在密歇根州东南部使用Sentinel-2卫星数据和谷歌地球引擎绘制覆盖作物物种的地图
Xuewei Wang1, Jennifer Blesh1, Preeti Rao1
1School for Environment and Sustainability, University of Michigan, Ann Arbor, MI, United States.
Frontiers in artificial intelligence
|September 4, 2023
概括
这项研究表明,Sentinel-2卫星数据如何准确地绘制单个覆盖作物物种,如谷物麦和红三叶草,对于了解它们在农业中的环境效益至关重要.
科学领域:
- 农业科学 农业科学
- 遥感 遥感 遥感 遥感
- 生态生态学 生态生态学
背景情况:
- 覆盖作物可以改善土壤健康,减少营养损失,但在规模上对其采用了解甚少.
- 识别特定的覆盖作物物种是至关重要的,因为不同种类提供不同的生态系统服务.
- 遥感提供了一种具有成本效益的方法,用于监测大面积的覆盖作物的采用.
研究的目的:
- 用Sentinel-2卫星数据绘制谷物麦 (草) 和红三叶草 (豆类) 覆盖作物的采用地图.
- 评估区分单个覆盖作物物种的准确性.
- 为了确定关键的光谱波段和时间窗口,以准确地对覆盖作物进行分类.
主要方法:
- 利用 Sentinel-2 卫星图像和一个随机的森林分类器.
- 在密歇根州莱辛河流域绘制谷物麦和红三叶草的地图.
- 分析了红边带的重要性和特定的图像采集日期 (4月底,7月初).
主要成果:
- 农业土地覆盖地图的准确度中等至高 (总体为83%).
- 谷物麦和红三叶草分类给出了F1分数在0.7和0.77.7之间.
- 确定了红边带和特定的时间数据,这些数据对分类准确性至关重要.
结论:
- 卫星Sentinel-2的图像可以有效地绘制单个覆盖作物物种的地图.
- 精确的物种级地图绘制对于评估各种覆盖作物策略对环境的影响至关重要.
- 这种方法支持更好地了解覆盖作物的好处,并促进可持续农业.
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