海冰信息在船舶运输、天气预报和全球气候预测等领域都起着重要作用。一直以来微波遥感是卫星监测海冰密集度的主要手段，目前基于可见光遥感的中分辨率海冰密集度产品还较少，其中只有NOAA发布了相关业务化产品，但其所采用的算法对低密集度海冰反演准确性仍存在提升空间。本文在Liu提出的算法基础上进行改进，提出了最邻近像素法确定纯冰典型反射率的改进算法，使用MODIS数据作为数据源计算海冰密集度，并使用30 m空间分辨率的Landsat 8 OLI数据作为验证数据进行对比验证。结果表明改进算法可以提高低密集度海冰的反演准确性，改善Liu算法存在过高估计的不足，在海冰密集度0—50%的情况下，Liu算法的平均偏差为13%，标准偏差为38%，改进算法的平均偏差为5%，标准偏差为32%；在海冰密集度0—100%的情况下，Liu算法的平均偏差为4%，标准偏差为32%，改进算法的平均偏差为-3%，标准偏差为28%。针对冰水过渡、碎冰覆盖等低密集度海冰区域，改进算法准确性更高。

Sea ice concentration, which refers to the percentage of sea ice in an area, is an important parameter describing the characteristics of sea ice. Remote sensing monitoring of sea ice is crucial to understand the role of polar regions in the global climate system and global warming. Sea ice information is of great importance in ship transportation, weather forecasting, and global climate forecast. At present, passive microwave radiometers are the main means of monitoring sea ice concentration; however, because microwaves present wavelength limitations, conventional sea ice concentration products cannot be used in practical applications in small areas. Visible-light-infrared remote sensing can retrieve sea ice concentrations, and its advantages over other sensing methods include high spatial resolution.This work focuses on the development of a sea ice concentration algorithm suitable for medium-resolution images. Few medium-resolution sea ice concentration products based on visible-light remote sensing are available, and only NOAA has released relevant operational products. However, the accuracy of its algorithm for low-concentration sea ice inversion is low. This paper proposes an improved algorithm based on the existing algorithm to determine the ice node via the nearest-pixel method. MODIS data are used as a data source to calculate the sea ice concentration, and Landsat 8 OLI data with a spatial resolution of 30 m are used for comparative verification.Results show that the improved algorithm can improve the inversion accuracy of low-concentration sea ice. The Liu algorithm has the disadvantage of overestimation. In the case of sea ice concentrations of 0% — 50%, the average deviation of the Liu algorithm is 13%, and its standard deviation is 38%. By comparison, the average deviation of the improved algorithm is 5%, and its standard deviation is 32%. In the case of sea ice concentrations of 0% — 100%, the average deviation of the Liu algorithm is 4%, and its standard deviation is 32%. By comparison, the average deviation of the improved algorithm is -3%, and its standard deviation is 28%. In the case of sea ice concentrations of 0% —50%, the accuracy of the improved algorithm is better than that of the Liu algorithm. When the sea ice concentration is close to 100%, the results of the two algorithms are highly similar. Overall, the improvement effect of the proposed algorithm is related to the actual sea ice concentration, and the improved algorithm is more accurate than the Liu algorithm for low-concentration sea ice regions, such as ice-water transitions and broken ice coverage.