1. INTRODUCTION. Copyright 2002 Royal Meteorological Society

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1 INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. 22: (2002) Published online in Wiley InterScience ( DOI: /joc.823 RELATIONSHIPS AMONG PHENOLOGICAL GROWING SEASON, TIME-INTEGRATED NORMALIZED DIFFERENCE VEGETATION INDEX AND CLIMATE FORCING IN THE TEMPERATE REGION OF EASTERN CHINA XIAOQIU CHEN* and WEIFENG PAN Department of Urban and Environmental Sciences, Peking University, Beijing , People s Republic of China Received 31 January 2002 Revised 17 April 2002 Accepted 17 June 2002 ABSTRACT Phenological, meteorological, and time-integrated normalized difference vegetation index (TI NDVI) data from 1982 to 1993, at three sample stations, were used to investigate the response of the growing season of local plant communities to climate change and the linkage of satellite sensor-derived greenness to the surface growing season. Results suggest that mean air temperature and growing degree days (GDDs) above 5 C during late winter and spring, and precipitation in autumn, are the most important controls on the beginning and end dates of the growing season (BGS and EGS). In contrast, annual mean air temperature, annual GDD totals, mean air temperature during late winter and spring, and growing season TI NDVI are the most important controls on length of the growing season (LGS). Using correlation and regression analysis, simple and multiple linear regression models were developed for individual and all stations. Since the standard error of the estimates (SE) of the BGS models for all stations are smaller than those of the EGS models, estimates of the beginning date of the growing season are probably more reliable than estimates of the end date. On average, if the mean air temperature in late winter and spring increases by 1 C, then the beginning date of the growing season will advance 5 6 days, and the end date will be 5 days later. Moreover, if autumn precipitation increases 100 mm, then the end date will advance 6 8 days. In terms of the LGS models, mean air temperature in late winter and spring, annual mean air temperature, and annual GDD totals have significant positive correlations with growing season duration, whereas growing season TI NDVI has a negative relationship. Comparing the SE of different LGS models, those developed with each of the three temperature variables fit the observed growing season duration data much better than those using growing season TI NDVI. Copyright 2002 Royal Meteorological Society. KEY WORDS: growing season modelling; plant phenology; time-integrated normalized difference vegetation index; climate variables; eastern China 1. INTRODUCTION Determining the growing season of terrestrial vegetation is crucial for estimating primary production (Kaduk and Heimann, 1996; Lieth, 1975) and explaining the seasonal CO 2 cycle (Hall et al., 1975; D Arrigo et al., 1987; Keeling et al., 1996). Since plant phenophases (such as bud-burst, bloom, leaf unfolding, leaf colouration, or defoliation) describe objectively the seasonal growth and senescence of terrestrial vegetation, the occurrence dates of plant phenophases from individual species have often been used to determine the beginning and end dates of the growing season (Schnelle, 1973; Reader et al., 1974; Chen, 1994; Chmielewski and Roetzer, 2001). As phenological data are comparatively scarce in many parts of the world, it is useful to determine how effectively the growing season can be measured using climate variables and satellite data. * Correspondence to: Xiaoqiu Chen, Department of Urban and Environmental Sciences, Peking University, Beijing , China; cxq@urban.pku.edu.cn Copyright 2002 Royal Meteorological Society

2 1782 X. CHEN AND W. PAN In the former case, Schwartz and Reiter (2000) calculated and examined the onset of the growing season of individual plant species across North America over the period using spring indices model output data based on daily maximum minimum temperatures and actual lilac phenological data. In the latter case, several authors have developed procedures to determine satellite sensor-derived growing season using normalized difference vegetation index (NDVI) data for a single pixel (Reed et al., 1994; White et al., 1997) or for large spatial scales (Myneni et al., 1997; Zhou et al., 2001). Other authors have used the onset and end of greenness and time-integrated (TI) NDVI as surrogates for growing season and primary production, and established statistical relationships between NDVI or TI NDVI and climate variables for gauging and monitoring vegetation dynamics (Yang W. et al., 1997; Yang L. et al., 1998; Fu and Wen, 1999). However, because NDVI metrics and thresholds may not directly correspond to conventional, ground-based phenological events, but rather provide indicators of vegetation dynamics (Justice et al., 1986; Lloyd, 1990), a detailed comparison of these satellite measures with ground-based phenological events is needed (Reed et al., 1994; Schwartz and Reed, 1999). In recent years, some studies have been carried out to compare surface phenological stages of individual plant species and mono-specific forests with satellite sensor-derived onset (beginning of growth) and offset (senescence) of greenness for selected biomes (Duchemin et al., 1999; Schwartz and Reed, 1999). Since NDVI measurements integrate observations of different plants and tend to provide