Green-up Date Research Articles

Mapping sugarcane plantation dynamics in Guangxi, China, by time series Sentinel-1, Sentinel-2 and Landsat images

Sugarcane is a major crop for sugar and ethanol production and its area has increased substantially in tropical and subtropical regions in recent decades. Updated and accurate sugarcane maps are critical for monitoring sugarcane area and production and assessing its impacts on the society, economy and the environment. To date, no sugarcane mapping tools are available to generate annual maps of sugarcane at the field scale over large regions. In this study, we developed a pixel- and phenology-based mapping tool to produce an annual map of sugarcane at 10-m spatial resolution by analyzing time-series Landsat-7/8, Sentinel-2 and Sentinel-1 images (LC/S2/S1) during August 31, 2017 - July 1, 2019 in Guangxi province, China, which accounts for 65% of sugarcane production of China. First, we generated annual maps of croplands and other land cover types in 2018. Second, we delineated the cropping intensity (single, double and triple cropping in a year) for all cropland pixels in 2018. Third, we identified sugarcane fields in 2018 based on its phenological characteristics. The resultant 2018 sugarcane map has producer, user and overall accuracies of 88%, 96% and 96%, respectively. According to the annual sugarcane map in 2018, there was a total of 8940 km2 sugarcane in Guangxi, which was ~1% higher than the estimate from the Guangxi Agricultural Statistics Report. Finally, we identified green-up dates of those sugarcane fields in 2019, which could be used to support the sugarcane planting and management activities. Our study demonstrates the potential of the pixel- and phenology-based sugarcane mapping tool (both the algorithms and the LC/S2/S1 time series images) in identifying croplands, cropping intensity and sugarcane fields in the complex landscapes with diverse crop types, fragmented crop fields and frequent cloudy weather. The resultant annual maps from this study could be used to assist farms and sugarcane mills for sustainable sugarcane production and environment.

Can changes in autumn phenology facilitate earlier green-up date of northern vegetation?

Climate warming has induced substantial advances in the onset of vegetation green-up in the northern hemisphere during recent decades. To date, however, the temporal changes in green-up date have not been adequately explained by the statistical relationships between green-up date and climatic factors, posing challenges in the attribution and prediction of phenological change. In this study, we thus turned to focus on autumn phenology, a critical biotic factor that is likely to affect the subsequent spring phenology of vegetation. Using satellite-retrieved start and end of growing season (SOS and EOS) over the period from 1982 to 2015, we examined the association between the EOS and the SOS in the following year in northern middle and high latitudes (north of 25°N). Interannual changes in SOS were significantly (P < 0.05) related to changes in EOS in the previous year in 26.4% of the total pixels, mostly in the boreal region, with a 1-day advance of EOS generally resulting in about a 0.5- to 1.0-day advance of the following SOS, suggesting that the advanced SOS may be associated with the advanced EOS. In temperate ecosystems, however, SOS showed a weak negative partial correlation with previous year's EOS (significant for 10.3% of the total pixels), suggesting that the delayed EOS may have limited contribution to the advanced SOS. Our analysis further revealed that changes in the EOS contributed little to the changes in the number of subsequent chilling days in temperate ecosystems and that the sum of forcing temperatures was weakly related with the number of the chilling days in the boreal region, suggesting that EOS may affect SOS through other mechanisms such as changes in the timing when the chilling requirement is met as well as in carbohydrate and nutrient economy. This study suggested that the timing of EOS may explain some of the temporal changes in SOS in the following year in 36.7% of the study region, but further studies are needed to identify the exact mechanisms.

Milk composition in a wild mammal: a physiological signature of phenological changes

Understanding how spring phenology influences early life can provide important insights into drivers of future development and survival. We combined unique, long-term data from a bighorn sheep population and satellite-derived phenology indices to quantify the relative importance of maternal and environmental influences on milk composition and lamb overwinter survival. Based on 216 milk samples from 34 females monitored over 6years, we found that longer snow-free and vegetation growing seasons increased milk fatty acid, iron and lactose concentrations. Structural equation modelling revealed no causality between milk energy content, lamb weaning mass and lamb overwinter survival. Our results suggest that spring conditions can affect milk energy content, but we did not detect any effect on lamb overwinter survival either directly or indirectly through lamb weaning mass. The effect of green-up date on milk composition and energy content suggests that herbivores living in seasonal environments, such as the bighorn sheep, might rely on a strategy intermediate between 'capital' and 'income' breeding when energy demands are high.

