1. Introduction
The presence of sea ice affects oceanic and atmospheric temperatures, as well as circulation patterns [
1]. The presence of sea ice moderates the reduction of tidal levels and constrains the movement of tidal currents, thereby reducing both tidal range and velocity. Additionally, it serves as a barrier to dampen wave height, hindering the transmission of waves and associated phenomena. Since sea ice has a considerably higher solar radiation reflectivity than sea water, the presence of sea ice inhibits the direct exchange of energy between the ocean and earth’s atmosphere, thus regulating the global atmosphere–ocean energy balance [
2]. Additionally, the reduction in sea ice cover serves to exacerbate the Earth’s greenhouse effect, thus impelling unforeseen consequences upon global climatic patterns. Arctic sea ice emerges through a process in which sea temperatures fall below −1.8 °C. Arctic sea ice surface temperature (IST) is a crucial index of Arctic sea ice change, and its change is extremely significant within the background of global climate warming. Studying the laws that govern change in marine environments and ecosystems through the observations and analysis of IST can aid a more thorough understanding of the impact of global warming on these environments.
Two primary methods are employed to acquire IST: field measurement and satellite remote sensing. In polar regions, the harsh climate conditions render it impracticable to establish numerous meteorological observatories, making it difficult to conduct in situ observations on the factors governing the energy and momentum equilibrium of sea ice. One approach to bridge this observational disparity is the utilization of satellites to measure the properties of sea ice [
3]. Compared with traditional measurement methods dependent on ships and buoys, satellite remote sensing offers numerous advantages including near real-time, all-weather, wide and long-term repeated coverage, which is better adapted to characteristics of ocean phenomena and can fill the gaps in field-measured data [
4]. Depending on the sensor type, the main IST algorithm for remote sensing inversion can be divided into a thermal infrared inversion algorithm and a microwave inversion algorithm. Thermal infrared sensors are characterized by high spatiotemporal resolution, and are widely used for IST inversion. However, the application of satellite infrared IST inversion is constrained by the existence of clouds. The IST data obtained in the presence of thin cirrus clouds are usually biased. Microwave radiometers are also widely used for sea ice observation [
5]. These receive microwave radiation that penetrates clouds, are unaffected by meteorological conditions such as sunlight and clouds, and can detect sea ice throughout the day in different meteorological conditions. Their disadvantage, however, is their relatively low spatial resolution.
Many scholars use microwaves to retrieve surface temperatures. In 1994, Key et al. [
6] proposed that if the surface type and emissivity of floating ice was known, the data from the Special Sensor Microwave Imager (SSM/I) could provide useful IST data. However, there needs to be some way to reduce the uncertainty in the ice classification in the floes, and the Synthetic Aperture Radar (SAR) data may need to be combined. In 1997, Comiso and Cavalieri [
7] used the 6 GHz channel of the Scanning Multichannel Microwave Radiometer (SMMR), carried by Nimbus-7, to calculate sea ice concentration (SIC) and IST. This approach addressed the limitations associated with using vertical polarization data at 18 and 37 GHz in the Bootstrap algorithm, thus enhancing its performance [
8]. In 2000, Wentz [
9] used the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) to describe a multiple linear regression method suitable for ocean observations. In 2006, Li and Guan et al. [
10] identified a forward model for the study of inversion algorithms, and proposed a physics-based multiple linear regression algorithm for estimating sea surface temperature (SST) derived from spaceborne microwave radiometry. Comparison with data from the European Centre for Medium-Range Weather Forecasts (ECMWF) showed the bias was 0.02 °C ± 0.68 °C and the correlation coefficient (Corr) was 0.993. In 2007, Wentz et al. [
11] divided the AMSR-E SST retrieval algorithm into two stages for a comprehensive elucidation of the algorithm’s structure and training methodology. In 2012, Holmes et al. [
12] proposed the use of vertical polarized brightness temperature (
Tb) at 37 GHz, rather than thermal infrared satellite sensors, for measurement of land surface temperature (LST). The theoretical bias was found to be within 1 K for 70% of vegetated land areas worldwide. In 2014, Scott et al. [
13] proposed the multimodality guided variational (MGV) approach, which combines data captured by a passive microwave sensor with data obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) in order to estimate IST. An evaluation of the MGV method’s performance was conducted by comparing the sea ice thickness derived from the swath surface temperature and the sea ice thickness obtained from the surface temperature by MGV. In 2015, Lee [
14] used the combined Fresnel equation, which was derived analytically by Sohn and Lee [
15], to retrieve various parameters, including emissivity, physical temperature, and refractive index of sea ice from microwave measurements, utilizing the
Tb at 6.9 GHz. Then, in 2018, Lee [
16] used SSM/I to invert winter snow and ice interface temperature (SIIT) in the Arctic, incorporating calculations for ice surface roughness and snow and ice volume scattering to determine apparent emissivity, and utilized ice mass balance (IMB) drift buoy data from the U.S. Navy Cold Regions Research and Engineering Laboratory (CRREL) to verify at the depth of zero ice. At present, inversion of microwave radiometry temperature observation is mostly used to retrieve SIIT, SST, or LST, and is seldom applied to IST.
