一个空间丰富,时间连贯的土壤光谱数据集用于土壤有机碳估计
Jeehwan Bae1, Inhye Seo2, Junge Hyun1
1Department of Environmental Science and Engineering, Kyung Hee University, Yongin, South Korea.
Scientific data
|January 21, 2026
概括
准确的土壤有机碳 (SOC) 估计至关重要. 本研究介绍了 Gyeonggi土壤光谱库 (G-SSL),这是开发强大的近红外 (NIR) 光谱模型的宝贵资源,用于在各种景观中精确评估SOC.
科学领域:
- 土壤科学 土壤科学
- 频谱学是一种光谱学.
- 环境科学 环境科学
背景情况:
- 准确的土壤有机碳 (SOC) 估计对于减缓气候变化和可持续土地管理至关重要.
- 近红外光谱 (NIR) 为SOC评估提供了一种快速且具有成本效益的方法.
- NIR光谱的性能在很大程度上依赖于具有足够时空覆盖的校准数据集.
研究的目的:
- 介绍 Gyeonggi土壤光谱库 (G-SSL),这是一个来自韩国表层土壤的NIR光谱的新数据集.
- 开发和验证一个强大的基于NIR的部分最小平方回归 (PLSR) 模型,用于SOC预测.
- 为在异质环境中创建特定区域的光谱图书馆建立一个基准.
主要方法:
- 在韩国金吉省11个土地覆盖类型中收集了1500个地表土壤样本 (0-15厘米).
- 所有样本的获得的NIR频谱 (1,400-2,500 nm).
- 校准的PLSR模型使用SOC测量从712个样本与70:30列车测试分割.
主要成果:
- 开发的PLSR模型表现出强大的性能,R2 = 0.95,RMSE = 0.39%,和RPD = 4.54.
- 江土壤光谱图书馆 (G-SSL) 包含1500个分布在空间上的土壤样本.
- 该图书馆涵盖了各种土地覆盖类型,提高了其适用性.
结论:
- G-SSL是一个高分辨率,空间强大的资源,用于数字SOC映射.
- 该研究为开发区域光谱图书馆提供了一种方法论的基准.
- 这项工作支持对环境管理进行改进的SOC评估.
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