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HP-LSP:来自与PhenoCam数据融合的协调Landsat和Sentinel-2的陆地表面现象学的参考

Khuong H Tran¹, Xiaoyang Zhang², Yongchang Ye¹

¹Geospatial Sciences Center of Excellence, Department of Geography & Geospatial Sciences, South Dakota State University, Brookings, SD, 57007, USA.

Scientific data

|October 11, 2023

概括

这项研究通过将卫星数据与地面观测数据相结合,创建了一个新的陆地表面现象学 (LSP) 数据集. 这种融合数据集提供了无差距的植被数据和准确的现象学日期,以更好地验证和分析生态系统变化.

科学领域:

地球系统科学地球系统科学
遥感遥感遥感遥感
生态生态学生态生态学

背景情况:

卫星衍生的陆地表面现象学 (LSP) 产品由于云层覆盖和有限的地面验证数据而存在不确定性.
现有的LSP产品需要提高精度和空间可比性,以进行强大的生态研究.

研究的目的:

通过合并卫星和地面观测,开发一个高质量的,没有差距的陆地表面现象学的参考数据集.
为验证卫星LSP产品和推进生态系统监测提供准确的现象学日期.

主要方法:

融合了Landsat 8和Sentinel-2 (HLS) 观测数据与近地PhenoCam时间序列数据.
创建一个30m分辨率的参考数据集,其中包含无间隙的EVI2时间序列和四个关键的现象学日期.
在2019-2020年期间,利用北美生态系统的78个10x10公里区域.

主要成果:

创建了一个新的HLS-PhenoCam LSP (HP-LSP) 参考数据集.
该数据集提供了为期3天的无间隙合成EVI2时间序列,用于监测植被发展.
提取了四个关键的现象学日期,精度≤5天,提供空间连续和可扩展的数据.

结论:

该HP-LSP数据集显著减少了陆地表面现象学监测中的不确定性.
这一参考数据集对于验证基于卫星的现象学产品和改进生态模型至关重要.
这些发现可以更好地分析气候对陆地生态系统的影响.

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