descriptive characteristics of phenological landscape events, rather than direct associations with the phenological performance of specific plants during the vegetation period (Achard and Blasco, 1990; Reed et al., 1994), phenological data from local plant communities are more suitable for satellite surface analysis than those from individual species. Based on the above consideration, a new bottom-up procedure has been developed to reveal the temporal relationships among plant community phenology and seasonal NDVI metrics at sample stations and the pixels overlying them. Further, the technique explores possibilities for extrapolating the growing season of terrestrial vegetation at regional scales, using threshold NDVI values obtained by the surface satellite analysis at individual stations/pixels (Chen et al., 2000, 2001). Finally, it is necessary to introduce climatic variables into the surface satellite analysis in order to measure the growing season based on climate data, and detect possible responses of growing season parameters to climate change, which is the focus of the present study. The interrelationship among phenological, climatic, and satellite-derived variables has three aspects. In terms of the phenology climate relationship, although there is little doubt that the phenological growing season of land vegetation is directly and primarily under climate control, the exact and quantitative nature of this control is not yet well understood, especially at the plant community level. Dealing with the remotesensing climate relationship, the influence of climatic conditions on vegetation growth, and its impacts on NDVI, are often indirect. Specific interpretation of observed variations in NDVI is not well documented in terms of interactions among vegetation, climate and soil (Yang et al., 1997). Last but not least, the mechanism of the phenology remote-sensing relationship is relatively clear, because NDVI derived from satellite measurements can be conceptually described as a function of visible and near-infraredreflectancefrom the plant canopy, soil, and atmosphere, in which plants play an essential role. As phenological development of the plant canopy is a key measure in changes of seasonal vegetation coverage or density, it is closely related to the seasonal NDVI profile. However, the relationship between the observed growing season duration and TI NDVI has not yet been analysed systematically. The objectives of this study, therefore, are to analyse the quantitative relationships among the phenological growing season, climate forcing, and TI NDVI on the interannual level, and to assess reliability of determining the growing season based on climate and NDVI data. 2. STUDY AREA The study area lies in the northern part of the monsoon region in eastern China. Monsoon climates are characterized by strong seasonal and interannual variability. The vegetation system driven by a temperate zone monsoon climate contains remarkable seasonal aspects and interannual dynamics, which may significantly influence the corresponding NDVI metrics. Therefore, it is an ideal area for identifying the temporal

3 GROWING SEASON NDVI AND CLIMATE 1783 interrelationships among phenological, climatic, and remotely sensed variables. Three national phenological stations at Mudanjiang (Tieling Middle School, N, E, 300 m), Beijing (Summer Palace, N, E, 50 m) and Luoyang (Botanical Garden, N, E, 155 m) were selected based on the following criteria: (1) the topography at the stations is nearly flat; (2) the number of plants observed at each station consists of at least 40 species in each year; and (3) the phenological data for each species include ten phenophases on average. The corresponding meteorological stations at Mudanjiang and Beijing are near the phenological stations, but there is no standard meteorological station at Luoyang. Therefore, Mengjin (34 50 N, E, m), located 18 km north of Luoyang, was chosen as the meteorological station for that site. In terms of environmental conditions at the sample stations, Mudanjiang is located in the middle temperate and humid climate zone of China with mean annual air temperature of 4.4 C and mean annual precipitation of mm ( ). The dominant vegetation types in the area include deciduous broad-leaved and coniferous mixed forests in the mountains and cultivated vegetation on the alluvial plain. Soil types include a dark-brown forest soil in the mountain area and a dark meadow soil on the plain. Beijing and Luoyang, however, are located in the warm temperate and sub-humid climate zone. Mean annual air temperature at Beijing and Luoyang is 12.5 C and 13.7 C ( ) respectively, and the mean annual precipitation at the two sites is mm and mm ( ) respectively. Dominant vegetation types in the Beijing area include deciduous broad-leaved forest and scrub in the adjacent mountain area, and cultivated vegetation on the plain, with cinnamon soil. Crops are the dominant vegetation type in the Luoyang area and the soil is of the yellow fluvo-aquic type. 3. DATA AND METHODS Phenological data were acquired from Yearbooks of Chinese Animal and Plant Phenological Observation (Institute of Geography at Chinese Academy of Science, 1988, 1989a,b, 1992) and from an unpublished database. The study period extended from 1982 to Although the plant community compositions at the three locations have different characteristics, all plant species observed were selected according to the Chinese