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Seminar Final Report

Monella Stefanía

2013

Soil Moisture Estimation Using Radiometers

Seminar Final Report Monella, Stefanía

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Table of Contents

Abstract ................................................................................................................... 2

1. Introduction ....................................................................................................... 3

2. Current and Near Future Missions ...................................................................... 4

3. Basic Principles of Radiometers ........................................................................ 11

3.1 Water in soils ...................................................................................................... 11

3.2 Soil Dielectric Properties .................................................................................... 12

3.3 Surface Roughness .............................................................................................. 14

3.4 Vegetation Effects .............................................................................................. 15

4. Surface Emission Model ................................................................................... 16

4.1 Soil Emission Model ............................................................................................ 16 4.2 Vegetation Cover Emission Model...................................................................... 16

5. Soil Moisture Retrieval ..................................................................................... 18

6. Latest Works on SMC ....................................................................................... 19

6.1 SMOS ................................................................................................................... 19 6.2 AMSR-E ............................................................................................................... 21 6.3 International Soil Moisture Network .................................................................. 22

7. Conclusion........................................................................................................ 24

8. References ....................................................................................................... 25


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Abstract

Surface soil moisture is one of the crucial variables in hydrological processes, which influences the exchange of water and energy fluxes at the land surface/atmosphere interface. Accurate estimate of the spatial and temporal variations of soil moisture is critical for numerous environmental studies, such as weather and climate forecasting skill, flood prediction and drought monitoring. Recent technological advances in satellite remote sensing have shown that soil moisture can be measured by a variety of remote sensing techniques, each with its own strengths and weaknesses. This report presents a literature review focus on soil moisture estimation from radiometers, which are instruments that measure the radiation emitted by an object. The basic principles of the passive microwave techniques and surface emission models are summarized, with a brief description of the soil moisture retrievals methods. Review of current/future mission is also included in this report along with mentions of latest works in estimating soil moisture content.


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1. Introduction A key element to understand the nature of global change is the ability to model two-way interactions between land and atmosphere. Perhaps the most important role that the land surface component performs is the partitioning of incoming radiative energy into sensible and latent heat fluxes. Soil moisture is the second most important forcing function, second only to the sea surface temperature in the mid-latitudes, and it becomes the most important forcing function in the summer months (Dubois et al, 1995). Surface soil moisture is the water that is in the upper 10 cm of soil, whereas root zone soil moisture is the water that is available to plants, which is generally considered to be in the upper 200 cm of soil. Compared with the total amount of water on the global scale, this thin layer of soil water may seem insignificant; nonetheless, it is of fundamental importance to many hydrological, biological and biogeochemical processes. The role of soil moisture in the top 1 to 2 meters of the Earth’s surface has been widely recognized as an important variable in numerous environmental studies; therefore it is important to accurately monitor and estimated spatial and temporal variations of soil moisture. Direct observation of soil moisture is often somewhat difficult to measure accurately in both time and space, especially at large spatial scales. Soil moisture exhibits extremely high spatial variability on both the small and large scale, due to the variability of precipitation and the heterogeneity of the land surface (vegetation, soil physical properties, topography, etc.). While in situ sampling of soil moisture is generally thought to be the most accurate, such observations are representative only of a relatively small area immediately surrounding the sample location. Subsequent areal averaging of a few point measurements, especially at scales of 10 –10 km, will often introduce large errors (Engman, 1991). In this context, satellite imagery is a powerful tool that can provide accurate and repetitive spatial data. Microwave remote sensing provides a unique capability for soil moisture estimation by measuring the electromagnetic radiation in the microwave region between 0.5 and 100 cm. Previous research had shown that radiometers sensors can be used to monitor surface soil moisture over land surfaces (Ulaby et al., 1986; Schmugge and Jackson, 1994; Jackson et al., 1995; Wigneron et al., 2003). These sensors measure the intensity of microwave emission from the soil, which is proportional to the brightness temperature, a product of the surface temperature and emissivity. This observed emission is related to its moisture content because of the large contrast between the dielectric properties of water (~80) and soil particles (~5) (Moran et al., 2004).


