The SMOS Soil Moisture Retrieval Algorithm

Abstract

International audienceThe Soil Moisture and Ocean Salinity (SMOS) mission is European Space Agency (ESA's) second Earth Explorer Opportunity mission, launched in November 2009. It is a joint program between ESA Centre National d'Etudes Spatiales (CNES) and Centro para el Desarrollo Tecnologico Industrial. SMOS carries a single payload, an L-Band 2-D interferometric radiometer in the 1400-1427 MHz protected band. This wavelength penetrates well through the atmosphere, and hence the instrument probes the earth surface emissivity. Surface emissivity can then be related to the moisture content in the first few centimeters of soil, and, after some surface roughness and temperature corrections, to the sea surface salinity over ocean. The goal of the level 2 algorithm is thus to deliver global soil moisture (SM) maps with a desired accuracy of 0.04 m3/m3. To reach this goal, a retrieval algorithm was developed and implemented in the ground segment which processes level 1 to level 2 data. Level 1 consists mainly of angular brightness temperatures (TB), while level 2 consists of geophysical products in swath mode, i.e., as acquired by the sensor during a half orbit from pole to pole. In this context, a group of institutes prepared the SMOS algorithm theoretical basis documents to be used to produce the operational algorithm. The principle of the SM retrieval algorithm is based on an iterative approach which aims at minimizing a cost function. The main component of the cost function is given by the sum of the squared weighted differences between measured and modeled TB data, for a variety of incidence angles. The algorithm finds the best set of the parameters, e.g., SM and vegetation characteristics, which drive the direct TB model and minimizes the cost function. The end user Level 2 SM product contains SM, vegetation opacity, and estimated dielectric constant of any surface, TB computed at 42.5$^{circ}$, flags and quality indices, and other parameters o- interest. This paper gives an overview of the algorithm, discusses the caveats, and provides a glimpse of the Cal Val exercises

Influence of meter-scale wind-formed features on the variability of the microwave brightness temperature around Dome C in Antarctica

“…This assumption is also required when emission models are run using snow properties measured in one point and results are compared to satellite data (Macelloni et al, 2006;Brucker et al, 2011). Advanced approaches for dealing with the subpixel heterogeneity, like unmixing (e.g., Painter et al, 2009), have not yet been considered in Antarctica for passive microwave remote sensing as opposed to other continental surfaces (Kerr et al, 2012).…”

MAPSM: A Spatio-Temporal Algorithm for Merging Soil Moisture from Active and Passive Microwave Remote Sensing

“…The average rainfall provided by the Indian Meteorological Department over the period from 2010 to 2013 is approximately 800 mm. Figure 2 shows the spatial distribution of the nominal (bare soil and low vegetation) and forest land cover used in the SMOS Level 2 soil moisture retrieval algorithm [5]. The Western part of the study area is covered by the forests of the Bandipur National park.…”

“…The mean antenna footprint for SMOS has a value of 1 in the center of SMOS pixel, and decreases with distance based on the antenna pattern. It has a value of around 0.5 at a distance of 20 km [5]. The time variant WCC combines two information that need to be identified: the direction (drying/wetting) and the magnitude of change.…”

“…Several studies have demonstrated soil moisture estimation from space using passive microwave (e.g., [2][3][4][5]) and active microwave (scatterometer) (e.g., [4,6,7]) at a coarse (25-150 km) spatial resolution. On the other hand, active microwave SAR data have been successfully used to retrieve the soil moisture at a finer (less than 100 m) spatial resolution but with a coarse (approximately 3 weeks) temporal resolution [8][9][10][11].…”

Intercomparison of Soil Moisture Retrievals From In Situ, ASAR, and ECV SM Data Sets Over Different European Sites

“…Since the late 1970s, coarse resolution (25 -50km) soil moisture products derived from past and present microwave radiometers (Advanced Microwave Scanning Radiometer (AMSR-E) (Njoku et al, 2003) and WindSat (Li et al, 2010)) and scatterometers (European Remote Sensing satellites (ERS) scatterometer (SCAT) (Wagner a (Bartalis et al, 2007;Naeimi et al, 2009)) have been available on an operational basis. Data from the European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) (Kerr et al, 2012;Mecklenburg et al, 2012) and the National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) (Entekhabi et al, 2010) dedicated soil moisture missions are strengthening this record of observations and further facilitating the study of long term soil moisture behaviour (please see Petropoulos et al (2015) and Zeng et al (2015) for further details of available satellite derived soil moisture products). With the availability of these products, it is necessary to validate them using independently derived soil moisture observations obtained through in-situ monitoring, models, or with different satellite sensors (van Doninck et al, 2012;Ochsner et al, 2013;Al-Yaari et al, 2014).…”