期刊
ECOLOGICAL INDICATORS
卷 130, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.ecolind.2021.108042
关键词
Remote sensing; Vegetation index; Phenology; Dryland
资金
- US Department of Agriculture's National Institute of Food and Agriculture [HAW01136-H, HAW01143-H, HAW01146-M]
The study reveals that satellite vegetation indices can effectively explain GPP and characterize phenological transition dates in dryland ecosystems. Among the indices, EVI performs the best at explaining variance in daily GPP. While most phenological events in GPP data can be detected from VI data, the accuracy varies among different vegetation indices.
Tracking dryland vegetation phenology under a changing climate is of great concern because dryland ecosystems have broad spatial coverage and are important drivers of global carbon cycles. However, dryland ecosystems often consist of two or more different vegetation types with divergent phenological patterns and are characterized by relatively low vegetation greenup signal compared to noise present in satellite imagery. Thus, accurately characterizing dryland phenology - including the start, peak, and end of season (SOS, POS, and EOS, respectively) - across large temporal and spatial scales with satellite vegetation indices (VIs) has proven challenging. Working across six dryland flux tower sites in the United States from 2003 to 2014, we asked: 1) How well do satellite VIs explain GPP? 2) How accurately can satellite VIs characterize the number and timing of phenological transition dates? and 3) Why does this accuracy vary across VIs? To address these questions, we used daily GPP data from the FLUXNET2015 dataset. We computed four daily VIs from the Moderate Resolution Imaging Spectrometer (MODIS) sensor including two greenness indices (i.e., NDVI and EVI), and two waterrelated indices (i.e., Land Surface Water Index (LSWI) and NDVI-LSWI difference). We derived phenological transition dates from spline smoothed VI and GPP time series. We found that all major phenological patterns apparent in GPP time series were present in VI data across sites and VIs. Among the four VIs, EVI explained the most variance in daily GPP (R2 = 0.65). Between 88 and 90% of phenological events found in GPP time series - especially recurring, large magnitude events but also some sporadic, small ephemeral events - were detected from MODIS VI data. The rate of omission, when an event was detected from GPP but not from VI data, was similar across VIs (10-12% of all GPP events). Commissions, when an event was detected from VI but not from GPP data, varied more widely from 18% for NDVI to 30% for LSWI. Transition date accuracy as measured by mean absolute difference (MAD) between GPP- and VI-derived SOS and POS ranged from 13 to 15 days across VIs but was lower for EOS (17-26 days). EOS from LSWI most accurately characterized GPP-derived EOS. This may be because LSWI is sensitive to leaf water content, which declines in certain dryland vegetation even when leaves remain green. Our results highlight the potential for satellite VI-derived metrics to accurately track spatiotemporal variation in the phenological event occurrence and timing in dryland ecosystems.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据