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2. Current and near future missions

Special Sensor Microwave Imager (SSM/I)

The SSM/I is a seven-channel, four frequency, linearly-polarized, passive microwave radiometric system that flown on board the United States Air Force Defense Meteorological satellite Program (DMSP), launched in June 1987. The instrument measures atmospheric, ocean and terrain microwave brightness temperatures at 19.35, 22.235, 37.0 and 85.5 GHz. The data are used to obtain synoptic maps of critical atmospheric, oceanographic and selected land parameters on a global scale.

The soil moisture product is generated by Fleet Numerical Meteorology and Oceanography Center (FNMOC); gives the percentage of moisture for the land regions. Values vary from 0 to 70 mm with accuracy of 1mm1.

Figure 1: SSM/I Soil Moisture Product, North America1.

1 http://www.osdpd.noaa.gov/ml/spp/

http://www.osdpd.noaa.gov/ml/spp/


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Tropical Rainfall Measuring Mission (TRMM/TMI)

The Tropical Rainfall Measuring Mission (TRMM) was launched in November of 1997 and it is a join space mission between NASA and Japan’s National Space Agency (JAXA) designed to monitor and study tropical precipitation and the associated release of energy. The satellite carries onboard several remote sensors such as: Precipitation Radar (PR), Microwave Imager (TMI), Visible and Infrared radiometer Scanner (VIRS), Clouds and Earth’s Radiant Energy System (CERES) and Lightning Image System (LIS).

TMI is a microwave radiometer with multi-channels in dual-polarization including 10.7GHz, 19.4GHz, 37.0GHz and 85.5GHz, and 21.3GHz channel only in vertical polarization. The lower frequency data have been successfully used to retrieve land surface vegetation and soil moisture (fig.2).

Figure 2: TMI based soil moisture estimations over US for July 10, 1999 (Jackson et al., 2003).

Windsat

Is a satellite-based polarimetric microwave radiometer aboard the joint DoD/Navy platform

Coriolis, launched in January 2003 with a planned 3-year life. WindSat measures the ocean surface wind vector, as well as cloud liquid water, sea surface temperature, total precipitable water, and rain rate (over water only). Derived products include soil moisture


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and sea ice. The Navy, NOAA, and UK Met Office frequently use WindSat data in several operational forecast models2

.

The WindSat radiometer operates in discrete bands at 6.8, 10.7, 18.7, 23.8, and 37.0 GHz. The 10.7, 18.7, and 37.0 GHz channels are fully polarimetric. The 6.8 channel is dual-polarization (vertical and horizontal), and is more sensitive to sea surface temperature (SST) than to winds.

The first release of WindSat land data products includes the daily global data of the surface soil moisture (data range 0 – 0.5 cm3/cm3), land surface temperature, land classification and the observation time. The vegetation water content data will be included in the second release following completion of the preliminary validation. Meanwhile the vegetation data is available through special request for validation purpose3.

The following figure shows an example of the soil moisture derived product,

Figure 3: Global Soil Moisture Map using WindSat data4.

2 http://www.nrl.navy.mil/WindSat/index.php

3 http://geobrain.laits.gmu.edu/windsat/index.jsp

4 http://www.nrl.navy.mil/WindSat/images/SoilMoisture.jpeg

http://www.nrl.navy.mil/WindSat/index.php

http://geobrain.laits.gmu.edu/windsat/index.jsp

http://www.nrl.navy.mil/WindSat/images/SoilMoisture.jpeg


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Advanced Microwave Scanning Radiometer for EOS (AMSR-E)

