基于Google Earth Engine的淮河流域越冬作物种植面积制图

越冬作物是中国重要的作物类型，其面积的变化不仅对中国粮食产量和经济产生直接的影响，还潜在地影响中国的粮食安全，因此有必要准确绘制越冬作物种植面积图来为决策制定者提供科学参考。该研究以淮河流域为例，基于Google Earth Engine云平台，融合时间序列Landsat-7/8和Sentinel-2A/B卫星影像，采用S-G（Savitzky-Golay）滤波器重构作物时间序列的归一化差异植被指数（Normalized Difference Vegetation Index，NDVI），根据不同植被类型物候期的差异，选取越冬作物生长旺盛期NDVI最大值、越冬作物播种期和收获期中相应的NDVI最小值和中位数，在像元尺度上构建越冬作物提取算法，绘制淮河流域越冬作物的种植面积。研究结果表明，所构建算法能够精确提取淮河流域越冬作物的种植面积，总体精度为95.8%，Kappa系数为0.912，该研究可为作物面积的提取和监测提供方法参考。

Mapping of the winter crop planting areas in Huaihe River Basin based on Google Earth Engine

Abstract: The winter crop has been one of the important crop types in China. Accurate and timely spatio-temporal distribution of planting area directly determines the grain output and economy, as well as the national food security. Taking the Huaihe River Basin as an example, this study aims to extract the planting areas of winter crops according to the phenology period using the Google Earth Engine cloud platform and the fusion of Landsat-7/8 and Sentinel-2A/B images. Firstly, a dataset of time-series images was constructed with a spatial resolution of 30m. A CFMask algorithm was selected to preprocess the images, thereby calculating the Normalized Difference Vegetation Index (NDVI). More importantly, the maximum NDVI of all high-quality images within 10 days was used to obtain time-series data with equal time intervals. The linear interpolation was utilized to fill the pixels without high-quality images. Savitzky-Golay (S-G) filtering (a second-order filter with a moving window of 9 observations) was adopted to smooth the NDVI time series for the removal of noise. As such, a smoothed NDVI time series was obtained with a 10-day interval. Secondly, the peak growth, sowing, and harvest periods were determined to select sample points of winter crops with different spatial distributions, according to the NDVI time series. Subsequently, the winter crops were sowed in mid-late October, when the NDVI values were the lowest. The NDVI values gradually increased, after the emergence of seedlings in early November. The crops stopped growing in January during the overwintering period, where the NDVI stayed the same over the whole period. Furthermore, the NDVI resumed growing and gradually reached the peak growth period, when the winter crops turned green in February. After that, the NDVI reached the peak at the heading stage, and then gradually decreased. Correspondingly, the NDVI dropped to the bottom, when the harvest was over from the end of May to June. According to these characteristics in the process of winter crops growth, the peak growth period was determined from March 20, 2018, to April 20, 2018, the sowing period was determined from October 11, 2017, to November 10, 2018, and the harvest period was determined from May 20, 2018, to June 30, 2018. Particularly, the maximum NDVI was achieved in the peak growth period and the minimum and median of NDVI in the sowing and harvest period. Finally, the classification model of a decision tree was constructed, according to the NDVI boxplots of winter crops and non-winter crops at different time periods. The planting area map of winter crops was also generated for the Huaihe River Basin. The results showed that the planting area of winter crops was 8.762×106 hm2 in the Huaihe River Basin in 2018. Specifically, the user accuracy was 0.926, the producer accuracy was 0.970, the total accuracy was 0.958, and the Kappa coefficient was 0.912. Consequently, the large-scale planting area of winter crops was extracted accurately for the decision-making in similar areas.