overall, SMOS tends to be slightly wetter than SMAP, excluding forests where some differences are observed. SMAP and Aquarius can only be compared for a little more than two months; they compare well, especially over low to moderately vegetated areas. SMAP and ASCAT show similar overall trends and spatial patterns with ASCAT providing wetter soil moistures than SMAP over moderate to dense vegetation. SMAP and AMSR2 largely disagree in their soil moisture trends and spatial patterns; AMSR2 exhibits an overall dry bias, while desert areas are observed to be wetter than SMAP.

Evaluation of the validated Soil Moisture product from the SMAP radiometer

NASAs Soil Moisture Active Passive (SMAP) mission launched on January 31, 2015 into a sun-synchronous 6 am/6 pm orbit with an objective to produce global mapping of high-resolution soil moisture and freeze-thaw state every 2-3 days using an L-band (active) radar and an L-band (passive) radiometer. The SMAP radiometer began acquiring routine science data on March 31, 2015 and continues to operate nominally. SMAPs radiometer-derived soil moisture product (L2_SM_P) provides soil moisture estimates posted on a 36 km fixed Earth grid using brightness temperature observations from descending (6 am) passes and ancillary data. A beta quality version of L2_SM_P was released to the public in September, 2015, with the fully validated L2_SM_P soil moisture data expected to be released in May, 2016. Additional improvements (including optimization of retrieval algorithm parameters and upscaling approaches) and methodology expansions (including increasing the number of core sites, model-based intercomparisons, and results from several intensive field campaigns) are anticipated in moving from accuracy assessment of the beta quality data to an evaluation of the fully validated L2_SM_P data product.

The sensitivity of ground-reflected GNSS signals to near-surface soil moisture, as recorded by spaceborne receivers

Spatial and temporal variations in near-surface soil moisture are important to measure for climate studies, numerical weather forecasts, and drought monitoring. Several previous studies have shown success in using ground-reflected Global Navigation Satellite System (GNSS) signals as a form of bistatic radar to sense soil moisture. However, the ability of this type of data to sense soil moisture variations from space is still a nascent field of study. In the past two years, three satellites have been launched that were either designed to capture ground-reflected GNSS signals or have been modified to record these signals. The data provided by these satellites are giving scientists an unprecedented opportunity to investigate their ability to detect changes in Earths land surface, including but certainly not limited to near-surface soil moisture. This paper will present spaceborne observations of ground-reflected GNSS signals and evaluate their sensitivity to near-surface soil moisture. This sensitivity will be compared to empirical and theoretical sensitivities of monostatic L-band radar measurements to soil moisture. We will also comment on possibilities for retrieval algorithm development, using techniques employed for monostatic radar as a guide.

Airborne Microwave Observatory of Subcanopy and Subsurface radar retrieval of root zone soil moisture: Preliminary results

We present an overview of the radar retrieval processing system for estimation of root zone soil moisture (RZSM) in several major North American biomes as one of the products of the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) project. The AirMOSS mission is briefly described along with the methodologies implemented to collect field data, to prepare several data layers required for retrievals, and to ultimately retrieve the soil moisture profiles. The retrieved soil moisture maps over several sites will be available when the radar data from AirMOSS flights are fully processed and calibrated, which is anticipated in early 2013.

An Optimal Nonnegative Eigenvalue Decomposition for the Freeman and Durden Three-Component Scattering Model

Model-based decomposition allows the physical interpretation of polarimetric radar scattering in terms of various scattering mechanisms. A three-component decomposition proposed by Freeman and Durden has been popular, though significant shortcomings have been identified. In particular, it can result in negative eigenvalues for the component terms and the remainder matrix, hence violating fundamental requirements for physically meaningful decompositions. In addition, since the algorithm solves for the canopy term first, the contribution of the canopy is often over-estimated. In this paper, we show how to determine the parameters for the Freeman–Durden model in a way that minimizes the total power in the remainder matrix without favoring any individual component in the model, while simultaneously satisfying the constraints of nonnegative eigenvalues. We illustrate our analytical solution by comparison with the Freeman–Durden algorithm, as well as the nonnegative eigenvalue decomposition (NNED) proposed by van Zyl et al. The results show that this optimum algorithm generally assigns less power to the volume scattering than either the original Freeman–Durden or the NNED algorithms.

Analysis of Polarimetric Radar Data and Soil Moisture From Aquarius: Towards a Regression-Based Soil Moisture Estimation Algorithm

Many soil moisture radar retrieval algorithms depend on substantial amounts of ancillary data, such as land cover type and soil composition. To address this issue, we examine and expand an empirical approach by Kim and van Zyl as an alternative; it describes radar backscatter of a vegetated scene as a linear function of volumetric soil moisture, thus reducing the dependence on ancillary data. We use 2.5 years of L-band Aquarius radar and radiometer derived soil moisture data to determine the two polarization dependent parameters on a global scale and on a weekly basis. We propose a look-up table based soil moisture estimation approach; it is promising due to its simplicity and independence of ancillary data. However, the estimation performance is found to be impacted by the used land cover classification scheme. Our results show that the sensitivity of the radar signal to soil moisture changes seasonally, and that the variation differs depending on vegetation class. While this seasonal variation can be relatively small, it must be properly accounted for as it impacts the soil moisture retrieval accuracy.