基于时间分辨率为逐月、空间分辨率为1 km的MODIS/NDVI数据,利用WS、S-G、A-G和D-L这4种重建方法对重庆市2010~2014年间历年逐月的NDVI时间序列数据进行重建,并采用视觉对比、分地类像元对比、相关系数（R）、均方根误差（RMSE）、赤池信息准则（AIC）和贝叶斯信息准则（BIC）对4种重建结果进行了评价。结果表明：S-G和WS重建后噪声少,S-G的曲线最为平滑,A-G与WS保真性较好。其中,A-G的R>0.8和RMSE

Chongqing is located in mountainous area where the vegetation is rich. It is greatly affected by the fog all year round. As a result, the noise of remote sensing image is serious and the affected area is large which have a great influence on the accuracy of related application.NDVI is one of the most important vegetation index which has an important role in the study of global climate change, vegetation change etc. In this paper, the MODIS/NDVI data are used, the time resolution of the data is monthly and the spatial resolution is 1km. The NDVI data of Chongqing city from 2010 to 2014 are reconstructed by four smoothing techniques: WS, S-G, A-G and D-L. The results are evaluated by visual comparison, pixel contrast of different land use types,correlation coefficient(R),the root mean square error (RMSE), Akaike Information Criteria (AIC) and BayesianInformation Criteria (BIC). In the visual contrast analysis, the results indicate that the noise of S-G technique and WS technique is lower than the others. In the pixel contrast of different land use types, the curve of S-G technique is the smoothest.In the fidelity analysis, R and RMSE are used to evaluate of the original data and data reconstructed by four technique, the result of A-G technique and WS technique is better. It also indicates that the A-G technique have the widest distribution, the value of R larger than 0.8 accounts for 89.41% of the total area and the value of RMSE smaller than 0.05 accounts for 66.40% of the total area. The WS technique is better than the other two techniques, accounting for 72.76% and 59.37% of the total area respectively. In the model effect analysis, the AIC and BIC evaluation results of the A-G technique are the best. The evaluation results of WS techniqueis good in Western Chongqing where the BIC evaluation results of other threetechniques are relatively poor, the result of S-G method is the worst.

基于植物物候的遥感监测对于研究植被对气候变化的响应具有重要的科学价值。本文在阐述植物物候遥感监测原理及其通用技术流程的基础上,分别从植被类型及其所处的地理条件、遥感数据源及其预处理、植物物候遥感识别方法和植物物候遥感监测结果评价4个方面分析了影响植物物候遥感监测精度的因素,并针对当前研究中存在的不足,探讨了提高植物物候遥感监测精度的可行性途径,即建立高分辨率的近地面遥感定点观测及数据共享网络,发展普适性更强的卫星遥感时序数据去噪及植被指数曲线重建方法,寻求稳定性更高的植物物候期遥感识别方法,探索综合运用地面观测、遥感监测与模型模拟实现物候观测空间尺度拓展的可能性。

Monitoring plant phenology with remote sensing data has important scientific value for studying the response of vegetation to climate change. A comprehensive analysis on the influencing factors of accuracy of plant phenology estimation based on principles and general technical processes of remote sensing application in vegetation monitoring was carried out by taking into account the following four aspects: the specific vegetation type and its geographical conditions; remote sensing data and pre-processing; techniques used to identify plant phenometrics; and evaluation of satellite-derived plant phenometrics. Potential methods for improving the accuracy of plant phenology monitoring are thoroughly discussed. These include: building high-resolution near-surface sensor-derived phenology observation and sharing network; developing universally applicable methods for noise removal of satellite remote sensing time-series data and reconstruction of vegetation index curves; searching more stable methods to estimate plant phenology; and exploring the possibility of synthesizing ground-based observation, remote sensing monitoring, and model simulation to achieve the spatial scaling-up of phenometrics.

基于2001—2020年归一化植被指数（NDVI）、增强型植被指数（EVI）2种遥感植被指数以及蒙古高原94个气象站点的月最高、最低温和降水量数据，利用累积NDVI的Logistic曲线曲率极值法和动态阈值法2种物候识别方法提取蒙古高原植被返青期，分析蒙古高原昼夜非对称变暖的时空变化及其对植被返青期的影响。结果表明：（1） 2001—2020年蒙古高原生长季开始前6个月（上一年11月—当年4月）平均最高温[0.7 ℃·(10a)^-1]和最低温[0.3 ℃·(10a)^-1]均呈上升趋势，最高温的变暖速率是最低温的2.3倍。（2） 季前昼夜非对称变暖对植被返青期均产生提前作用，但与最高温相比，最低温对返青期的影响程度更大，且影响范围更为广泛。（3） 季前昼夜非对称变暖对不同植被类型返青期产生不同影响，白天变暖对灌丛、农田和稀疏植被返青期的影响更明显，夜间变暖对森林和草地返青期的影响更强，特别是森林地区（25.5%）。研究白天和夜间非对称变暖对蒙古高原植被物候的影响，对揭示温度对春季植被物候的影响机制有重要意义。

