ABSTRACT

Timely mapping of corn and soybean plays an important role in food security in the USA. A subpixel fraction derived from Land Remote-Sensing Satellite (System, Landsat) imagery during growing seasons is desired in order to help local farmers monitor crop growth and manage them in a timely fashion. However, two obstacles need to be surpassed before such fractional information can be made available: 1) the endmember spectral reflectance of corn and soybean varies with time and location; 2) no methods have been tested for deriving fractional maps of corn and soybean throughout the growing season. Therefore, in this research, we have set aside two objectives: 1) To account for endmember variability of corn and soybean during their growing seasons; 2) To derive multi-temporal fractional maps for corn and soybeans with Landsat and monitor the growing status of corn and soybean. Accordingly, we employed three endmember optimization methods and the state-of-the-art unmixing method Multiple Endmember Spectral Mixture Analysis (MESMA) to acquire corn and soybean fractional maps based on Google Earth Engine (GEE). Applying the method on Landsat 8 images from April to September 2017, we generated multi-temporal fractional maps of corn and soybean in Grundy County and analysed their changes. Up to 94.76% of our study area was successfully explained by the unmixing model. The crop fraction in both corn and soybean fields was about 15.00% during the planting stage, and increased to nearly 80.00% in the peak growing season. The crop fractions remained high during harvest, which could be attributed to crop residues in the field. These findings correspond well with the growth stages provided by the United States Department of Agriculture (USDA). That the corn growing season was earlier than soybeans was also well represented by the fractional change analysis. Moreover, among all the fractional maps, the results in the peak growing time (29 July 2017 in this study) had the highest agreement with classification results, with an overall accuracy of 85.07%. This research shows the great potential of monitoring corn and soybean growth conditions with fractional maps. The methods in this study, implemented in GEE, can be easily transferred to other crops and other locations.

摘要

在美国，及时绘制玉米和大豆图谱对粮食安全起着重要作用。为了帮助当地农民监控作物生长并及时进行管理，需要在生长季节从陆地遥感卫星（系统，Landsat）影像中获得的亚像素部分。但是，在获得此类分数信息之前，需要克服两个障碍：1）玉米和大豆的末端成员光谱反射率随时间和位置而变化；2）没有测试过整个生长期的玉米和大豆分数图的方法。因此，在这项研究中，我们预留了两个目标：1）考虑玉米和大豆在其生长季节的最终成员变异性；2）用Landsat导出玉米和大豆的多时间分数图，并监测玉米和大豆的生长状况。因此，我们采用了三种端成员优化方法和最新的解混方法多端成员谱混合分析（MESMA）来基于Google Earth Engine（GEE）获取玉米和大豆的分数图。将方法应用于2017年4月至9月的Landsat 8图像，我们生成了Grundy县玉米和大豆的多时相分数图，并分析了它们的变化。分解模型成功地解释了我们研究区域的高达94.76％。玉米和大豆田在播种阶段的作物比例约为15.00％，在高峰生长期增至近80.00％。收割期间农作物的比例仍然很高，这可能归因于田间的农作物残留。这些发现与美国农业部（USDA）提供的成长阶段非常吻合。分数变化分析也很好地说明了玉米的生长季节早于大豆。此外，在所有分数图中，峰值生长时间（本研究于2017年7月29日）中的结果与分类结果具有最高的一致性，总准确度为85.07％。这项研究显示了利用分数图监测玉米和大豆生长状况的巨大潜力。在GEE中实施的这项研究中的方法可以轻松地转移到其他农作物和其他地方。分数变化分析也很好地说明了玉米的生长季节早于大豆。此外，在所有分数图中，峰值生长时间（本研究于2017年7月29日）中的结果与分类结果具有最高的一致性，总准确度为85.07％。这项研究显示了利用分数图监测玉米和大豆生长状况的巨大潜力。在GEE中实施的这项研究中的方法可以轻松地转移到其他农作物和其他地方。分数变化分析也很好地说明了玉米的生长季节早于大豆。此外，在所有分数图中，峰值生长时间（本研究于2017年7月29日）中的结果与分类结果具有最高的一致性，总准确度为85.07％。这项研究显示了利用分数图监测玉米和大豆生长状况的巨大潜力。在GEE中实施的这项研究中的方法可以轻松地转移到其他农作物和其他地方。这项研究显示了利用分数图监测玉米和大豆生长状况的巨大潜力。在GEE中实施的这项研究中的方法可以轻松地转移到其他农作物和其他地方。这项研究显示了利用分数图监测玉米和大豆生长状况的巨大潜力。在GEE中实施的这项研究中的方法可以轻松地转移到其他农作物和其他地方。