Mapping and Yield Estimation of Cultivated Alfalfa Using Cutting-Induced NDVI Peak–Trough Features from Sentinel-2 Time Series

Alfalfa (Medicago sativa) is an important forage source for grassland agricultural development; developing accurate and efficient methods for alfalfa identification and yield estimation using remote sensing is of considerable interest. However, the traditional methods of identifying large areas of crops and yield estimation have some problems, such as the limited spatial resolution of remote sensing data and a strong dependence on training data. In this study, using Sentinel-2 high-resolution imagery and the Google Earth Engine (GEE) platform, we constructed a cloud-free normalized difference vegetation index (NDVI) time-series dataset and proposed an effective method for alfalfa feature extraction and yield estimation. The results show that: (1) the producer’s accuracy, user’s accuracy, overall accuracy, and Kappa coefficient of alfalfa identification using the trough recognition algorithm were 98.51%, 91.67%, 94.26%, and 0.88, respectively. The total area of cultivated alfalfa identified in the study area in 2020 was estimated at 46,793.21 hm2, and was mainly distributed in the northern region of the Qilian Mountains. (2) NDVI showed a highly significant correlation with alfalfa hay yield, and the power function regression model performed best, with an R2 greater than 0.65. (3) The annual unit hay yield of four alfalfa cuttings was estimated at 17,497.55–32,962.10 kg/hm2, with a total hay yield of 4.838 × 108 kg and an average hay yield of 4464.95 kg/hm2. The proposed method has significant application potential for automated and rapid remote sensing-based identification and yield estimation of large-scale alfalfa cultivation.