Comparison of MODIS-based vegetation indices and methods for winter wheat green-up date detection in Huanghuai region of China

Satellite vegetation index (VI) time series data provide a feasible option for monitoring crop phenology at a large scale. However, there are limited researches that investigated the accuracy of different methods for crop phenology detection with various VIs over a large-scale region. In this study, we used four VIs, i.e. Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Two-band Enhanced Vegetation Index (EVI2), and Normalized Difference Phenology Index (NDPI) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) data, combined with six methods, i.e. relative threshold at 10%, 20% or 50% of the VI's amplitude (RT10, RT20 and RT50), maxima of the curvature change rate of the fitted logistic curve (CCRmax), maxima of the first derivation of the VI curve (βmax), and cross-correlogram spectral matching-phenology (CCSM-P), to detect winter wheat green-up dates (GUDs) for the period of 2009–2013 in the Huanghuai winter wheat region of China. The performance of the combinations of these methods and VIs was evaluated using ground-observed GUDs from agrometeorological stations with correlation coefficient (r), regression coefficient (a), root mean square error (RMSE) and bias. We further investigated the spatial trend of residuals from a linear model between satellite- and ground-observed GUDs. Results show that NDPI outperforms the other VIs with the highest consistency with ground data in the whole region. RT10, CCRmax and CCSM-P show higher accuracy in the northern region, while in the southern region, RT20 shows relatively higher accuracy in the case of poor performance of all six methods. However, the residuals of these six methods based on NDPI show significantly positive correlations with latitude in the whole region, suggesting an uneven spatial distribution of accuracy with a tendency of underestimating GUDs at the low latitude region and overestimating GUDs at the high latitude region when applying the same method to detect GUDs over a large-scale region. It is suggested to develop a new method or combine several methods to reduce the spatial incoherence of residuals.

Local residents’ perceptions of climate and ecological changes in the eastern Tibetan Plateau

The Tibetan Plateau is highly sensitive to climate change. Knowing local Tibetans’ perceptions of climate and ecological changes is important for exploring the climate and vegetation changes in this vast and remote plateau. We interviewed residents in three counties, Dege, Seda, and Ruoergai, on the eastern Tibetan Plateau and analyzed their perceptions of climate and ecological changes; then, we compared the results with those from instrumental observations. The results showed that residents are sensitive to changes in temperature and extreme weather, and the perceptions of temperature change are consistent with meteorological observations. Both remote sensing observations and local perception showed an advanced green-up date in the eastern Tibetan Plateau. More than half of informants perceived an earlier start date and a later end date of Ophiocordyceps sinensis harvesting, which indicated an advance of vegetation spring phenology and a possibly upward distribution extension in elevation under climate warming, respectively. The results also indicated that it is necessary to explore the vegetation phenology of the Tibetan Plateau from the perspective of sub-regions. More research on the views of environmental change should be carried out in different sub-regions of the Tibetan Plateau, and the interrelationship between perceptions is also worth exploring.

Impact of spring phenology variation on GPP and its lag feedback for winter wheat over the North China Plain

Spring green-up date (GUD) is a sensitive indicator of climate change, and of great significance to winter wheat production. However, our knowledge of the chain relationships among them is relatively weak. In this study, based on 8-day Enhanced Vegetation Index (EVI) data from Moderate Resolution Imaging Spectroradiometer (MODIS) from 2001 to 2015, we first assessed the performance of four algorithms for extracting winter wheat GUD in the North China Plain (NCP). A multiple linear regression model was then established to quantitatively determine the contributions of the time lag effects of hydrothermal variation on GUD. We further investigated the interactions between GUD and gross primary production (GPP) comprehensively. Our results showed that the rate of change in curvature algorithm (RCCmax) had better performance in capturing the spatiotemporal variation of winter wheat GUD relative to the other three methods (Kmax, CRmax, and cumCRmax). Regarding the non-identical lag time effects of hydrothermal factors, hydrothermal variations could explain winter wheat GUD variations for 82.05% of all pixels, 36.78% higher than that without considering the time lag effects. Variation in GUD negatively correlated with winter wheat GPP after green up in most parts of the NCP, significantly in 35.75% of all pixels with a mean rate of 1.89 g C m−2 yr−1 day−1. Meanwhile, winter wheat GPP exerted a strongly positive feedback on GUD in >82.42% of all pixels (significant in 28.01% of all pixels), characterized by a humped-shape pattern along the long-term average plant productivity. This finding highlights the complex interaction between spring phenology and plant productivity, and also suggests the importance of preseason climate factors on spring phenology.

