Abstract: Alfalfa is a perennial crop to serve as a key feed variety for the development of herbivorous animal husbandry and food safety in China. Timely and accurate acquisition of alfalfa spatial distribution can greatly contribute to the data support for the scientific management of grass production. In this study, a new extraction was proposed to obtain the remote sensing characteristics of alfalfa using the Findpeaks function of MATLAB, combined with the change of spectral reflectance of alfalfa with the growth stage. A Normalized Difference Vegetation Index (NDVI) dataset was also constructed using high-resolution GF-1/WFV (Wide Field of View) and Sentinel-2 remote sensing images in Jinchang City, Gansu Province, China. The limitation of automatic identification and area extraction was solved to extract the spatial distribution of alfalfa via determining Minimum Peak Prominence (MPP) value. Firstly, an analysis was made on the time series of alfalfa NDVI. It was found that the alfalfa NDVI increased many times, as the peak value decreased in one year. Specifically, there were many peaks and troughs in the NDVI time series curve, among which the peaks represented the high value of NDVI in a growing period (the flourishing period of alfalfa growth and development), whereas, the troughs reflected the alfalfa from the peak period to the cutting state. Then, a field investigation was conducted to determine the peaks and troughs number of alfalfa, where the trough number was 3-4, and the peak number was 3-5 in the NDVI time series curve. Thirdly, a verification of position accuracy found that the classification accuracy increased when the value of MPP was in the range of 0.3 to 0.4 and reached the maximum when the value of MPP was 0.4, while the classification accuracy tended to decrease with the increase of MPP value. Therefore, the MPP value of 0.4 was set to extract the potential spatial distribution of alfalfa using the Findpeaks function of MATLAB software. As such, the spatial distribution dataset of alfalfa planting area was established in the study area by masking the terrain and cultivated land with the removal of forests and other land objects. Finally, the spatial distribution of alfalfa in the study area in 2019 was obtained using ENVI software for the subsequent classification post-processing, such as multiplicity filtering and fragment elimination. The results show that: 1) The recognition accuracy and Kappa coefficient of Sentinel-2 remote sensing data were more than 85% and 0.7, better than that of GF-1/WFV. The larger density of NDVI time series curve in Sentinel-2 data than that of GF-1/WFV was attributed to better capture the key time points of alfalfa. 2) In terms of identification methods, it was found that the find troughs presented the higher overall accuracy, Kappa coefficient, user accuracy, and mapping accuracy of extracted alfalfa in the study area, compared with the find peaks. 3) The find troughs using Sentinel-2 image performed the best for the remote sensing recognition of alfalfa, with an overall accuracy of 92.25%, a Kappa coefficient of 0.81, and a position accuracy of 86.44%, indicating an excellent monitoring performance in terms of spatial location. 4) The spatial distribution of alfalfa showed a gradual increase from the north to south, while most continuous areas were mainly concentrated in the south-central and southwest, and there was only sporadic distribution in the north of the study area. Specifically, the alfalfa planting area that identified by find troughs using Sentinel-2 image was 15 449.07 hm2 in 2019, of which the alfalfa area of Jinchuan district was 1 353.42 hm2, accounting for 8.76% of the total alfalfa area of Jinchang, and the alfalfa area of Yongchang county was 14 095.65 hm2, accounting for 91.24% of the total area. The research data confirmed that the find troughs using Sentinel-2 remote sensing data can be expected to effectively identify alfalfa in the study area. The finding can provide important practical support to the refined management of pasture for the precise monitoring of grass production.