phenological observation guide (Institute of Geography at Chinese Academy of Science, 1965) to assure spatial comparability. In order to obtain a temporal profile of seasonal phenological progression at each station, and in each year, all observed phenophases of deciduous trees and shrubs (including bud-burst, first leaf unfolding, 50% leaf unfolding, first bloom, 50% bloom, end bloom, fruit maturing, beginning of leaf colouration, 100% leaf area colouration, beginning of defoliation, 100% defoliation, etc.) have been used to establish a mixed data set. Then the cumulative frequency of occurrence dates of all phenophases in every 10-day period throughout each year, and for each station, was computed in order to correspond with the NDVI data. The sequential and overlapping occurrences of these phenophases, illustrated by the phenological cumulative frequency curve within a year, represent the seasonal succession of the observed plant community (Chen et al., 2000). Figure 1 shows the average 10-day time series of phenological cumulative frequency from 1982 to 1993 at the sample stations. The phenological curves are characterized by four turning points. From the first to the second turning points, the phenological cumulative frequency increases rapidly, which reflects the progression of the plant canopy from winter conditions through to bud-burst, bloom, and leaf unfolding. From the second to the third turning points, little change occurs in the phenological cumulative frequency. Since dark-green leaves cover the plant canopy, the seasonal state remains relatively steady. From the third to the fourth turning points, the curves display another rapid increase in phenological cumulative frequency, which represents the dynamic progression of seasonal change of the plant canopy from dark-green to yellow or red, and then, finally, to defoliation. After the fourth turning point, phenological cumulative frequency changes very slowly again. Most deciduous trees and shrubs enter dormancy and the seasonal state is again steady. The spectral data used in this study were acquired from the NOAA/NASA Earth Observing System AVHRR Pathfinder data set for the period from January 1982 to December 1993 (James and Kalluri, 1994). TI NDVI was derived from NDVI composites at 8 km spatial resolution. The composites were generated by selecting the highest NDVI value over each 10-day period in order to reduce the effect of cloud contamination. The

4 1784 X. CHEN AND W. PAN Cumulative frequency Mudanjiang Beijing Luoyang Ten-daily period Figure 1. Average cumulative frequency curves of plant phenophases at the three sample stations ( ) original data in pixels overlying the three sample stations were then modified using the adjustment method suggested by Chen et al. (2000) to get a more homogeneous time series. TI NDVI can be expressed as TI NDVI = NDVI i where i is the ith 10-day interval ranging from onset to end period of the phenological growing season or over a specific period in a year. The annual total TI NDVI is defined as the integral of NDVI values from the first 10-day interval to the last 10-day interval of a year. Climate data were collected from each meteorological station. The data set contains daily mean air temperature and daily precipitation, based on which monthly and annual mean air temperatures, monthly and annual precipitation, and growing degree days (GDDs) were calculated. GDD was determined by subtracting a base temperature from the daily average temperature. A base temperature of 5 C is used in this study to calculate GDD, by accumulating daily mean air temperatures above 5 C over a year. The three climate variables chosen generally represent energy, heat, and moisture regimes affecting the phenological growing season of plant communities. In order to reveal the interannual relationships among growing season parameters, remotely sensed variables, and climatic variables, the beginning and end dates of the growing season were first determined by the phenological cumulative frequency curve of each station and each year using the method developed by Chen and Cao (1999). For detecting how phenology TI NDVI climate relations vary when different lengths of the growing season are considered, two specific growing season types were defined. Growing season 1 shows the time interval from 5 to 95% of the phenological cumulative frequency in each year, whereas growing season 2 represents the period from 10 to 90% of the phenological cumulative frequency (Chen et al., 2001). Figure 2 shows the interannual variations of the beginning of the growing season (BGS) and the end of the growing season (EGS) and the length of the growing season (LGS) of the two growing seasons at all three stations. A similar variation pattern was found between the two growing seasons at each individual station. However, except for growing season duration at Beijing, no significant linear trends are present in the growing season parameter time series of these three stations (Chen et al., 2001). Correlation analyses were then performed between the BGS, EGS, and LGS time series and the climate and TI NDVI variables for all three stations, to examine statistical relationships among these phenomena. As a last step, simple and multiple linear regression models were created to describe quantitatively the response of the growing season to climate change, and the linkage of satellite sensor-derived greenness to the surface growing season.

