Assessing the inter-annual variability of vegetation phenological events observed from satellite vegetation index time series in dryland sites

被引:12
作者
Kato, Anna [1 ,3 ]
Carlson, Kimberly M. [1 ,2 ]
Miura, Tomoaki [1 ]
机构
[1] Univ Hawaii Manoa, Dept Nat Resources & Environm Management, 1910 East West Rd, Honolulu, HI 96822 USA
[2] NYU, Dept Environm Studies, 285 Mercer St, New York, NY USA
[3] No Arizona Univ, Sch Informat Comp & Cyber Syst, 1295 Knoles Dr, Flagstaff, AZ 86011 USA
基金
美国食品与农业研究所;
关键词
Remote sensing; Vegetation index; Phenology; Dryland; LAND-SURFACE PHENOLOGY; GROSS PRIMARY PRODUCTION; CARBON-DIOXIDE; PRIMARY PRODUCTIVITY; SEMIARID ECOSYSTEMS; SPRING PHENOLOGY; WATER INDEX; SOIL-WATER; DATA SET; MODIS;
D O I
10.1016/j.ecolind.2021.108042
中图分类号
X176 [生物多样性保护];
学科分类号
090705 ;
摘要
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.
引用
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页数:17
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