Martti Hallikainen

Microwave Dielectric Behavior of Wet Soil-Part II: Dielectric Mixing Models

This paper is the second in a series evaluating the microwave dielectric behavior of soil-water mixtures as a function of water content and soil textural composition. Part II draws upon the data presented in Part 1 [13] to develop appropriate empirical and theoretical dielectric mixing models for the 1.4-to 18-GHz region. A semiempirical mixing model based upon the index of refraction is presented, requiring only easily ascertained soil physical parameters such as volumetric moisture and soil textural composition as inputs. In addition, a theoretical model accounting explicitly for the presence of a hydration layer of bound water adjacent to hydrophilic soil particle surfaces is presented. A four-component dielectric mixing model treats the soil-water system as a host medium of dry soil solids containing randomly distributed and randomly oriented disc-shaped inclusions of bound water, bulk water, and air. The bulk water component is considered to be dependent upon frequency, temperature, and salinity. The soil solution is differentiated by means of a soil physical model into 1) a bound component and 2) a bulk soil solution. The performance of each model is evaluated as a function of soil moisture, soil texture, and frequency, using the dielectric measurements of five soils ranging from sandy loam to silty clay (as presented in Part I [13]) at frequencies between 1.4 and 18 GHz. The semiempirical mixing model yields an excellent fit to the measured data at frequencies above 4 GHz. At 1.

Microwave Dielectric Behavior of Wet Soil-Part 1: Empirical Models and Experimental Observations

This is the first paper in a two-part sequence that evaluates the microwave dielectric behavior of soil-water mixtures as a function of water content, temperature, and soil textural composition. Part I presents the results of dielectric constant measurements conducted for five different soil types at frequencies between 1.4 and 18 GHz. Soil texture is shown to have an effect on dielectric behavior over the entire frequency range and is most pronounced at frequencies below 5 GHz. In addition, the dielectric properties of frozen soils suggest that a fraction of the soil water component remains liquid even at temperatures of -24° C. The dielectric data as measured at room temperature are summarized at each frequency by polynomial expressions dependent upon both the volumetric moisture content m and the percentage of sand and clay contained in the soil; separate polynomial expressions are given for the real and imaginary parts of the dielectric constant. In Part II, two dielectric mixing models will be presented to account for the observed behavior: 1) a semiempirical refractive mixing model that accurately describes the data and requires only volumetric moisture and soil texture as inputs, and 2) a theoretical four-component mixing model that explicitly accounts for the presence of bound water.

HUT snow emission model and its applicability to snow water equivalent retrieval

The derivation, testing, and employment to parameter retrieval of the Helsinki University of Technology (HUT) snow microwave emission model is presented. The radiative transfer-based semi-empirical model describes the emission behavior of a homogeneous snowpack as a function of water equivalent (SWE), effective grain size, and density of snow. Additionally, the modeling approach takes into account the influence of soil surface, forest canopy, and atmosphere to spaceborne observed brightness temperature by using empirical and semi-empirical formulas. The comparison of model predictions with independent experimental data shows good correlations, especially in terms of spectral characteristics. This enables the development of a new inversion technique for the SWE retrieval from spaceborne data based on the developed model. The test results using special sensor microwave/imager (SSM/I) data from boreal forest zone showed SWE retrieval accuracies considerably higher than those obtained with conventional algorithms. A discussion and analysis on the feasibility of the new SWE retrieval technique for operational applications is also included.

Dielectric properties of snow in the 3 to 37 GHz range

Microwave dielectric measurements of dry and wet snow were made at nine frequencies betweeo 3 and 18 GHz, and at 37 GHz, using two free-space transmission systems. The measurements were conducted during the winters of 1982 and 1983. The following parametric ranges were covered: 1) liquid water content, 0 to 12.3 percent by volume; 2) snow density, 0.09 to 0.42 g cm-3; 3) temperature, 0 to -5 \deg C and -15\deg C (scattering-loss measurements); and 4) crystal size, 0.5 to 1.5 mm. The experimental data indicate that the dielectric behavior of wet snow closely follows the dispersion behavior of water. For dry snow, volume scattering is the dominant loss mechanism at 37 GHz. The applicability of several empirical and theoretical mixing models was evaluated using the experimental data. Both the Debye-like semi-empirical model and the theoretical Polder-Van Santen mixing model were found to describe adequately the dielectric behavior of wet snow. However, the Polder-Van Santen model provided a good fit to the measured values of the real and imaginary parts of wet snow only when the shapes of the water inclusions in snow were assumed to be both nonsymmetrical and dependent upon snow water content. The shape variation predicted by the model is consistent with the variation suggested by the physical mechanisms governing the distribution of liquid water in wet snow.

Lake water quality classification with airborne hyperspectral spectrometer and simulated MERIS data

We study the use of airborne and simulated satellite remote sensing data for classification of three water quality variables: Secchi depth, turbidity, and chlorophyll a. An extensive airborne spectrometer and ground truth data set obtained in four lake water quality measurement campaigns in southern Finland during 1996–1998 was used in the analysis. The class limits for the water quality variables were obtained from two operational classification standards. When remote sensing data is used, a combination of them proved to be the most suitable. The feasibility of the system for operational use was tested by training and testing the retrieval algorithms with separate data sets. In this case, the classification accuracy is 90% for three Secchi depth classes, 79% for five turbidity classes, and 78% for five chlorophyll a classes. When Airborne Imaging Spectrometer for Applications (AISA) data was spectrally averaged corresponding to Envisat Medium Resolution Imaging Spectrometer (MERIS) channels, the classification accuracy was about the same as in the case of the original AISA channels.

