Jiachun Shi

The Soil Moisture Active Passive (SMAP) Mission

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 Councils Decadal Survey. SMAP will make global measurements of the soil moisture present at the Earths 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.

A Comparison of Soil Moisture Retrieval Models Using SIR-C Measurements over the Little Washita River Watershed

Abstract SIR-C L-band measurements over the Little Washita River watershed in Chickasha, Oklahama during 11–17 April 1994 have been analyzed for studying the change of soil moisture in the region. Two algorithms developed recently for estimation of moisture content in bare soil were applied to these measurements and the results were compared with those sampled on the ground. There is a good agreement between the values of soil moisture estimated by either one of the algorithms and those measured from ground sampling for bare or sparsely vegetated fields. The standard error from this comparison is on the order of 0.05–0.06 cm 3 /cm 3 , which is comparable to that expected from a regression between backscattering coefficients and measured soil moisture. Both algorithms provide a poor estimation of soil moisture or fail to give solutions to areas covered with moderate or dense vegetation. Even for bare soils the number of pixels that bear no numerical solution from the application of either one of the two algorithms to the data is not negligible. Results from using one of these algorithms indicate that the fraction of these pixels becomes larger as the bare soils become drier. The other algorithm generally gives a larger fraction of these pixels when the fields are vegetation-covered. The implication and impact of these features are discussed in this article.

Modeling the effect of surface roughness on the back-scattering coefficient and emissivity of a soil-litter medium using a numerical model

In the context of the SMOS mission, a new numerical method based on the finite element method is presented which can be used to model the radiometric L-band emission of soil and litter layers in forests. Many different characteristics of these layers that affect the soil-litter emission can be incorporated into the model, including surface roughness, inclusions and volume effects. Soil moisture is incorporated into the model as a function of the dielectric permittivity constant. The model is validated for a single dielectric layer with a surface roughness of Gaussian autocorrelation function by comparing results of the backscattering coefficient with those calculated by the 2D method of moments. Good general agreement is obtained between these results. An emissivity calculation for a single layer rough surface is also presented and compared with the emissivity of a flat layer.

Comparison of soil moisture retrieval algorithms using simulated HYDROS brightness temperatures

The HYDROS mission objective is to collect global scale measurements of the Earths soil moisture and land surface freeze/thaw conditions, using a combined L band radiometer and radar system operating at 1.41 and 1.26 GHz, respectively. In order to examine how HYDROS soil moisture retrieval will be performed and how the retrieval accuracy will be impacted by vegetation water content and surface heterogeneity, an observing system simulation experiment (OSSE) was conducted using a modeled geophysical domain in the south-central United States centered on the Arkansas-Red River basin for a one-month period in 1994. Three separate radiometer retrieval algorithms were evaluated: (1) a single-channel algorithm (H polarization), (2) a two-channel iterative algorithm, and (3) a two-channel reflectivity ratio algorithm. Analysis indicates that the HYDROS accuracy goal of 4% volumetric soil moisture can be met anywhere in the test basin except woodland areas. Nonlinear scaling of higher resolution ancillary vegetation data can adversely affect algorithm retrieval accuracies, especially in heavy tree areas on the east side of the basin