评估多模型方法的准确性,以降低不同农业气候区的土地表面温度
Debasish Roy1, Bappa Das2, Pooja Singh3
1ICAR-Indian Agricultural Research Institute, New Delhi, 110012, India.
Scientific reports
|March 29, 2025
概括
随机森林 (RF) 模型有效地将陆地表面温度 (LST) 数据缩小到10米分辨率. 这种增强的LST数据支持印度多样化的农业气候区的精密农业和环境监测.
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
- 农业科学 农业科学
背景情况:
- 陆地表面温度 (LST) 对陆地与大气的相互作用至关重要.
- 目前的LST数据分辨率限制了现场农业和生态应用.
- 将LST缩小到更好的分辨率对于实际的地面作战至关重要.
研究的目的:
- 为了比较三个缩放模型的有效性:热敏化 (TsHARP),薄板支线 (TPS) 和随机森林 (RF).
- 通过使用Sentinel-1,2数据,将LST的分辨率从100米降低到10米.
- 评估印度不同农业气候地区的模型性能.
主要方法:
- 使用TsHARP,TPS和RF模型缩小LST的规模.
- 使用高分辨率的Sentinel-1,2光学微波数据.
- 使用R2,RMSE和nRMSE指标对模型校准和验证准确性的分析.
主要成果:
- 随机森林 (RF) 模型显示出更高的校准精度 (R2: 0.961-0.997).
- 在验证方面,RF和TPS模型始终超过了TsHARP模型.
- 使用累积局部效应 (ALE) 图表进行了特征重要性分析.
结论:
- 射频模型是LST缩小规模的有效和适应性的策略.
- 更高分辨率的LST数据提高了农业和生态决策的精度.
- 该研究强调了RF在各种农业气候区域和土地覆盖类型中的更广泛应用.
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