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Comparison of the classification accuracy of three soil freeze–thaw discrimination algorithms in China using SSMIS and AMSR-E passive microwave imagery

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This study compared the classification accuracies of three soil freeze–thaw discrimination algorithms in China using Special Sensor Microwave Imager/Sounder (SSMIS) and Advanced Microwave Scanning Radiometer Earth Observing System (AMSR-E) passive microwave imagery from 2008. The algorithms used were the dual-index algorithm (DIA), the decision tree algorithm (DTA), and the discriminant function algorithm (DFA). The comparison was conducted based on 0 cm land-surface temperature data from 756 meteorological stations across China by constructing error meta-matrices, and it is divided into two parts. The first part compared the overall classification accuracies from two aspects: temporal variation and spatial distribution. In the second part, the classification accuracies of frozen and thawed soils were evaluated. Results showed that both SSMIS and AMSR-E data can be applied to the DIA, DTA, and DFA algorithms, although they were originally developed from different satellite data sets. However, each of the three algorithms has its own advantages and disadvantages. Possible improvements in the three algorithms for future work are also discussed.
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Document Type: Research Article

Affiliations: State Key Laboratory of Remote Sensing Science and School of Geography and Remote Sensing Science, Beijing Normal University, Beijing, 100875, China

Publication date: November 17, 2014

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