Developed and provided to the National Aeronautics and Space Administration’s (NASA) EOS Aqua satellite by the National Space Development Agency of Japan (NASDA), as one of the indispensable instruments for Aqua’s mission, which has been launched on May 2002. It’s a multichannel passive microwave radiometer that observes atmospheric, land, oceanic and cryospheric parameters, including precipitation, sea surface temperatures, ice concentrations, snow water equivalent, surface wetness, wind speed, atmospheric cloud water and water vapor (Table 1). AMSR-E is a six-frequency total-power microwave radiometer system with dual polarization capability for all frequency bands. The frequency bands include 6.925, 10.65, 18.7, 23.8, 36.5, and 89.0 GHz. Vertically and horizontally polarized measurements are taken at all channels. Spatial resolution of the individual measurements varies from 5.4 km at 89.0 GHz to 56 km at 6.9 GHz. Conical scanning at 40 r/min is employed to observe the earth’s surface with a constant incidence angle of 55° (Kawanishi et al., 2003).

Table 1: Standard Products from AMSR-E (AMSR-E Brochure).


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Soil Moisture Ocean Salinity (SMOS) Mission Launched on November the 2nd 2009, is the first satellite ever attempted to globally measure the Earth’s soil moisture and ocean salinity by means of L-band microwave radiometry. The SMOS mission is a direct response to the current lack of global observations of soil moisture and ocean salinity, and was thought of as a cost-effective, demonstrator mission with a nominal (extended) lifetime of 3 (5) years. The SMOS objectives are to demonstrate the use of L-band to:

Monitor on a global scale the surface soil moisture over land surface. Monitor on a global scale the surface salinity over the ocean Improve the characterization of ice and snow covered surfaces.

The satellite has a Sun-synchronous, quasi-circular, dusk-dawn orbit, with a mean altitude of 758 km, and with 6 am/6 pm overpass times. The SMOS single payload is a completely new type of instrument: an L-band two dimensional synthetic aperture radiometer with multiangular and dual-polarization/full-polarimetric capabilities, the Microwave Imaging Radiometer using Aperture Synthesis (MIRAS). SMOS is expected to provide global maps of soil moisture every 3 days –compatible with the temporal variability of the near surface soil moisture over continental surfaces–, with a ground resolution better than 50 km, and an accuracy of 0.04 m3/m3 volumetric humidity (ESA, 2003). The SMOS mission requirements are summarized in the following Table.

Table 2: Main scientific requirements of SMOS Mission (Piles, 2011)

The following figure shows the first map of global soil moisture retievals, released on June 30th of 2010.


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Figure 4: Global Soil Moisture Map5.

Aquarius/ SAC-D Is a joint international science mission, between the National Aeronautics and Space Administration (NASA) of United States and the Argentine Space Agency (CONAE) launched on June 10, 2011. This mission will provide measurements of the global sea surface salinity, which contributes to understanding climatic changes in the global water cycle and how these variations influence the general ocean circulation. Aquarius/SAC-D provides opportunities to explore new approaches to soil moisture retrieval. It’s the first space borne data that can be used to assess the synergy of L-band passive and active observation for improving remote sensing of soils and vegetation. The two main instruments of the mission are: the Aquarius, which is an integrated L-band radiometer/scatterometer and the Microwave Radiometer (MWR), a three channel radiometer measures surface brightness temperature in one polarization at 23.8 GHz (H-pol) and a 36.5 GHz frequency in both polarizations. With data obtained from both sensors (plus auxiliary data) soil moisture retrieval algorithm are being develop by Jackson and Karszenbaum6.

5 http://spaceinimages.esa.int/Images/2010/06/First_map_of_global_soil_moisture_retrievals

6 6th Aquarius/SAC-D Science Meeting (2010), power point presentation.

http://en.wikipedia.org/wiki/Argentina


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Soil Moisture Active Passive (SMAP) Mission Developed by NASA in response to the National Research Council’s Decadal Survey, SMAP will make global measurements of the soil moisture present at the Earth’s land surface and will distinguish frozen from thawed land surfaces. SMAP hydrosphere state measurements will yield a critical data set that enable science and applications users to:

Understand processes that link the terrestrial water energy, and carbon cycles. Estimate global water and energy fluxes at the land surface. Quantify net carbon flux in boreal landscapes; enhance weather and climate

forecast skill. Develop improved flood prediction and drought monitoring capability.