5 GROWING SEASON NDVI AND CLIMATE 1785 (a) Day of year Mudanjiang GS1 Mudanjiang GS2 Beijing GS1 Beijing GS2 Luoyang GS1 Luoyang GS Year (b) Day of year Mudanjiang GS1 Mudanjiang GS2 Beijing GS1 Beijing GS2 Luoyang GS1 Luoyang GS Year (c) Days Mudanjiang GS1 Mudanjiang GS2 Beijing GS1 Beijing GS Year Luoyang GS1 Luoyang GS2 Figure 2. Interannual variations of growing season parameters at the three sample stations: (a) beginning date; (b) end date; (c) length

6 1786 X. CHEN AND W. PAN 4. RESULTS AND DISCUSSION 4.1. BGS and EGS model: growing season climate relationship It is well known that the spring phenophases of individual plants are mainly triggered by the seasonal temperature regime (Chen, 1994; Chmielewski and Roetzer, 2001). However, it is unclear whether this relationship is applicable to the phenological development of whole plant communities. In order to explore plant community phenology climate TI NDVI relationships comprehensively, a correlation analysis was undertaken among the BGS and EGS, seasonal climate variables, and seasonal TI NDVI at the three sample stations. Table I shows that the BGS is mainly influenced by mean air temperature and GDD above 5 C from January to March at Beijing and Luoyang, and from February to April at Mudanjiang. In addition, some monthly mean temperatures also correlate highly with the BGS at Mudanjiang and Beijing. The overall negative correlation indicates that higher mean temperatures and GDD totals in late winter and spring may induce an earlier onset of the phenological growing seasons of local plant communities. However, there is no significant correlation between BGS and seasonal precipitation. The correlation between seasonal TI NDVI and BGS is also insignificant, which means that previous environmental and biological conditions measured by TI NDVI do not obviously influence the BGS. In contrast to the BGS, a significant correlation between EGS and climate variables was only detectable at Luoyang. The positive correlation between EGS and September November mean air temperature, and between EGS and September November GDD above 5 C, indicates that higher mean temperatures and GDD totals in autumn result in a later EGS at Luoyang. The negative correlation between the end date of growing season 2 and September November precipitation suggests that a drier autumn with less precipitation delays the end of growing season 2 at Luoyang. Similar to the BGS, no significant correlation was found between seasonal TI NDVI total and the EGS (Table II). Based on the correlation analysis, linear regression models were developed between the BGS and EGS (as dependent variables) and seasonal climatic elements (as independent variables) using the time series from individual sites (phenological and weather stations) and the spatial temporal data set pooled across all three sites. Table III shows the summary statistics of these BGS models. In terms of the BGS models for individual sites, both the standard error of the estimates (SE) and the slope of the models show an increasing tendency Table I. Correlation between BGS and climate TI NDVI variables at individual sample stations from 1982 to 1993 Variables a Mudanjiang Beijing Luoyang BGS 1 BGS 2 BGS 1 BGS 2 BGS 1 BGS 2 T 1 (2) T 2 (3) T 3 (4) T 1 3(2 4) GDD 1 3(2 4) P 1 3(2 4) TI NDVI 1 3(2 4) Correlation is significant at the 0.05 level. Correlation is significant at the 0.01 level. a T 1 (2) : mean air temperature of January (at Beijing and Luoyang) or February (at Mudanjiang). T 2 (3) : mean air temperature of February (at Beijing and Luoyang) or March (at Mudanjiang). T 3 (4) : mean air temperature of March (at Beijing and Luoyang) or April (at Mudanjiang). T 1 3(2 4) : mean air temperature from January to March (at Beijing and Luoyang) or from February to April (at Mudanjiang). GDD 1 3(2 4) : accumulated GDD above 5 C from January to March (at Beijing and Luoyang) or from February to April (at Mudanjiang). P 1 3(2 4) : accumulated precipitation from January to March (at Beijing and Luoyang) or from February to April (at Mudanjiang). TI NDVI 1 3(2 4) : TI NDVI from January to March (at Beijing and Luoyang) or from February to April (at Mudanjiang).

