Due to significantly reduced microbial activity pronounced soil carbon reservoirs have been accumulating in the circumpolar northern cold climate zone (boreal forest/tundra), outweighing the vegetation and atmospheric carbon pools together [1
]. At the same time, the higher Northern latitudes are especially sensitive to climate change due to above-average rising temperatures (e.g., [3
]). At present still mostly locked in permafrost, there is a high potential that on thawing, this storage is subject to release to the atmosphere in form of greenhouse gases or in a dissolved state to the hydrosphere (e.g., [5
]). The permafrost—carbon coupling is recognized as potentially the largest positive terrestrial feedback to anthropogenic climate change. Nevertheless, significant knowledge gaps remain concerning the quantification of the impact of thawing ground on the global carbon cycle (i.e., magnitude, type and timing of greenhouse gas emissions, e.g., [7
]). Soil moisture plays a key role in determining the rate of soil carbon cycling and the type of carbon emission (e.g., [7
]). Hence, there is a strong need to monitor hydrologic states and water redistribution processes in these regions, where pronounced organic surface horizons are common standard (a soil horizon is a layer whose physical characteristics differ from the layers above and beneath).
Space-borne remote sensing techniques are the only means to acquire such observations at high temporal resolution and with complete spatial coverage. The Soil Moisture and Ocean Salinity (SMOS) satellite [9
] was the first space mission to be launched (November 2009) with the objective of soil moisture observations. It carries a passive L-band microwave (1.4 GHz) radiometer, on board. These lowest microwave frequency measurements presently available from space are considered as most suited for satellite soil moisture retrieval due to the absence of day-light and cloud restrictions, smallest surface roughness and vegetation impacts, and largest emission depths [11
]. Two space missions using the same microwave band but differing technologies followed closely: Aquarius aboard the Argentine SAC-D spacecraft [12
] operational between June 2011 and June 2015 as well as the Soil Moisture Active Passive (SMAP) mission launched in January 2015 [13
]. From the global L-band brightness temperatures (TB) acquired by these space-borne sensors, moisture content of the soil surface layer (~0–5 cm depth) can be retrieved, taking advantage of the very large difference between the relative permittivity (also referred to as dielectric constant, ε) of dry soil and liquid water. Spatial resolutions are of around ~40–50 km in case of the SMOS and SMAP missions, and ~75–150 km in case of Aquarius, with temporal repeat cycles of 2–3 and 7 days, respectively.
The SMOS and SMAP soil moisture retrieval algorithms are based on the inversion of radiative transfer equations [14
]. In these radiative transfer forward models, a dielectric model is used to relate an initial soil moisture guess to the soil’s relative permittivity using auxiliary information such as soil texture, bulk density, and temperature. In the SMOS soil moisture algorithm first the Dobson model [16
] using the formulation by Peplinksi et al. [17
] was used. In April 2012 it was substituted with the one by Mironov et al. [18
] and Mironov and Fomin [21
] as the latter is more physically-based, offers a wider soil texture calibration range up to 100% sand, requires less auxiliary input that is often hardly available and error-prone on a global scale, as well as better numerical stability [22
]. For the generation of the SMAP retrieval algorithm this choice was followed, supported by studies at local scale [24
]. While the well-established Mironov and Dobson models differ in their concepts, they were both designed for application in purely mineral soils.
The structural characteristics of the above-mentioned organic surface horizons are differing from the ones of mineral soils. Organic material exhibits complex structures, small bulk densities, high porosities and large specific surface areas, leading to extreme water holding capacities up to 0.8–0.9 cm3
compared to around 0.4–0.6 cm3
in common mineral soils (e.g., [26
]) as well as a higher fraction of bound water. During the application of an electric field, water molecules close to solid surfaces are rotationally hindered due to the active binding forces, resulting in considerably smaller relative permittivity of bound water compared to the one of free water (e.g., [28
]). The dielectric characteristics of bound water in soils resemble the one of water in solid state where the molecules are bound in the rigid ice structure. Consequently, at a given water content the bulk relative permittivity of organic substrates with large specific surface areas is reduced compared to the one of mineral soils (with the exception of very clayey soils).
