NDVI-based winter wheat unmixing for accurate acreage estimation

Based on the linear mixing model, the authors suggested the NDVI linear unmixing model, where the multi-temporal NDVI data are used to substitute bands´ data. In this study, we used Landsat-TM and NOAA-AVMRR NDVI data in combination. Firstly, the land use classification was made using TM to get the distribution of winter wheat land. Then, the Landsat TM data were used to estimate the NDVI value for each land use end-members of the NOAA-NDVI data. Finally, we applied the NDVI linear unmixing model to estimate the winter wheat acreage and compare it with winter wheat acreage in TM classification. The result shows that there is a 5.1% error in the total area of winter wheat proportion between the pixel-unmixing classification and the classification using Landsat TM data. They have very similar spatial distribution patterns for the winter wheat patches in the 2 land use classification images. This method is satisfactory, which is promising for large-area winter wheat monitoring with coarse-resolution remotely-sensed imagery