7 GROWING SEASON NDVI AND CLIMATE 1787 Table II. Correlation between EGS and climate TI NDVI variables at individual sample stations from 1982 to 1993 Variables a Mudanjiang Beijing Luoyang EGS 1 EGS 2 EGS 1 EGS 2 EGS 1 EGS 2 T 9 (8) T 10 (9) T 11 (10) T 9 11(8 10) GDD 9 11(8 10) P 9 11(8 10) TI NDVI 9 11(8 10) Correlation is significant at the 0.05 level. Correlation is significant at the 0.01 level. a T 9 (8) : mean air temperature of September (at Beijing and Luoyang) or August (at Mudanjiang). T 10 (9) : mean air temperature of October (at Beijing and Luoyang) or September (at Mudanjiang). T 11 (10) : mean air temperature of November (at Beijing and Luoyang) or October (at Mudanjiang). T 9 11(8 10) : mean air temperature from September to November (at Beijing and Luoyang) or from August to October (at Mudanjiang). GDD 9 11(8 10) : accumulated GDD above 5 C from September to November (at Beijing and Luoyang) or from August to October (at Mudanjiang). P 9 11(8 10) : accumulated precipitation from September to November (at Beijing and Luoyang) or from August to October (at Mudanjiang). TI NDVI 9 11(8 10) : TI NDVI from September to November (at Beijing and Luoyang) or from August to October (at Mudanjiang). from north to south across the research region, namely the smallest SE and slope appear at Mudanjiang, whereas the largest SE and slope are at Luoyang. According to the slopes of the regression models, a warming of 1 C during late winter and spring in the temperate region of eastern China would lead to an advanced onset of the growing season by 3 days in the north and 7 8 days in the south, whereas an increase of 100 degree days (GDD above 5 C) during the late winter and spring would cause an advanced onset of the growing season by days in the north and days in the south. Comparing the beginning of growing season 1 (BGS1) model with the beginning of growing season 2 (BGS2) model at the same site, the SE of the BGS2 model is normally smaller than that of the BGS1 model, which indicates that estimating the BGS2 would be more reliable than estimating the BGS1. Figure 3(a) shows an example of the correlation between BGS2 predicted by February April mean temperature and that derived from the phenological observations at Mudanjiang. The BGS models for all three sites include only one seasonal climate variable that is statistically significant in relation to the BGS (p <0.001): the mean air temperature from January to March at Beijing and Luoyang, and from February to April at Mudanjiang. The correlation between the BGS and the three-monthly mean temperature is negative. The SEs of the models are 6.5 days for growing season 1 and 5.8 days for growing season 2, which are identical with those of the models for the individual sites. According to the slopes of the regression equations, on average, if the three-monthly mean temperature increases by 1 C, then the beginning date of the phenological growing season would advance 5.9 days for growing season 1 and 5.2 days for growing season 2 (Table III). Figure 3(b) illustrates the correlation between the predicted and observed beginning dates of growing season 2 for all three sites. Two kinds of EGS model for individual sites were developed at Luoyang. The five simple linear models describe the quantitative relationship between the EGS and a seasonal climate variable for the two growing seasons. For a given independent variable, the SE of the end of growing season 1 (EGS1) model is obviously smaller than that of the end of growing season 2 (EGS2) model, which implies that estimating the end date of growing season 1 would be more reliable than growing season 2. According to the slopes of the models, a warming of 1 C in three-monthly mean temperature or an increase of 100 degree days (GDD above 5 C) during autumn (September November) would induce a delayed end of the growing season by 7 8 days, whereas an increase of 100 mm in autumn precipitation would cause the growing season to end 8 days earlier (Table IV).

