使用多谱信息融合估计小麦作物产量
1Department of Information Technology, Indian Institute of Information Technology - Allahabad, Prayagraj, India.
Journal of the science of food and agriculture
|October 7, 2023
概括
这项研究使用多谱卫星数据融合准确估计了印度的小麦作物面积和产量. 这种新方法为农业监测提供了可靠的数据.
科学领域:
- 农业遥感 农业遥感
- 地理空间分析的研究.
- 农作物监测 农作物监测
背景情况:
- 估计小麦作物面积和产量对于印度北方邦等地区的粮食安全至关重要.
- 来自Sentinel-2,Landsat-8和Landsat-9的多光谱卫星图像是在2021年和2022年的收获前季节 (三月) 中获得的.
- 为了准确的作物评估,开发了一种多光谱信息融合方法.
研究的目的:
- 估计印度Uttar Pradesh的Etah地区的小麦作物面积和产量.
- 评估用于作物监测的多光谱信息融合方法的有效性.
- 为了比较不同分类算法和植被指数的性能.
主要方法:
- 使用支持矢量机 (SVM),人工神经网络 (ANN) 和最大概率 (ML) 算法进行图像分类.
- 基于植被指数的提取,使用规范差异植被指数 (NDVI) 和部分植被覆盖 (FVC).
- 分离性分析以确定土地覆盖分类和小麦作物提取的最佳图像带.
主要成果:
- 通过使用ANN的Sentinel-2数据实现了98.34%的最大分类准确度.
- 2021年Sentinel-2的估计小麦面积 (260,540公) 与参考数据 (238,600公) 非常接近.
- SVM分类器表现良好,使用Sentinel-2数据计算了2022年的8,408,490小麦.
结论:
- 拟议的方法为作物生产监测提供了一种新的方法,通过每年使用单一图像与地面真实数据.
- 多光谱数据和先进的分类技术的融合提高了作物面积和产量估计的准确性.
- 该方法为农业规划和粮食安全评估提供了可靠的工具.
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