Abstract: A great challenge has been found to limit the per capita cultivated land and the scarcity of land resources in China. However, the cultivated land can be abandoned across the country with the rapid growth of industrialization and urbanization. It is crucial to accurately and rapidly monitor the abandoned farmland with high accuracy for farmland protection. Land use can also be evaluated to guarantee national food security for the decision-making on modern agriculture. Among them, there is fragmented farmland with a wide distribution in the Loess Plateau of China. The complex terrain has made some challenge to investigate and monitor the abandoned farmland. This study aims to monitor the abandoned farmland on the loess plateau using feature optimization. Data sources were collected from the Chinese satellite images with high spatial resolution and multi-spectrum. Some feature factors were selected to effectively distinguish the cultivated land from other land types. The prior knowledge was combined with the distinct separability, including the spectral, phenological, topographic and texture features. The spectral feature differences were statistically analyzed from May to October. Spectral time series feature factors were developed to prove the effectiveness and superiority of the schemes. Five types of experimental scheme were also constructed for comparison. The binary particle swarm optimization (BPSO) coupled with the genetic algorithm (GA) was selected to modify the index factor for the optimal selection of feature factors in classification. Random forest (RF) classification and detection were performed post-classification. The spatiotemporal distribution was obtained for the abandoned farmland at field scale between 2011 and 2022. The results showed that the feature optimization improved the accuracy and efficiency in the classification of remotely sensed images with the high spatial resolution. According to an importance assessment for all features, NDVI was found to have the highest MDA score. The final preferred feature was ranked in the order of the green band, red band, near-infrared band, (b1-b2)/(b1+b2), Slope, NDVI, Variance and Contrast. The combination of GA and BPSO exhibited high efficiency in the global search and search speed. The Optimum Index Factor effectively evaluated the overall combination. Feature optimization can be expected to avoid information redundancy and over-fitting issues. The field validation was performed using image interpretation and field investigation. It infers that the abandoned farmland at field scale was recognized from the high spatial resolution images of Chinese satellites with an accuracy as high as 92.48%. There was a 96% overlapping ratio between patches from field investigation and those extracted from images. Among them, the recognition rate of fragmented patches with areas as small as 200-400 m² reached 90.11%. The classification showed that there was abandoned farmland with a total area of 5 492.51 hm² between 2011 and 2022, indicating the maximum abandoned percentage (21.09%) in 2011-2014 and the minimum abandoned percentage (0.99%) in 2020-2022. The large-scale pattern of abandonment of farmland was gradually shifted into a smaller fragmented one by peasant households over this period. As such, the abandoned farmland was monitored at a field scale on the loess plateau region using remote sensing. The finding can provide a strong reference to explore the abandoned farmland using high spatial resolution images of Chinese satellites.