The SMAP mission will utilize a combination of radiometer high resolution radar with multiple polarizations in L-band to provide high resolution and high-accuracy global maps of soil moisture and freeze/thaw state every two to three days (Table 3). In addition, the SMAP project will use these observations with advanced modeling and data assimilation to provide deeper root-zone soil moisture and net ecosystem exchange of carbon. SMAP is scheduled for launch in the 2014–2015 timeframe.

Table 3: SMAP Mission Requirements (Entekhabi et al, 2010).


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3. Basic Principles of Radiometers Passive microwave remote sensing is based on the measurement of thermal radiation from the land surface in the centimeter wave band. This radiation is determined largely by the physical temperature and the emissivity of the radiating body, and may be approximated by,

(1)

where TB is the observed microwave brightness temperature, T is the physical temperature of the emitting layer and is the surface emissivity; the subscript p denotes

either vertical or horizontal polarization. Emissivity may be further related to the reflectivity

( ) (2)

where is the smooth surface reflectivity (Owe et al, 2001).. For a flat surface, and a

medium of uniform dielectric constant, the expressions for reflectivity at vertical and horizontal polarization are given by the power Fresnel reflection coefficients, as:

| √

√ |

| √

√ |

Where θ is the incidence angle and εT is the complex dielectric constant of soils, which is in turn governed by the moisture content and the soil type (Ulaby et al., 1981).

3.1 Water in soils Water in soils is commonly classified as bound and free water; bound water is the water absorbed by the surface of soil particles, while free water is the liquid water located in the pore spaces. The porosity of a soil determines the total volume occupied by pores per unit volume of soil. Soils with small pores (clayey soils) will hold more water per unit volume than soils with large pores (sandy soils). While pore spaces of dry soils are mostly filled with air, pore spaces of wet soils are filled with water. Processes such as infiltration, ground-water movement, and storage occur in these void spaces.


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The soil moisture, or water in a soil, is expressed as a ratio, which can range from 0

(completely dry) to the value of the materials’ porosity at saturation (∼ 0.5). It is usually expressed in per cent, and can be determined in two ways:

a. Gravimetric soil moisture , which is defined as the mass of water per unit mass

of dry soil, and can be calculated from the wet mw and dry md weights of a soil sample, as:

b. Volumetric soil moisture , defined as the volume of water per unit volume of soil, determined from the volume of water Vw and the total volume VT (soil volume +water volume + void space), and related to mg through the soil bulk density :

3.2 Dielectric properties of soils

The dielectric properties of wet soils have been studied in detail by several investigators (e.g. Wang and Schmugge, 1980; Dobson et al, 1985). Soil emission at microwaves frequencies is related to the soil water content by the dielectric constant, which is a measure of the soil response to an electromagnetic wave. It is defined as a complex number (

), where the real part determines the propagation characteristics

of the energy as it passes through the soil, and the imaginary part determines the energy loses. The high dielectric constant of water significantly increases both the real and imaginary parts of the dielectric constant of the soil as the volume fraction of water in soil increases. Figure 5 shows the relationship between dielectric constant and volumetric soil moisture content for a variety of soil types at a frequency of 1.4 GHz. The dependence on soil texture is due to differences in the percentage of water bound to the particle surfaces in the different soils. Bound water is less freely able to exhibit molecular rotation at microwave frequency and hence has a smaller dielectric effect than free water in the pore spaces. This is most evident in clay soils which have greater particle surface areas and greater affinities for binding water molecules (Njoku and Entekhabi, 1994). The dependence of dielectric constant on soil texture introduces some uncertainty into estimation of soil moisture if the soil textural composition is unknown.


