DOSIF:从OCO-3开始的长期每日SIF,具有全球连续覆盖
Longlong Yu1, Xiang Zhang1, Lizhi Wang1
1College of Computer Science, Chengdu University, Chengdu 610106, China.
Sensors (Basel, Switzerland)
|November 13, 2025
概括
研究人员开发了一种新方法,以创建每日卫星太阳诱导的叶绿素光 (SIF) 数据. 这种长期的每日SIF数据集增强了植被生产率的监测和分析.
科学领域:
- 地球和环境科学 地球和环境科学
- 遥感 遥感 遥感 遥感
- 生态生态学 生态生态学
背景情况:
- 卫星采集的太阳引起的叶绿素光 (SIF) 是全球植被生产力的关键指标.
- 现有的SIF数据集缺乏准确的植被动态研究所需的长期,每日分辨率.
- 挑战包括日常建模准确性,大数据量和计算强度.
研究的目的:
- 开发一种数据驱动的方法来重建长期的每日SIF数据.
- 克服现有SIF数据集的局限性,用于植被动态研究.
- 从2001年到现在提供全球连续的每日SIF数据集.
主要方法:
- 使用谷歌地球引擎 (GEE) 平台进行数据处理.
- 采用移动时空窗口采样 (MSTWS) 策略.
- 通过将轨道碳观测台 (OCO) - 3 SIF与MODIS表面反射率联系起来,开发了每日预测模型.
主要成果:
- 成功重建了全球覆盖的长期每日OCO-3 SIF (DOSIF).
- 在R2=0.92 (培训) 和R2=0.81 (验证) 实现了高预测准确度.
- 通过交叉传感器比较验证了数据集的时空准确性和可靠性.
结论:
- 开发的DOSIF数据集提供了强大的,每日,长期的植被生产力洞察力.
- 这一进步有助于更精确地探索全球的植被动态.
- 数据驱动方法为生成高分辨率SIF数据提供了一种可靠的方法.
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