The impact of land surface temperature on soil moisture anomaly detection from passive microwave observations

Abstract

Abstract. For several years passive microwave observations have been used to retrieve soil moisture from the Earth's surface. Low frequency observations have the most sensitivity to soil moisture, therefore the current Soil Moisture and Ocean Salinity (SMOS) and future Soil Moisture Active and Passive (SMAP) satellite missions observe the Earth's surface in the L-band frequency. In the past, several satellite sensors such as the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) and WindSat have been used to retrieve surface soil moisture using multi-channel observations obtained at higher microwave frequencies. While AMSR-E and WindSat lack an L-band channel, they are able to leverage multi-channel microwave observations to estimate additional land surface parameters. In particular, the availability of Ka-band observations allows AMSR-E and WindSat to obtain coincident surface temperature estimates required for the retrieval of surface soil moisture. In contrast, SMOS and SMAP carry only a single frequency radiometer and therefore lack an instrument suited to estimate the physical temperature of the Earth. Instead, soil moisture algorithms from these new generation satellites rely on ancillary sources of surface temperature (e.g. re-analysis or near real time data from weather prediction centres). A consequence of relying on such ancillary data is the need for temporal and spatial interpolation, which may introduce uncertainties. Here, two newly-developed, large-scale soil moisture evaluation techniques, the triple collocation (TC) approach and the R value data assimilation approach, are applied to quantify the Correspondence to: R. M. Parinussa (robert.parinussa@falw.vu.nl) global-scale impact of replacing Ka-band based surface temperature retrievals with Modern Era Retrospective-analysis for Research and Applications (MERRA) surface temperature output on the accuracy of WindSat and AMSR-E based surface soil moisture retrievals. Results demonstrate that under sparsely vegetated conditions, the use of MERRA land surface temperature instead of Ka-band radiometric land surface temperature leads to a relative decrease in skill (on average 9.7 %) of soil moisture anomaly estimates. However the situation is reversed for highly vegetated conditions where soil moisture anomaly estimates show a relative increase in skill (on average 13.7 %) when using MERRA land surface temperature. In addition, a pre-processing technique to shift phase of the modelled surface temperature is shown to generally enhance the value of MERRA surface temperature estimates for soil moisture retrieval. Finally, a very high correlation (R 2 = 0.95) and consistency between the two evaluation techniques lends further credibility to the obtained results.

Behavior of multitemporal and multisensor passive microwave indices in Southern Hemisphere ecosystems

“…Many contributing factors (e.g., soil moisture, superficial profile of soil temperature, and phenology) are required to explain the behavior of the two passive microwave indices in Alice Springs and Sturt Plains. In particular, in these sparse vegetated sites, the observed radiation was not simply produced at the air/soil interface, and FI variations can be explained by variations of temperature profile, in agreement with previous works [Parinussa et al, 2011;Norouzi et al, 2012].…”

A Quasi-Global Approach to Improve Day-Time Satellite Surface Soil Moisture Anomalies through the Land Surface Temperature Input

“…A consequence is that the existing linear regression is extrapolated outside its calibrated range, resulting in LST errors which manifest themselves in the soil moisture algorithm. A previous study [7] demonstrated this, and showed that refining this LST regression can lead to enhanced soil moisture anomalies with significantly larger improvements possible for the day-time over the night-time observations. In contrast with the degrading impact of T s and T c differences, it was suggested that higher LST observed during the day-time may result in a higher transparency of the vegetation, allowing for better penetration of the soil moisture emission through the canopy.…”

“…The R value verification technique (Section 3.2.1) is based on the connection between precipitation and the subsequent changes in soil moisture and therefore requires precipitation data as an input [7,16]. The precipitation product that was used here is the Tropical Rainfall Monitoring Mission (TRMM) 3B42 (v7) product.…”

Towards an integrated soil moisture drought monitor for East Africa

“…TCA is a statistical method for characterizing consensus and discrepancies across multiple independent datasets. Though developed originally for oceanographic applications (Stoffelen, 1998), the method has recently been applied successfully to the problem of estimating soil moisture variability at regional to global scale Hain et al, 2011;Parinussa et al, 2011;Yilmaz et al, 2012). TCA is of particular value in regions that lack in situ soil moisture monitoring networks, as consensus anomaly estimates derived from multiple independent datasets can be interpreted as a measure of confidence in the absence of adequate in situ evaluation data.…”