Assessing plant senescence reflectance index-retrieved vegetation phenology and its spatiotemporal response to climate change in the Inner Mongolian Grassland

Plant phenology is a key link for controlling interactions between climate change and biogeochemical cycles. Satellite-derived normalized difference vegetation index (NDVI) has been extensively used to detect plant phenology at regional scales. Here, we introduced a new vegetation index, plant senescence reflectance index (PSRI), and determined PSRI-derived start (SOS) and end (EOS) dates of the growing season using Moderate Resolution Imaging Spectroradiometer data from 2000 to 2011 in the Inner Mongolian Grassland. Then, we validated the reliability of PSRI-derived SOS and EOS dates using NDVI-derived SOS and EOS dates. Moreover, we conducted temporal and spatial correlation analyses between PSRI-derived SOS/EOS date and climatic factors and revealed spatiotemporal patterns of PSRI-derived SOS and EOS dates across the entire research region at pixel scales. Results show that PSRI has similar performance with NDVI in extracting SOS and EOS dates in the Inner Mongolian Grassland. Precipitation regime is the key climate driver of interannual variation of grassland phenology, while temperature and precipitation regimes are the crucial controlling factors of spatial differentiation of grassland phenology. Thus, PSRI-derived vegetation phenology can effectively reflect land surface vegetation dynamics and its response to climate change. Moreover, a significant linear trend of PSRI-derived SOS and EOS dates was detected only at small portions of pixels, which is consistent with that of greenup and brownoff dates of herbaceous plant species in the Inner Mongolian Grassland. Overall, PSRI is a useful and robust metric in addition to NDVI for monitoring land surface grassland phenology.

[1]  P. Gong,et al.  Modeling grassland spring onset across the Western United States using climate variables and MODIS-derived phenology metrics , 2015 .

[2]  C. Tucker,et al.  Interannual variations in satellite-sensed vegetation index data from 1981 to 1991 , 1998 .

[3]  Lin Xu,et al.  Phenological responses of Ulmus pumila (Siberian Elm) to climate change in the temperate zone of China , 2012, International Journal of Biometeorology.

[4]  Geoffrey M. Henebry,et al.  Land surface phenology and temperature variation in the International Geosphere–Biosphere Program high‐latitude transects , 2005 .

[5]  Shilong Piao,et al.  Increasing altitudinal gradient of spring vegetation phenology during the last decade on the Qinghai–Tibetan Plateau , 2014 .

[6]  O. Sonnentag,et al.  Climate change, phenology, and phenological control of vegetation feedbacks to the climate system , 2013 .

[7]  Jesslyn F. Brown,et al.  Measuring phenological variability from satellite imagery , 1994 .

[8]  A. Gitelson,et al.  Non‐destructive optical detection of pigment changes during leaf senescence and fruit ripening , 1999 .

[9]  David W. Inouye,et al.  Phenology at High Latitudes , 2013 .

[10]  P. Ciais,et al.  Leaf onset in the northern hemisphere triggered by daytime temperature , 2015, Nature Communications.

[11]  Xiaoqiu Chen,et al.  Modeling greenup date of dominant grass species in the Inner Mongolian Grassland using air temperature and precipitation data , 2014, International Journal of Biometeorology.

[12]  M. D. Schwartz,et al.  Determining the growing season of land vegetation on the basis of plant phenology and satellite data in Northern China , 2000, International journal of biometeorology.

[13]  G. Arturo Sanchez-Azofeifa,et al.  Changes in Spectral Properties, Chlorophyll Content and Internal Mesophyll Structure of Senescing Populus balsamifera and Populus tremuloides Leaves , 2008, Sensors.

[14]  Murugesu Sivapalan,et al.  Soil moisture controls on patterns of grass green-up in Inner Mongolia: an index based approach , 2012 .

[15]  R. Koster,et al.  Hydroclimatic Controls on the Means and Variability of Vegetation Phenology and Carbon Uptake , 2014 .

[16]  J. Seaquist,et al.  Phenology and its role in carbon dioxide exchange processes in northern peatlands , 2014 .

[17]  N. Delbart,et al.  Remote sensing of spring phenology in boreal regions: A free of snow-effect method using NOAA-AVHRR and SPOT-VGT data (1982-2004) , 2006 .

[18]  Chang-Hoi Ho,et al.  Phenology shifts at start vs. end of growing season in temperate vegetation over the Northern Hemisphere for the period 1982–2008 , 2011 .

[19]  Xiaoqiu Chen,et al.  Spatial and temporal variation of phenological growing season and climate change impacts in temperate eastern China , 2005 .

[20]  Ranga B. Myneni,et al.  Assessing Performance of NDVI and NDVI3g in Monitoring Leaf Unfolding Dates of the Deciduous Broadleaf Forest in Northern China , 2013, Remote. Sens..

[21]  E. Linacre,et al.  Climate Data and Resources: A Reference and Guide , 2003 .

[22]  A. Strahler,et al.  Monitoring vegetation phenology using MODIS , 2003 .

[23]  Michele Meroni,et al.  Remote sensing of larch phenological cycle and analysis of relationships with climate in the Alpine region , 2010 .

[24]  Xiangming Xiao,et al.  Spatial analysis of growing season length control over net ecosystem exchange , 2005 .

[25]  A. Strahler,et al.  Climate controls on vegetation phenological patterns in northern mid‐ and high latitudes inferred from MODIS data , 2004 .

[26]  The Carbon Cycle: Grasslands and the Global Carbon Cycle: Modeling the Effects of Climate Change , 2000 .

[27]  M. D. Schwartz,et al.  High-Resolution Phenological Data , 2013 .

[28]  Xiaoqiu Chen,et al.  Temperature controls on the spatial pattern of tree phenology in China's temperate zone , 2012 .

[29]  S. Quegan,et al.  Spring phenology in boreal Eurasia over a nearly century time scale , 2008 .

[30]  C. Tucker,et al.  Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999 , 2001 .

[31]  Beth Cole,et al.  Spectral monitoring of moorland plant phenology to identify a temporal window for hyperspectral remote sensing of peatland , 2014 .

[32]  T. Rötzer,et al.  Response of tree phenology to climate change across Europe , 2001 .

[33]  A. Menzel.  Plant Phenological Anomalies in Germany and their Relation to Air Temperature and NAO , 2003 .

[34]  Xiaoyang Zhang,et al.  Reconstruction of a complete global time series of daily vegetation index trajectory from long-term AVHRR data , 2015 .

[35]  D. Milchunas,et al.  Grassland vegetation changes and nocturnal global warming , 1999, Science.

[36]  J. Mustard,et al.  Cross-scalar satellite phenology from ground, Landsat, and MODIS data , 2007 .

[37]  Xiaoqiu Chen,et al.  Relationships among phenological growing season, time‐integrated normalized difference vegetation index and climate forcing in the temperate region of eastern China , 2002 .

[38]  G. Henebry.  Phenologies of North American Grasslands and Grasses , 2013 .

[39]  Mitchell D. Goldberg,et al.  Monitoring fall foliage coloration dynamics using time-series satellite data , 2011 .

[40]  Miina Rautiainen,et al.  Seasonal reflectance dynamics of common understory types in a northern European boreal forest , 2011 .

[41]  Josep Peñuelas,et al.  Phenology Feedbacks on Climate Change , 2009, Science.

[42]  X. Chen,et al.  An analysis of relationships among plant community phenology and seasonal metrics of Normalized Difference Vegetation Index in the northern part of the monsoon region of China , 2001, International journal of biometeorology.