Generation and evaluation of the VIIRS land surface phenology product

Zhang, Xiaoyang; Liu, Lingling; Liu, Yan; Senthilnath, J.; Jian‐min, Wang; Moon, Minkyu; Henebry, Geoffrey M.; Friedl, M. A.; Schaaf, Crystal B.

doi:10.1016/j.rse.2018.06.047

Cited by 136 publications

(76 citation statements)

References 91 publications

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“…Specifically, a hybrid piecewise logistic model (HPLM) algorithm was used to describe the temporal NDVI/EVI2 trajectory. Method details can be found in the existing literature [33,34]. Based on the HPLM and curvature change rates, a set of phenological metrics were identified.…”

Section: Land Surface Autumn Phenology Datasetsmentioning

confidence: 99%

“…During a senescence period, there are three extreme points (two minimum and one maximum values) in the curvature change rate, where the two minimum extreme points correspond to senescence onset and dormancy onset, separately, while the maximum point corresponds to the date at the mid-senescent phase that is close to 50% of the total seasonal amplitude [34]. The autumn phenology occurrence date in our research is defined as the middle senescence date (MSD) (Figure 3), which is identified using the maximum extreme point in the curvature change rate of HPLM [34]. It should be noted that only the first growing cycle of grassland is addressed in this study if there are two or more growing cycles.…”

Section: Land Surface Autumn Phenology Datasetsmentioning

confidence: 99%

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Precipitation and Minimum Temperature are Primary Climatic Controls of Alpine Grassland Autumn Phenology on the Qinghai-Tibet Plateau

Chen

Zhang

et al. 2020

Remote Sensing

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Autumn phenology is a crucial indicator for identifying the alpine grassland growing season’s end date on the Qinghai-Tibet Plateau (QTP), which intensely controls biogeochemical cycles in this ecosystem. Although autumn phenology is thought to be mainly influenced by the preseason temperature, precipitation, and insolation in alpine grasslands, the relative contributions of these climatic factors on the QTP remain uncertain. To quantify the impacts of climatic factors on autumn phenology, we built stepwise linear regression models for 91 meteorological stations on the QTP using in situ herb brown-off dates, remotely sensed autumn phenological metrics, and a multi-factor climate dataset during an optimum length period. The results show that autumn precipitation has the most extensive influence on interannual variation in alpine grassland autumn phenology. On average, a 10 mm increase in autumn precipitation during the optimum length period may lead to a delay of 0.2 to 4 days in the middle senescence date (P < 0.05) across the alpine grasslands. The daily minimum air temperature is the second most important controlling factor, namely, a 1 °C increase in the mean autumn minimum temperature during the optimum length period may induce a delay of 1.6 to 9.3 days in the middle senescence date (P < 0.05) across the alpine grasslands. Sunshine duration is the third extensive controlling factor. However, its influence is spatially limited. Moreover, the relative humidity and wind speed also have strong influences at a few stations. Further analysis indicates that the autumn phenology at stations with less autumn precipitation is more sensitive to precipitation variation than at stations with more autumn precipitation. This implies that autumn drought in arid regions would intensely accelerate the leaf senescence of alpine grasslands. This study suggests that precipitation should be considered for improving process-based autumn phenology models in QTP alpine grasslands.

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Section: Land Surface Autumn Phenology Datasetsmentioning

confidence: 99%

Section: Land Surface Autumn Phenology Datasetsmentioning

confidence: 99%

Precipitation and Minimum Temperature are Primary Climatic Controls of Alpine Grassland Autumn Phenology on the Qinghai-Tibet Plateau

Chen

Zhang

et al. 2020

Remote Sensing

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“…NDVI and EVI are widely used as proxies of canopy "greenness", an integrative composite property of the green leaf area, canopy structure, and leaf chlorophyll content [36]. Both NDVI and EVI have been used for deriving vegetation phenological metrics at regional to global scales [5,[37][38][39][40]. Vegetation Indices (VIs) are robust and seamless biophysical measures, computed identically across all pixels in time and space, regardless of the biome type, land cover condition and soil type [41].…”

Section: Vegetation Indicesmentioning

confidence: 99%

Interaction of Seasonal Sun-Angle and Savanna Phenology Observed and Modelled using MODIS