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Figure 5: Dielectric constant as a function of volumetric soil moisture for five soils with different textural composition at 1.4 GHz (Ulaby, 1986).

Also, figure 5 shows that the relationship between dielectric constant and volumetric soil moisture is almost linear, except at low moisture contents. This non-linearity at low moisture content is due to the strong bonds developed between the surfaces of soil particles and the thin films of water that surrounds them, which impede the free rotation of the water molecules. As more water is added, the molecules are further from the particle surface and are able to rotate more freely, hence increasing the soil dielectric constant (Schmugge, 1983). Soil moisture, through its relationship to the real and imaginary parts of the dielectric

constant, has an impact on the soil penetration depth p. It is defined as the thickness of the top surface layer of the soil medium governing the emission .The plot shown in figure 6 describes the variation of the penetration depth with volumetric moisture content mv at three microwave frequencies for a homogeneous loamy soil. At L-band (1.3 GHz), the depth decreases from 1 m at mv = 1% down to 6 cm at 40%.


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Figure 6: Penetration depth as a function of moisture content for loamy soil at three microwave frequencies (Ulaby et al., 1982).

The dielectric constant of dry soils is almost independent of temperature; for wet soils, the dielectric constant is only weakly dependent on temperature, and for the range of temperatures encountered in nature this dependence may be ignore. However, frozen soils have much lower dielectric constants that unfrozen soils, as the contained water is no longer in liquid phase. This feature has led to studies of microwave radiometry for detecting areas of permafrost and freeze-thaw boundaries in soil (England, 1990).

3.3 Surface roughness

The effect of surface roughness on the microwave emission from bare soils is illustrated in figure 7, which shows experimental data measured at 1.4 GHz for three fields with different surface roughness conditions (Newton and Rouse, 1980). It shows that surface roughness increases the emissivity of natural surfaces –due to the increase in the soil area interacting with the atmosphere- and reduces the difference between the vertical and horizontal polarization. Also, the sensitivity of emissivity to soil moisture variation decreases significantly as the surface roughness increases, since it reduces the range in measurable emissivity from dry to wet conditions (Wang, 1983).


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Figure 7: Comparison between normalized antenna temperatures (emissivity) vs. incidence angle at 1.4GHz for three bare soil fields with different surface roughness (Newton and Rouse, 1980)

The effect of soil roughness on the emissivity has been an issue widely addressed in the literature and different approaches have been proposed to model it. A simpler, semi-empirical expression for rough surface reflectivity was proposed by Choudhury et al.

(7)

where r0 is the smooth surface reflectivity and is the soil roughness parameter, related to the electromagnetc wavenumber k and the standard deviation of the surface height σs, and θ is the incidence angle.

3.4 Vegetation effects

When the soil is covered by vegetation, its emission is affected by the canopy layer: it absorbs and scatters the radiation emanating from the soil and also its ads its own contribution. In areas of sufficiently dense canopy, the emitted soil radiation will become masked, and the observed emissivity will be due largely to the vegetation. The magnitude of the absorption by the canopy depends upon the wavelength and the vegetation water content. The most frequently used wavelengths for soil moisture sensing are in the L-band


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and C-band, with L-band sensors having a greater penetration of vegetation (1.4 and 6 GHz, respectively). While observations at all frequencies are subject to scattering and absorption and require some correction if the data are to be used for soil moisture retrieval, shorted wave bands are more susceptible to vegetation influences. A variety of models have been developed to account for the effects of vegetation on the observed microwave signal, and rage from empirical models to more physically-based radiative transfer treatments (Owe et al, 2001).

4. Surface Emission Model

4.1 Soil emission model The most commonly used model that describes the bare soil surface emission as a function of the surface roughness and dielectric properties is the so-called Q/H model (Choudhury et al, 1979; Wang and Choudhury, 1981):

[ ] (8)

where

and are the surface effective reflectivity and emissivity at polarization p,

respectively. The subscript describes the polarization state v or h; is the surface reflectivity for flat surface. The roughness parameter Q describes the energy emitted in orthogonal polarization due to the surface roughness effects. The roughness parameter H is a measure of the roughness effect on surface effective reflectivity. Both parameters Q and H are determined empirically form the experimental data (Wang et al., 1983; Schmugge, 1987; Shi et al., 2005).

