在半干旱的农业生态系统中,对不同叶角作物的RUSLE C因子基于地块的测量进行升级
Melis Özge Pınar1, Günay Erpul2
1Dept. of Soil & Water Res. Research, Transitional Zone Agricultural Research Inst. TAGEM (TR Ministry of Agriculture and Forestry), 26080, Eskisehir, Turkey. melisozge.pinar@tarimorman.gov.tr.
Environmental monitoring and assessment
|October 19, 2023
概括
这项研究开发了使用规范差异植被指数 (NDVI) 和叶面积指数 (LAI) 的简化模型,以估计向日和小麦作物的土壤损失率 (SLR). 这些模型为可持续的土地管理提供了传统现场测量的实际替代方案.
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:
- 评估时间覆盖变化对于水文和侵蚀过程管理至关重要.
- 叶片角分布 (LAD) 影响用于估计覆盖管理因子 (C-因子) 的植被指数 (VIs).
- 可持续的土地和土壤管理需要了解保护性树冠覆盖的变化.
研究的目的:
- 为了评估向日和小麦的C因子,不同的叶面取向.
- 开发使用NDVI和LAI估计作物阶段土壤损失比率 (SLR) 的缩小模型.
- 提供适用于半干旱作物系统的植物特异方程.
主要方法:
- 使用渐进线性回归与NDVI和LAI的减少模型.
- 通过远程和近距离传感工具测量NDVI.
- 使用近距离手持作物传感器获得LAI.
- 对特定作物生长阶段的上调SLR (向日1P,小麦3P).
主要成果:
- 开发了SLR的预测方程,R平方值为0.85的向日和0.93的小麦.
- 证明了NDVI和LAI在估计不同作物生长阶段的SLR中的实用性.
- 验证了简化模型对树冠覆盖评估的有效性.
结论:
- 开发的模型提供了一种有效的方法,用于估计半干旱作物系统中的SLR.
- 这些模型减少了对传统,劳动密集型现场测量的需求.
- 该方法通过准确评估覆盖管理因素来支持可持续的土地和土壤管理.
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