Fusion of Active and Passive Microwave Observations to Create an Essential Climate Variable Data Record on Soil Moisture

Abstract

Abstract. Soil moisture was recently included in the list of Essential Climate Variables (ECVs) that are deemed essential for IPCC (Intergovernmental Panel on Climate Change) and UNFCCC (United Nations Framework Convention on Climate Change) needs and considered feasible for global observation. ECVs data records should be as long, complete and consistent as possible, and in the case of soil moisture this means that the data record shall be based on multiple data sources, including but not limited to active (scatterometer) and passive (radiometer) microwave observations acquired preferably in the low-frequency microwave range. Among the list of sensors that can be used for this task are the C-band scatterometers on board of the ERS and METOP satellites and the multi-frequency radiometers SMMR, SSM/I, TMI, AMSR-E, and Windsat. Together, these sensors already cover a time period of more than 30 years and the question is how can observations acquired by these sensors be merged to create one consistent data record? This paper discusses on a high-level possible approaches for fusing the individual satellite data. It is argued that the best possible approach for the fusion of the different satellite data sets is to merge Level 2 soil moisture data derived from the individual satellite data records. This approach has already been demonstrated within the WACMOS project (http://wacmos.itc.nl/) funded by European Space Agency (ESA) and will be further improved within the Climate Change Initiative (CCI) programme of ESA (http://www.esa-cci.org/).

A multi-sourced assessment of the spatiotemporal dynamics of soil moisture in the MARINE flash flood model

“…The SMOS-IC product provides daily SSM at 25 km resolution (Fernandez-Moran et al, 2017). The SMOS-IC soil moistures are derived from the SMOS satellite data, based on the algorithm presented by Wigneron et al (2007). This method uses the new calibrated values of the soil roughness and effective scattering albedo parameters presented by Li et al (2020).…”

“…Estimation of the root-zone soil moisture from intermittent remotely sensed surface data has focused on the assimilation of such data into land surface models. Many studies now also suggest that constraining those land surface models using various types of earth observations, including vegetation-related earth observations, may lead to a better representation of the root-zone soil moisture (Bolten et al, 2009;Pezij et al, 2019;Wagner et al, 2012). In addition, simplified approaches (e.g., Soil Water Index) have also been developed for obtaining root-zone soil moisture.…”

Review on "Aerosol optical depth trend over the Middle East"

“…A long-term global surface soil moisture (SSM) data record based on satellite mounted active and passive microwave sensors is provided by the European Space Agency Climate Initiative (ESA-CCI) (Liu et al, 2012(Liu et al, , 2011Wagner et al, 2012;ESA-CCI soil moisture, 2015). We use the COM-BINED data set which covers a time period up to and including the year 2013.…”

“…In addition, it introduces a merged AOD product, combining retrievals from the Deep Blue and Dark Target algorithms to produce a consistent data set covering a multitude of surface types ranging from oceans to bright deserts. A crucial aspect is the problem of spurious AOD trends due to instrumental drift found in earlier MODIS collections (Wang et al, 2012;Zhang and Reid, 2010;Levy et al, 2010), which has been addressed recently (Levy et al, 2013). Another difference to previous studies is the extended time period considered: to verify the persistence of the AOD increase, we take MODIS data up to 2015 into account.…”

Partitioning of Historical Precipitation Into Evaporation and Runoff Based on Hydrologic Dynamics Identified With Recent SMAP Satellite Measurements

“…First, the variational estimation approach directly assimilates TB observations into a water-balanced constrained objective function; therefore, in addition to the ET and drainage function parameters, soil moisture estimates are also provided. Therefore, long-term harmonized soil moisture products and data sets such as the ESA Climate Change Initiative (CCI) (Dorigo et al, 2015;Wagner et al, 2012) are not inputs. Furthermore, to the best our knowledge, no harmonized SMAP-SMOS TB data products exists which can be leveraged.…”