Using high spatial resolution satellite images to accurately monitor the Ulva prolifera (Ulva) green tide has important application value for early disaster detection, dynamic tracking, and coastal defense. Although there are many methods to extract Ulva from high-resolution satellite images, the performances of different remote sensing algorithms are influenced by the common various observing conditions including seawater background (turbid and clear seawater) and external observation conditions (such as cloud cover, sun glint, and observation geometry). Therefore, taking domestic high-resolution GF-WFV and HJ-CCD images as examples, this work compares the advantages and disadvantages of the normalized difference vegetation index (NDVI), virtual baseline floating macroalgae height (VB-FAH), and tasseled cap greenness (TCG) index in extracting Ulva under different environmental backgrounds. The results show that NDVI, VB-FAH, and TCG algorithms have good performance of Ulva extraction under clear water, turbid water, and weak sun glint conditions. Their accuracy evaluation indicators, F1-score and OA (overall accuracy), are greater than 95.6% and 95.2%, respectively. Under different geometric observation conditions, VB-FAH and TCG indexes are not sensitive to the change of observation geometric angle with high stability, performing better than NDVI. Under the background of cloud cover and strong sun glint, TCG method has the best ability to identify Ulva, and can effectively eliminate the interference of cloud cover and sun glint, whose accuracy evaluation indicators, F1-score and OA, are greater than 95.2% and 95.0%, respectively.