用Vis-NIR光谱方法预测干旱土地土壤中的土壤生物属性的可行性
Elias Hosseini1, Mehdi Zarei1,2, Ali Akbar Moosavi1
1Department of Soil Science, College of Agriculture, Shiraz University, Shiraz, Iran.
PloS one
|September 25, 2024
概括
可见和近红外 (Vis-NIR) 光谱学有效地使用光谱转移函数 (STF) 预测土壤的生物属性. 这种快速方法为评估农田土壤健康提供了可靠的替代方法.
科学领域:
- 土壤科学 土壤科学
- 频谱学是一种光谱学.
- 生物地质化学生物地质化学
背景情况:
- 可见和近红外 (Vis-NIR) 反射光谱是一种成本效益高的土壤监测工具.
- 评估土壤的生物属性对于了解土壤健康和营养循环至关重要.
研究的目的:
- 使用Vis-NIR光谱数据预测石灰质土壤的基本生物属性.
- 开发和比较从部分最小方位回归 (PLSR) 和渐进多重线性回归 (SMLR) 技术中衍生出的光谱转移函数 (STF).
主要方法:
- 收集了97个土壤样本从农业用地 (甘,小麦,棕树) 在胡塞斯坦省,伊朗.
- 使用标准实验室方法和近距离传感在Vis-NIR范围 (400-2500nm) 中分析样品.
- 通过测量酶活性 (ACP,ALP,DEH),微生物呼吸 (SMR) 和微生物生物质 (Pmic,Cmic) 来评估土壤的生物状态.
主要成果:
- 与STF相比,PLSR模型对大多数生物参数的预测性能略高.
- STFs准确预测了酸酸酶 (ACP),酸酸酶 (ALP),脱酶 (DEH),土壤微生物呼吸 (SMR),微生物生物质 (Pmic) 和微生物生物质碳 (Cmic),其验证R2值从0.65到0.76.
- 开发的模型证明了Vis-NIR光谱在土壤生物评估中的潜力.
结论:
- 与STF相结合的Vis-NIR光谱学提供了一种快速可靠的技术来评估土壤的生物质量.
- 应强调开发实用且易于部署的STF,以利用光谱学预测土壤特性.
- 这种方法为农业管理中的土壤健康监测提供了有价值的工具.
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