Development and Assessment of the SMAP Enhanced Passive Soil Moisture Product.

Launched in January 2015, the National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) observatory was designed to provide frequent global mapping of high-resolution soil moisture and freeze-thaw state every two to three days using a radar and a radiometer operating at L-band frequencies. Despite a hardware mishap that rendered the radar inoperable shortly after launch, the radiometer continues to operate nominally, returning more than two years of science data that have helped to improve existing hydrological applications and foster new ones. Beginning in late 2016 the SMAP project launched a suite of new data products with the objective of recovering some high-resolution observation capability loss resulting from the radar malfunction. Among these new data products are the SMAP Enhanced Passive Soil Moisture Product that was released in December 2016, followed by the SMAP/Sentinel-1 Active-Passive Soil Moisture Product in April 2017. This article covers the development and assessment of the SMAP Level 2 Enhanced Passive Soil Moisture Product (L2_SM_P_E). The product distinguishes itself from the current SMAP Level 2 Passive Soil Moisture Product (L2_SM_P) in that the soil moisture retrieval is posted on a 9 km grid instead of a 36 km grid. This is made possible by first applying the Backus-Gilbert optimal interpolation technique to the antenna temperature (TA) data in the original SMAP Level 1B Brightness Temperature Product to take advantage of the overlapped radiometer footprints on orbit. The resulting interpolated TA data then go through various correction/calibration procedures to become the SMAP Level 1C Enhanced Brightness Temperature Product (LiC_TB_E). The LiC_TB_E product, posted on a 9 km grid, is then used as the primary input to the current operational SMAP baseline soil moisture retrieval algorithm to produce L2_SM_P_E as the final output. Images of the new product reveal enhanced visual features that are not apparent in the standard product. Based on in situ data from core validation sites and sparse networks representing different seasons and biomes all over the world, comparisons between L2_SM_P_E and in situ data were performed for the duration of April 1, 2015 - October 30, 2016. It was found that the performance of the enhanced 9 km L2_SM_P_E is equivalent to that of the standard 36 km L2_SM_P, attaining a retrieval uncertainty below 0.040 m3/m3 unbiased root-mean-square error (ubRMSE) and a correlation coefficient above 0.800. This assessment also affirmed that the Single Channel Algorithm using the V-polarized TB channel (SCA-V) delivered the best retrieval performance among the various algorithms implemented for L2_SM_P_E, a result similar to a previous assessment for L2_SM_P.

Ensemble uncertainty of inherent optical properties.

We present a method to evaluate the combined accuracy of ocean color models and the parameterizations of inherent optical proprieties (IOPs), or model-parametrization setup. The method estimates the ensemble (collective) uncertainty of derived IOPs relative to the radiometric error and is directly applicable to ocean color products without the need for inversion. Validation shows a very good fit between derived and known values for synthetic data, with R(2) > 0.95 and mean absolute difference (MADi) <0.25 m(-1). Due to the influence of observation errors, these values deteriorate to 0.45 < R(2) < 0.5 and 0.65 < MADi < 0.9 for in-situ and ocean color matchup data. The method is also used to estimate the maximum accuracy that could be achieved by a specific model-parametrization setup, which represents the optimum accuracy that should be targeted when deriving IOPs. Application to time series of ocean color global products collected between 1997-2007 shows few areas with increasing annual trends of ensemble uncertainty, up to 8 sr m(-1) decade(-1). This value is translated to an error of 0.04 m(-1) decade(-1) in the sum of derived absorption and backscattering coefficients at the blue wavelength 440 nm. As such, the developed method can be used as a tool for assessing the reliability of model-parametrization setups for specific biophysical conditions and for identifying hot-spots for which the model-parametrization setup should be reconsidered.

L band brightness temperature observations over a corn canopy during the entire growth cycle.

During a field campaign covering the 2002 corn growing season, a dual polarized tower mounted L-band (1.4 GHz) radiometer (LRAD) provided brightness temperature (T(B)) measurements at preset intervals, incidence and azimuth angles. These radiometer measurements were supported by an extensive characterization of land surface variables including soil moisture, soil temperature, vegetation biomass, and surface roughness. In the period May 22 to August 30, ten days of radiometer and ground measurements are available for a corn canopy with a vegetation water content (W) range of 0.0 to 4.3 kg m(-2). Using this data set, the effects of corn vegetation on surface emissions are investigated by means of a semi-empirical radiative transfer model. Additionally, the impact of roughness on the surface emission is quantified using T(B) measurements over bare soil conditions. Subsequently, the estimated roughness parameters, ground measurements and horizontally (H)-polarized T(B) are employed to invert the H-polarized transmissivity (γ(h)) for the monitored corn growing season.

Impact of Soil Moisture Dynamics on ASAR σo Signatures and Its Spatial Variability Observed over the Tibetan Plateau.

This paper reports on the analysis of a 2.5 year-long time series of ASAR wide swath mode (WSM) observations for characterizing the soil moisture dynamics. The employed ASAR WSM data set consists of 152 VV-polarized scenes acquired in the period between April 2005 and September 2007 over the Naqu river basin located on the Tibetan Plateau. For four different spatial domains, with areas of 30x30 km², 5x5 km² and (two domains of) 1x1 km², the mean backscatter (σo) and the standard deviation (stdev) have been computed for each ASAR acquisition. Comparison of the mean σo values with the stdev values results in a specific triangular distribution of data points for all spatial domains. Analysis of the mean σo and stdev with respect to in-situ soil moisture measurements demonstrates that this triangular shaped distribution can be explained by soil moisture dynamics during monsoon and winter periods. This shows that the relationship between the spatial mean soil moisture and variability is not uniquely defined and may change throughout seasons. Downscaling of coarse resolution soil moisture products should, therefore, be ideally based on additional near real time data sources. In this context, the presented results could form a basis for the development of SAR-based soil moisture downscaling methodologies.

Estimation of the Total Atmospheric Water Vapor Content and Land Surface Temperature Based on AATSR Thermal Data.

The total atmospheric water vapor content (TAWV) and land surfacetemperature (LST) play important roles in meteorology, hydrology, ecology and some otherdisciplines. In this paper, the ENVISAT/AATSR (The Advanced Along-Track ScanningRadiometer) thermal data are used to estimate the TAWV and LST over the Loess Plateauin China by using a practical split window algorithm. The distribution of the TAWV isaccord with that of the MODIS TAWV products, which indicates that the estimation of thetotal atmospheric water vapor content is reliable. Validations of the LST by comparingwith the ground measurements indicate that the maximum absolute derivation, themaximum relative error and the average relative error is 4.0K, 11.8% and 5.0%respectively, which shows that the retrievals are believable; this algorithm can provide anew way to estimate the LST from AATSR data.