A within-season approach for detecting early growth stages in corn and soybean using high temporal and spatial resolution imagery

Crop emergence date is a critical input to models of crop development and biomass accumulation. The ability to robustly detect and map emergence date using remote sensing would greatly benefit operational yield estimation and crop monitoring efforts; however, this has proven to be challenging. Previous remote-sensing phenology algorithms showed that crop stages can typically be detected starting only around the V3-V4 (3 to 4 established leaves) vegetative stage. Furthermore, traditional approaches have a strong assumption regarding the temporal evolution of plant growth and normally require a complete growth period of observations to define seasonal changes. Most approaches were not designed for within-season operational mapping, particularly in the early growing season. In the current paper, we describe a new within-season emergence (WISE) approach to mapping crop green-up date using satellite observations available during early growth stages. The approach was first optimized using high spatiotemporal resolution (10 m, 2-day revisit) imagery from the Vegetation and Environment monitoring New MicroSatellite (VENμS) research mission, and assessed using ground observations of early crop growth stages (emergence VE and one leaf V1 stages for corn, and emergence VE and unifoliolate VC stages for soybeans) collected over the Beltsville Agricultural Research Center (BARC) experimental fields in Beltsville, MD during the 2019 growing season. Results show that early crop growth stages can be reliably detected at sub-field scale about two weeks after crop emergence. The remote-sensing green-up dates were about 4–5 days after crop emergence on average. Coefficients of determination (R2) between green-up dates and the mid-point dates of the early growth stages were above 0.90. The mean absolute differences, standard deviations, and root mean square errors comparing to the early growth stage mid-point dates were within six days. The maximum differences were within ±10 days across all fields. The WISE approach was assessed using operational Sentinel-2 data (10 m, 5-day revisit) over BARC. The detected green-up dates from Sentinel-2 were consistent with those from VENμS. Some fields were not detected due to the lack of observations around the emergence dates. For independent evaluation, the WISE approach was applied over an agricultural watershed on the Maryland Eastern Shore using both VENμS and the Harmonized Landsat and Sentinel-2 (HLS) data (30 m, 3–4-day revisit). The detected green-up dates were compared with emergence dates in crop progress reports from the National Agricultural Statistics Service (NASS) at the state-level. The WISE-detected green-up dates at the regional scale are within VE stage ranges but slightly earlier than NASS crop progress reports at the state-level. The WISE approach uses remote-sensing observations during the early crop growth stages and has potential for operational application within the season using Sentinel-2 and HLS data.

Change Analysis of Spring Vegetation Green-Up Date in Qinba Mountains under the Support of Spatiotemporal Data Cube

In recent decades, global and local vegetation phenology has undergone significant changes due to the combination of climate change and human activities. Current researches have revealed the temporal and spatial distribution of vegetation phenology in large scale by using remote sensing data. However, researches on spatiotemporal differentiation of remote sensing phenology and its changes are limited which involves high-dimensional data processing and analysing. A new data model based on data cube technologies was proposed in the paper to efficiently organize remote sensing phenology and related reanalysis data in different scales. The multidimensional aggregation functions in the data cube promote the rapid discovery of the spatiotemporal differentiation of phenology. The exploratory analysis methods were extended to the data cube to mine the change characteristics of the long-term phenology and its influencing factors. Based on this method, the case study explored that the spring phenology of Qinba Mountains has a strong dependence on the topography, and the temperature plays a leading role in the vegetation green-up date distribution of the high-altitude areas while human activities dominate the low-altitude areas. The response of green-up trend slope seems to be the most sensitive at an altitude of about 2000 meters. This research provided a new approach for analysing phenology phenomena and its changes in Qinba Mountains that had the same reference value for other regional phenology studies.