4.2 Emission model for vegetation covered areas The effects of vegetation cover can be well approximated by a simple radiative transfer model, commonly referred to as the model (Wigneron et al., 2003). This model is based on two parameters, the optical depth and the single scattering albedo which are used to parameterize, respectively, the vegetation attenuation properties and the scattering effects within the canopy layer. Using the model, the brightness temperature Tb of a soil and vegetation layer is the sum of three terms: (1) the canopy attenuated soil emission, (2) the direct vegetation emission and (3) the vegetation emission reflected by the soil and attenuated by the canopy:


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[ ]

[ ] ,

(9) where and are the physical temperatures (K) of the soil and vegetation canopy, is

the surface emissivity, is the vegetation optical depth and is the single scattering albedo. Several studies found that can be estimated through its relationship to the total vegetation water content Wc (kg/m2) given by:

The b parameter can be calibrated for each crop type or for large categories of vegetation (leaf-dominated, steam-dominated, grass). It was found also that b depends on polarization and θ is the incident angle, especially for vegetation with a dominant vertical structure (Ulaby and Wilson, 1985; Jackson and Schmugge, 1991). The model can applied successfully if other factors that influence the brightness temperature, such as instrument configuration and target characteristics, are invariant for a particular locality (Schmugge, 1983).The spatial variability of the soil texture and temperature, surface roughness and vegetation from one locality to another and even within a single instrument footprint complicates the application of this technique. More recently, polarization indexes have been proposed to monitor soil moisture and vegetation development, as the microwave signatures of soil and vegetation exhibit distinct response to polarization effects. The most common index is the microwave polarization difference index (also referred to as the polarization ratio) defined as:

where and are brightness temperature at V and H polarization, respectively (Owe et al., 2001). As the MPDI is a normalized calculation of brightness temperature, it primarily depends on the polarization difference, thereby minimizing the variable surface temperature effects.


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5. Soil Moisture Retrieval Methods The brightness temperatures of land covers is influenced by many variables, the most important being soil moisture, soil roughness (parameterized by the soil roughness parameter hs), soil temperature Ts, and vegetation characteristics such as albedo and opacity τ. The challenge of retrieval or inversion techniques is to reconstruct the environmental parameters from the measured signal by using a minimum of auxiliary data.

Many approaches have been developed to retrieve soil moisture from microwave radiometric measurements, which can be grouped into three main categories:

i. Statistical Techniques: based on deriving an empirical relationship between the geophysical variables and the radiative transfer equation through a regression technique. Statistical approaches are simple and efficient, but are site-specific, as they can only be used for the similar conditions during which they were calibrated, while are not applicable for monitoring events or trends out of the domain of calibration.

ii. Forward Model Inversion: a model is used to simulate remotely sensed signatures (output) on the basis of land surface parameters (input). Inversion methods are developed to produce an “inverse model” in which outputs are the relevant land surface variables. The inversion methods are usually based on a iterative minimization routine of the root mean square error (RMSE) between forward model simulations and observation, Other method suggest the use of look up tables (LUT)or neural network(NN)

iii. Explicit Inverse: explicit inverse of the physical process can be built by transferring input (remote sensing measurements) into output (land surface parameters). In most studies, neural networks are used to create this explicit inverse model.


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6. Latest Works on SMC

6.1 SMOS From the ESA website it’s possible to obtain SMOS generates products up to level 2 of Soil Moisture and Ocean Salinity. The generation of global products (level 3 products) implies the application of different averaging and analysis methods. Level 4 products are global maps derived from SMOS level 3 products by means of physical models and other auxiliary data.

