A dynamic approach for evaluating coarse scale satellite soil moisture products

160

125

Abstract. Currently, no extensive, near real time, global soil moisture observation network exists. Therefore, the Met Office global soil moisture analysis scheme has instead used observations of screen temperature and humidity. A number of new space-borne remote sensing systems, operating at microwave frequencies, have been developed that provide a more direct retrieval of surface soil moisture. These systems are attractive since they provide global data coverage and the horizontal resolution is similar to weather forecasting models. Several studies show that measurements of normalised backscatter (surface soil wetness) from the Advanced Scatterometer (ASCAT) on the meteorological operational (MetOp) satellite contain good quality information about surface soil moisture. This study describes methods to convert ASCAT surface soil wetness measurements to volumetric surface soil moisture together with bias correction and quality control. A computationally efficient nudging scheme is used to assimilate the ASCAT volumetric surface soil moisture data into the Met Office global soil moisture analysis. This ASCAT nudging scheme works alongside a soil moisture nudging scheme that uses observations of screen temperature and humidity. Trials, using the Met Office global Unified Model, of the ASCAT nudging scheme show a positive impact on forecasts of screen temperature and humidity for the tropics, North America and Australia. A comparison with in-situ soil moisture measurements from the US also indicates that assimilation of ASCAT surface soil wetness improves the soil moisture analysis. Assimilation of ASCAT surface soil wetness measurements became operational during July 2010.

Section: Comparison Of Model With In-situ Soil Moisture Measurementssupporting

confidence: 71%

Operational assimilation of ASCAT surface soil wetness at the Met Office

Dharssi

Bovis

Macpherson

et al. 2011

160

125

“…8, is that daytime observations from both satellites become of higher quality when the vegetation density increases compared to the night-time observations over the same areas. Several studies (Loew and Schlenz, 2011;Brocca et al, 2011) indicated this already, but none of them explained this phenomenon. One possible explanation is that the vegetation water content during the day decreases due to transpiration induced by photosynthesis, making the vegetation more transparent to microwave emission, and consequently increasing the sensitivity to the underlying soil moisture signal.…”

Section: Ka-band Scenariosmentioning

confidence: 99%

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

Parinussa

Holmes

Yılmaz

et al. 2011

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.

“…These effects can give rise to temporal autocorrelation in errors and undermine the linearity assumption between coincident measures. Finally, the non-stationary characteristic of noise in satellite SM (Loew and Schlenz , 2011;Zwieback et al, 2013;Su et al, 2014a) due to e.g. dynamical land surface characteristics such as soil moisture (Su et al, 2014b), is not treated here.…”

Section: Multi-scale Decomposition Of Soil Moisturementioning

confidence: 99%

“…One possible remedy is to apply bias correction, either TC or statistical-moment matching, only to anomaly time series (Miralles et al, 2011;Liu et al, 2012;Su et al, 2014a), but it remains unclear how these methods affect the signal and noise components in the corrected data. Alternatively a moving time window can be used to examine the time-varying statistics of time series (Loew and Schlenz , 2011;Zwieback et al, 2013;Su et al, 2014a).…”

Section: Introductionmentioning

confidence: 99%

Multi-scale analysis of bias correction of soil moisture

Ryu

2015

Abstract. Remote sensing, in situ networks and models are now providing unprecedented information for environmental monitoring. To conjunctively use multi-source data nominally representing an identical variable, one must resolve biases existing between these disparate sources, and the characteristics of the biases can be non-trivial due to spatiotemporal variability of the target variable, inter-sensor differences with variable measurement supports. One such example is of soil moisture (SM) monitoring. Triple collocation (TC) based bias correction is a powerful statistical method that is increasingly being used to address this issue, but is only applicable to the linear regime, whereas the non-linear method of statistical moment matching is susceptible to unintended biases originating from measurement error. Since different physical processes that influence SM dynamics may be distinguishable by their characteristic spatio-temporal scales, we propose a multi-timescale linear bias model in the framework of a wavelet-based multi-resolution analysis (MRA). The joint MRA-TC analysis was applied to demonstrate scale-dependent biases between in situ, remotely sensed and modelled SM, the influence of various prospective bias correction schemes on these biases, and lastly to enable multiscale bias correction and data-adaptive, non-linear de-noising via wavelet thresholding.