The water body extraction algorithm based on satellite remote sensing is mainly aimed at large- and medium-sized lakes or large rivers. When applied to small water bodies, misjudging often occurs. The multi-spectral remote sensing data of the Sentinel 2 satellite has a spatial resolution of 10, 20, and 60 meters; a dual-star time resolution of 5 days; and a high temporal and spatial resolution. Therefore, this paper uses the sentinel-2 green light band (560 nm), red-edge band (705 nm), near-infrared band (842 nm, 865 nm), and short wave-infrared band (2190 nm) for remote sensing reflectance. A new water index algorithm (red edge-based water index, RWI for short) is proposed for the extraction of fine water. The normalized remote sensing reflectance of vegetation, shadow, building, mixed pixel, bare soil, and water body is compared and analyzed. The mechanism explains why RWI has a better effect of extracting fine water bodies compared with other water body indexes.To quantitatively evaluate the advantages of the RWI water body extraction algorithm proposed in this paper, this paper compares several current water body extraction algorithms, including the improved normalized difference water index MNDWI (modified normalized difference water index), multi-band water body index MBWI (multi-band water index), and AWEI (automatic water extraction index). The water body extraction results obtained by several algorithms and the area statistics of the artificial water interpretation of the water body results are calculated using the error formula to obtain RWI. The errors of MNDWI, MBWI, AWEI_sh, and AWEI_nsh are 3.6%, 4.2%, 12.2%, 8.8%, and 19.8%, respectively. Results show that the RWI algorithm has the highest accuracy. At the same time, through analysis, the advantages and disadvantages of several water extraction results are quantitatively evaluated. From the results of image extraction, this paper proposes that the water boundary extraction method extracted by the RWI water body extraction algorithm is better, and it can eliminate mountains, building shadows, and clouds. The effect of shadows can eliminate the effects of mixed pixels.At the same time, this paper carried out multi-temporal water extraction in Xiong’an New District, Shendong Mining Area, and Yongcheng Mining Area. In the time range from January 2016 to December 2018, a total of 43 Sentinel-2 images without clouds were screened, and the small broken water bodies in the three areas of Xiong’an New District, Shendong Mining Area, and Yongcheng Mining Area were selected by the algorithm proposed in this paper. The distribution carried out multi-temporal analysis. Results from statistical area analysis indicate that Xiong’an New District has the largest water body area in spring, followed by summer and autumn. No significant change in water area was observed in the Shendong and Yongcheng mining areas. After careful observation, the results of each phase are very good, the boundary of the small water body is highly differentiated, and no data are misleading or missing, and the algorithm has good applicability and stability.

water extraction;water body index;small water body;Sentinel-2;RWI;MNDWI;MBWI;AWEI