6.1.1 SMOS-BEC The SMOS Barcelona Expert Centre on Radiometric Calibration and Ocean Salinity (SMOS-BEC) is a joint initiative of the Spanish Research Council (CSIC) and the Universitat Politècnica de Catalunya (UPC) to contribute to the ground segment of the Soil Moisture and Ocean Salinity (SMOS) mission of the European Space Agency (ESA). This research center developed high resolution soil moisture maps now available in near real-time (fig. 7). Soil moisture maps at 1 km spatial resolution over the Iberian Peninsula are obtained by optimally merging SMOS and MODIS data using the downscaling algorithm (Piles et al., 2011). Maps from the first two years of SMOS in orbit (historical dataset) are available. Furthermore, near real-time maps are daily generated at 4.30 am and 4.30 pm, corresponding to SMOS ascending and descending orbits7.

Figure 7: Soil Moisture Map over the Iberian Peninsula6.

7 http://www.smos-bec.icm.csic.es/high_resolution_soil_moisture_maps_now_available_in_near_real_time

http://www.csic.es/

http://www.upc.edu/

http://www.esa.int/esaLP/LPsmos.html

http://www.esa.int/esaCP/index.html

http://www.smos-bec.icm.csic.es/high_resolution_soil_moisture_maps_now_available_in_near_real_time


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6.1.2 Centre d'Etudes Spatiales (CESBIO) The Centre d'Etudes Spatiales de la Biosphere, Toulouse (France) is a join partner of the SMOS mission and has developed a new drought index by including SMOS surface soil moisture to a double bucket model. The index is computed operationally and is part of the level 4 SMOS products, which are end-level products obtained by combination of the SMOS data to physical models (hydrological, statistical with/without data assimilation, etc.) or by using remote sensing data from other sensors (disaggregation, data fusion, synergism, etc.). Figure 8 shows an example of the Drought Index and figure 9 shows the African root zone soil moisture Index (SARI) developed along with the Land Surface Hydrology group at Princeton8.

Figure 8: Map of SMOS drought root zone soil moisture index9.

Figure 9: African Drought Monitor

8 http://www.cesbio.ups-tlse.fr/SMOS_blog/?page_id=2589#comments

9 http://www.cesbio.ups-tlse.fr/SMOS_blog/?page_id=2589

http://www.cesbio.ups-tlse.fr/SMOS_blog/?page_id=2589#comments

http://www.cesbio.ups-tlse.fr/SMOS_blog/?page_id=2589


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The index represents the available water in the root zone. Drought is driven by scarcity of water in the first meters of the surface. This creates stress and drying of the vegetation. The SMOS observation of soil moisture in the first centimeters is very precious information to model the drought. The surface soil moisture (~5cm) is intermediate water content between the skin surface and the root zone soil moisture. By delivering the soil moisture at the surface SMOS mission reduces the uncertainties in other processes at the surface that contribute to the water budget7.

6.2 AMSR-E The Microwave Remote Sensing Group from the Institute of Applied Physics in Sesto Fiorentino (Italy) developed and implemented an algorithm for generate maps of snow depth (SD) and soil moisture content (SMC) from AMSR-E data. As auxiliary output, the algorithm also generates maps of vegetation biomass. The algorithm was calibrated using a wide set of experimental data collected on Mongolia and Australia (for SMC) and Siberia (for SD), integrated with model simulation. The results were validated by comparing the algorithm outputs with experimental data collected in Northern Italy and Scandinavia. An additional test of the algorithm was also performed on a large scale, including sites characterized by different climatic and meteorological conditions. Soil Moisture content maps of the entire world obtained at different dates (December 2009, February, April, August and October 2010) are shown in figure 10. Snow cover and forest are masked in the images. At least 4 levels of SMC can easily be identified. Although no ancillary information is available, the results are in reasonable agreement with the climatic and seasonal meteorological conditions of the various zones. The seasonal variation in SMC shows an opposite trend in the two hemispheres, e.g. Australia is wetter in August than February (Santi et al., 2012).


