在地强度的空间变化和地下水趋势的模式在部分下游河平原的部分
Piyasa Gayen1, Dipanwita Dutta2
1Department of Remote Sensing and GIS, Vidyasagar University, Midnapore, 721102, West Bengal, India.
Environmental monitoring and assessment
|February 7, 2025
概括
米种植显著影响地下水位,70%的种植面积呈现水平面下降的情况. 先进的遥感和机器学习准确地绘制了田强度,这对于云地区的可持续水资源管理至关重要.
科学领域:
- 农业科学 农业科学
- 环境科学 环境科学
- 遥感 遥感 遥感 遥感
背景情况:
- 米种植需要大量的水,因此需要对地下水进行监测.
- 传统的遥感技术不足以在云的地区进行详细分析,如下游江河平原.
- 整合机器学习与SAR数据对于评估田强度及其对水资源的影响至关重要.
研究的目的:
- 用Sentinel SAR数据评估机器学习模型进行高分辨率 (10米尺度) 米绘图.
- 为了评估云地区的田强度的时空变化.
- 分析两十年来米种植对地下水位趋势的影响.
主要方法:
- 利用了2022年的多时间哨兵SAR图像用于地图绘制.
- 采用随机森林 (RF) 机器学习模型进行分类.
- 分析了中央地下水局 (CGWB) 2002年至2022年的地下水位趋势数据.
主要成果:
- 该RF模型在识别米种植区和季节性分布方面取得了90%以上的准确性.
- 来自Sentinel-1A数据的反向散射系数 (σ VH) 与大米生长阶段 (Aus,Aman,Boro) 有很强的相关性.
- 70%的研究的米种植区显示地下水位下降趋势.
结论:
- 使用SAR数据的机器学习为在具有挑战性的环境中绘制田强度提供了优越的方法.
- 米种植与河下游平原地下水的严重枯竭有关.
- 这种综合方法为可持续的农业和水资源管理提供了关键的见解.
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