Soil moisture outweighs temperature for triggering the green-up date in temperate grasslands

Investigating grassland phenology and its relationship with climatic factors is crucial for understanding ecosystem responses to climate change. However, there have been few studies on the impact of soil moisture on grassland phenology. In this study, we extracted the green-up date in the Inner Mongolia grasslands, China, using the normalized difference vegetation index (NDVI) data from the Global Inventory Modeling and Mapping Studies (GIMMS) NDVI3g dataset (1982–2015) and the Moderate Resolution Imaging Spectroradiometer (2001–2015). We investigated the spatiotemporal pattern of the green-up date and its relationship with four climatic factors, including growing degree days, chilling days, soil moisture, and solar radiation. Most areas (67.1%) exhibited a trend towards earlier green-up date, but the significant trends (p < 0.05) were only found in 13.4% of the areas. For both datasets, the pixels with a negative regression coefficient of soil moisture accounted for more than 87% of the total area (more than 21% significantly, p < 0.05). Regarding the other factors, the sign of regression coefficients was not consistent among pixels. Thus, soil moisture correlated more significantly with the green-up date in temperate grasslands compared with the other climatic factors. This study provides the scientific basis for better understanding the mechanism of phenological response to climate change in temperate grasslands.

Delayed autumn leaf senescence date prolongs the growing season length of herbaceous plants on the Qinghai–Tibetan Plateau

Plant phenological shifts on the Qinghai–Tibetan Plateau (QTP) have been a hot research topic during the recent decade. Most previous studies based on remote sensing data suggest that earlier spring green-up is the main contributor to the prolonged vegetation growing season on the QTP. Given the large uncertainties of satellite-derived phenology, this study collected a large number (1455+ records) of long-term and continuous ground phenological observation data across the QTP. Based on these data, we conducted comprehensive analyses of temporal changes in spring green-up date (GUD), autumn leaf senescence date (LSD), and growing season length (GSL) of herbaceous plants from 1981 to 2017 through ordinary least squares regression and meta-analysis, and evaluated the relative contribution of shifts in GUD and LSD to change in GSL using the C-index. Results showed that the overall trends of GUD, LSD, and GSL insignificantly advanced at a rate of –0.17 days/decade, significantly delayed at a rate of 5.29 days/decade, and significantly lengthened at a rate of 5.46 days/decade, respectively. On average, the relative contribution of LSD shift to GSL change was 69.3% which was more than twice that of GUD shift (30.7%). Therefore, it was mainly the delayed LSD, rather than an advanced GUD, that prolonged GSL of QTP herbaceous plants, which was contrary to the prevailing conclusion reported by studies based on remote sensing data. Besides, among three functional groups of herbaceous plants, we found that change trends and patterns of the growing seasons seemed to be more favorable to the growth of forbs, which might shift community structure and further influence ecosystem function. Our study suggests that the importance of autumn phenology has been underestimated on the QTP, and more attention should be paid to the shift in autumn phenology and its influences on ecosystem structure and function under global warming.

Uncertainty of Vegetation Green-Up Date Estimated from Vegetation Indices Due to Snowmelt at Northern Middle and High Latitudes

Vegetation green-up date (GUD), an important phenological characteristic, is usually estimated from time-series of satellite-based normalized difference vegetation index (NDVI) data at regional and global scales. However, GUD estimates in seasonally snow-covered areas suffer from the effect of spring snowmelt on the NDVI signal, hampering our realistic understanding of phenological responses to climate change. Recently, two snow-free vegetation indices were developed for GUD detection: the normalized difference phenology index (NDPI) and normalized difference greenness index (NDGI). Both were found to improve GUD detection in the presence of spring snowmelt. However, these indices were tested at several field phenological camera sites and carbon flux sites, and a detailed evaluation on their performances at the large spatial scale is still lacking, which limits their applications globally. In this study, we employed NDVI, NDPI, and NDGI to estimate GUD at northern middle and high latitudes (north of 40° N) and quantified the snowmelt-induced uncertainty of GUD estimations from the three vegetation indices (VIs) by considering the changes in VI values caused by snowmelt. Results showed that compared with NDVI, both NDPI and NDGI improve the accuracy of GUD estimation with smaller GUD uncertainty in the areas below 55° N, but at higher latitudes (55°N-70° N), all three indices exhibit substantially larger GUD uncertainty. Furthermore, selecting which vegetation index to use for GUD estimation depends on vegetation types. All three indices performed much better for deciduous forests, and NDPI performed especially well (5.1 days for GUD uncertainty). In the arid and semi-arid grasslands, GUD estimations from NDGI are more reliable (i.e., smaller uncertainty) than NDP-based GUD (e.g., GUD uncertainty values for NDGI vs. NDPI are 4.3 d vs. 7.2 d in Mongolia grassland and 6.7 d vs. 9.8 d in Central Asia grassland), whereas in American prairie, NDPI performs slightly better than NDGI (GUD uncertainty for NDPI vs. NDGI is 3.8 d vs. 4.7 d). In central and western Europe, reliable GUD estimations from NDPI and NDGI were acquired only in those years without snowfall before green-up. This study provides important insights into the application of, and uncertainty in, snow-free vegetation indices for GUD estimation at large spatial scales, particularly in areas with seasonal snow cover.

