Abstract: In cloudy and rainy areas, the humid and hot climate and cloud contamination during the rainy season often cause the loss of optical data. Hence, optical data alone fail to enable the accurate monitoring of abandoned land. This study proposed a method for monitoring abandoned land in cloudy and rainy areas based on multisource remote sensing data. By integrating optical and synthetic aperture Radar (SAR) remote sensing data, this study extracted the multitemporal optical and SAR-derived features of vegetation and assessed their importance using the GINI index. Employing the random forest classifier, this study mapped the spatial distribution of abandoned land in Jiexi County in 2021. The results show that the proposed method achieved a relatively high accuracy in identifying abandoned land in cloudy and rainy areas, yielding an overall accuracy of 87.0%. This value represents an improvement of 6.7 and 13.8 percentage points, respectively, compared to the results derived solely from optical and SAR remote sensing features. The analysis reveals that the normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), polarization entropy, normalized difference water index (NDWI), and anti-entropy are crucial for identifying abandoned land. Additionally, key months for distinguishing abandoned from non-abandoned land include February, April, June, August, and December. This study establishes a monitoring model for abandoned land based on multisource features and multitemporal phases, providing technical support for monitoring abandoned land in cloudy and rainy areas.