Two phenological identification methods, namely, the logistic curve curvature extreme value method and the dynamic threshold method of cumulative NDVI, were used to extract the data based on the normalized difference vegetation index (NDVI) and enhanced vegetation index remote sensing vegetation indices from 2001 to 2020, as well as the highest temperature, the lowest temperature, and precipitation data of 94 meteorological stations in the Mongolian Plateau. The temporal and spatial changes of the Mongolian Plateau’s asymmetric circadian warming and its impact on the start of growing season were analyzed. The research shows the following: (1) The average maximum temperature [0.7 ℃·(10a)-1] and the minimum temperature [0.3 ℃·(10a)-1] in the preseason 6 months (November of the previous year to April of the current year) in the Mongolian Plateau from 2001 to 2020 both showed an upward trend, and the warming rate of the highest temperature was 2.3 times that of the lowest temperature. (2) The asymmetric warming of the preseason day and night has an early effect on the start of growing season; however, compared with the highest temperature, the lowest temperature has a greater impact on the start of growing season, and the impact range is wider. (3) The preseason asymmetrical warming of day and night has different effects on the start of growing season of different vegetation types. The effect of daytime warming on the start of the growing season of shrubs, farmland, and sparse vegetation is more obvious, and the effect of nighttime warming on the start of the growing season of forest and grassland is more obvious, especially in forest areas (25.5%). Studying the effects of daytime and nighttime asymmetric warming on vegetation phenology on the Mongolian Plateau is of great significance to reveal the mechanism of temperature effects on vegetation phenology in spring.

Land cover (LC) determines the energy exchange, water and carbon cycle between Earth's spheres. Accurate LC information is a fundamental parameter for the environment and climate studies. Considering that the LC in China has been altered dramatically with the economic development in the past few decades, sequential and fine-scale LC monitoring is in urgent need. However, currently, fine-resolution annual LC dataset produced by the observational images is generally unavailable for China due to the lack of sufficient training samples and computational capabilities. To deal with this issue, we produced the first Landsat-derived annual China land cover dataset (CLCD) on the Google Earth Engine (GEE) platform, which contains 30m annual LC and its dynamics in China from 1990 to 2019. We first collected the training samples by combining stable samples extracted from China's land-use/cover datasets (CLUDs) and visually interpreted samples from satellite time-series data, Google Earth and Google Maps. Using 335 709 Landsat images on the GEE, several temporal metrics were constructed and fed to the random forest classifier to obtain classification results. We then proposed a post-processing method incorporating spatial-temporal filtering and logical reasoning to further improve the spatial-temporal consistency of CLCD. Finally, the overall accuracy of CLCD reached 79.31% based on 5463 visually interpreted samples. A further assessment based on 5131 third-party test samples showed that the overall accuracy of CLCD outperforms that of MCD12Q1, ESACCI_LC, FROM_GLC and GlobeLand30. Besides, we intercompared the CLCD with several Landsat-derived thematic products, which exhibited good consistencies with the Global Forest Change, the Global Surface Water, and three impervious surface products. Based on the CLCD, the trends and patterns of China's LC changes during 1985 and 2019 were revealed, such as expansion of impervious surface (+148.71 %) and water (+18.39 %), decrease in cropland (-4.85 %) and grassland (-3.29 %), and increase in forest (+4.34 %). In general, CLCD reflected the rapid urbanization and a series of ecological projects (e.g. Gain for Green) in China and revealed the anthropogenic implications on LC under the condition of climate change, signifying its potential application in the global change research.