To the knowledge of the authors, thus far, only few dielectric models exist for the above-mentioned organic substrates. Mironov et al. [29
] developed one temperature-dependent model valid throughout the 1 to 16 GHz frequency range that was likewise based on the generalized refractive mixing dielectric model (GRMDM) as their above-mentioned version for mineral soils used in the SMOS and SMAP retrieval algorithms. They fit their physically-based model to relative permittivity measurements conducted on an organic tundra soil collected from the Alaska North Slope near Toolik. Recently, this model was reduced to only one frequency (1.4 GHz) and to cover the full temperature range from 25 to −30 °C [30
]. Concurrently, Mironov and Savin [31
] developed a multi-relaxation spectroscopic dielectric model (MRSDM) valid in the frequency range of 0.05–15 GHz and from 25 to −30 °C. This model was fit to dielectric measurements using an organic tundra soil sample collected from the Siberian Yamal Peninsula. While the development of such models is without question of need, the application of the above-described Mironov et al. models for organic substrates in satellite data retrieval algorithms might be problematic at this stage. This is acknowledged by the authors themselves, who state that each model was solely built and validated on soil samples from one specific region. Furthermore, these models are not calibrated over the full wetness range, and for the laboratory measurements the material was packed to significantly higher constant bulk densities as measured in situ, while the dry bulk density of the organic soil is required as model input. In addition, this quantity is not available at global scale.
All the above motivated the ESA project “SMOSHiLat” [32
] whose overall aim was to improve our understanding of L-band emission of organic soil surface layers and thus, enhance the quality of SMOS soil moisture data in the higher Northern latitudes. In the scope of SMOSHiLat different samples from organic soil surface layers were collected from various sites in Denmark, Finland, Scotland and Siberia, spanning a wide range of humus (soil organic substrate) types. On all collected samples relative permittivity measurements were carried out directly at the L-band frequency, expected to give a comprehensive insight into the sensitivity of L-band relative permittivity and thus, emission behavior of soil organic matter. Where available, measurements of samples from the underlying sandy mineral A-horizons (topmost mineral soil layer) were also considered in order to demonstrate the above-mentioned bound water effect of the organic material. These datasets are described in this paper in detail. In addition, we show simple empirical models for organic soil surface layers as well as sandy mineral soils that could be developed based on the acquired water content—relative permittivity couples. We then make use of these measured datasets and derived simple models to evaluate the above-described dielectric models for organic substrates developed by Mironov et al. [30
] and Mironov and Savin [31
] including cross-comparison with corresponding simulations using the Dobson et al. 1985 [16
] and Mironov et al. [18
]/Mironov and Fomin [21
] dielectric models for mineral soils. The study sites and collected soil samples as well as the applied methods (for resonant cavity measurements, derivation of simple models and evaluation of existing dielectric models) are provided in Section 2
. Section 3
presents the results, followed by a discussion in Section 4
. Section 5
provides a summary and conclusive remarks on the presented work.
shows all average resonant cavity permittivity estimates (real and imaginary parts in left and right panel, respectively) of samples from organic horizons at room temperature for the entire wetness range and filtered as specified in Section 2.2.3
, in grey when using an alpha filling factor for the full tube length and in red specifically calibrated for the 4 cm tube insertion depth.
