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This study investigates differences in the soil moisture product and brightness temperatures between 6 p.m. and 6 a.m. local solar time Soil Moisture Ocean Salinity (SMOS) passes for a region in the Midwest United States. This region has uniform land cover, consisting largely of maize and soybean row crops. The comparison was restricted to periods with no rainfall. There were 19 days available for analysis of the soil moisture product. It was found that there was a significant difference in the soil moisture product for all 19 days, with lower soil moisture for most mornings. The difference between the soil moisture products on some days exceeded the allowable error of 0.04 m3 m-3. In-situ and model results indicate that there should be virtually no change in soil moisture between the evening and morning. In order to investigate this discrepancy, measured brightness temperature was converted to a polarization index (PI), and evening and morning values were compared. Investigation of the measured brightness temperature was limited to five days where a large range in incidence angles was available. Large differences between evening and morning passes were found for incidence angles less than 40° that could not be explained with radiative transfer theory but may be attributed to technical issues. There was also a difference in the PI values between the evening and morning passes for incidence angles greater than 40°. This can be caused by a decrease in soil moisture from evening to morning or could be attributed to an increase in the volumetric water content of the vegetation. [ABSTRACT FROM AUTHOR]

Radiometric sensitivity to soil moisture at 1.4 GHz through a corn canopy at a maximum biomass of 8.0 kg m−2 (water column density of 6.3 kg m−2) was much higher than expected. The magnitude of the measured sensitivity of horizontally polarized brightness temperature to the 0-3 cm volumetric soil water content was at least 1.5 K per 0.01 m3 m−3 and could have been as high as 2.5 K per 0.01 m3 m−3. Vertically polarized brightness temperature was 0.5 K per 0.01 m3 m−3 less sensitive than horizontally polarized brightness temperature. A widely used radiative transfer model that assumes a uniform distribution of vegetation in the canopy underestimated this soil moisture sensitivity at horizontal polarization by over 1 K per 0.01 m3 m−3. Given an appropriate emission model that correctly accounts for the differences in transparency between heterogeneous canopies (as compared to the wavelength) such as corn and relatively homogeneous canopies such as grass, it appears that there will be practical sensitivity to soil moisture through corn (and most, if not all, row crops) throughout the growing season. [ABSTRACT FROM AUTHOR]

At the watershed scale, soil moisture is the major control for rainfall-runoff response, especially where saturation excess runoff processes dominate. From the ecological point of view, the pools of soil moisture are fundamental ecosystem resources providing the transpirable water for plants. In drylands particularly, soil moisture is one of the major controls on the structure, function, and diversity in ecosystems. In terms of the global hydrological cycle, the overall quantity of soil moisture is small, ~0.05%; however, its importance to the global energy balance and the distribution of precipitation far outweighs its physical amount. In soils it governs microbial activity that affects important biogeochemical processes such as nitrification and CO2 production via respiration. During the past 20 years, technology has advanced considerably, with the development of different electrical sensors for determining soil moisture at a point. However, modeling of watersheds requires areal averages. As a result, point measurements and modeling grid cell data requirements are generally incommensurate. We review advances in sensor technology, particularly emerging geophysical methods and distributed sensors, aimed at bridging this gap. We consider some of the data analysis methods for upscaling from a point to give an areal average. Finally, we conclude by offering a vision for future research, listing many of the current scientific and technical challenges. [ABSTRACT FROM AUTHOR]

In the near future, the SMOS (Soil Moisture and Ocean Salinity) mission will provide global maps of surface soil moisture (SM). The SMOS baseline payload is an L-band (1. 4 GHz) two dimensional interferometric microwave radiometer which will provide multi-angular and dual-polarization observations. In the framework of the ground segment activities for the SMOS mission an operational SMOS Level 2 Soil Moisture algorithm was developed. The principle of the algorithm is to exploit multi-angular data in order to retrieve simultaneously several surface parameters including soil moisture and vegetation characteristics. The algorithm uses an iterative approach, minimizing a cost function computed from the differences between measured and modelled brightness temperature (T B) data, for all available incidence angles. In the algorithm, the selected forward model is the so-called L-MEB (L-band Microwave Emission of the Biosphere) model which was the result of an extensive review of the current knowledge of the microwave emission of various land covers. This model is a key element in the SMOS L2 algorithm and could be used in future assimilation studies. There is thus a strong need for a reference study, describing the model and its implementation. In order to address these needs a detailed description of soil and vegetation modelling in L-MEB is given in this study. In a second step, the use of L-MEB in soil moisture retrievals is evaluated for several experimental data sets over agricultural crops. Calibrations of the soil and vegetation L-MEB parameters are investigated for corn, soybean and wheat. Over the different experiments, very consistent results are obtained for each vegetation type in terms of calibration and soil moisture retrievals. [Copyright &y& Elsevier]