Initial soil moisture retrievals from the METOP‐A Advanced Scatterometer (ASCAT)

Abstract

[1] This article presents first results of deriving relative surface soil moisture from the METOP-A Advanced Scatterometer. Retrieval is based on a change detection approach which has originally been developed for the Active Microwave Instrument flown onboard the European satellites ERS-1 and ERS-2. Using model parameters derived from eight years of ERS scatterometer data, first global soil moisture maps have been produced from ASCAT data. The ASCAT data were distributed by EUMETSAT for validation purposes during the ASCAT product commissioning activities. Several recent cases of drought and excessive rainfall are clearly visible in the soil moisture data. The results confirm that seamless soil moisture time series can be expected from the series of two ERS and three METOP scatterometers, providing global coverage on decadal time scales (from 1991 to about 2021). Thereby, operational, nearreal-time ASCAT soil moisture products will become available for weather prediction and hydrometeorological applications.

Microwave remote sensing of short‐term droughts during crop growing seasons

“…For example, land surface models can produce substantially different drought classifications even when forced with identical meteorological conditions [Mo and Lettenmaier, 2014]. In contrast, the microwave remote sensing of soil moisture not only has global coverage [Bartalis et al, 2007]. Although the sensing depth of satellite data is only a few centimeters, there is generally a close relationship between surface soil moisture and soil moisture in the upper 10 cm [Albergel et al, 2008], which is the depth of the first soil layer for most land surface models.…”

“…As an important short-term drought indicator, soil moisture can be measured at in situ stations [Robock et al, 2000] or retrieved from satellite remote sensing [Bartalis et al, 2007], and it can also be reconstructed by land surface modeling [Wang et al, 2011]. The in situ measurements have the best estimation at local scale, but they have deficiency in representing the soil moisture dynamics and thus drought at large scale due to insufficient data coverage.…”

Improving soil moisture and runoff simulations over Europe using a high-resolution data-assimilation modeling framework

“…For the study period of 2000 to 2006, the CCI-SM data are based on passive microwave observations (i.e. DMSP SSM/I, TRMM TMI, Aqua AMSR-E and Coriolis WindSat; Owe et al, 2008), whereas the active data products are based on observations from the C-band scatterometers on board of the ERS-1 and ERS-2 (Wagner et al, 2013;Bartalis et al, 2007) satellites. In this product, the absolute soil moisture was re-scaled against the 0.25° land surface modeling soil moisture (GLDAS-NOAH, Rodell et al, 2004) using cumulative density function matching.…”

Downscaling soil moisture over regions that include multiple coarse-resolution grid cells

“…Active microwave sensing has also been used to estimate soil moisture. In particular, the Advanced Scatterometer (ASCAT) produces backscatter measurements from transmitted linear frequency-modulated pulses (C-band) to estimate global soil moisture at a 25 km resolution [Bartalis et al, 2007]. The Soil Moisture Active and Passive (SMAP) mission combines active and passive microwave sensing to obtain 9 km resolution global soil moisture, but currently only the passive radiometer is operational [Das et al, 2011, Entekhabi et al, 2010.…”