Modeling grassland spring onset across the Western United States using climate variables and MODIS-derived phenology metrics

Xin, Qinchuan; Broich, Mark; Zhu, Peng; Gong, Ping

doi:10.1016/j.rse.2015.02.003

Cited by 88 publications

(52 citation statements)

References 92 publications

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“…This is, to some extent, in line with the results of two similar studies based on satellite-derived green-up date and ground meteorological data [47,63]. Some process-based phenology modelling and experimental studies have also previously highlighted the critical effects of water availability on SOS in North American grasslands [11,18] and Inner Mongolian grasslands [64,65]. In contrast, however, temperature has been reported as key factor of determining spring phenological events of woody plants in temperate ecosystems [66], and herbaceous species in alpine ecosystems [67].…”

Section: Key Factors Of Controlling Sos/eos For the Whole Study Regionsupporting

confidence: 85%

“…Ground data are commonly recorded at the species level and used for conducting species-specific phenology analysis at local scales [10][11][12][13]. Meanwhile, satellite data are mainly employed to define the growing season characteristics of entire landscapes at regional to global scales [14][15][16][17][18][19]. Various satellite-derived vegetation indices have been developed to extract vegetation phenological metrics, such as the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) [20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

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Diverse Responses of Vegetation Phenology to Climate Change in Different Grasslands in Inner Mongolia during 2000–2016

Ren

Peichl

et al. 2017

Remote Sensing

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Vegetation phenology in temperate grasslands is highly sensitive to climate change. However, it is still unclear how the timing of vegetation phenology events (especially for autumn phenology) is altered in response to climate change across different grassland types. In this study, we investigated variations of the growing season start (SOS) and end (EOS), derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016), for meadow steppe, typical steppe, and desert steppe in the Inner Mongolian grassland of Northern China. Using gridded climate data (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015), we further analyzed correlations between SOS/EOS and pre-season average air temperature and total precipitation (defined as 90-day period prior to SOS/EOS, i.e., pre-SOS/EOS) in each grid. The results showed that both SOS and EOS occurred later in desert steppe (day of year (doy) 114 and 312) than in meadow steppe (doy 109 and 305) and typical steppe (doy 111 and 307); namely, desert steppe has a relatively late growing season than meadow steppe and typical steppe. For all three grasslands, SOS was mainly controlled by pre-SOS precipitation with the sensitivity being largest in desert steppe. EOS was closely connected with pre-EOS air temperature in meadow steppe and typical steppe, but more closely related to pre-EOS precipitation in desert steppe. During 2000-2015, SOS in typical steppe and desert steppe has significantly advanced by 2.2 days and 10.6 days due to a significant increase of pre-SOS precipitation. In addition, EOS of desert steppe has also significantly advanced by 6.8 days, likely as a result from the combined effects of elevated preseason temperature and precipitation. Our study highlights the diverse responses in the timing of spring and autumn phenology to preceding temperature and precipitation in different grassland types. Results from this study can help to guide grazing systems and to develop policy frameworks for grasslands protection.

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Section: Key Factors Of Controlling Sos/eos For the Whole Study Regionsupporting

confidence: 85%

Section: Introductionmentioning

confidence: 99%

Diverse Responses of Vegetation Phenology to Climate Change in Different Grasslands in Inner Mongolia during 2000–2016

Ren

Peichl

et al. 2017

Remote Sensing

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“…However, no study has conducted a comparative analysis of the performance of MODIS C5 and C6 NDVI in monitoring vegetation phenology. Given that MODIS C5 NDVI has been extensively used for monitoring vegetation phenology [21][22][23], it is necessary to analyze the difference between vegetation phenology derived from C5 and C6 NDVI and consequently investigate the uncertainty in monitoring vegetation phenology due to sensor degradation. Due to the fact that Terra data is more affected by the sensor degradation than Aqua data [13,17], this study focused on the Terra MODIS NDVI products.…”

