Uncertainty of Remote Sensing Data in Monitoring Vegetation Phenology: A Comparison of MODIS C5 and C6 Vegetation Index Products on the Tibetan Plateau

Zheng, Zhoutao; Zhu, Wenquan

doi:10.3390/rs9121288

Cited by 25 publications

(22 citation statements)

References 48 publications

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“…Collection 6 of the MODIS data was released in the fall of 2015. Some papers have highlighted significant sensor degradation in collection 5 and notable differences between time series in collections 5 (V005) and 6 (V006) (Wang et al 2012, Zheng and Zhu 2017, Lyapustin et al 2014. In this paper we evaluate the land surface phenology for these two collections, and we investigate how well the land surface phenology results from the two collections correlate with large scale climate indices.…”

Section: Introductionmentioning

confidence: 99%

Large scale climate oscillation impacts on temperature, precipitation and land surface phenology in Central Asia

Beurs

Henebry

Owsley

et al. 2018

Environ. Res. Lett.

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Central Asia has been rapidly changing in multiple ways over the past few decades. Increases in temperature and likely decreases in precipitation in Central Asia as the result of global climate change are making one of the most arid regions in the world even more susceptible to large-scale droughts. Global climate oscillations, such as the El Niño-Southern Oscillation, have previously been linked to observed weather patterns in Central Asia. However, until now it has been unclear how the different climate oscillations act simultaneously to affect the weather and subsequently the vegetated land surface in Central Asia. We fit well-established land surface phenology models to two versions of MODIS data to identify the land surface phenology of Central Asia between 2001 and 2016. We then combine five climate oscillation indices into one regression model and identify the relative importance of each of these indices on precipitation, temperature, and land surface phenology, to learn where each climate index has the strongest influence. Our analyses illustrate that the North Atlantic Oscillation, the East Atlantic/West Russia pattern, and the Atlantic Multi-Decadal Oscillation predominantly influence temperature in the northern part of Central Asia. We also show that the Scandinavia index and the Multivariate ENSO index both reveal significant impacts on the precipitation in this region. Thus, we conclude that the land surface phenology across Central Asia is affected by several climate modes, both those that are strongly linked to far northern weather patterns and those that are forced by southern weather patterns, making this region a 'climate change hotspot' with strong spatial variations in weather patterns. We also show that regional climate patterns play a significant role in Central Asia, indicating that global climate patterns alone might not be sufficient to project weather patterns and subsequent land surface changes in this region.

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Section: Introductionmentioning

confidence: 99%

Large scale climate oscillation impacts on temperature, precipitation and land surface phenology in Central Asia

Beurs

Henebry

Owsley

et al. 2018

Environ. Res. Lett.

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“…Another possible solution would be including more newly released MODIS reflectance product Collection 6 (C6). A recent study found that, in the Tibetan Plateau, NDVI from Collection 5 (C5) SOS was more consistent than C6 SOS in terms of ground-observed green-up dates for alpine grassland [63]. Although there was improved quality at high latitudes from use of all available observations in C6 than C5 who used only four observations per day.…”

Section: Fi-hyymentioning

confidence: 99%

Limitations and Challenges of MODIS-Derived Phenological Metrics Across Different Landscapes in Pan-Arctic Regions