8 1788 X. CHEN AND W. PAN Table III. Summary statistics of BGS models for individual and all three stations a Site Y X r Sig. Intercept Slope SE 3 sites BGS1 T 1 3(2 4) sites BGS2 T 1 3(2 4) MD BGS1 T MD BGS2 T BJ BGS1 T BJ BGS2 T LY BGS1 T LY BGS2 T MD BGS1 GDD MD BGS2 GDD BJ BGS2 GDD LY BGS1 GDD LY BGS2 GDD a MD: Mudanjiang; BJ: Beijing; LY: Luoyang; BGS1: beginning date of growing season 1; BGS2: beginning date of growing season 2. Two multiple linear models were developed among the EGS and three seasonalclimate variablesfor growing season 2, in which the correlation coefficients between the independent variables (mean air temperature, GDD, and precipitation in autumn) are insignificant. Since the SEs of the multiple linear models for growing season 2 are smaller than those of the simple linear models, using the multiple linear models may improve the accuracy for estimating the end date of growing season 2. The EGS models for all three sites consist of two seasonal climate variables that are statistically significant (p <0.001): mean temperature during late winter and spring (January March temperature at Beijing and Luoyang; February April temperature at Mudanjiang) and precipitation in autumn (September November precipitation at Beijing and Luoyang; August October precipitation at Mudanjiang). The three-monthly mean temperature during late winter and spring is positively correlated with both end dates of the growing season, and precipitation in autumn is negatively correlated with the end dates. The R 2 values for both the EGS1 and EGS2 models combining all three sites are highly significant. Exploring the significant correlation between mean temperature during late winter and spring and the end date of the growing season, we can assume that, because the mean temperature correlates closely with the beginning of the growing season, a higher mean temperature in this period would induce an earlier beginning of that year s photosynthetic activities. Therefore, more organic material and chemical energy would be produced and stockpiled in plants, which would sustain the seasonal growth of plants over a longer period of time and consequently delay the end date of the growing season. More detailed studies and explanations are needed to understand the physiological and ecological mechanisms of this correlation. Since the SEs of the EGS models of all three sites are considerably larger than those of the corresponding BGS models (Tables III and IV), estimating the beginning date of the growing season at regional scales is more reliable than estimating the end date. The slope analysis indicates that (i) if mean temperature during late winter and spring increases by 1 C, then the growing season end date will be delayed 5 days; (ii) if precipitation in autumn decreases 100 mm, then the growing season end date will be delayed 6 to 8 days (Table IV) LGS model: growing season climate TI NDVI relationship Eight LGS models for all three sites were constructed between the growing season duration (as the dependent variable) and the mean temperature during late winter and spring, the annual mean temperature, the annual total GDD above 5 C and the growing season TI NDVI (as independent variables). All four independent variables correlate significantly with the length of growing seasons 1 and 2 (p <0.001). Mean temperature

9 GROWING SEASON NDVI AND CLIMATE 1789 (a) 112 Observed value in day of year Predicted value in day of year (b) Observed value in day of year Predicted value in day of year Figure 3. Correlations between beginning date of growing season 2 predicted by mean air temperature in late winter and spring and that derived from phenological observations: (a) Mudanjiang; (b) all three stations ( : Luoyang; : Beijing; : Mudanjiang) during late winter and spring, annual mean temperature, and annual total GDD have positive correlations with length of the growing season, whereas growing season TI NDVI has an inverse relationship with growing season duration (Table V). In addition, the correlation between growing season duration and annual total precipitation was very low, which means that annual total precipitation was not the limiting factor on growing season duration in the study area. The negative correlation between growing season duration and growing season TI NDVI (also the annual total TI NDVI, not shown) may be caused indirectly by temperature and moisture regimes in the temperate monsoon region. High temperatures in spring and early summer often result in a depletion of soil moisture, which may restrict the growth of land vegetation and decrease levels of the 10-day NDVI and the growing season TI NDVI. This explanation can be partially verified by the significant negative correlation between growing season TI NDVI and mean temperature during late winter and spring, and between growing season TI NDVI and annual mean temperature (Table VI).

