Sea ice as a disaster has recently attracted a great deal of attention in China. Its monitoring has become a routine task for the maritime sector. Remote sensing, which depends mainly on SAR and optical sensors, has b...Sea ice as a disaster has recently attracted a great deal of attention in China. Its monitoring has become a routine task for the maritime sector. Remote sensing, which depends mainly on SAR and optical sensors, has become the primary means for sea-ice research. Optical images contain abundant sea-ice multi-spectral in-formation, whereas SAR images contain rich sea-ice texture information. If the characteristic advantages of SAR and optical images could be combined for sea-ice study, the ability of sea-ice monitoring would be im-proved. In this study, in accordance with the characteristics of sea-ice SAR and optical images, the transfor-mation and fusion methods for these images were chosen. Also, a fusion method of optical and SAR images was proposed in order to improve sea-ice identification. Texture information can play an important role in sea-ice classification. Haar wavelet transformation was found to be suitable for the sea-ice SAR images, and the texture information of the sea-ice SAR image from Advanced Synthetic Aperture Radar （ASAR） loaded on ENVISAT was documented. The results of our studies showed that, the optical images in the hue-intensi-ty-saturation （HIS） space could reflect the spectral characteristics of the sea-ice types more efficiently than in the red-green-blue （RGB） space, and the optical image from the China-Brazil Earth Resources Satellite （CBERS-02B） was transferred from the RGB space to the HIS space. The principal component analysis （PCA） method could potentially contain the maximum information of the sea-ice images by fusing the HIS and texture images. The fusion image was obtained by a PCA method, which included the advantages of both the sea-ice SAR image and the optical image. To validate the fusion method, three methods were used to evaluate the fused image, i.e., objective, subjective, and comprehensive evaluations. It was concluded that the fusion method proposed could improve the ability of image interpretation and sea-ice identification.展开更多
Rapid declines in Arctic sea ice have captured attention and pose significant challenges to a variety of stakeholders. There is a rising demand for Arctic sea ice prediction at daily to seasonal time scales, which is ...Rapid declines in Arctic sea ice have captured attention and pose significant challenges to a variety of stakeholders. There is a rising demand for Arctic sea ice prediction at daily to seasonal time scales, which is partly a sea ice initial condition problem. Thus, a multivariate data assimilation that integrates sea ice observations to generate realistic and skillful model initialization is needed to improve predictive skill of Arctic sea ice. Sea ice data assimilation is a relatively new research area. In this review paper, we focus on two challenges for implementing multivariate data assimilation systems for sea ice forecast. First, to address the challenge of limited spatiotemporal coverage and large uncertainties of observations, we discuss sea ice parameters derived from satellite remote sensing that(1) have been utilized for improved model initialization, including concentration, thickness and drift, and(2) are currently under development with the potential for enhancing the predictability of Arctic sea ice, including melt ponds and sea ice leads. Second, to strive to generate the ‘‘best" estimate of sea ice initial conditions by combining model simulations/forecasts and observations, we review capabilities and limitations of different data assimilation techniques that have been developed and used to assimilate observed sea ice parameters in dynamical models.展开更多
基金The National Science Foundation for Young Scientists of China under contract No.41306193the National Special Research Fund for Non-Profit Marine Sector of China under contract No.201105016the ESA-MOST Dragon 3 Cooperation Programme under contract No.10501
文摘Sea ice as a disaster has recently attracted a great deal of attention in China. Its monitoring has become a routine task for the maritime sector. Remote sensing, which depends mainly on SAR and optical sensors, has become the primary means for sea-ice research. Optical images contain abundant sea-ice multi-spectral in-formation, whereas SAR images contain rich sea-ice texture information. If the characteristic advantages of SAR and optical images could be combined for sea-ice study, the ability of sea-ice monitoring would be im-proved. In this study, in accordance with the characteristics of sea-ice SAR and optical images, the transfor-mation and fusion methods for these images were chosen. Also, a fusion method of optical and SAR images was proposed in order to improve sea-ice identification. Texture information can play an important role in sea-ice classification. Haar wavelet transformation was found to be suitable for the sea-ice SAR images, and the texture information of the sea-ice SAR image from Advanced Synthetic Aperture Radar （ASAR） loaded on ENVISAT was documented. The results of our studies showed that, the optical images in the hue-intensi-ty-saturation （HIS） space could reflect the spectral characteristics of the sea-ice types more efficiently than in the red-green-blue （RGB） space, and the optical image from the China-Brazil Earth Resources Satellite （CBERS-02B） was transferred from the RGB space to the HIS space. The principal component analysis （PCA） method could potentially contain the maximum information of the sea-ice images by fusing the HIS and texture images. The fusion image was obtained by a PCA method, which included the advantages of both the sea-ice SAR image and the optical image. To validate the fusion method, three methods were used to evaluate the fused image, i.e., objective, subjective, and comprehensive evaluations. It was concluded that the fusion method proposed could improve the ability of image interpretation and sea-ice identification.
基金supported by the National Key R&D Program of China (2018YFA0605901)the NOAA Climate Program Office (NA15OAR4310163)+1 种基金the National Natural Science Foundation of China (41676185)and the Key Research Program of Frontier Sciences of Chinese Academy of Sciences (QYZDY-SSW-DQC021)
文摘Rapid declines in Arctic sea ice have captured attention and pose significant challenges to a variety of stakeholders. There is a rising demand for Arctic sea ice prediction at daily to seasonal time scales, which is partly a sea ice initial condition problem. Thus, a multivariate data assimilation that integrates sea ice observations to generate realistic and skillful model initialization is needed to improve predictive skill of Arctic sea ice. Sea ice data assimilation is a relatively new research area. In this review paper, we focus on two challenges for implementing multivariate data assimilation systems for sea ice forecast. First, to address the challenge of limited spatiotemporal coverage and large uncertainties of observations, we discuss sea ice parameters derived from satellite remote sensing that(1) have been utilized for improved model initialization, including concentration, thickness and drift, and(2) are currently under development with the potential for enhancing the predictability of Arctic sea ice, including melt ponds and sea ice leads. Second, to strive to generate the ‘‘best" estimate of sea ice initial conditions by combining model simulations/forecasts and observations, we review capabilities and limitations of different data assimilation techniques that have been developed and used to assimilate observed sea ice parameters in dynamical models.