The rapid development of cities increases urban heat island (UHI) effect. Urban greening can alleviate the heat island effect, but plant phenology could be affected by the heat island effect. Using remote sensing images, we compared the plant phenology between UHI areas and non-UHI areas as well as the different levels of UHI intensity in Beijing. Landsat-8 images for Beijing were used to invert the land surface temperature (LST). Spatial analysis methods were used to identify UHI zones and the UHI intensity classification. With data of normalized differential vegetation index (NDVI) of MOD13A1, three phenological parameters (start of growth season (SOG), end of growth season (EOG), and length of growth season (LOG)) were extracted by the Dallimer mean value method and dynamic threshold method. The three parameters were used to examine the differences in plant phenology between the UHI areas and the non-UHI areas and between different UHI intensity levels. The UHI effect of Beijing in summer was more obvious than that in winter. The SOG in the UHI areas extracted by Dallimer mean value method was four days earlier, the EOG was nine days later, and the LOG was 13 days longer than that in non-UHI areas. The SOG in UHI areas extracted by dynamic threshold method was 10 days earlier, the EOG was four days later, and the LOG was 14 days longer than that in nonUHI areas. Compared with the Dallimer mean value method, the phenology calculated by the dynamic threshold method was closer to the observation data. Under higher UHI intensity, the SOG would be earlier, the EOG would be later, and the LOG would be longer. This study enriches the response mechanism of plant phenology to the UHI effect in North China, which helps us to understand the effects of global warming on ecosystems.

基于2001—2020年NDVI和EVI两种遥感植被指数及蒙古国60个气象站点的气温和降水数据，利用累积植被指数的Logistic曲线曲率极值法和动态阈值法提取蒙古国植被返青期，并结合偏相关分析方法探讨二者与冬春季昼夜气温非对称变化之间的关系。结果表明：（1）近20 a来，最高气温在冬春季的变暖速率分别为0.07 ℃·a^-1、0.15 ℃·a^-1（PR²=0.33），最低气温在冬春季的变化速率分别为-0.01 ℃·a^-1、0.04 ℃·a^-1，冬春季气温日较差的变化速率分别为0.08 ℃·a^-1、0.11 ℃·a^-1（PR²=0.52），存在明显的季节性差异。（2）蒙古国植被返青期对冬春季气候变暖的季节性响应是非对称的，冬季最高气温和气温日较差的影响大，而春季则是最低气温影响大，且均表现为负相关。（3）蒙古国气候变暖对植被NDVI产生的非对称影响主要表现在春季，即春季最高气温和气温日较差对植被NDVI主要表现为负相关，而春季最低气温对植被NDVI主要表现为正相关。研究结果可为进一步了解全球气候变暖背景下植被春季物候及植被后期生长的季节性影响提供重要参考价值。

<p id="p00010">Based on the normalized difference vegetation index (NDVI) and enhanced vegetation index obtained from remote sensing and on temperature and precipitation data from 60 meteorological stations in Mongolia from 2001 to 2020, the logistic curve method and dynamic threshold method of the cumulative vegetation index were used to extract the vegetation green-up period in Mongolia. Partial correlation analysis was used to explore the relationship between the vegetation green-up period and the asymmetric changes in diurnal temperature in winter and spring. The results were as follows: (1) In the past 20 years, the warming rates for highest winter and spring temperatures were 0.07 ℃·a⁻¹ and 0.15 ℃·a⁻¹ (<i>P</i><0.05, <i>R</i>²=0.33) respectively, while the change rates of the lowest winter and spring temperatures were −0.01 ℃·a⁻¹ and 0.04 ℃·a⁻¹ respectively. The change rate of the winter and spring diurnal temperature ranges were 0.08 ℃·a⁻¹ and 0.11 ℃·a⁻¹ (<i>P</i><0.05, <i>R</i>²=0.52) respectively, showing clear seasonal differences. (2) The seasonal response of the start of the growing season to winter and spring climate warming is asymmetric, with the highest temperature and diurnal temperature range having a greater impact in winter, while the lowest temperature has a greater impact in spring; both show negative correlations. (3) The asymmetric impact of climate warming on NDVI in Mongolia is mainly manifested in spring. The highest spring temperature and spring diurnal temperature range have a mainly negative correlation with NDVI, while the lowest spring temperature has a mainly positive correlation with NDVI. This study provides an important reference in the study of the seasonal effects of climate warming on vegetation phenology and late-stage growth.</p>

Spring warming substantially advances leaf unfolding and flowering time for perennials. Winter warming, however, decreases chilling accumulation (CA), which increases the heat requirement (HR) and acts to delay spring phenology. Whether or not this negative CA-HR relationship is correctly interpreted in ecosystem models remains unknown. Using leaf unfolding and flowering data for 30 perennials in Europe, here we show that more than half (7 of 12) of current chilling models are invalid since they show a positive CA-HR relationship. The possible reason is that they overlook the effect of freezing temperature on dormancy release. Overestimation of the advance in spring phenology by the end of this century by these invalid chilling models could be as large as 7.6 and 20.0 days under RCPs 4.5 and 8.5, respectively. Our results highlight the need for a better representation of chilling for the correct understanding of spring phenological responses to future climate change.