IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
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A New Sea-Ice Concentration Algorithm Based on Microwave Surface Emissivities—Application to AMSU Measurements Cezar Kongoli, Sid-Ahmed Boukabara, Banghua Yan, F. Weng, and Ralph Ferraro
Abstract—Passive microwave sea-ice retrieval algorithms are typically tuned to brightness temperature measurements with simple treatments of weather effects. The new technique presented is a two-step algorithm that variationally retrieves surface emissivities from microwave remote sensing observations, followed by the retrieval of sea-ice concentration from surface emissivities. Surface emissivity spectra are interpreted for determining sea-ice fraction by comparison with a catalog of sea-ice emissivities to find the closest match. This catalog was computed off-line from known ocean, first-year, and multiyear sea-ice reference emissivities for a range of fractions. The technique was adjusted for application to the Advanced Microwave Sounding Unit (AMSU)/Microwave Humidity Sensor observations, and its performance was compared to the National Oceanic and Atmospheric Administration (NOAA)’s AMSU heritage sea-ice algorithm and to NOAA’s operational Interactive Multi-sensor Snow and Ice Mapping System taken as ground truth. Assessment results indicate a performance that is superior to the heritage algorithm particularly over multiyear ice and during the warm season. Index Terms—Microwave radiometry, remote sensing, sea ice, variational methods.
I. I NTRODUCTION
S
EA ICE covers a significant extent of the ocean (about 5%–8%). Because of its vast extent, global monitoring of sea ice can be accomplished most effectively through the use of satellite data. The most comprehensive source of global sea-ice data has been satellite passive microwave sensors. The high contrast in microwave emissivity between open water and sea ice allows separation between the two surface types. The contrast is frequency dependent and is higher at lower frequencies. Passive microwave radiation at specific atmospheric
Manuscript received October 1, 2008; revised April 17, 2009, August 14, 2009, February 2, 2010, and April 26, 2010. C. Kongoli and B. Yan are with the Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD 20740 USA, and also with the Center for Satellite Applications and Research, National Environmental Satellite Data and Information Service, National Oceanic and Atmospheric Administration, Camp Springs, MD 20746 USA (e-mail: cezar.kongoli@noaa. gov;
[email protected]). S.-A. Boukabara, F. Weng, and R. Ferraro are with the Center for Satellite Applications and Research, National Environmental Satellite Data and Information Service, National Oceanic and Atmospheric Administration, Camp Springs, MD 20746 USA (e-mail:
[email protected]; fuzhong.weng@ noaa.gov;
[email protected]). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TGRS.2010.2052812
windows also allows sea-ice monitoring in cloudy atmospheres. However, the polar weather influences on passive microwave cannot be ignored. These influences can often increase to significant levels even at lower frequency channels, e.g., close to the water vapor absorption line at 22 GHz and near the oxygen absorption band at 50 GHz. Typically, polar regions are cloud covered (in particular, the Arctic), and it has been noted that surprisingly high liquid cloud fractions can be found even at low temperatures [1]. Weather effects are particularly enhanced over mixed ocean water—sea-ice scenes due to the large radiometric contrast between the weakly emissive ocean water surface and the atmospheric signal. Atmospheric absorption by water vapor and cloud liquid water (CLW) in polar regions can cause substantial underestimates in multiyear ice concentrations by decreasing polarization and spectral gradients between lower frequencies [2]. In this paper, spectral emissivity difference is defined as εν2 − εν1 , where ν indicates frequency and ν2 > ν1 , and thus, spectral gradient is referred to as “negative” for decreasing emissivities with increasing frequency and as “positive” for increasing emissivities with increasing frequency. Regarding the synoptic weather patterns over the polar regions, increasing cyclonicity trends are predicted [3], [4], consistent with projected global warming and increased high-latitude precipitation [5]. The sea-ice dynamics as they relate to such weather conditions are therefore extremely important to understand and to monitor, with implications for the treatment of weather influences in sea-ice algorithms. In addition to atmospheric correction, it is also important for these algorithms to provide a way to distinguish between the effects of temperature and surface emissivities on the brightness temperature signal. Most of the passive microwave sea-ice algorithms use simple weather correction schemes. The AMSR Bootstrap Algorithm [5] uses a technique that distinguishes between surface emissivities and temperature from multiple-channel brightness temperature measurements, but ignores atmospheric contributions. Next, sea-ice concentration is computed from a linear combination of retrieved surface emissivities at 19 and 37 GHz following the technique used in the Bootstrap Algorithm [6]. Note that the Bootstrap Algorithm computes sea-ice concentration based on linear combination of brightness temperature measurements at 19 and 37 GHz and thus does not provide a way to distinguish between surface temperature fluctuations and emissivity on the one hand and atmospheric contributions on the other. The Norwegian Remote Sensing Experiment