illustrates the filtered average resonant cavity measurements of relative permittivity (real and imaginary parts) over the entire wetness range and at room temperature with different colors for individual (a) mineral and (b) organic samples (shades of blue and yellow-red-purple, respectively, see Table 1
). The simple empirical models (3rd order polynomials) fit through the cavity datasets obtained from mineral and organic samples are plotted along in Figure 4
a,b, respectively (blue and red lines). The corresponding model coefficients are listed in Table 3
. Respective statistics are provided in Table 4
In Figure 5
a our simple empirical model for sandy mineral soils (light blue stars) is depicted (real and imaginary parts in top and bottom panel, respectively) together with the outputs of the Dobson and Mironov mineral model runs (blue and dark blue circles, respectively), with input corresponding to average conditions encountered in the mineral soil samples used for our measurements (Table 2
). These models’ calibration ranges are denoted by filled icons. For cross-comparison, the simple empirical model for organic substrates (red stars) is also included here. Figure 5
b illustrates again our empirical model for organic substrates, now together with the functions fit through the output of the two dielectric models for organic substrates adopted from literature (Mironov et al. [30
] and Mironov and Savin [31
], orange and salmon triangles, respectively). The data ranges available from these references for curve fitting are denoted by filled icons. Additionally, the relative permittivity measurements obtained from all organic samples after filtering are shown (grey dots). The statistics for the comparison of the different model outputs with our cavity measurement datasets for mineral and organic substrates, respectively, are provided in Table 4
The objective of the here presented work in the scope of ESA’s “SMOSHiLat” project was to improve our currently insufficient knowledge of L-band emissions of organic surface layers by means of L-band permittivity measurements as well as to evaluate the two existing dielectric modes for organic substrates by Mironov et al. [30
] and Mironov and Savin [31
]. To this end, L-band relative permittivity measurements were carried out on a wealth of samples from organic surface horizons from different regions (Denmark, Finland, Scotland and Siberia), spanning a wide range of organic substrate types. Where available, measurements of samples from the underlying sandy mineral A-horizons were included for cross-comparison. The here presented data was measured at room temperature using the resonant cavity (weak perturbation, 1.26 GHz) technique. This method yielded satisfactory results for organic substrates over the entire wetness range after the height of the tube containing the sample inserted into the resonant cavity was adjusted. The necessary specific calibration of the alpha-factor used for the calculation of ε was carried out by means of standard acetic acid-water mixtures.
The obtained datasets for organic and sandy mineral substrates showed uniform trends with increasing relative permittivity as the water content increases, though with a noticeable spread in the data measured from samples of organic character. Neither in case of the datasets obtained from mineral nor organic soil material there was a distinct difference in the behavior of measurements on samples from different sites. This implies that generally, a uniform dielectric response, and thus, L-band emission behavior can be expected from organic soil layers. These findings allowed the fitting of simple empirical models through the water content—relative permittivity couples obtained from mineral and organic soil samples, respectively. These models turned out to be consistent with the theory of increased bound water fraction (where water molecules are rotationally hindered) in organic substrates, and thus, lower ε compared to the sandy mineral soils, as well as with earlier published findings by Bircher et al. [44
] using conventional in situ soil moisture sensors.
The simple empirical model for sandy mineral soils agreed well with corresponding simulations using the Mironov mineral model and underlined its validity in the high sandy texture range. Respective output from the Dobson et al. [16
] mineral model showed constantly positively biased values of ε′ and unrealistic behavior of ε″, most likely due to the fact that this dielectric model was not calibrated for sandy soils. At the same time, our simple empirical model for organic substrates compared well with outputs of the Mironov et al. [30
] and Mironov and Savin [31
] organic models. This general consensus strengthens confidence in the validity of all of these dielectric models for organic substrates. Compared to the more sophisticated Mironov et al. models that are based on organic samples from only one specific region, the newly developed empirical model considers a significant variety of organic substrate types. Furthermore, it is calibrated over the entire expected wetness range and does not require globally unavailable auxiliary input data. Hence, it should be generically applicable wherever organic soil surface layers are present (also in lower latitude regions), and thus, should be suited for its purpose, namely the application in coarse-resolution L-band microwave satellite soil moisture retrieval algorithms. Currently, the model is being tested in the SMOS Soil Moisture Level 2 Prototype Processor (SML2PP) in prospect of its implementation in the operational one. The temperature-dependent Mironov et al. dielectric models for organic substrates should be exploited for use in satellite data applications where negative temperatures are one of the major drivers (e.g., freeze-thaw, permafrost or snow-related products). Ideally, in the future, common efforts with the research team of V. Mironov should be undertaken to merge the advantages of the simple empirical approach and the Mironov et al. organic models.