Results for “sea surface salinity” — Page 20

NASA NTRS1990

The diffusive boundary layer of sediments: oxygen microgradients over a microbial mat

Oxygen microelectrodes were used to analyze the distribution of the diffusive boundary layer (DBL) at the sediment-water interface in relation to surface topography and flow velocity. The sediment, collected from saline ponds, was covered by a microbial mat that had high oxygen consumption rate and well-defined surface structure. Diffusion through the DBL constituted an important rate limitation to the oxygen uptake of the sediment. The mean effective DBL thickness decreased from 0.59 to 0.16 mm as the flow velocity of the overlying water was increased from 0.3 to 7.7 cm s-1 (measured 1 cm above the mat). The oxygen uptake rate concurrently increased from 3.9 to 9.4 nmol cm-2 min-1. The effects of surface roughness and topography on the thickness and distribution of the DBL were studied by three-dimensional mapping of the sediment-water interface and the upper DBL boundary at 0.1-mm spatial resolution. The DBL boundary followed mat structures that had characteristic dimensions > 1/2 DBL thickness but the DBL had a dampened relief relative to the mat. The effective surface area of the sediment-water interface and of the upper DBL boundary were 31 and 14% larger, respectively, than a flat plane. Surface topography thereby increased the oxygen flux across the sediment-water interface by 49% relative to a one-dimensional diffusion flux calculated from the vertical oxygen microgradients.

NASA Center ARC↗

NASA NTRS2011

SMOS/SMAP Synergy for SMAP Level 2 Soil Moisture Algorithm Evaluation

Soil Moisture Active Passive (SMAP) satellite has been proposed to provide global measurements of soil moisture and land freeze/thaw state at 10 km and 3 km resolutions, respectively. SMAP would also provide a radiometer-only soil moisture product at 40-km spatial resolution. This product and the supporting brightness temperature observations are common to both SMAP and European Space Agency's Soil Moisture and Ocean Salinity (SMOS) mission. As a result, there are opportunities for synergies between the two missions. These include exploiting the data for calibration and validation and establishing longer term L-band brightness temperature and derived soil moisture products. In this investigation we will be using SMOS brightness temperature, ancillary data, and soil moisture products to develop and evaluate a candidate SMAP L2 passive soil moisture retrieval algorithm. This work will begin with evaluations based on the SMOS product grids and ancillary data sets and transition to those that will be used by SMAP. An important step in this analysis is reprocessing the multiple incidence angle observations provided by SMOS to a global brightness temperature product that simulates the constant 40 degree incidence angle observations that SMAP will provide. The reprocessed brightness temperature data provide a basis for evaluating different SMAP algorithm alternatives. Several algorithms are being considered for the SMAP radiometer-only soil moisture retrieval. In this first phase, we utilized only the Single Channel Algorithm (SCA), which is based on the radiative transfer equation and uses the channel that is most sensitive to soil moisture (H-pol). Brightness temperature is corrected sequentially for the effects of temperature, vegetation, roughness (dynamic ancillary data sets) and soil texture (static ancillary data set). European Centre for Medium-Range Weather Forecasts (ECMWF) estimates of soil temperature for the top layer (as provided as part of the SMOS ancillary data) were used to correct for surface temperature effects and to derive microwave emissivity. ECMWF data were also used for precipitation forecasts, presence of snow, and frozen ground. Vegetation options are described below. One year of soil moisture observations from a set of four watersheds in the U.S. were used to evaluate four different retrieval methodologies: (1) SMOS soil moisture estimates (version 400), (2) SeA soil moisture estimates using the SMOS/SMAP data with SMOS estimated vegetation optical depth, which is part of the SMOS level 2 product, (3) SeA soil moisture estimates using the SMOS/SMAP data and the MODIS-based vegetation climatology data, and (4) SeA soil moisture estimates using the SMOS/SMAP data and actual MODIS observations. The use of SMOS real-world global microwave observations and the analyses described here will help in the development and selection of different land surface parameters and ancillary observations needed for the SMAP soil moisture algorithms. These investigations will greatly improve the quality and reliability of this SMAP product at launch.

Bindlish, Rajat↗