Modeling growing season phenology in North American forests using seasonal mean vegetation indices from MODIS
Modeling growing season phenology in North American forests using seasonal mean vegetation indices from MODIS
复制标题
DOI：
10.1016/j.rse.2014.03.001
复制
发表时间：
2014-05
影响因子：
13.5
通讯作者：
Chaoyang Wu;A. Gonsamo;C. Gough;J. Chen;Shiguang Xu
中科院分区：
文献类型：
--
作者：
Chaoyang Wu;A. Gonsamo;C. Gough;J. Chen;Shiguang Xu
植被物候对陆地生态系统碳循环具有重要的控制作用。由于时间序列的噪声和常绿林冠度的季节性变化有限，森林关键物候阶段（如生长季节的春季开始和秋季结束）的遥感仍然具有挑战性。利用北美4个落叶阔叶林（DBF）和6个常绿针叶林（ENF） 94个站点年的碳通量数据，研究了春季（4 - 5月）和秋季（9 - 11月）观测的平均植被指数（VIs）是否可以遥感生长季节物候。基于MODIS数据，采用归一化植被指数（NDVI）、陆地地表水指数（LSWI）、增强型植被指数（EVI）、宽动态范围植被指数（WDRVI）和优化土壤调整植被指数（OSAVI） 5种植被指数。我们的研究结果表明，生长季节转换可以从平均季节VIs推断，尽管不同的VIs在不同的地点和植物功能类型中具有不同的预测强度。广泛使用的NDVI和EVI在跟踪ENF生态系统生长季节物候方面潜力有限，而对水分敏感的指数（如LSWI）或受土壤影响较小的指数（如OSAVI）在指示物候转变方面可能具有未被揭示的能力。OSAVI是ENF生态系统生长季节结束的一个强有力的预测因子，这表明该指数可能为ENF物候的建模提供了一种新的策略。结果表明，多指数组合可以改善地表物候的遥感，在我们的评估中，生长季节转换的模型和观测值及其长度之间的一致性很好。
The phenology of vegetation exerts an important control over the terrestrial ecosystem carbon (C) cycle. Remote sensing of key phenological phases in forests (e.g., the spring onset and autumn end of growing season) remains challenging due to noise in time series and the limited seasonal variation of canopy greenness in evergreen forests. Using 94 site-years of C flux data from four deciduous broadleaf forests (DBF) and six evergreen needleleaf forests (ENF) in North America, we examine whether growing season phenology can be remotely sensed from mean vegetation indices (VIs) derived from spring (Apr.–May) and autumn (Sep.–Nov) observations. Five VIs were used based on Moderate Resolution Imaging Spectroradiometer (MODIS) data, including the normalized difference vegetation index (NDVI), the land surface water index (LSWI), the enhanced vegetation index (EVI), the wide dynamic range vegetation index (WDRVI) and the optimized soil-adjusted vegetation index (OSAVI). Our results show that growing season transitions can be inferred from mean seasonal VIs, though the different VIs varied in their predictive strength across sites and plant functional types. Widely used NDVI and EVI exhibited limited potential in tracking growing season phenology of ENF ecosystems, while indices sensitive to water (i.e., LSWI) or less influenced by soil (i.e., OSAVI) may have unrevealed powers in indicating phenological transitions. OSAVI was shown to be a strong predictor of the end of the growing season in ENF ecosystems, suggesting that this VI may offer a new strategy for modeling the phenology of ENF sites. We conclude that combinations of multiple indices may improve the remote sensing of land surface phenology, as evidenced by the good agreement between modeled and observed growing season transitions and its length in our evaluation.