The importance of mapping regional and global cropland distribution in timely ways has been recognized, but separation of crop types and multiple cropping patterns is challenging due to their spectral similarity. This study developed a new approach to identify crop types (including soy, cotton and maize) and cropping patterns (Soy-Maize, Soy-Cotton, Soy-Pasture, Soy-Fallow, Fallow-Cotton and Single crop) in the state of Mato Grosso, Brazil. The Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) time series data for 2015 and 2016 and field survey data were used in this research. The major steps of this proposed approach include: (1) reconstructing NDVI time series data by removing the cloud-contaminated pixels using the temporal interpolation algorithm, (2) identifying the best periods and developing temporal indices and phenological parameters to distinguish croplands from other land cover types, and (3) developing crop temporal indices to extract cropping patterns using NDVI time-series data and group cropping patterns into crop types. Decision tree classifier was used to map cropping patterns based on these temporal indices. Croplands from Landsat imagery in 2016, cropping pattern samples from field survey in 2016, and the planted area of crop types in 2015 were used for accuracy assessment. Overall accuracies of approximately 90%, 73% and 86%, respectively were obtained for croplands, cropping patterns, and crop types. The adjusted coefficients of determination of total crop, soy, maize, and cotton areas with corresponding statistical areas were 0.94, 0.94, 0.88 and 0.88, respectively. This research indicates that the proposed approach is promising for mapping large-scale croplands, their cropping patterns and crop types.

人们已经认识到及时绘制区域和全球农田分布图的重要性，但是由于它们的光谱相似性，将作物类型和多种种植方式分开是一项挑战。这项研究开发了一种新的方法来确定马托州的作物类型（包括大豆，棉花和玉米）和种植方式（大豆玉米，大豆棉，大豆草皮，大豆休闲，休闲棉和单一作物）巴西格罗索。本研究使用了中分辨率成像光谱仪（MODIS）的2015年和2016年归一化植被指数（NDVI）时间序列数据以及实地调查数据。该提议方法的主要步骤包括：（1）通过使用时间插值算法去除受云污染的像素来重建NDVI时间序列数据，（2）确定最佳时期并开发时间指标和物候参数，以将耕地与其他土地覆被类型区分开；（3）开发作物时间指标，以利用NDVI时间序列数据和将作物模式分组为作物类型来提取作物模式。决策树分类器用于基于这些时间索引来映射种植模式。使用2016年Landsat影像中的耕地，2016年实地调查中的耕作模式样本以及2015年中作物类型的种植面积来进行准确性评估。农田，种植方式和作物类型的总体准确度分别约为90％，73％和86％。具有相应统计面积的总作物，大豆，玉米和棉花面积的确定测定系数分别为0.94、0.94、0.88和0.88。