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sea-ice algorithm [7] employs a simplified radiative transfer equation that relates satellite brightness temperature at window frequencies in the 10–37-GHz range to the surface parameters of effective emissivity and temperature and to the atmospheric parameters of total opacity and temperature. Only two typical values of atmospheric opacity were initially used in the radiative transfer equation representing warmer (sub-Arctic) and colder (Arctic) atmospheres, which were finally replaced with atmospheric opacities computed from linear interpolation between the two values. This adjustment was made following large variations observed in measured opacities, indicating that opacities vary considerably with atmospheric conditions. The ARTIST Sea Ice algorithm [8], [9] presents an interesting approach in that it uses only 90-GHz channels, which have higher spatial resolution than the lower channels (up to 37 GHz). Over ocean water surfaces, there is a large difference between the vertical and horizontal polarization brightness temperature measurements, which remains almost unchanged over the 20–100-GHz frequency range. In contrast, the difference between vertical and horizontal polarization brightness temperatures is almost independent of sea-ice type in the higher frequency bands. Another simplified form of the radiative transfer equation is described in [10]. It relates the satellite brightness temperature polarization difference near 90 GHz to sea-ice concentration, surface emissivity difference at vertical and horizontal polarization, and total atmospheric opacity, the last estimated for typical Arctic environments. The radiative transfer equation was derived for an isothermal atmosphere and also under the basic assumption that the atmospheric influence can be represented as a smooth function of ice concentration that varies between open water and ice-covered scene. The SEA LION algorithm [11] also uses measurements near 90 GHz to retrieve sea-ice concentration but employs a radiative transfer scheme that interactively corrects for weather influences. Meteorological parameters such as water vapor, wind speed, and CLW are obtained externally from remote sensing data or weather prediction models. A similar approach to weather correction is followed in [12], where measurements near 90 GHz are applied within a sea-ice retrieval system. Recognizing the influence of weather effects, a physically based atmospheric treatment was incorporated into the so-called NASA Team 2 algorithm [13]. Significant improvements were reported compared to its heritage algorithm, commonly referred to as the NASA algorithm [14], [15] by incorporating measurements at the 85-GHz frequency channel (in addition to the 19 and 37 GHz), and a radiative transfer model. Atmospheric contribution is accounted for by comparing satellite observations with radiative transfer model simulations for 12 cataloged atmospheric profiles. In this paper, we present a new methodology for the retrieval of sea-ice concentration based on the surface emissivity spectra and temperature, both retrieved simultaneously with the atmospheric parameters using a comprehensive physical algorithm. The integrated retrievals of geophysical parameters and surface emissivities are performed first, optimally and consistent with a sophisticated radiative transfer model that is valid in all weather conditions. This generality of physical treatment constitutes a departure from the aforementioned techniques and
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
a significant leverage in that it can provide monitoring of the sea-ice–ocean–atmosphere system from environmental remote sensing satellites in a wide variety of weather conditions. Next, in a separate step, the surface emissivities and temperature are used as inputs to retrieve sea-ice concentration. The emissivitybased techniques are also preferred to those based on brightness temperatures, because the emissivities are directly related to a material’s intrinsic physical properties. The layout of this paper is as follows. Section II-A provides an overview of the integrated retrieval system, including its mathematical basis and application details. Section II-B describes the new sea-ice concentration algorithm. Section II-C describes the application of the algorithm to the Advanced Microwave Sounding Unit/Microwave Humidity Sensor (AMSU-MHS). Observations from other sensor configurations are being processed and implemented under the same umbrella, e.g., the Special Sensor Microwave Imager/Sounder (SSMI/S) (not reported here). Section III describes the data and the method of intercomparison between the new sea-ice algorithm and the National Oceanic and Atmospheric Administration (NOAA)’s 4-km Interactive Multi-sensor Snow and Ice (IMS) Mapping System taken as reference “ground truth.” In addition, NOAA’s AMSU heritage sea-ice algorithm is also intercompared with IMS. Section IV presents and discusses the intercomparison results, and finally, Section V presents the main conclusions of this study.