新疆天山山区禾本科牧草物候区域差异及其驱动力分析

PDF HTML阅读 XML下载 导出引用 引用提醒 新疆天山山区禾本科牧草物候区域差异及其驱动力分析 DOI: 10.5846/stxb201811262570 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 中央级公益性科研院所基本科研业务费项目（IDM2018008，IDM2016004）；国家重点研发计划（2018YFC1507100） Regional differences of gramineae herbage phenology and its driving forces in Tianshan Mountains, Xinjiang Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:天山山区地形复杂，同种植被因所处地理位置、地形特征的不同，其生育期对气候变化的响应特征差异明显。基于天山山区1990-2015年气象及物候观测数据，利用数理统计及空间插值等方法，分析了典型禾本科牧草物候空间差异及其关键驱动因子，结果表明：（1）26年来，天山山区年平均气温和年平均0 cm地温明显上升，北坡升温幅度大于南坡；年降水量增加，大致为"北坡趋多，南坡趋少"；气候总体呈明显暖湿化趋势。（2）天山山区4个牧业气象观测站所共有的优势禾本科牧草物候呈现较明显的差异，春季返青期普遍提前，黄枯期大多为提前，生长季变化差异较大。（3）返青期主要受温度影响，与当月及春季平均气温、平均0 cm地温显著负相关，春季平均温度每升高1℃，返青日期提前1.98-5.77 d，春季平均0 cm地温每升高1℃，返青日期提前1.40-5.08 d；黄枯期主要受前期气温、0 cm地温和降水的综合影响，与前三个月的气温、0 cm地温显著负相关，与降水显著正相关。（4）在热量条件适合时，降水增加，对牧草的生育期是促进作用，相反，若热量条件不足时，降水增加，则会对牧草的发育产生抑制作用。（5）牧草生育期对于海拔高度的敏感性存在明显差异，随海拔高度的增加，物候对气候变化的响应程度更为显著。 Abstract:The Tianshan Mountains have very complex topographies and the response characteristics of the same kind of vegetation to climate change during its growth period are greatly different as the result of different geographical locations and topographic features. In this study, we analyzed the spatial differences of typical gramineae herbage phenology and the key driving forces by using the meteorological and phenological observation data collected from 1990 to 2015 as well as the mathematical statistics and spatial interpolation methods. The results showed that:(1) During the 26 years, the annual mean temperature and annual mean 0 cm earth temperature increased significantly, and the warming range in the north slope of Tianshan Mountains was greater than that in the south slope. In addition, the annual precipitation varied, roughly being "north slope more, south slope less". Thus, the climate in this region generally showed a warm and humid trend. (2) The phenology of dominant gramineae herbage shared by the four agrometeorological stations in the region of Tianshan Mountains had different features. Generally, the spring greenup date of gramineae herbage started earlier than before, so did its wilting period. Great changes were found in the growth season of the gramineae herbage. (3) The greenup date showed a clear negative correlation with the mean temperatures and mean 0 cm earth temperatures in the current month and spring. For every 1℃ rose in the spring mean temperature, the greenup date was 1.98-5.77 d earlier; for every 1℃ rose in the spring 0 cm earth temperature, the greenup date was 1.40-5.08 d in advance. The dominant factors that affected the wilting dates jointly were the temperature, 0 cm earth temperature and precipitation in the previous three months, which means that the wilting period had an obviously negative correlation with the temperature and 0 cm earth temperature, but a clearly positive correlation with precipitation. (4) The increase of precipitation could promote the growth stage of gramineae herbage when the heat condition was suitable; on the contrary, the increase of precipitation could inhibit the growth stage of the herbage if the heat was insufficient. (5) There was a significant difference in the sensitivity of the herbage growth period to elevation. With the increase of elevation, the response of phenology to climate change was more noticeable. 参考文献 相似文献 引证文献

Interactions among the Phenological Events of Winter Wheat in the North China Plain-Based on Field Data and Improved MODIS Estimation