10 1790 X. CHEN AND W. PAN Table IV. Summary statistics of EGS models for individual and all three stations Site Y X r Sig. R 2 Sig. Intercept Slope SE 3 sites EGS1 T 1 3(2 4) P 9 11(8 10) sites EGS2 T 1 3(2 4) P 9 11(8 10) LY EGS1 T LY EGS2 T LY EGS1 GDD LY EGS2 GDD LY EGS2 P LY EGS2 T P LY EGS2 GDD P EGS1: end date of growing season 1; EGS2: end date of growing season 2. Table V. Summary statistics of LGS models for all three stations a Y X r Sig. Intercept Slope SE LGS1 T 1 3(2 4) LGS2 T 1 3(2 4) LGS1 AT LGS2 AT LGS1 GDD LGS2 GDD LGS1 TI NDVI GS LGS2 TI NDVI GS a AT: annual mean temperature; GDD: total growing degree days in a year; TI NDVI GS1 : timeintegrated NDVI during growing season 1; TI NDVI GS2 : time-integrated NDVI during growing season 2; LGS1: length of growing season 1; LGS2: length of growing season 2. Table VI. Correlation matrix among climate variables and TI NDVI for all three stations and two growing seasons T 1 3(2 4) AT GDD TI NDVI GS1 /TI NDVI GS2 T 1 3(2 4) / AT / GDD / TI NDVI GS1 /TI NDVI GS /1.000 Correlation is significant at the 0.01 level. A stronger linkage between LGS and growing season TI NDVI was observed over growing season 2 than growing season 1 (Table V). This is likely caused by NDVI being primarily associated with, and affected by, chlorophyll absorption and the density of healthy green leaves (Tucker and Sellers, 1986). In the early stage of growing season 1, when green leaf density is low, NDVI values tend to be biased towards the spectral reflectance of non-vegetation land cover components, such as soil background. Similarly, in the late stage

11 GROWING SEASON NDVI AND CLIMATE 1791 of growing season 1, when plants near senescence, reduced near-infrared reflection will result in a dramatic decrease in NDVI. Therefore, changes in NDVI during the early and late periods of the growing season are not necessarily closely linked to phenological development of local plant communities as affected by climatic conditions. Moreover, slightly stronger correlations between LGS and annual mean temperature and between LGS and annual total GDD were also detected over growing season 2 than growing season 1. Comparing standard errors of the estimates of the eight LGS models, the models developed with the three temperature variables fit the observed growing season duration data much better than the models using growing season TI NDVI. On average, if annual mean temperature and mean temperature during late winter and spring increase by 1 C, then the growing seasons would lengthen days and days respectively, whereas if annual total GDD increases 100 degree days, then the growing seasons would lengthen 4 5 days. In contrast to the temperature variables, if growing season TI NDVI increases by one unit, then the growing season would shorten by days. Because there are close dependencies among the three climate variables and growing season TI NDVI (Table VI), no multiple linear models were constructed in this study. As the LGS models were based on the spatial temporal series of all three sample stations, they should not only be useful in determining the growing season at the sample stations but also in estimating the growing season at other adjacent sites with similar vegetation. Thus, it appears that further application of these models to sites lacking phenological data would be productive. 5. CONCLUSIONS This study demonstrates that phenological cumulative frequency of plant communities provides a sensible and real metric for monitoring vegetation dynamics and for determining the growing season at local scales. Growing-season climate relationships established by BGS and EGS models for all three stations indicate that higher mean air temperatures during late winter and spring can induce an earlier onset of the growing season of local plant communities, whereas higher mean air temperatures during late winter and spring and less precipitation in autumn can cause a later end of the growing season. The SE analyses show that estimating the beginning and end date of growing season 2 would be more reliable than growing season 1. Generally speaking, estimating the beginning date of the growing season is more reliable than estimating the end date. The growing-season climate TI NDVI relationships established by LGS models show that mean air temperature during late winter and spring, annual mean air temperature, and annual GDD totals have positive correlations with growing season duration, whereas growing season TI NDVI has a negative relationship. Lastly, models developed with each of the three temperature variables fit the observed growing season duration data much better than those using growing season TI NDVI. This study suggests an integrated and surface satellite approach involving statistical models, phenological and meteorological observations, and satellite remote sensing as an effective means to monitor interannual vegetation dynamics and to estimate growing season parameters using meteorological and satellite data at regional scales. ACKNOWLEDGEMENTS This work was supported by the Excellence Young Teacher Program (1998), administered by the Ministry of Education, People s Republic of China, and by the National Natural Science Foundation of China under grant no REFERENCES Achard F, Blasco F Analysis of vegetation seasonal evolution and mapping of forest cover in West Africa with the use of NOAA AVHRR HRPT data. Photogrammetric Engineering and Remote Sensing 56: Chen X Untersuchung zur zeitlich-raeumlichen Aehnlichkeit von phaenologischen und klimatologischen Parametern in Westdeutschland und zum Einfluss geooekologischer Faktoren auf die phaenologische Entwicklung im Gebiet des Taunus. Selbstverlag des Deutschen Wetterdienstes: Offenbach.

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