Huete

Tran

2019

Remote Sensing

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Remote sensing of phenology usually works at the regional and global scales, which imposes considerable variations in the solar zenith angle (SZA) across space and time. Variations in SZA alters the shape and profile of the surface reflectance and vegetation index (VI) time series, but this effect on remote-sensing-derived vegetation phenology has not been adequately evaluated. The objective of this study is to understand the behaviour of VIs response to SZA, and to further improve the interpretation of satellite observed vegetation dynamics, across space and time. In this study, the sensitivity of two widely used VIs—the normalised difference vegetation index (NDVI) and the enhanced vegetation index (EVI)—to SZA was investigated at four northern Australian savanna sites, over a latitudinal distance of 9.8° (~1100 km). Complete time series of surface reflectances, as acquired with different SZA configurations, were simulated using Bidirectional Reflectance Distribution Function (BRDF) parameters provided by MODerate Resolution Imaging Spectroradiometer (MODIS). The sun-angle dependency of the four phenological transition dates were assessed. Results showed that while NDVI was very sensitive to SZA, such sensitivity was nearly absent for EVI. A negative correlation was also observed between NDVI sensitivity to SZA and vegetation cover, with sensitivity declining to the same level as EVI when vegetation cover was high. Different sun-angle configurations resulted in considerable variations in the shape and magnitude of the phenological profiles. The sensitivity of VIs to SZA was generally greater during the dry season (with only active trees present) than in the wet season (with both active trees and grasses), thus, the sun-angle effect on VIs was phenophase-dependent. The sun-angle effect on NDVI time series resulted in considerable differences in the phenological metrics across different sun-angle configurations. Across four sites, the sun-angle effect caused 15.5 days, 21.6 days, and 20.5 days differences in the start, peak, and the end of the growing season derived from NDVI time series, with seasonally varying SZA at local solar noon, as compared to those metrics derived from NDVI time series with fixed SZA. In comparison, those differences in the start, peak, and end of the growing season for EVI were significantly smaller, with only 4.8 days, 4.9 days, and 3 days, respectively. Our results suggest the potential importance of considering the seasonal SZA effect on VI time series prior to the retrieval of phenological metrics.

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“…Global climate change has influenced vegetation phenology considerably over the past decades (Chen et al, ; Dannenberg, Song, Hwang, & Wise, ; Gonsamo, Chen, & Ooi, ; Piao, Fang, Zhou, Philippe, & Zhu, ; Richardson et al, ; Zhang et al, ). While numerous research focused upon the spring phenology (i.e., the start of growing season, SOS) (Piao et al, ; Seyednasrollah, Swenson, Domec, & Clark, ; Shen, Tang, Chen, Zhu, & Zheng, ; White et al, ), influences of climate change on autumn leaf senescence (i.e., the end of the growing season, EOS) received less attention (Liu, Fu, Zhu, et al, ; Wu, Chen, et al, ; Zhu et al, ).…”

Section: Introductionmentioning

confidence: 99%

Satellite detection of cumulative and lagged effects of drought on autumn leaf senescence over the Northern Hemisphere

Peng

Zhang

et al. 2019

Global Change Biology

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Climate change has substantial influences on autumn leaf senescence, that is, the end of the growing season (EOS). Relative to the impacts of temperature and precipitation on EOS, the influence of drought is not well understood, especially considering that there are apparent cumulative and lagged effects of drought on plant growth. Here, we investigated the cumulative and lagged effects of drought (in terms of the Standardized Precipitation-Evapotranspiration Index, SPEI) on EOS derived from the normalized difference vegetation index (NDVI3g) data over the Northern Hemisphere extra-tropical ecosystems (>30°N) during 1982-2015. The cumulative effect was determined by the number of antecedent months at which SPEI showed the maximum correlation with EOS (i.e., R max-cml ) while the lag effect was determined by a month during which the maximum correlation between 1-month SPEI and EOS occurred (i.e., R max-lag ). We found cumulative effect of drought on EOS for 27.2% and lagged effect for 46.2% of the vegetated land area. For the dominant time scales where the R max-cml and R max-lag occurred, we observed 1-4 accumulated months for the cumulative effect and 2-6 lagged months for the lagged effect. At the biome level, drought had stronger impacts on EOS in grasslands, savannas, and shrubs than in forests, which may be related to the different root functional traits among vegetation types. Considering hydrological conditions, the mean values of both R max-cml and R max-lag decreased along the gradients of annual SPEI and its slope, suggesting stronger cumulative and lagged effects in drier regions as well as in areas with decreasing water availability. Furthermore, the average accumulated and lagged months tended to decline along the annual SPEI gradient but increase with increasing annual SPEI. Our results revealed that drought has strong cumulative and lagged effects on autumn phenology, and considering these effects could provide valuable information on the vegetation response to a changing climate. K E Y W O R D Sautumn phenology, cumulative effect, drought, lagged effect

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Generation and evaluation of the VIIRS land surface phenology product

Cited by 136 publications

References 91 publications

Precipitation and Minimum Temperature are Primary Climatic Controls of Alpine Grassland Autumn Phenology on the Qinghai-Tibet Plateau

Precipitation and Minimum Temperature are Primary Climatic Controls of Alpine Grassland Autumn Phenology on the Qinghai-Tibet Plateau

Interaction of Seasonal Sun-Angle and Savanna Phenology Observed and Modelled using MODIS

Satellite detection of cumulative and lagged effects of drought on autumn leaf senescence over the Northern Hemisphere

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