Existing IST products include MODIS MYD29, Advanced Very High Resolution Radiometer (AVHRR), Visible Infrared Imaging Radiometer Suite (VIIRS), and some fusion products, such as the Arctic Ocean sea and ice surface temperature data (L4 SST/IST) and the Northern High Latitude Level 3 Sea and Sea Ice Surface Temperature (NHL L3 SST/IST) data. In a previous paper, we evaluated the Arctic
Tb data of FengYun-3D Medium Resolution Spectral Imager-II (FY-3D/MERSI-II) thermal infrared channels (channels 24 and 25), and used the calibrated
Tb data from MERSI-II to retrieve the IST with good results. From January to May, the monthly mean bias of MERSI IST and L4 IST data was less than 2.7510 °C, with the Std of less than 3.5774 °C [
17]. Most of these products have been validated, with absolute values of bias less than 3 °C, and the standard deviation (Std) and root mean square error (RMSE) mostly below 4.5 °C [
1,
17,
18,
19,
20,
21,
22].
In this study, the channels Tb data of FengYun-3D Microwave Radiation Imager (FY-3D/MWRI) were used to invert the IST. Compared with microwave IST inversion algorithm, IST inversion algorithm using thermal infrared band has been relatively mature. There are few algorithms and studies on microwave IST data inversion, and no IST inversion algorithm for Tb data observed by FY-3D/MWRI has been published. The FY-3D and Aqua satellites both operate in the afternoon, making their sensors suitable for comparisons. Measured data on IST parameters in the Arctic are difficult to obtain. We aimed to establish the relationship between Tb data in FY-3D/MWRI and Aqua/MODIS MYD29 IST data to obtain a microwave IST inversion equation in 2019. The retrieved MWRI IST data were assessed against the NHL L3 IST product and the Operation IceBridge (OIB) KT19 IR Surface Temperature data.
5. Conclusions
Based on the FY-3D/MWRI microwave Tb data and MODIS MYD29 IST data from January to December 2021, we inverted IST using multivariate statistical regression. NHL L3 IST data and OIB IST data were used as validation data. We evaluated and analyzed the MWRI IST data for the months of January to April, November, and December 2019.
A comprehensive comparison between the inversion-obtained MWRI IST and the NHL L3 IST for the six months showed that the results were better: the Corr was 0.81, the mean bias was −1.6 °C, and the Std was 4.02 °C. Overall, the inversion-obtained MWRI IST was lower than that of the NHL L3 IST data, and the matching points exhibited a nearly symmetrical distribution on either side of the 1:1 line. The MWRI daily IST distribution is basically consistent with the MYD29 and NHL L3 daily ISTs. When the inversion-obtained MWRI IST was compared with OIB KT19 IST data, there were 3254 matching data points in April, with a bias of 0.51 °C, and a Std of 4.34 °C. The obtained result exhibits a level of accuracy on par with that of most IST products. We thus infer that the FY-3D/MWRI Tb data have the potential to be utilized for inverting IST through multiple linear regressions in the Arctic.
We used the MYD29 IST data as the baseline data. Despite the validation of the MODIS MYD29 product, current measurements obtained from Arctic buoys are lacking, and this represents an area for the future improvement. The microwave data can be used as supplementary data as it is not affected by clouds, although its resolution is low. Inversion of microwave IST data and fusion of thermal infrared data and microwave data are the directions for additional discussion and analysis.