基于光使用效率 (LUE) 的双月总初级生产率 (GPP) 在30米空间分辨率 (2000-2022) 的全球草原
Mustafa Serkan Isik1, Leandro Parente1, Davide Consoli1
1OpenGeoHub, Doorwerth, Netherlands.
PeerJ
|August 18, 2025
概括
一个新的高分辨率 (30米) 数据集使用Landsat数据估计了草原原产量 (GPP) 从2000年到2022年. 这种宝贵的资源有助于土地退化分析和温室气体流量建模.
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:
- 准确估计总初级生产率 (GPP) 对于理解生态系统功能和碳循环至关重要.
- 现有的GPP数据集往往缺乏详细的生态和土地管理应用所需的空间和时间分辨率.
- 光利用效率 (LUE) 模型为从卫星观测中估计GPP提供了一个强大的框架.
研究的目的:
- 为了产生一个高空间分辨率 (30米) 二次月 GPP 数据集的全球草原从2000年到2022年.
- 为了校准和验证GPP数据集,使用广泛的旋共变量流量塔数据.
- 为环境监测和环境研究中的各种应用公开提供GPP数据集.
主要方法:
- 使用重新构建的全球Landsat档案 (400TB) 与MOD11A1温度和CERES PAR数据相结合.
- 实施了一个具有生物群特定生产率因子的LUE模型,在全球范围内应用,然后对草原进行校准.
- 以30米分辨率推导的每两个月的GPP地图,与527个旋共变量流量塔进行验证.
主要成果:
- 在0.48-0.71之间的R平方值和所有土地覆盖类的RMSE低于~2.3 gCm−2d−1的值.
- 经验证的草原特异性GPP产品显示R平方在0.51-0.70之间,RMSE<2 gCm−2d−1使用92个流量塔.
- 最终的数据集包括云优化的GeoTIFF格式的每两个月和每年的GPP地图,总计约23TB.
结论:
- 开发的30m GPP数据集为监测草原生产力和健康提供了重大进展.
- 数据集的高分辨率和开放可访问性促进了详细的趋势分析,包括土地退化和作物产量估计.
- 该资源支持在更精细的空间尺度上改进温室气体流量的建模,为气候变化研究做出贡献.
关键词:
准备分析的数据 (ARD)在Ceres的Ceres.地球观测 地球观测这是GPP GPP GPP.全球草原监测全球草原监测草原的生产力 草原的生产力毛初级生产率的总值陆地卫星 (Landsat) 是一个地球卫星.灯光使用效率的效率.莫迪斯 (Modis) 的方式更多相关视频
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