Soil moisture affects the spatial variation of land–atmosphere interactions through its influence on the balance of latent and sensible heat fluxes. Wetter soils are more prone to flooding because a smaller fraction of rainfall can infiltrate into the soil. The Soil Moisture Ocean Salinity (SMOS) satellite carries a remote sensing instrument able to make estimates of near-surface soil moisture on a global scale. One way to validate satellite observations is by comparing them with observations made with sparse networks of in situ soil moisture sensors that match the extent of satellite footprints. The rate of soil drying after significant rainfall observed by SMOS is found to be higher than the rate observed by a U.S. Department of Agriculture (USDA) soil moisture network in the watershed of the South Fork Iowa River. This leads to the conclusion that SMOS and the network observe different layers of the soil: SMOS observes a layer of soil at the soil surface that is a few centimeters thick, while the network observes a deeper soil layer centered at the depth at which the in situ soil moisture sensors are buried. It is also found that SMOS near-surface soil moisture is drier than the South Fork network soil moisture, on average. The conclusion that SMOS and the network observe different layers of the soil, and therefore different soil moisture dynamics, cannot explain the dry bias. However, it can account for some of the root-mean-square error in the relationship. In addition, SMOS observations are noisier than the network observations.

NASA’s Soil Moisture Active Passive (SMAP) Level 2 soil moisture products are not meeting mission goals in the U.S. Corn Belt according to our seasonal evaluation conducted at a SMAP Core Validation Site in central Iowa. The single-channel algorithm (SCA) soil moisture products are too dry in early spring and late fall before and after crops are present, and too noisy in late spring and early summer when crops begin to grow. We investigated likely contributing factors. The climatology of vegetation’s effect on soil moisture retrieval in the SCA can differ by more than 14 days from what is retrieved by SMAP’s dual-channel algorithm (DCA). Soil and vegetation temperatures, assumed to be equal by all retrieval algorithms, are not: vegetation is about 2 K colder at 6:00 a.m. and about 2 K warmer at 6:00 p.m.. The effective temperature in version 2 products is too warm as compared to in situ soil temperatures. We propose a new effective temperature model that is consistent with observations, decreases the unbiased root-mean-square-error (ubRMSE) overall, and increases the coefficient of determination (R²) of the DCA in every month. However, some monthly dry biases increase to more than 0.10 m³ m⁻³. The single-scattering albedo, $\omega$ , has a significant impact on soil moisture retrieval. While the DCA has its lowest ubRMSE and highest R² when $\omega$ is non-zero, the SCA have their lowest ubRMSE and highest R² when $\omega =0$ , and the dry bias of all algorithms increases as $\omega$ increases. Errors in soil texture are not significant, but soil surface roughness should not be static and have a higher overall value. Our findings make it clear that a new retrieval algorithm that can account for changing soil roughness and vegetation conditions is needed.

Microwave-based soil moisture remote sensing is impacted by the overlying vegetation layer, which is typically characterized by its vegetation water content (VWC).¹ Agricultural regions in particular have a large seasonal dynamic for vegetation biomass and water content as compared to nonagricultural regions. The SMAP passive algorithm products have been validated and satisfy the accuracy requirements of the mission for vegetation with $\mathrm{VWC}<5.0{\mathrm{kg}/\mathrm{m}}^2$.² 3. ^{– 4} The operational algorithms use a long-term average (climatological) vegetation parameterization based on MODIS data in order to facilitate a low latency in the soil moisture estimate.⁵ This approach introduces some uncertainty if the actual vegetation conditions deviate from the climatology. Previous experiments have addressed the vegetation parameterization by conducting physical sampling to provide accurate estimates of vegetation conditions at the time of the experiment. For instance, the Soil Moisture Experiments in 2002, SMEX02, established equations for corn and soybeans for central Iowa during the field campaign in early June of 2002.⁶ Follow-up experiments furthered this analysis for short time periods of intensive observation periods,⁷ 8. ^{– 9} but most focused only on a limited two- to three-week period of time and did not include peak biomass conditions of agricultural crops. These methodologies usually take advantage of high-resolution optical datasets such as Landsat, to produce 30 m vegetation index maps. From these maps, relationships can be developed to estimate VWC. In sparsely vegetated regions, the normalized difference vegetation index (NDVI) can be used effectively. For more densely vegetated regions, such as in the U.S. Corn Belt, a more resilient index is necessary. Reference 6 demonstrated that the normalized difference water index (NDWI) in central Iowa is a suitable measure for dense vegetation, especially corn. However, that study did not have the peak biomass conditions that can be observed during the reproductive stages of corn and soybeans.