Large-scale crop mapping is vitally important to agriculrural monitoring and management. However, traditional methods cannot well meet the needs of large-scale applications. Therefore, this study proposed a method for large-scale crop mapping based on multisource remote sensing images. To be specific, 1) harmonic analysis was conducted on normalized difference vegetation index time-series derived from moderate resolution imaging spectroradiometer images and synthetic aperture radar backscattering coefficient time-series derived from Sentinel-1 data, respectively, extracting harmonic-derived phenological features and harmonic-derived backscattering features, and then combined with spectral features from Landsat-8 and Sentinel-2 images to construct the final multisource feature set for crop classification; 2) it employed prior constraints of crop dominance and cropland distribution to reduce misclassifications in large scale crop mapping; and 3) the whole process was conducted on the Google Earth Engine online platform, which can reduce the computational burdens caused by the spatiotemporal data. In the experimental study, we evaluated three crops, including wheat, rapeseed, and corn in Qinhai in 2018, based on the classification and regression tree classifier. The results show that the Jeffries–Matusita distances between crop samples are close to 2, and the overall accuracy is 84.25%. Furthermore, this study found that the distribution of the crops in Qinghai is associated with climate, topography, and cultivation habits.

中文翻译：

---

谷歌地球引擎中多源遥感图像的大规模作物映射

大规模作物测绘对于农业监测和管理至关重要。然而，传统方法不能很好地满足大规模应用的需要。因此，本研究提出了一种基于多源遥感影像的大尺度作物制图方法。具体而言，1)分别对中分辨率成像光谱仪图像的归一化差异植被指数时间序列和Sentinel-1数据的合成孔径雷达后向散射系数时间序列进行谐波分析，提取谐波衍生的物候特征和谐波衍生后向散射特征，然后结合来自 Landsat-8 和 Sentinel-2 图像的光谱特征，构建最终的多源特征集用于作物分类；2）利用作物优势和耕地分布的先验约束来减少大规模作物制图中的错误分类；3）整个过程在谷歌地球引擎在线平台上进行，可以减少时空数据带来的计算负担。在实验研究中，我们基于分类和回归树分类器对 2018 年青海小麦、油菜和玉米 3 种作物进行了评价。结果表明，作物样本之间的Jeffries-Matusita距离接近2，总体准确率为84.25%。此外，本研究发现青海农作物的分布与气候、地形和栽培习惯有关。3）整个过程在谷歌地球引擎在线平台上进行，可以减少时空数据带来的计算负担。在实验研究中，我们基于分类和回归树分类器对 2018 年青海小麦、油菜和玉米 3 种作物进行了评价。结果表明，作物样本之间的Jeffries-Matusita距离接近2，总体准确率为84.25%。此外，本研究发现青海农作物的分布与气候、地形和栽培习惯有关。3）整个过程在谷歌地球引擎在线平台上进行，可以减少时空数据带来的计算负担。在实验研究中，我们基于分类和回归树分类器对 2018 年青海小麦、油菜和玉米 3 种作物进行了评价。结果表明，作物样本之间的Jeffries-Matusita距离接近2，总体准确率为84.25%。此外，本研究发现青海农作物的分布与气候、地形和栽培习惯有关。基于分类和回归树的分类器。结果表明，作物样本之间的Jeffries-Matusita距离接近2，总体准确率为84.25%。此外，本研究发现青海农作物的分布与气候、地形和栽培习惯有关。基于分类和回归树的分类器。结果表明，作物样本之间的Jeffries-Matusita距离接近2，总体准确率为84.25%。此外，本研究发现青海农作物的分布与气候、地形和栽培习惯有关。