Soil moisture at high spatial resolution is required for various land processes related studies. However, currently the resolution of passive microwave retrieved soil moisture is low. To solve this problem, a soil moisture disaggregation algorithm based on thermal inertia relationship between daily temperature change and average soil moisture modulated by vegetation conditions has been formulated. This algorithm was applied to the SMAP (Soil Moisture Active/Passive) to produce the 1 km downscaled soil moisture over the SMAPVEX15 (SMAP Validation Experiment 2015). The disaggregated soil moisture has been compared to in situ observations and the results of this approach are very encouraging.

Soil water content is a basic hydrologic state variable. It is used in applications that range in scale from irrigation management to flood forecasting. Soil water content has also increased in importance in weather forecasting and climate modeling, as these disciplines have begun to recognize its importance in large-scale water cycles.

The National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) satellite mission was launched on January 31, 2015. The observatory was developed to provide global mapping of high-resolution soil moisture and freeze-thaw state every two to three days using an L-band (active) radar and an L-band (passive) radiometer. After an irrecoverable hardware failure of the radar on July 7, 2015, the radiometer-only soil moisture product became the only operational soil moisture product for SMAP. The product provides soil moisture estimates posted on a 36 km Earth-fixed grid produced using brightness temperature observations from descending passes. Within months after the commissioning of the SMAP radiometer, the product was assessed to have attained preliminary (beta) science quality, and data were released to the public for evaluation in September 2015. The product is available from the NASA Distributed Active Archive Center at the National Snow and Ice Data Center. This paper provides a summary of the Level 2 Passive Soil Moisture Product (L2_SM_P) and its validation against in situ ground measurements collected from different data sources. Initial in situ comparisons conducted between March 31, 2015 and October 26, 2015, at a limited number of core validation sites (CVSs) and several hundred sparse network points, indicate that the V-pol Single Channel Algorithm (SCA-V) currently delivers the best performance among algorithms considered for L2_SM_P, based on several metrics. The accuracy of the soil moisture retrievals averaged over the CVSs was 0.038 m 3 /m 3 unbiased root-mean-square difference (ubRMSD), which approaches the SMAP mission requirement of 0.040 m 3 /m 3 .

Validation is important for any satellite-based remote sensing products. In this paper, in situ soil moisture observations from 38 stations over the 1 °×1 ° domain from the central Tibetan Plateau Soil Moisture/Temperature Monitoring Network (CTP-SMTMN) and 152 stations from the Soil Climate Analysis Network (SCAN) over continental U.S. are used to determine the reliability of Aquarius level-2 soil moisture products. Evaluation of the time series in CTP-SMTMN shows good performances of the products to capture surface soil moisture annual cycle with the correlation coefficient of 0.767 and RMSD of 0.078m 3m-3. The evaluation results in SCAN suggest that the average correlation is 0.58 and 71.83% sites have correlation larger than 0.5 but differences are observed over many other sites and need to be addressed. The evaluation results also show that the retrieval results performed better for descending orbits (6 AM overpass time).

Soil Moisture Active Passive (SMAP), a proposed mission in support of the Earth Science Decadal Survey, conducted afield campaign in June 2010 to support algorithm development. As part of the experiment in situ soil moisture measurements were made over a two week period in which multiple UAVSAR flights were conducted. Repeat-pass polarimetric-interferometric data generated from these flights were analyzed to see if phase changes could be correlated with soil moisture changes. Also, we compared the data to that predicted by simple surface scattering models and showed moderate agreement with the Oh model [4].

Coincidental airborne brightness temperature (TB) and normalized radar-cross section (NRCS) measurements were carried out with the PALS (Passive and Active L- and S-band) instrument in the SMAPVEX08 (SMAP Validation Experiment 2008) field campaign. This paper describes results obtained from a set of flights which measured a field in 45(sup o) steps over the azimuth angle. The field contained mature soy beans with distinct row structure. The measurement shows that both TB and NRCS experience modulation effects over the azimuth as expected based on the theory. The result is useful in development and validation of land surface parameter forward models and retrieval algorithms, such as the soil moisture algorithm for NASA's SMAP (Soil Moisture Active and Passive) mission. Although the footprint of the SMAP will not be sensitive to the small resolution scale effects as the one presented in this paper, it is nevertheless important to understand the effects at smaller scale.

This paper discusses the importance of the proper characterization of scattering and attenuation in trees needed for accurate retrieval of soil moisture in the presence of trees. Emphasis is placed on determining an accurate estimation of the propagation properties of a vegetation canopy using the complex frequency correlation function (FCF). A new technique for determining the canopy attenuation that uses the measured stepped frequency radar backscatter response is proposed. It makes use of the details found in a transient solution where the canopy (volume scattering) and the tree-ground (double interaction) effects appear at different times. The proposed technique is based on separating the backscattering sources within a forest canopy in the time response. The technique has been used with L band data collected over deciduous trees to verify that the algorithm results match the simulated data.

We present the WindSat land algorithm that retrieves global soil moisture and vegetation water content simultaneously using the physically-based multi-channel maximum-likelihood estimation. The retrieval results agree well with soil moisture climatology, in-situ observations, precipitation patterns and the AVHRR vegetation data, potentially satisfying soil moisture science requirements of 6% under low to moderate vegetation conditions.