Spring green-up date derived from GIMMS3g and SPOT-VGT NDVI of winter wheat cropland in the North China Plain

Abstract Satellite temporal resolution affects the fitting accuracy of vegetation growth curves. However, there are few studies that evaluate the impact of different satellite data (including temporal resolution and time series change) on spring green-up date (GUD) extraction. In this study, four GUD algorithms and two different temporal resolution satellite data (GIMMS3g during 1982–2013 and SPOT-VGT during 1999–2013) were used to investigate winter wheat GUD in the North China Plain. Four GUD algorithms included logistic-NDVI (normalized difference vegetation index), logistic-cumNDVI (cumulative NDVI), polynomial-NDVI and polynomial-cumNDVI algorithms. All algorithms and data were first regrouped into eight controlled cases. At site scale, we evaluated the performance of each case using correlation coefficient (r), bias and root mean square error (RMSE). We further compared spatial patterns and inter-annual trends of GUD inferred from different algorithms, and then analyzed the difference between GIMMS3g-based GUD and SPOT-VGT-based GUD. Our results showed that all satellite-based GUD were correlated with observations with r ranging from 0.32 to 0.57 (p

[1]  Jin Chen,et al.  A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter , 2004 .

[2]  J. Diez,et al.  Drought-induced changes in the phenology, productivity and diversity of Spanish fungi , 2015 .

[3]  Jose Oteros,et al.  Variations in cereal crop phenology in Spain over the last twenty-six years (1986–2012) , 2015, Climatic Change.

[4]  Chongcheng Chen,et al.  Winter wheat mapping combining variations before and after estimated heading dates , 2017 .

[5]  Wenfeng Chi,et al.  Understanding long-term (1982-2013) patterns and trends in winter wheat spring green-up date over the North China Plain , 2017, Int. J. Appl. Earth Obs. Geoinformation.

[6]  Andrew E. Suyker,et al.  Interannual and spatial impacts of phenological transitions, growing season length, and spring and autumn temperatures on carbon sequestration: A North America flux data synthesis , 2012 .

[7]  Qingyuan Zhang,et al.  Monitoring interannual variation in global crop yield using long-term AVHRR and MODIS observations. , 2016, ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing.

[8]  Agus Santoso,et al.  Recent intensification of wind-driven circulation in the Pacific and the ongoing warming hiatus , 2014 .

[9]  M. Yokozawa,et al.  Climate changes and trends in phenology and yields of field crops in China, 1981-2000 , 2006 .

[10]  John M. Baker,et al.  Simulating crop phenology in the Community Land Model and its impact on energy and carbon fluxes , 2015 .

[11]  The 20th century cooling trend over the southeastern United States , 2012, Climate Dynamics.

[12]  Miaogen Shen,et al.  Changes in autumn vegetation dormancy onset date and the climate controls across temperate ecosystems in China from 1982 to 2010 , 2015, Global change biology.

[13]  R. Zurita-Milla,et al.  Trends and Natural Variability of Spring Onset in the Coterminous United States as Evaluated by a New Gridded Dataset of Spring Indices , 2015 .

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

[15]  Michael T. Coe,et al.  Testing the performance of a dynamic global ecosystem model: Water balance, carbon balance, and vegetation structure , 2000 .

[16]  Jean-Pierre Wigneron,et al.  Monitoring elevation variations in leaf phenology of deciduous broadleaf forests from SPOT/VEGETATION time-series , 2011 .

[17]  Zheng Niu,et al.  Extracting grassland vegetation phenology in North China based on cumulative SPOT-VEGETATION NDVI data , 2014 .

[18]  P. Atkinson,et al.  Inter-comparison of four models for smoothing satellite sensor time-series data to estimate vegetation phenology , 2012 .

[19]  B. Holben.  Characteristics of maximum-value composite images from temporal AVHRR data , 1986 .

[20]  Jonathan Seaquist,et al.  The effect of the temporal resolution of NDVI data on season onset dates and trends across Canadian broadleaf forests , 2011 .

[21]  A. Croitoru,et al.  Air temperature trend and the impact on winter wheat phenology in Romania , 2012, Climatic Change.

[22]  H. Mooney,et al.  Shifting plant phenology in response to global change. , 2007, Trends in ecology & evolution.

[23]  M. Hutchinson,et al.  Development and Testing of Canada-Wide Interpolated Spatial Models of Daily Minimum–Maximum Temperature and Precipitation for 1961–2003 , 2008 .

[24]  Bin Wang,et al.  Climate change impacts on phenology and yields of five broadacre crops at four climatologically distinct locations in Australia , 2015 .

[25]  Antje Müller,et al.  Climate changes and trends in phenology of fruit trees and field crops in Germany, 1961-2000 , 2004 .

[26]  S. Piao,et al.  Spring vegetation green-up date in China inferred from SPOT NDVI data: A multiple model analysis , 2012 .

[27]  B. Duchemin,et al.  VEGETATION/SPOT: an operational mission for the Earth monitoring; presentation of new standard products , 2004 .

[28]  G. Churkina,et al.  Investigating the impact of climate change on crop phenological events in Europe with a phenology model , 2012, International Journal of Biometeorology.

