NDVI and vegetation phenology dynamics under the influence of sunshine duration on the Tibetan plateau

Wang, Hongshuo; Liu, Desheng; Lin, Hui; Montenegro, Álvaro; Zhu, Xiaolin

doi:10.1002/joc.4013

Cited by 90 publications

(49 citation statements)

References 53 publications

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“…warming would cause earlier SOS in the Tibetan Plateau, while increased preseason precipitation would promote the onset of green-up especially in arid areas. However, due to the fact that increased preseason precipitation may cause lower temperatures by accompanying deficient sunshine intensity and duration, later SOS may also be expected [62,64].…”

Section: Discussionmentioning

confidence: 99%

Spatiotemporal Variability in Start and End of Growing Season in China Related to Climate Variability

Dai

Cui

et al. 2016

Remote Sensing

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Satellite-derived vegetation phenophases are frequently used to study the response of ecosystems to climate change. However, limited studies have identified the common phenological variability across different climate and vegetation zones. Using NOAA/Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) dataset, we estimated start of growing season (SOS) and end of growing season (EOS) for Chinese vegetation during the period 1982-2012 based on the Midpoint method. Subsequently, the empirical orthogonal function (EOF) analysis was applied to extract the main patterns of phenophases and their annual variability. The impact of climate parameters such as temperature and precipitation on phenophases was investigated using canonical correlation analysis (CCA). The first EOF mode of phenophases exhibited widespread earlier or later SOS and EOS signals for almost the whole country. The attendant time coefficients revealed an earlier SOS between 1996 and 2008, but a later SOS in 1982-1995 and 2009-2012. Regarding EOS, it was clearly happening later in recent years, mainly after 1993. The preseason temperature contributed to such spatiotemporal phenological change significantly. The first pair of CCA patterns for phenology and preseason temperature was found to be similar and its time coefficients were highly correlated to each other (correlation coefficient >0.7). These results indicate that there is a substantial amount of common variance in SOS and EOS across different vegetation types that is related to large-scale modes of climate variability.

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Section: Discussionmentioning

confidence: 99%

Spatiotemporal Variability in Start and End of Growing Season in China Related to Climate Variability

Dai

Cui

et al. 2016

Remote Sensing

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“…The dynamics of vegetation were significantly influenced by climate change on the Tibetan Plateau (Wang et al 2015). Alpine grassland covers an area of about 1.525*10 6 km 2 , accounting for 59.28 % of the Tibetan Plateau.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, the monthly correlation coefficient for monthly average Normalized Difference Vegetation Index (NDVI) and precipitation is 0.75, which is greater than the correlation coefficient of 0.63 for monthly average NDVI and temperature in the Lhasa area (Chu et al 2007). Previous studies have analyzed NDVI variations and their relationships with temperature and precipitation in the Tibetan Plateau (Chu et al 2007;Xu et al 2011;Ding et al 2013;Wang et al 2015), but correlations between NDVI in different vegetation types and climatic factors have not been observed. In actuality, different vegetation types respond differently to temperature and precipitation.…”

Section: Introductionmentioning

confidence: 99%

The response of vegetation dynamics of the different alpine grassland types to temperature and precipitation on the Tibetan Plateau

Sun

Qin

Yang

2015

Environ Monit Assess

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The spatiotemporal variability of the Normalized Difference Vegetation Index (NDVI) of three vegetation types (alpine steppe, alpine meadow, and alpine desert steppe) across the Tibetan Plateau was analyzed from 1982 to 2013. In addition, the annual mean temperature (MAT) and annual mean precipitation (MAP) trends were quantified to define the spatiotemporal climate patterns. Meanwhile, the relationships between climate factors and NDVI were analyzed in order to understand the impact of climate change on vegetation dynamics. The results indicate that the maximum of NDVI increased by 0.3 and 0.2 % per 10 years in the entire regions of alpine steppe and alpine meadow, respectively. However, no significant change in the NDVI of the alpine desert steppe has been observed since 1982. A negative relationship between NDVI and MAT was found in all these alpine grassland types, while MAP positively impacted the vegetation dynamics of all grasslands. Also, the effects of temperature and precipitation on different vegetation types differed, and the correlation coefficient for MAP and NDVI in alpine meadow is larger than that for other vegetation types. We also explored the percentages of precipitation and temperature influence on NDVI variation, using redundancy analysis at the observation point scale. The results show that precipitation is a primary limiting factor for alpine vegetation dynamic, rather than temperature. Most importantly, the results can serve as a tool for grassland ecosystem management.

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“…This could also increase the importance of sunshine duration for the climate modeling community. Another important scientific field of sunshine duration applications is in the field of agriculture (e.g., [16]) and vegetation (e.g., [17,18]). …”

Section: Introductionmentioning

confidence: 99%

A Satellite-Based Sunshine Duration Climate Data Record for Europe and Africa

et al. 2017

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Besides 2 m temperature and precipitation, sunshine duration is one of the most important and commonly used parameter in climatology, with measured time series of partly more than 100 years in length. EUMETSAT's Satellite Application Facility on Climate Monitoring (CM SAF) presents a climate data record for daily and monthly sunshine duration (SDU) for Europe and Africa. Basis for the advanced retrieval is a highly resolved satellite product of the direct solar radiation from measurements by Meteosat satellites 2 to 10. The data record covers the time period 1983 to 2015 with a spatial resolution of 0.05 • × 0.05 • . The comparison against ground-based data shows high agreement but also some regional differences. Sunshine duration is overestimated by the satellite-based data in many regions, compared to surface data. In West and Central Africa, low clouds seem to be the reason for a stronger overestimation of sunshine duration in this region (up to 20% for monthly sums). For most stations, the overestimation is low, with a bias below 7.5 h for monthly sums and below 0.4 h for daily sums. A high correlation of 0.91 for daily SDU and 0.96 for monthly SDU also proved the high agreement with station data. As SDU is based on a stable and homogeneous climate data record of more than 30 years length, it is highly suitable for climate applications, such as trend estimates.

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NDVI and vegetation phenology dynamics under the influence of sunshine duration on the Tibetan plateau

Cited by 90 publications

References 53 publications

Spatiotemporal Variability in Start and End of Growing Season in China Related to Climate Variability

Spatiotemporal Variability in Start and End of Growing Season in China Related to Climate Variability

The response of vegetation dynamics of the different alpine grassland types to temperature and precipitation on the Tibetan Plateau

A Satellite-Based Sunshine Duration Climate Data Record for Europe and Africa

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