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Polar sea ice plays a critical role in regulating the global climate. Seasonal variation in sea ice extent, however, coupled with the difficulties associated with in situ observations of polar sea ice, makes remote sensing the only practical way to estimate this important climatic variable on the space and time scales required. Unfortunately, accurate retrieval of sea ice extent from satellite data is a difficult task. Sea ice and high cold clouds have similar visible reflectance, but some other types of clouds can appear darker than sea ice. Moreover, strong atmospheric inversions and isothermal structures, both common in winter at some polar locations, further complicate the classification. This paper uses a combination of feed-forward neural networks and 1.6-µm data from the new Chinese Fengyun-1C satellite to mitigate these difficulties. The 1.6-µm data are especially useful for detecting illuminated water clouds in polar regions because 1) at 1.6 µm, the reflectance of water droplets is significantly higher than that of snow or ice and 2) 1.6-µm data are unaffected by atmospheric inversions. Validation data confirm the accuracy of the new classification technique. Application to other sensors with 1.6-µm capabilities also is discussed. [ABSTRACT FROM AUTHOR]

Presents a study that reviewed the parameter-elevation regression on independent slopes model (PRISM), its Alaskan input data and its limitations. Patterns of variability in temperature and precipitation from PRISM modeled maps; Relation of the patterns to climactic processes; Alaskan vegetation ecosystem map based on a phenological classification from advanced very high resolution radiometer data.

Presents information on a study which developed the neural network (NN) approaches for detecting clear land, cloud and areal extent of snow in satellite data. Steps of a neural network procedure; Background material and design of the neural networks; Training sets, network training and linear mixing model classifications of NN; Results and discussion; Conclusions.

Presents a study which proposed a hybrid method for automatic clustering of satellite-observed natural scenes. Complex task of classification of natural features in multivariate satellite data; Improved Split-And-Merge Clustering algorithm; ISODATA clustering algorithm; Improved image clustering of natural scenes; Results and discussion.

Provides information on a study which developed a method to correct the effect of anisotropic reflectance on Advanced Very High Resolution Radiometer (AVHRR) images in Channels 1 and 2. AVHRR images which show dependence of the satellite-observed reflectance of snow on the geometry; Simulation of the anisotropic reflectance of snow over sea ice; Operational algorithm for anisotropic correction.

Presents a study which considered arbitrary viewing and illumination conditions. General case of cloud shadow detection; Extension of cloud shadow detection to arbitrary viewing and illumination conditions; Validation of cloud shadow retrieval; Conclusions.

Presents information on a study which investigated the bidirectional reflectance anisotropy in Advanced Very High Resolution Radiometer (AVHRR) Channel 1 and 2 spectral regions for snow- and ice-covered surfaces. Description and development of the radiative transfer model; Modeling of the bidirectional reflectance for snow and sea ice surfaces; Conclusion.

Presents information on a study which tracked the behavior of the geostationary meteorological satellite (GMS)-5 visible infrared spin scan radiometer (VISSR) from July 1, 1995 through June 30, 1996. Overview of the calibration method; Theoretical basis for the calibration method; Results and discussion.

Focuses on a study which presented a multispectral algorithm used in cloud shadow detection and the removal in daytime AVHRR scenes over land. Importance of the accurate detection of cloud shadows in AVHRR scenes; Methodology used to conduct the study; Results of the study.