Crop planting extraction based on multi-temporal remote sensing data in Northeast China

Crop area and its spatial distribution are generally considered to be essential data inputs for crop yield estimation, assessment of water productivity and adjustment of cropping structure to support science and policy applications focused on understanding the role and response of the agricultural sector to environmental change issues. The objective of this research was to evaluate the applicability of time-series MODIS 250m normalized difference vegetation index (NDVI) data for large-area crop mapping over Northeast China. Spatial pattern of crop planting was obtained based on 16-day time-series MODIS 250m NDVI data from 2007 to 2008, Landsat enhanced thematic mapper plus (ETM+) images, and ground truth data using Optimal Iteration Unsupervised Classification, spectral matching technique (SMT) and Google Earth. Sub-pixel area fraction estimate was applied to estimate cropland area, rice area, spring maize area and soybean area. We found that the position precision was 85.7%, their correlation coefficient compared with statistic was 0.916, 0.685, 0.746 and 0.681 respectively, and that there was significant difference between these groups by using paired samples test. Results indicated that the method can accurately reflect various crop distributions in Northeast China and be applied for large-area crops classification and crop planting extraction．