Retrieval of regional Snow Water equivalent from space-borne passive microwave observations

Abstract The feasibility of space-borne microwave radiometers for monitoring the evolution of snow cover in a drainage area is investigated. Four winter sets (1993/94, 1995/96, 1996/97, 1997/98) of SSM/I radiometer observations for the 51,000 km 2 River Kemijoki drainage area, Northern Finland, are used for analyses. The Snow Water Equivalent (SWE) of dry snow cover is estimated by employing the Helsinki University of Technology (HUT) Snow Emission Model-based automatic inversion algorithm. For comparison, the SWE estimates are also determined by using a conventional empirical Spectral and Polarization Difference algorithm. The results indicate that the HUT Snow Emission Model-based automatic algorithm can estimate the regional SWE under dry snow conditions with an overall RMSE of about 30 mm without using any training reference data on SWE (e.g., in situ reference values). The retrieval error was found to vary considerably from year to year. At best, the annual SWE retrieval RMSE showed values as low as 20 mm.

Application of an empirical neural network to surface water quality estimation in the Gulf of Finland using combined optical data and microwave data

Abstract Since neural networks have been widely applied to the nonlinear transfer function approximation, we present an empirical neural network algorithm to estimate major parameters in surface waters from combined optical data and microwave data in the Gulf of Finland. Concurrent in situ surface water quality measurements, optical (Landsat TM) data and microwave (ERS-2 SAR) data were obtained in selected locations in August 1997. The TM and ERS-2 SAR data from locations of water samples were extracted and digital data were examined in numerous transformations. Although significant correlations were observed between digital data and chlorophyll- a (Chl- a ), suspended sediment concentration (SSC), turbidity (Turb), and Secchi disk depth (SDD), application of neural networks appears to yield a superior performance in modeling transfer functions in this study area. Here, an empirical neural network algorithm is applied to estimate the transfer functions between the major characteristics of surface waters and the satellite optical and microwave data. The results show that the estimation accuracy for major characteristics of surface waters using the neural network is much better than those from regression analysis. The results also indicate that microwave data can assist to improve the estimation of these characteristics. Therefore, it may be possible to develop surface water quality algorithms in which microwave data are used as supplementary data to optical observations. However, this improvement of optical data retrieval algorithm is limited in this case study. The technique still needs to be refined in detail in order to detect differences within the typical range of these water quality parameters found in the area under study.

Comparison of algorithms for retrieval of snow water equivalent from Nimbus-7 SMMR data in Finland

The correlation between the brightness temperature of snow-covered terrain (dry snow) and the snow-water equivalent in Finland was investigated using Nimbus-7 SMMR data. The satellite data set covers the winters of 1978-9 through 1981-2. The correlation analysis was performed for 17 different brightness temperature functions, each involving one or several frequencies and polarizations. The highest correlation coefficients between the satellite-derived brightness temperature functions and the manually measured snow-water equivalent values were obtained by using the brightness temperature difference between 37 GHz and either 18 GHz or 10.7 GHz, vertical polarization. >

A semi-operative approach to lake water quality retrieval from remote sensing data

A semi-operative approach to retrieve chlorophyll-a concentration from airborne/spaceborne spectrometer observations has been developed and tested using the airborne imaging spectrometer (AISA) data from 11 lakes located in southern Finland. The retrieval approach is empirical and requires nearly simultaneous in situ training data on water quality for the determination of regression coefficients. However, the training data does not have to be collected from every lake under investigation. Instead, the results obtained indicate that reliable estimates on the level of chlorophyll-a (chl-a) for an individual lake can be achieved without employing in situ data representing this specific lake. This enables the estimation of water quality from remotely sensed data for numerous lakes with the aid of reference data only for a few selected lakes representing the region under investigation. In addition, it is shown that the remotely sensed spectrum shape characteristics are highly affected by the trophic and humic state of the lake water.

Retrieval of biomass in boreal forests from multitemporal ERS-1 and JERS-1 SAR images

The response of JERS-1 and ERS-1 synthetic aperture radar (SAR) to the forest stem volume (biomass) was investigated by employing a digital stem volume map and weather information. The stem volume map was produced from the National Forest Inventory sample plot data together with a LANDSAT thematic mapper (TM) image. A new indirect inversion method was developed and tested to estimate the forest blockwise stem volume from JERS-1 and/or ERS-1 SAR images. The method is based on using a semiempirical backscatter model for inversion. The model presumes that backscatter from a forest canopy is determined by the stem volume, soil moisture, and vegetation moisture. The area of interest is divided into a training and test area. In this study, the training area was 10% of the test site, while the remaining 90% was used for testing the method. The inversion algorithm is carried out in the following three steps. 1) For the training area, the soil and vegetation moisture parameters are estimated from the backscattering coefficients and stem volume (must be known for training areas) with the semiempirical backscatter model. 2) For the area of interest, the stem volume is estimated from the moisture parameters and backscattering coefficients with the semiempirical backscattering model. 3) If several SAR images are used, the stem volume estimates are combined with a multiple linear regression. The regression equation is defined using the stem volume estimates for the training area. The results for the stem volume estimation using L-band and/or C-band SAR data showed promising accuracies: the relative retrieval rms error varied from 30 to 5% as the size of the forest area varied from 5 to 30000 ha.