[29]  Jennifer Pontius,et al.  Remote sensing of spring phenology in northeastern forests: A comparison of methods, field metrics and sources of uncertainty , 2014 .

[30]  Conghe Song,et al.  Empirical evidence of El Niño–Southern Oscillation influence on land surface phenology and productivity in the western United States , 2015 .

[31]  Enli Wang,et al.  Increased uncertainty in simulated maize phenology with more frequent supra-optimal temperature under climate warming , 2015 .

[32]  M. Schaepman,et al.  Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982–2006 , 2009 .

[33]  M. D. Schwartz,et al.  Spring onset variations and trends in the continental United States: past and regional assessment using temperature‐based indices , 2013 .

[34]  E. Lin,et al.  Future climate change, the agricultural water cycle, and agricultural production in China , 2003 .

[35]  Ruyin Cao,et al.  Temperature sensitivity as an explanation of the latitudinal pattern of green‐up date trend in Northern Hemisphere vegetation during 1982–2008 , 2015 .

[36]  Li Guo,et al.  Reconciling the discrepancy in ground‐ and satellite‐observed trends in the spring phenology of winter wheat in China from 1993 to 2008 , 2016 .

[37]  Jiyuan Liu,et al.  Spatiotemporal characteristics, patterns, and causes of land-use changes in China since the late 1980s , 2014, Journal of Geographical Sciences.

[38]  P. Ciais,et al.  Variations in satellite‐derived phenology in China's temperate vegetation , 2006 .

[39]  Jinwei Dong,et al.  Mapping paddy rice planting areas through time series analysis of MODIS land surface temperature and vegetation index data. , 2015, ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing.

[40]  E. Lokupitiya,et al.  Incorporation of crop phenology in Simple Biosphere Model (SiBcrop) to improve land-atmosphere carbon exchanges from croplands , 2009 .

[41]  G. Dedieu,et al.  SMAC: a simplified method for the atmospheric correction of satellite measurements in the solar spectrum , 1994 .

[42]  Chaoyang Wu,et al.  Land surface phenology of China's temperate ecosystems over 1999–2013: Spatial–temporal patterns, interaction effects, covariation with climate and implications for productivity , 2016 .

[43]  W. Zhuang,et al.  Impacts of recent climate warming, cultivar changes, and crop management on winter wheat phenology across the Loess Plateau of China , 2015 .

[44]  Yu Kosaka,et al.  Recent global-warming hiatus tied to equatorial Pacific surface cooling , 2013, Nature.

[45]  William J. Sacks,et al.  Crop management and phenology trends in the U.S. Corn Belt: Impacts on yields, evapotranspiration and energy balance , 2011 .

[46]  Alfredo Huete,et al.  Abrupt shifts in phenology and vegetation productivity under climate extremes , 2015 .

[47]  Jinwei Dong,et al.  Green-up dates in the Tibetan Plateau have continuously advanced from 1982 to 2011 , 2013, Proceedings of the National Academy of Sciences.

[48]  Rogier de Jong,et al.  Variability and evolution of global land surface phenology over the past three decades (1982–2012) , 2016, Global change biology.

[49]  Jin Chen,et al.  An improved logistic method for detecting spring vegetation phenology in grasslands from MODIS EVI time-series data , 2015 .

[50]  M. Yokozawa,et al.  Modelling the impacts of weather and climate variability on crop productivity over a large area: A new process-based model development, optimization, and uncertainties analysis , 2009 .

[51]  T. Sakamoto,et al.  A crop phenology detection method using time-series MODIS data , 2005 .

[52]  Dailiang Peng,et al.  Improved modeling of gross primary production from a better representation of photosynthetic components in vegetation canopy , 2017 .

[53]  G. Henebry,et al.  Exploration of scaling effects on coarse resolution land surface phenology , 2017 .

[54]  Fulu Tao,et al.  Remote sensing of crop production in China by production efficiency models: models comparisons, estimates and uncertainties , 2005 .

[55]  M. Wang,et al.  Observed changes in winter wheat phenology in the North China Plain for 1981–2009 , 2013, International Journal of Biometeorology.

[56]  Huadong Guo,et al.  Detecting winter wheat phenology with SPOT-VEGETATION data in the North China Plain , 2014 .

[57]  Steven W. Running,et al.  Effects of precipitation and soil water potential on drought deciduous phenology in the Kalahari , 2004 .

[58]  Ranga B. Myneni,et al.  Monitoring spring canopy phenology of a deciduous broadleaf forest using MODIS , 2006 .

[59]  Tao Wang,et al.  Changes in satellite‐derived spring vegetation green‐up date and its linkage to climate in China from 1982 to 2010: a multimethod analysis , 2013, Global change biology.

[60]  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 .

[61]  Jinwei Dong,et al.  Evolution of regional to global paddy rice mapping methods: A review , 2016 .

[62]  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 .

[63]  J. Schaber,et al.  Responses of spring phenology to climate change , 2004 .

[64]  Peter M. Atkinson,et al.  The use of MERIS Terrestrial Chlorophyll Index to study spatio-temporal variation in vegetation phenology over India , 2010 .

[65]  Compton J. Tucker,et al.  A Non-Stationary 1981-2012 AVHRR NDVI3g Time Series , 2014, Remote. Sens..