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Figure 10: (a) SMC maps (in m3/ m3) of the entire world obtained in December 2009, February and April 2010 using HydroAlgo (some AMSR-E scans are missing: Africa and North America in February

2010). (b) SMC maps (in m3/ m3) of the entire world obtained in June, August and October 2010 using HydroAlgo, some AMSR-E scans are missing: black lines in Africa and South America (Santi et

al., 2012).

6.3 International Soil Moisture Network

Is an international cooperation to establish and maintain a global in-situ soil moisture database. This database is an essential means of the geoscientific community for validating and improving global satellite observation and land surface models. This international initiative is coordinated by the Global Energy and Water Cycle Experiment (GEWEX) in cooperation with the Group of Earth Observation (GEO) and the Committee on Earth Observation Satellites (CEOS)10. A main purpose of the International Soil Moisture Network is for validation of satellite observed soil moisture data. This has become an increasingly important task given that several global soil moisture datasets derived from microwave radiometers (SMOS, AMSR-E, Windsat, TRMM, and SMMR) and scatterometers (ERS SCAT, ASCAT) have become freely available in recent years.

10

http://www.ipf.tuwien.ac.at/insitu/

http://www.gewex.org/

http://earthobservations.org/

http://www.ceos.org/

http://www.ipf.tuwien.ac.at/insitu/


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Users of the ISMN are required to register before being able to download data from the ISMN; the registration will provide full access to the data hosting facility (fig. 11). Data distributed through the ISMN are provided for free and shall be used for scientific purposes only.

Figure 11: Web interface of ISMN. Red droplets indicate the center coordinates of the different networks contained in the database, status of May 2011 (Dorigo et al., 2011).

Incoming soil moisture data are automatically transformed into common volumetric soil moisture units and checked for outliers and implausible values. Apart from soil water measurements from different depths, important metadata and meteorological variables (e.g., precipitation and soil temperature) are stored in the database. Currently (status May 2011), the ISMN contains data of 19 networks and more than 500 stations located in North America, Europe, Asia, and Australia. The time period spanned by the entire database runs from 1952 until the present. The database is rapidly expanding, which means that both the number of stations and the time period covered by the existing stations are still growing. Hence, it will become an increasingly important resource for validating and improving satellite-derived soil moisture products and studying climate related trends (Dorigo et al., 2011).


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7. Conclusions

Microwave remote sensing is an effective technique for soil moisture estimation, with advantages for all-weather observations and solid physics. The current state of the art indicates that surface soil moisture measurements from space are feasible in regions of bare soil or low vegetation cover using sensor with frequencies in the range 1-5 GHz. Such measurements, acquired on a global and repetitive basis, would be extremely useful for hydrologic and climate studies. The perturbing effects of surface roughness and vegetation cover are well understood qualitatively. Passive microwave has more potential for large-scale soil moisture monitoring but has a low spatial resolution. For future soil moisture retrieval algorithms, a lot of effort is being put in integrating the space borne measurements from multiple sensors, especially high resolution SAR images with radiometric data like the SMAP mission.


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8. References

1. Bindlish R., Jackson T.J., Wood E., Gao H., Starks P., Bosch D., Lakshmi V., (2003). “Soil Moisture Mapping the Southern U.S. with the TRMM Microwave Imager observations over the Southern United States”. Remote Sensing of Environment, vol. N° 85, pp. 507-515.

2. Choudhury B., Schmugge T., Chang A., Newton R., (1979). “Effect of surface

roughness on the microwave emission from soils”. Journal of Geophysical

Research, vol. N° 84, 5699-5706.

3. Dobson M.C., Ulaby F.T., Hallikainen M.T., El-Rayes M.A., (1985). “Microwave Dielectric Behaviour of Wet Soil- Part II: Dielectric Mixing Models”. IEEE Trans Geoscience Remote Sensing, vol. N°23, pp. 35-46.

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