Abstract: Satellite remote sensing technology is the main means for monitoring tidal flat photovoltaics, but the research on photovoltaic remote sensing mainly focuses on land-based systems. While the characteristics of tidal flat photovoltaic are obviously different from that of land photovoltaic, and its background environment is complex, 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 relevant 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 the proposed method is good in different tidal flat photovoltaic areas, the recall rate is 86.28%, and the F1-Score is 0.91. Compared to Support Vector Machine (SVM) and Random Forest algorithms (RF), the recall rate and F1-Score are improved by more than 10% and 7%, respectively. Additionally, the method exhibits the advantages of low false extraction rate, fine edge extraction and fast training speed, making it an effective technical solution for tidal flat photovoltaic remote sensing monitoring and extraction.