Abstract: To efficiently monitor citrus greening (also called Huanglongbing in Chinese) at the large plot scale, this study investigated the healthy, yellowing, and Huanglongbing-affected citrus leaves sampled quarterly from the ground in Mengshan County in Guangxi Province. By performing polymerase chain reaction (PCR), chlorophyll content, and hyperspectral detections on these leaf samples, this study analyzed the variation patterns of citrus characteristics under different states, extracting the effective bands and image features for Huanglongbing monitoring. Furthermore, this study constructed a monitoring model for healthy citrus to reduce the objects to be discriminated and identify abnormal citrus growth plots. Finally, this study extracted the Huanglongbing-affected plots using a multi-classifier algorithm based on the effective features from multitemporal Sentinel-2 images. The results of this study indicate that the Huanglongbing-affected and yellowing leaf samples yielded highly similar chlorophyll contents. In March and December, the Huanglongbing-affected citrus exhibited higher chlorophyll content compared to the yellowing citrus. However, the case was the opposite in June and September. The hyperspectral curves suggest that December is a significant period for identifying Huanglongbing and yellowing. The wavelengths ranging from 530 nm to 650 nm and 740 nm to 1050 nm proved effective for diagnosing Huanglongbing and yellowing. The feature indices based on the Sentinel-2 image for December, including the normalized difference vegetation index (NDVI), land surface water index (LSWI), nitrogen reflectance index (NRI), green normalized difference vegetation index (GNDVI), and inverted red edge chlorophyll index (IRECI), could effectively distinguish between healthy and abnormal growth plots of citrus. The feature indices based on the Sentinel-2 images covering periods from October to December and January to February of the following year, including the NDVI, modified normalized difference water index (MNDWI), normalized difference water index (NDWI), GNDVI, inverted red-edge chlorophyll index (IRECI), modified chlorophyll absorption ratio index 2 (MCARI2), normalized difference index based on Landsat bands 4 and 5 (NDI45), and pigment specific simple ratio chlorophyll index (PSSRa), showed advantages in monitoring Huanglongbing. The identification accuracy of Huanglongbing-affected plots in Mengshan County was 86.6 %, with a missed detection rate of 7.8 % and an error rate of 10.4 %. In 2021, Mengshan County held 964 Huanglongbing-affected plots covering an area of 220.13 hm², with an incidence rate of 2.02 % for large-scale Huanglongbing, mainly concentrated in Xinxu, Wenxu, and Mengshan towns, and Xiayi Yao Township. The combination of satellite remote sensing and ground measurement enables large-scale monitoring of Huanglongbing-affected plots. The monitoring technology in this study provides novel insights for the large-scale monitoring, prevention, and control of Huanglongbing.