Identification of complete drivers for phenology changes is crucial for developing prediction models of plant phenology. In addition to climatic factors, the interaction among phenological events has recently been reported as an important driver for the phenology changes of forests, savannas, and grasslands. However, open questions remain as to whether the phenological interaction exists in agricultural ecosystems, among which winter wheat plays a vital role in feeding human beings. In this study, we investigated the interaction among the phenological events of winter wheat in the North China Plain (NCP) using both field and satellite data. Considering the large discrepancies between the existing satellite estimation and field measurements of winter wheat phenology, we first improved the MODIS-based estimation of green-up date (GUD), heading date (HD), and maturity date (MD) through a re-calibrated relative threshold method (RTM) in the NCP. The GUD, HD, and MD were accurately estimated with the mean absolute errors (MAE) and root mean squared errors (RMSE) lower than 7.5 days, compared with the RMSEs ranging from 12.0 to 36.1 days in previous studies. Then, the relationships among the GUD, HD, and MD were analyzed using the field data collected at agricultural meteorological stations. The GUD (HD) showed a significantly positive correlation with the HD (MD). Quantitatively, a one-day earlier GUD (HD) would result in an earlier HD (MD) of 0.57 days (0.60 days). Furthermore, we applied the partial correlation analysis to the improved MODIS estimation of GUD, HD, and MD to investigate their interactions by considering the simultaneous influences from climatic factors. The results showed that the HD (MD) with 85.2% (94.5%) of all winter wheat pixels presented a significantly positive correlation with the GUD (HD). Meanwhile, the GUD (HD) with 84.2% (33.3%) of the entire winter wheat area presented a significantly negative correlation with pre-season temperature. These results suggest that both the climatic factors and phenological interactions should be included in the future development of winter wheat phenology models to improve the prediction accuracies.

Vegetation green up under the influence of daily minimum temperature and urbanization in the Yellow River Basin, China

Abstract Climate conditions are the major driving factors of vegetation phenology. However, there is limited effort to monitor dynamics of vegetation phenology and its responses to climate change and urbanization. Using NDVI data from 1982 to 2015, this study investigated the spatiotemporal change of spring green-up date (GUD) across the Yellow River Basin (YRB) and estimated the possible effects of different climatic factors on it. Additionally, the urban-rural differences in GUD and its linkage to spring land surface temperature (LST) for YRB’s major cities over 2001–2015 were investigated. The results showed that the GUD significantly advanced at a rate of 0.42 days yr−1 and delayed spatially from southeast to northwest. The interannual variations in regionally averaged GUD were driven mainly by preseason min-temperature (Tmin). Spatially, the effect of preseason Tmin was strongest in the central and western region. The confounding effects of preseason maximum temperature (Tmax) and precipitation jointly affected the GUD, while insolation had a weak impact on GUD. Moreover, the sensitivity of GUD to preseason Tmin and precipitation weakened with an increasing mean annual preseason precipitation gradient, but the sensitivity to preseason Tmax was gradually enhanced. Furthermore, the difference in the GUD between urban and rural areas presented a significant logarithmic relationship with the distance away from the urban center, and it was strongly related to the regional LST. Our findings confirmed the importance of Tmin and urbanization in regulating changes in GUD and further suggested that LST should be considered to develop an improved model of GUD under future climate change and urban development.

How Does Scale Effect Influence Spring Vegetation Phenology Estimated from Satellite-Derived Vegetation Indexes?

As an important land-surface parameter, vegetation phenology has been estimated from observations by various satellite-borne sensors with substantially different spatial resolutions, ranging from tens of meters to several kilometers. The inconsistency of satellite-derived phenological metrics (e.g., green-up date, GUD, also known as the land-surface spring phenology) among different spatial resolutions, which is referred to as the “scale effect” on GUD, has been recognized in previous studies, but it still needs further efforts to explore the cause of the scale effect on GUD and to quantify the scale effect mechanistically. To address these issues, we performed mathematical analyses and designed up-scaling experiments. We found that the scale effect on GUD is not only related to the heterogeneity of GUD among fine pixels within a coarse pixel, but it is also greatly affected by the covariation between the GUD and vegetation growth speed of fine pixels. GUD of a coarse pixel tends to be closer to that of fine pixels with earlier green-up and higher vegetation growth speed. Therefore, GUD of the coarse pixel is earlier than the average of GUD of fine pixels, if the growth speed is a constant. However, GUD of the coarse pixel could be later than the average from fine pixels, depending on the proportion of fine pixels with later GUD and higher growth speed. Based on those mechanisms, we proposed a model that accounted for the effects of heterogeneity of GUD and its co-variation with growth speed, which explained about 60% of the scale effect, suggesting that the model can help convert GUD estimated at different spatial scales. Our study provides new mechanistic explanations of the scale effect on GUD.