Wang

Cheng

et al. 2018

Remote Sensing

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Recent efforts have been made to monitor the seasonal metrics of plant canopy variations globally from space, using optical remote sensing. However, phenological estimations based on vegetation indices (VIs) in high-latitude regions such as the pan-Arctic remain challenging and are rarely validated. Nevertheless, pan-Arctic ecosystems are vulnerable and also crucial in the context of climate change. We reported the limitations and challenges of using MODerate-resolution Imaging Spectroradiometer (MODIS) measurements, a widely exploited set of satellite measurements, to estimate phenological transition dates in pan-Arctic regions. Four indices including normalized vegetation difference index (NDVI), enhanced vegetation index (EVI), phenology index (PI), plant phenological index (PPI) and a MODIS Land Cover Dynamics Product MCD12Q2, were evaluated and compared against eddy covariance (EC) estimates at 11 flux sites of 102 site-years during the period from 2000 to 2014. All the indices were influenced by snow cover and soil moisture during the transition dates. While relationships existed between VI-based and EC-estimated phenological transition dates, the R 2 values were generally low (0.01-0.68). Among the VIs, PPI-estimated metrics showed an inter-annual pattern that was mostly closely related to the EC-based estimations. Thus, further studies are needed to develop region-specific indices to provide more reliable estimates of phenological transition dates. sensors [5], is measurable by remote sensing methods [6][7][8]. However, the scale disparity between the spatial extent of the organisms and the spatial resolution of the sensor results in an ambiguous mixture of target and background or signal and noise [9,10].LSP metrics are often compared with direct observations of physical changes in the canopy structure. Eddy covariance (EC) measurements of carbon exchange offer a spatially and temporally perspective for extracting key phenological dates by assessing seasonal cycles of gross primary productivity (GPP) [8,11]. The long-term durability of CO 2 flux measurements in EC tower sites worldwide allows comparisons with remote sensing-based estimates at spatial footprints similar to coarse and medium resolution satellite pixels [4,12,13]. Productive efforts have been made to improve the estimates of phenological transitions based on remote sensing observations [12,[14][15][16], using new methods such as digital cameras [8,17], developing improved vegetation indices, and applying remotely sensed solar-induced fluorescence (SIF) from the Global Ozone Monitoring Experiment-2 (GOME-2) and the Orbiting Carbon Observatory-2 (OCO-2) [18][19][20].Optical remote sensing observations are widely applied to estimate key phenological dates, often using vegetation indices (VIs) based on a combination of reflectances in different spectral bands. The most commonly applied VI is the normalized difference vegetation index (NDVI), which involves red and near-infrared reflectances. The NDVI is sufficiently stable to permit meaningful ...

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Section: Datasetsmentioning

confidence: 99%

“…In addition, the maximum value composite (MVC) method was used to construct the monthly NDVI data for the calculation of monthly NPP [32]. To avoid the uncertainty caused by land cover change, we only considered pixels where grassland cover type remained identical during 2001-2015, according to the criteria set in a previous study [33]: (1) the average NDVI for June-September should be greater than 0.1; (2) the annual maximum NDVI should exceed 0.15 and occur within July-September; (3) the average NDVI for July-September should be greater than 1.2 times the average NDVI for November-March; and, (4) the average NDVI in winter (December-February) should be lower than 0.4. In addition, the green-up (also termed as MCD12Q2derived BGS) derived from the MODIS Land Cover Dynamics Collection 6 dataset (MCD12Q2) (https://lpdaac.usgs.gov/products/mcd12q2v006/) was compared in performance with BGS derived from MOD13Q1 NDVI in this study.…”

Section: Datasetsmentioning

confidence: 99%

Direct and Lagged Effects of Spring Phenology on Net Primary Productivity in the Alpine Grasslands on the Tibetan Plateau

2020

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As a key biotic factor, phenology exerts fundamental influences on ecosystem carbon sequestration. However, whether spring phenology affects the subsequent seasonal ecosystem productivity and the underlying resource limitation mechanism remains unclear for the alpine grasslands of the Tibetan Plateau (TP). In this study, we investigated the direct and lagged seasonal responses of net primary productivity (NPP) to the beginning of growing season (BGS) along a precipitation gradient by integrating field observations, remote sensing monitoring and ecosystem model simulations. The results revealed distinct response patterns of seasonal NPP to BGS. Specifically, the BGS showed a significant and negative correlation with spring NPP (R = −0.73, p < 0.01), as evidenced by the direct boosting effects of earlier BGS on spring NPP. Moreover, spring NPP was more responsive to BGS in areas with more annual precipitation. The boosting effects of earlier BGS on NPP tended to weaken in summer compared with that in spring. Sequentially, BGS exhibited stronger positive correlation with autumn NPP in areas with less annual precipitation, which suggested the enhanced lagged suppressing effects of earlier spring phenology on ecosystem carbon assimilation during the later growing season under aggravated water stress. Overall, the strengthened NPP in spring was offset by its decrement in autumn, resulting in no obvious relationship between BGS and annual NPP (R = −0.34, p > 0.05) for the entire grasslands on the TP. The findings of this study imply that the lagged effects of phenology on the ecosystem productivity during the subsequent seasons should not be neglected in the future studies.

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Uncertainty of Remote Sensing Data in Monitoring Vegetation Phenology: A Comparison of MODIS C5 and C6 Vegetation Index Products on the Tibetan Plateau

Cited by 25 publications

References 48 publications

Large scale climate oscillation impacts on temperature, precipitation and land surface phenology in Central Asia

Large scale climate oscillation impacts on temperature, precipitation and land surface phenology in Central Asia

Limitations and Challenges of MODIS-Derived Phenological Metrics Across Different Landscapes in Pan-Arctic Regions

Direct and Lagged Effects of Spring Phenology on Net Primary Productivity in the Alpine Grasslands on the Tibetan Plateau

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