在Uttarakhand的高河平原,使用地理空间技术进行作物分类和作物强度估计
Arjun Shreepad Hegde1, Rajeev Ranjan2, Samarth Shreepad Hegde3
1Division of Agriculture Physics, Indian Agricultural Research Institute, New Delhi, India.
Heliyon
|December 3, 2024
概括
使用Sentinel-2卫星数据和人工神经网络准确地绘制作物地图对于粮食安全至关重要. 这项研究估计了乌塔拉罕德邦的作物面积和作物强度,达到174.4%的多种作物指数.
科学领域:
- 农业科学 农业科学
- 遥感 遥感 遥感 遥感
- 地理空间技术是什么
背景情况:
- 准确的作物测绘对于粮食安全,经济规划和环境监测至关重要.
- 卫星遥感提供定期,大规模的观测,对于特征作物类型和分布至关重要.
- 之前的研究没有使用地空间技术估计在Uttarakhand的上游河流平原的作物强度.
研究的目的:
- 用Sentinel-2卫星数据绘制Uttarakhand的作物类型和估计面积.
- 计算该地区的多种作物指数 (MCI).
- 率先使用地理空间技术在这个特定区域进行作物强度估计.
主要方法:
- 利用了从2021年10月到2022年3月的高分辨率,多光谱的Sentinel-2卫星图像.
- 使用"Mapmarker"安卓应用程序和谷歌地球收集地面真相数据.
- 使用Sentinel应用平台 (SNAP) 处理卫星数据,并通过人工神经网络 (ANN) 进行作物分类/面积估计.
主要成果:
- 估计大米面积为108,884公,甘面积为11,479公 (2021年10月图片).
- 估计豆类作物面积为6227公 (2021年12月图片).
- 估计小麦面积为105,334公,子面积为2,018公 (2022年3月图片).
- 计算了研究区域的174.4%的多重作物指数 (MCI).
结论:
- 地理空间技术,特别是Sentinel-2数据和ANN,对于乌塔拉罕德邦的作物绘图和种植面积估计是有效的.
- 该研究首次成功估计了乌塔拉罕德邦上游河流平原的作物强度.
- 高的MCI表明密集的农业实践和优化土地利用的潜力.
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