Considering the low accuracy of water area extraction from remote sensing images due to great spatial difference of water quality and spectral characteristics in inland lakes as well as complex tributary structures, a water area extraction algorithm combining spectral principal component analysis (PCA) and support vector machine (SVM) was proposed. Based on GF-1 satellite images, PCA dimensionality reduction analysis was conducted to derive texture feature vectors, including entropy, variance, and differentiation. Together with original 4-band spectrums and normalized difference water index (NDWI), an 8-dimensional optimal feature vector was constructed, and then water area was extracted using the SVM algorithm. With the GF-1 remote sensing image of Chaohu Lake area in flood and non-flood periods used as a case study, the NDWI method, traditional SVM algorithm, and PCA-SVM algorithm were applied to extracting lake water area. Furthermore, based on the PCA-SVM algorithm, the flooding evolution process of Chaohu Lake in the flood period of 2020 was inverted and tracked, and the influences of feature vector combination and the penalty parameter C of SVM were quantitatively analyzed. Results show that the lake area extracted with the PCA-SVM algorithm is complete with continuous tributaries, and mis-extractions due to blue algae and building confusions are significantly overcome；the F1 scores of the PCA-SVM algorithm in the flood and non-flood periods are 95.08% and 97.95%, and the false alarm rates are 5.43% and 1.13%, respectively, demonstrating a significant improvement of the PCA-SVM algorithm in extraction accuracy, as compared with the NDWI method and SVM algorithm.