在印度热带岛屿用C频段SAR和人工神经网络模型绘制土壤湿度图
Sirisha Adamala1,2, A Velmurugan1,3, T P Swarnam1,4
1ICAR-Central Island Agricultural Research Institute (CIARI), Port Blair, 744105, India.
Environmental monitoring and assessment
|June 17, 2025
概括
这项研究使用Sentinel-1A SAR和其他数据分析了南安达曼岛的土壤水分. 人工神经网络模型准确地估计了土壤水分,优于线性回归,尤其是植被数据.
科学领域:
- 地球和环境科学 地球和环境科学
- 遥感 遥感 遥感 遥感
- 水文学的水文学
背景情况:
- 准确的土壤湿度估计对于水文建模和灌管理至关重要.
- 卫星遥感为大规模的土壤湿度监测提供了一种可行的方法.
- 整合多个数据源可以提高土壤水分检索的准确性.
研究的目的:
- 用综合遥感方法分析南安达曼地区的土壤湿度模式.
- 开发和比较人工神经网络 (ANN) 和多重线性回归 (MLR) 模型用于土壤湿度估计.
- 用SAR数据评估植被覆盖对土壤湿度估计的影响.
主要方法:
- 使用 Sentinel-1A 的 C 波段 SAR 数据, Sentinel-2A 和 Landsat 8 的辅助数据.
- 采集了60个地表土壤样本 (0-10厘米),以测定土壤水分含量 (SMCv).
- 开发了ANN和MLR模型,其中包括反向散射系数 (σ°:VV和VH),NDVI,SMI和介电常数 (ε).
主要成果:
- 在所有土地用途中,SAR反向散射 (σ°:VV+VH) 和土壤湿度 (SMCv) 之间发现了正相关性.
- 植被覆盖干扰了基于SAR的土壤湿度估计;然而,结合NDVI和SMI提高了准确性.
- 在ANN模型中,与MLR相比,ANN模型表现优越,达到高R2 (0.67-0.99) 和效率 (η: 62.6-99.9).
结论:
- 集成的遥感数据和先进的建模 (ANN) 提供了准确的土壤湿度估计.
- 该研究强调了考虑植被指数和热量数据的重要性,以改善植被地区的土壤水分回收.
- 研究结果支持开发高效的灌系统,并为水文建模提供基线数据.
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