The detailed area and spatial distribution of irrigated and rainfed wheat can help
forecast wheat yield and study water use efficiency. However, the similar spectral characteristics
of irrigated and rainfed wheat make it difficult to separate them with low-spatial resolution or
several high-spatial resolution images on the high heterogeneity of the southern Loess Plateau.
To solve this challenge, this study used the Spatial and Temporal Adaptive Reflectance Fusion
Model (STARFM) and Enhanced STARFM (ESTARFM) to generate time series of the normalized
difference vegetation index (NDVI) and the normalized difference water index (NDWI) at
a 30-m resolution by fusing Moderate Resolution Imaging Spectroradiometer and Landsat data.
Then, the phenological feature extracted from the predicted NDVI is combined with an auxiliary
dataset to classify irrigated and rainfed wheat using the support vector machine classifier. An
overall classification accuracy of 93.7% and a Kappa coefficient of 0.91 are achieved. Compared
with corresponding high-resolution Google Earth images, the spatial distribution of the classification
was consistent with actual land cover. This study demonstrates that the classification
approach could classify irrigated and rainfed wheat in high heterogeneity regions and crops
with smaller spectral characteristic differences. Moreover, it could be implemented across larger
geographic regions