Seasonal Evaluation of SMAP Soil Moisture in the U.S. Corn Belt

Abstract

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 (R 2 ) of the DCA in every month. However, some monthly dry biases increase to more than 0.10 m 3 m −3 . The single-scattering albedo, ω, has a significant impact on soil moisture retrieval. While the DCA has its lowest ubRMSE and highest R 2 when ω is non-zero, the SCA have their lowest ubRMSE and highest R 2 when ω = 0, and the dry bias of all algorithms increases as ω 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.

Quantitative Assessment of Satellite L-Band Vegetation Optical Depth in the U.S. Corn Belt

“…1 were sampled each measurement day. In addition, 23 separate fields were sampled, more than the 20 soil moisture sites used to determine pixel-scale South Fork soil moisture [29]. We do not know of a more extensive set of in situ crop plant water measurements.…”

“…3) SMAP-Reprocessed: SMAP-r was generated using SMAP Level 1 brightness temperature but with an effective surface temperature colder than SMAP-v2 that better matches in situ soil temperature in the South Fork [29].…”

Dielectric database of organic Arctic soils (DDOAS)

“…The permittivity model of soils is an essential element in the physical-based algorithms of soil moisture retrieval with using remote sensing data of the current radiometric satellites. Currently, Mironov's models (Mironov et al, 2009(Mironov et al, , 2012 of mineral soils used in current SMAP (Walker et al, 2019) and SMOS (Wigneron et al, 2017) physical-based retrievals algorithms. For the reason that, surface horizons of Arctic land cover represents organic-rich soils, the structural characteristics of which are differing from the ones of mineral soils, the error of soil moisture retrievals in Northern regions is substantially higher than for moderate latitudes (Al-Yaari et al, 2017;Wrona et al, 2017).…”

Using SMAP and SMOS vegetation optical depth to measure crop water in vegetation

“…SMAP v2 uses the assumption that T soil ≈ T veg so that T eff can be used as the approximate temperature for the entire surface. Walker et al (2019) found that this is not the case and that SMAP v2 T eff values are too warm. What will be referred to as SMAP repr in this chapter is a reprocessed SMAP v2 that makes the changes suggested in Walker et al (2019).…”

“…SMAP VOD was adjusted to nadir by taking the original SMAP DCA VOD value and multiplying by the cosine of 40 degrees for comparison to SMOS VOD. A reprocessed SMAP product is also used that was generated at Iowa State University (Walker et al, 2019). This product takes the original SMAP v2…”