土壤总预测基于不同土地使用类型下的多时间合成Sentinel-2图像
Yi-Lin Ouyang1, Wei Wu2, Hong-Bin Liu3
1College of Resources and Environment, Southwest University, Beibei, Chongqing, 400715, China.
Scientific reports
|November 26, 2025
概括
准确预测土壤总 (TN) 对农业至关重要. 这项研究表明,多时远程传感数据与机器学习相结合,可显著提高跨不同土地用途的TN预测精度.
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:
- 总土壤 (TN) 是植物发育的关键因素,影响农业生产力和环境健康.
- 精确的TN监测对于有效的农业生产和环境管理至关重要.
研究的目的:
- 为了提高总土壤 (TN) 的预测准确度.
- 使用机器学习算法将多时代合成遥感图像与环境数据集成.
主要方法:
- 获取了七张单时间的Sentinel-2图像 (2019-2020年).
- 生成四种多时间合成图像类型 (最大,最小,平均,中位数合成).
- 提取光谱带和植被指数,然后使用极端梯度增强与远程传感,地形和气候数据在各种空间分辨率的TN预测.
主要成果:
- 模型性能因土地使用而异:单时间图像在果园中表现出色 (R2 = 0.73).
- 多时间平均合成图像显示在干旱土地 (R2 = 0.63) 和田 (R2 = 0.61) 中的性能优越.
- 光谱带 (B8,B11) 被确定为最有影响力的预测变量.
结论:
- 多时间遥感合成技术为土壤总气监测提供了显著的价值.
- 这种方法为推进精准农业和可持续土地管理实践提供了可行的技术途径.
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