Abstract: Satellite remote sensing technology is the main technical means for monitoring tidal flat photovoltaic, but the research on photovoltaic remote sensing mainly focuses on land photovoltaic. The characteristics of tidal flat photovoltaic are obviously different from that of land photovoltaic, and its background environment is complicated, so it is difficult to extract accurately. To solve this problem, based on the remote sensing data of Gaofen-1 (GF-1) Satellite, this paper uses Elastic Net to select the spatial spectrum response characteristics of tidal flat photovoltaic, and selects seven features that can characterize tidal flat photovoltaic, namely blue band reflectance, green band reflectance, the mean value, Normalized Difference Water Index (NDWI), information entropy, the difference and ratio of red and blue wavebands, and brightness. On this basis, this paper constructs the tidal flat photovoltaic remote sensing extraction method based on eXtreme gradient boosting algorithm. The experimental results show that the extraction effect of this method is good in different tidal flat photovoltaic areas, the recall rate is 86.28%, and the F1-Score is 0.91. Compared with Support Vector Machine (SVM) and Random Forest algorithm (RF), the recall rate and F1-Score are increased by more than 10% and 7%, respectively, and have the advantages of low false extraction rate, fine edge extraction and fast training speed, which can provide effective technical support for tidal flat photovoltaic remote sensing monitoring and extraction.