Section: Introductionmentioning

confidence: 99%

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

Zheng

Zhu

2017

Remote Sensing

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Vegetation phenology is considered a sensitive indicator of climate change, which controls carbon, nitrogen, and water cycles within terrestrial ecosystems. The Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) is an important moderate resolution remote sensing data for monitoring vegetation phenology. However, Terra MODIS Collection 5 (C5) vegetation index products were identified to be affected by sensor degradation, which has been addressed in the recently released MODIS Collection 6 (C6) vegetation index products. In order to compare the difference between MODIS C5 and C6 NDVI in monitoring vegetation phenology, the start and end of growing season (SOS and EOS) of the alpine grassland on the Tibetan Plateau (TP) were extracted using four common methods. Then, the C5 and C6 NDVI-derived SOS (SOS C5 and SOS C6 ) and EOS (EOS C5 and EOS C6 ) were compared with ground-observed phenology data. Results showed that the multi-year average growing season NDVIs of C6 were lower than those of C5 in most areas, while the inter-annual variation patterns of regional average SOS C5 and SOS C6 (EOS C5 and EOS C6 ) were consistent. However, large spatial differences in phenological trends were found between C5 and C6 NDVI products. From C5 to C6, pixels with a SOS (EOS) trend shifting from significant to insignificant or from insignificant to significant accounted for at least 14.58% (9.07%) of the total pixels. SOS C5 was more consistent than SOS C6 with the ground-observed green-up dates. C5 NDVI may be more appropriate for monitoring SOS than C6 NDVI in the study region, but more ground-observed phenology records are needed to confirm it due to only four observational sites in this study. However, large differences and poor correlations existed between EOS C5 (EOS C6 ) and the ground-observed beginning of leaf coloring. To further evaluate the uncertainty of MODIS C5 and C6 NDVI in monitoring vegetation phenology, higher resolution near-surface remote sensing data and corresponding validation methods should be applied.

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“…Vegetation consumption by herds is an important dynamics in evaluating and defining protocols based on vegetation indices when annual and inter-annual averages present good correlations with grassland status (Wessels et al, 2007;Reeves & Bagget, 2014). This phenomenon is present mainly in tropical grassland regions, where most of the rainfall occurs during the growing season and corresponds to changes in the vegetation index; however, the onset of spring in dry grasslands or moist environments indicates a strong relationship with climate data (Xin et al, 2015). Remote sensing data can greatly assist in the identification and mapping of existing grasslands and their conditions.…”

Section: Introductionmentioning

confidence: 99%

Vegetative growth of grasslands based on hyper-temporal NDVI data from the Modis sensor

Hott

Carvalho

Antunes

et al. 2016

Pesq. agropec. bras.

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-The objective of this work was to analyze the development of grasslands in Zona da Mata, in the state of Minas Gerais, Brazil, between 2000 and 2013, using a parameter based on the growth index of the normalized difference vegetation index (NDVI) from the moderate resolution imaging spectroradiometer (Modis) data series. Based on temporal NDVI profiles, which were used as indicators of edaphoclimatic conditions, the growth index (GI) was estimated for 16-day periods throughout the spring season of 2012 to early 2013, being compared with the average GI from 2000 to 2011, used as the reference period. Currently, the grassland areas in Zona da Mata occupy approximately 1.2 million hectares. According to the used methods, 177,322 ha (14.61%) of these grassland areas have very low vegetative growth; 577,698 ha (45.96%) have low growth; 433,475 ha (35.72%) have balanced growth; 39,980 ha (3.29%) have high growth; and 5,032 ha (0.41%) have very high vegetative growth. The grasslands had predominantly low vegetative growth during the studied period, and the NDVI/Modis series is a useful source of data for regional assessments.Index terms: growth index, pastures, remote sensing, time series, Zona da Mata. Termos para indexação: índice de crescimento, pastagens, sensoriamento remoto, séries temporais, Zona da Mata. Crescimento vegetativo de pastagens baseado em dados hipertemporais de NDVI do sensor Modis

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Modeling grassland spring onset across the Western United States using climate variables and MODIS-derived phenology metrics

Cited by 88 publications

References 92 publications

Diverse Responses of Vegetation Phenology to Climate Change in Different Grasslands in Inner Mongolia during 2000–2016

Diverse Responses of Vegetation Phenology to Climate Change in Different Grasslands in Inner Mongolia during 2000–2016

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

Vegetative growth of grasslands based on hyper-temporal NDVI data from the Modis sensor

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