Deduction of a meteorological phenology indicator from reconstructed MODIS LST imagery

Phenology is a valuable attribute of vegetation to assess the biological impacts from climate change. A challenge of phenological research is to obtain information on both high temporal resolution and fine spatial scale observations. Here, we constructed an air temperature map based on temporal merging and spatial interpolation algorithms to overcome the cloud-related problem from the MODIS LST product. Then, we derived the accumulated growing degree days (AGDD) from the constructed mean air temperature map to use as a meteorological indicator. Further, we verified the indicator with the seasonal mean air temperature and the green-up date of a Quercus mongolica forest determined from the field-based measurements. The AGDD threshold for each Q. mongolica forest when the first leaf has unfolded was detected from the EXG trajectory extracted from digital camera images. A comparison between meteorological and MODIS-derived AGDD showed good agreement between them. There was also high consistency between DoYs extracted from AGDD and EVI based on curvature K for Q. mongolica forests of 30 sampling sites throughout South Korea. The results prove that microclimatic factors such as elevation, waterbody, and land-use intensity were faithfully reflected in the reconstructed images. Therefore, the results of this study could be applied effectively in areas where microclimatic variation is very severe and for monitoring phenology of undergrowth, which is difficult to detect from reflectance imaging.

Winter Wheat Green-up Date Variation and its Diverse Response on the Hydrothermal Conditions over the North China Plain, Using MODIS Time-Series Data

Vegetation phenology plays a critical role in the dynamic response of terrestrial ecosystems to climate change. However, the relationship between the phenology of winter wheat and hydrothermal factors is inadequate, especially in typical agricultural areas. In this study, the possible effects of preseason climate changes on the green-up date (GUD) of winter wheat over the North China Plain (NCP) was investigated, using the MODIS EVI 8-day time-series data from 2000 to 2015, as well as the concurrent monthly mean temperature (Tm), mean maximum (Tmax) and minimum temperature (Tmin) and total precipitation (TP) data. Firstly, we quantitatively identified the time lag effects of winter wheat GUD responses to different climatic factors; then, the major driving factors for winter wheat GUD were further explored by applying multiple linear regression models. The results showed that the time lag effects of winter wheat GUD response to climatic factors were site- and climatic parameters-dependent. Negative temperature effects with about a 3-month time lag dominated in most of the NCP, whereas positive temperature effects with a zero-month lag were most common in some of the southern parts. In comparison, total precipitation had a negative zero-month lag effect in the northern region, but two lagged months occurred in the south. Regarding the time lag effects, the explanation power of climatic factors improved relatively by up to 77%, and the explanation area increased by 41.20%. Additionally, change in winter wheat GUD was primarily determined by temperature rather than by TP, with a marked spatial heterogeneity of the Tmax and Tmin effect. Our results confirmed different time lag effects from different climatic factors on phenological processes in spring, and further suggested that both Tmax and Tmin should be considered to improve the performance of spring phenology models.

Soil organic carbon and nutrient losses resulted from spring dust emissions in Northern China

Dust emissions due to wind erosion have significant impacts on air quality, climate, and biogeochemical processes. Arid and semi-arid regions in Northern China are a major contributor to global dust emissions. However, how dust emissions affect soil organic carbon (SOC) and nutrient losses in this region are poorly understood. In this study, we estimated the spatial patterns and temporal dynamics of SOC and nutrient (total nitrogen (TN) and total phosphorus (TP)) losses in spring using a process-based dust emission model in Northern China during 1982–2011. Spatial patterns of SOC and nutrient losses are consistent with dust emission rates across the research region. Annual losses of SOC, TN, and TP resulted from wind erosion in spring were 0.985 ± 0.149 Tg yr−1, 0.094 ± 0.014 Tg yr−1, and 0.089 ± 0.013 Tg yr−1, respectively. Two distinct periods with opposite trends were identified in dust emissions and SOC and nutrient losses, declining from 1982 to 1997 and then increasing. The opposite patterns in Northern China are largely attributed to the changes in vegetation growth due to climate change and shifts of green-up date of vegetation. The findings could help to reduce the uncertainties in simulating regional biogeochemical cycling in Northern China.

