global product and is consistent in its retrieval approach for the entire period of data record. The moisture retrievals are made with a radiative transfer-based land parameter retrieval model. The various sensors have different technical specifications, including primary wavelength, spatial resolution, and temporal frequency of coverage. These sensor specifications and their effect on the data retrievals are discussed. The model is described in detail, and the quality of the data with respect to the different sensors is discussed as well. Examples of the different sensor retrievals illustrating global patterns are presented. Additional validation studies were performed with large-scale observational soil moisture data sets and are also presented. The data will be made available for use by the general science community.

/pdf/multisensor-historical-climatology-of-satellite-derived-36mwvdy3f2.pdf

Multisensor historical climatology of satellite-derived global land surface moisture

Within the scope of the upcoming launch of a new water related satellite mission (SMOS) a global evaluation study was performed on two available global soil moisture products. ERS scatterometer surface wetness data was compared to AMSR-E soil moisture data. This study pointed out a strong similarity between both products in sparse to moderate vegetated regions with an average correlation coefficient of 0.83. Low correlations were found in densely vegetated areas and deserts. The low values in the vegetated regions can be explained by the limited soil moisture retrieval capabilities over dense vegetation covers. Soil emission is attenuated by the canopy and tends to saturate the microwave signal with increasing vegetation density, resulting in a decreased sensor sensitivity to soil moisture variations. It is expected that the new low frequency satellite mission (SMOS) will obtain soil moisture products with a higher quality in these regions. The low correlations in the desert regions are likely due to volume scattering or to the dielectric dynamics within the soil. The volume scattering in dry soils causes a higher backscatter under very dry conditions than under conditions when the sub-surface soil layers are somewhat wet. In addition, at low moisture levels the dielectric constant has a reduced sensitivity in response to changes in the soil moisture content. At a global scale the spatial correspondence of both products is high and both products clearly distinguish similar regions with high seasonal and inter annual variations. Based on the global analyses we concluded that the quality of both products was comparable and in the sparse to moderate vegetated regions both products may be beneficial for large scale validation of SMOS soil moisture. Some limitations of the studied products are different, pointing to significant potential for combining both products into one superior soil moisture data set.

https://link.springer.com/content/pdf/10.1007%2Fs10712-008-9044-0.pdf

Global Soil Moisture Patterns Observed by Space Borne Microwave Radiometers and Scatterometers

https://people.eng.unimelb.edu.au/jwalker/papers/awr01.pdf

One-dimensional soil moisture profile retrieval by assimilation of near-surface observations: a comparison of retrieval algorithms

In order to assess the retrieval of soil moisture from ERS 1 (European Remote Sensing Satellite) synthetic aperture radar (SAR) data, an inversion procedure based oft the integral equation model (IEM) [Fung et al, 1992] is developed First, the IEM is used to analyze the sensitivity of radar echoes (in terms of the backscattering coefficient σ0) to the surface parameters (roughness and dielectric constant) under ERS 1 SAR configuration Results obtained for random rough bare soil fields show that the effect of surface roughness is very strong, particularly in the case of smooth surfaces, and that the sensitivity of σ0 to dielectric constant is independent of the radar configuration and the roughness conditions This means that the range of variation of backscattering with respect to the dielectric constant variation of dry to wet soil remains the same (about 5 dB) for any roughness condition and radar configuration The possibility of applying the inversion procedure to retrieve soil moisture is investigated using a set of data collected in a test site situated near Naples, Italy, during the SeIe Synthetic Aperture Radar experiment (SESAR) campaign (November 1993) Simultaneous with ERS 1 overpasses, dielectric constant and roughness measurements were taken over two flat bare fields From this analysis it is found that the inversion of backscattering from ERS 1 SAR into soil moisture is not reliable without accurate information on roughness if the surface is smooth In this case it is observed that the sensitivity to the roughness parameters is much higher than the sensitivity to dielectric constant, so that even a small error in the measurement of this parameter can affect the retrieved value of soil moisture significantly The inversion procedure provides more reliable soil moisture estimates when surfaces rougher than those analyzed in the field experiment are considered

Retrieving Soil Moisture Over Bare Soil from ERS 1 Synthetic Aperture Radar Data: Sensitivity Analysis Based on a Theoretical Surface Scattering Model and Field Data

[1] Remotely sensed soil moisture studies have mainly focused on retrievals using active and passive microwave (MW) sensors, which provide measurements that are directly related to soil moisture (SM). MW sensors have obvious advantages such as the ability to retrieve through nonprecipitating cloud cover which provides shorter repeat cycles. However, MW sensors offer coarse spatial resolution and suffer from reduced retrieval skill over moderate to dense vegetation. A unique avenue for filling these information gaps is to exploit the retrieval of SM from thermal infrared (TIR) observations, which can provide SM information under vegetation cover and at significantly higher resolutions than MW. Previously, an intercomparison of TIR-based and MW-based SM has not been investigated in the literature. Here a series of analyses are proposed to study relationships between SM products during a multiyear period (2003–2008) from a passive MW retrieval (AMSR-E), a TIR based model (ALEXI), and a land surface model (Noah) over the continental United States. The three analyses used in this study include (1) a spatial anomaly correlation analysis, (2) a temporal correlation analysis, and (3) a triple collocation error estimation technique. In general, the intercomparison shows that the TIR and MW methods provide complementary information about the current SM state. TIR can provide SM information over moderate to dense vegetation, a large information gap in current MW methods, while serving as an additional independent source of SM information over low to moderate vegetation. The complementary nature of SM information from MW and TIR sensors implies a potential for integration within an advanced SM data assimilation system.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2011JD015633

An intercomparison of available soil moisture estimates from thermal infrared and passive microwave remote sensing and land surface modeling

Four major objectives are proposed: (1) to study the sensitivity of active and passive microwave remote sensing approaches to soil moisture variations; (2) to investigate the effect of vegetation cover on microwave backscatter and emission; (3) to test theoretical models of microwave backscatter and emission from a natural terrain against the observations obtained from SIR-B and aircraft radiometer flights; and (4) to estimate vegetation biomass with airborne visible and infrared sensors.

P. Cuddapah

Papers

Remote sensing of soil moisture