II. M ETHODOLOGY A. Integrated Retrievals of Geophysical Parameters Surface emissivities and temperature are retrieved using a comprehensive physical algorithm called the Microwave Integrated Retrieval System (MIRS) [16], [17]. The algorithm is based on the 1-D variational assimilation scheme (1-DVAR). The inversion is performed iteratively and optimally, with the end result being a set of geophysical parameters that are computed simultaneously and, when used as inputs to the forward model, would fit the measured radiances to within the noise level. The retrieval is performed in a reduced space by using empirical orthogonal function decomposition to allow a more stable inversion, a faster retrieval, and to avoid null space errors. MIRS is coupled with the Joint Center for Satellite Data Assimilation (JCSDA) Community Radiative Transfer Model (CRTM) [18], [19], which is valid in clear, cloudy, and precipitating conditions. A comprehensive theoretical description of inverse methods for remote sensing is provided in [20]. Applications of the 1-DVAR method to microwave remote sensing observations are described in [21]–[23]. The mathematical basis for 1-DVAR is to find a vector X, in this case, a set of geophysical parameters, given a vector of measurements Ym , in this case, a vector of radiometric data (radiances or brightness temperatures). Two important assumptions are made, namely, the local linearity of the forward problem and the Gaussian nature of both the geophysical state vector and the simulated radiometric vector around the measured vector. With these assumptions, the mathematical
KONGOLI et al.: NEW SEA-ICE CONCENTRATION ALGORITHM
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Fig. 1. Diagram depicting radiometric and geophysical data flow in MIRS, starting from radiances and ending in the postprocessed surface properties and the column-integrated atmospheric parameters such as total precipitable water (TPW), cloud liquid water (CLW), rain water path (RWP), and ice water path (IWP).
problem can be formulated as a minimization of the following cost function: 1 (X − X0 )T × B −1 × (X − X0 ) J(X) = 2 1 T −1 (Ym − Y (X)) × E × (Ym − Y (X)) + (1) 2 where X0 is the mean background vector, B is the background error covariance matrix, Y is the forward operator, in this case, the CRTM, capable of simulating the measurementlike vector, and E is the measurement and/or forward modeling error covariance matrix. CRTM is applied assuming specular reflection over ocean and diffuse reflection over snow, sea ice, and snowfree land. The downwelling components of radiation above the diffuse surfaces are calculated at an effective observation angle of 53◦ , which is a good approximation for consideration of the downwelling contributions in operational applications [24]. An insightful discussion on the effect of surface type on emissivity is provided in [25] and [26]. The left term in (1) represents the penalty in departing from the background value (a priori information), and the right term represents the penalty in departing from the measurements. 1-DVAR also assumes independence of background and observation errors. This allows one to write separate terms for the aforementioned cost function without cross terms of the form (X − X0)T B −1 (Ym − Y (X))E −1 . The solution that minimizes the cost function is found by solving ∂J(X) = J (X) = 0 ∂X
(2)
which has the following iterative solution: −1 ΔXn+1 = BKnT Kn BKnT + E × [(Ym − Y (Xn )) + Kn ΔXn ]
(3)
where n is the iteration index, K is the matrix of partial derivatives of Y with respect to the state vector X (Jacobian), and ΔX is the departure from the background value. At each iteration n, a new optimal departure from the background is computed given the current geophysical and radiometric departures. This is an iterative-based numerical solution that accommodates
slightly nonlinear problems or parameters with slightly nonGaussian distributions. The unconstrained cost function is used as a metric for deciding if convergence has been reached χ2 = [Ym − Y (X)]T E −1 [Ym − Y (X)] .
(4)
Convergence is first attempted assuming a nonprecipitating atmosphere. Convergence is reached when χ2