Improved Spring Vegetation Phenology Calculation Method Using a Coupled Model and Anomalous Point Detection

High temporal resolution remote sensing satellite data can be used to collect vegetation phenology observations over regional and global scales. Logistic and polynomial functions are the most widely used methods for fitting time series normalized difference vegetation index (NDVI) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS). Furthermore, the maximum in the curvature of the fitted curve is usually considered as the spring green-up date. However, the existing green-up date calculation methods have low accuracy for sparse vegetation. This paper proposes an improved green-up date calculation method using a coupled model and anomalous point detection (CMAPD). This model is based on a combination of logistic and polynomial functions, which is used to fit time series vegetation index. Anomalous values were identified using the nearest neighbor algorithm, and these values were corrected by the combination of growing degree-days (GDD) and land use type. Then, the trends and spatial patterns of green-up date was analyzed in the Sanjiangyuan area. The results show that the coupled model fit the time series data better than a single logistic or polynomial function. Besides, the anomalous point detection method properly controlled the green-up date within the local threshold, and could reflect green-up date more accurately. In addition, a weak statistically significant advance trend for average vegetation green-up date was observed from 2000 to 2016. However, in 10.4% of the study area, the the green-up date has significant advanced. Regression analysis showed that the green-up date is correlated to elevation: the green-up date is clearly later at higher elevations.

RELATIONSHIP BETWEEN PHENOLOGY, PRODUCTIVITY, AND METEOROLOGICAL FACTORS IN RECENT 15 YEARS IN THE PASTORAL AREA OF QINGHAI, CHINA

Through studying vegetation phenology and its relation to meteorological factors in a representative region, we can get the responsiveness of vegetation phenophase in different sites to climate changes and how it affects vegetation productivity by the changes of growing season. It is crucial to enhance the accuracy of carbon sequestration estimate. We use phenological observation data and corresponding meteorological data from weather stations of six counties in Qinghai province. The conclusions are (1) In the background of global climate change, the temperature of the pastoral area of Qinghai takes a significantly increasing trend. The precipitation also takes an increasing trend, but only the site of Qumalai is significant. (2) The green-up dates of forages in every site are not significant, but the withered dates of forages are significantly put off. In the sites of Gande and Haiyan, the major forages put off in withered dates are Gramineae, in the site of Henan, Cyperaceae is significantly put off. Significantly extended growing season of representative forages of Gramineae, Cyperaceae and other broad-leaved herb exist in the pastoral area of Qinghai. (3) In terms of relations between green-up date and temperature, the influence of site scale is greater than the influence of different species, and advanced green-up dates of forages in the sites of Henan and Haiyan are longer than the dates of the same forages in the site of Gande. Overall, green-up dates of forages advanced in the site of Henan (Avg3.67 days/[Formula: see text]C) are longer than the dates in the site of Gande (Avg1.31 days/[Formula: see text]C). In terms of relations between green-up date and precipitation, divergences of sites and species exist. The green-up dates of forages in the sites of both Qumalai and Tongde have significant negative correlation with precipitation. In the site of Haiyan, the green-up dates of some forages advance, and the green-up dates of some forages put off. On the whole, Gramineae is best responded, then Cyperaceae and broad-leaved herb show hardly any responsiveness. (4) The responsiveness of temperature is less than precipitation in the pastoral area of Qinghai. Forages in Tongde and Qumalai have significantly positive correlation with the accumulation precipitation from August to September. The differences are the delayed dates of the withered dates of Cyperaceae (2.97 days/10[Formula: see text]mm) which are longer than the delayed dates of the withered dates of Gramineae (2.02 days/10[Formula: see text]mm) in the site of Qumalai. Conversely, the delayed dates of the withered dates of Gramineae (3.13 days/10[Formula: see text]mm) are longer than the delayed dates of the withered dates of Cyperaceae (1.68 days/10[Formula: see text]mm) in the site of Tongde. (5) Only parts of sites in the pastoral area of Qinghai have the tendency of significant increment of forage yield. The precipitations in the sites of Gande, Qumalai and Xinghai can significantly increase the forage yield. (6) The main vegetation phenology and productivity changes are affected by the drivers of climate factors. The response patterns of vegetation phenology of different functional groups and forage yield of different regions are inconsistent with the climate factors which are related to the biologic characteristics of different kinds of vegetations and climate sensitivities.