Estimating canopy photosynthetic parameters in maize field based on multi-spectral remote sensing

Aims Determination of canopy photosynthetic parameters is key to accurate simulation of ecosystem function by using remote sensing methods. Currently, remote estimation of vegetation canopy structure characteristics has been widely adopted. However, directly estimating photosynthetic variables (photosynthetic capacity and efficiency) at canopy scale based on field spectrometry combined with CO₂ flux measurements is rare.
Methods In this study, we remotely estimated solar radiation use efficiency (εN, net ecosystem CO₂ exchange/absorbed photosynthetically active radiation (NEE_CO2/APAR); εG, gross primary productivity/absorbed photosynthetically active radiation (GPP/APAR); α, apparent quantum efficiency) and photosynthetic capacity (P_max) based on in situ measurements of spectral reflectance and ecosystem CO₂ fluxes, along with observational data on micrometeorological factors during the entire growing season for a maize canopy in Northeast China.
Important findings Results showed that the seasonal variations in P_max and α exhibited a single peak; whereas the values of εN and εG were higher at the start of vegetative stage and then rapidly decreased with the development of maize until displaying a single peak at the intermediate and late stages of the growing season, coinciding with the occurrence of peak values in P_max. A comparison was made on the predictive performance based on normalized difference vegetation index (NDVI), ratio vegetation index (RVI), wide dynamic range vegetation index (WDRVI), 2-band enhanced vegetation index (EVI2), and chlorophyll index (CI) in estimating four canopy photosynthetic parameters with any combination of two separate wavelengths at the range of 400–1 300 nm, which showed that EVI2 was most closely and linearly related to photosynthetic capacity and efficiency. This study demonstrates that multi-spectral remote sensing information is sensitive to the variations in canopy photosynthetic parameters in maize field and can be used to quantitatively monitor seasonal dynamics of canopy photosynthesis, and to accurately assess crop productivity and ecosystem CO₂ exchange capacity.     

Landscape,soil and meteorological influences on canopy dynamics of northern flooding Pampa grasslands,Argentina

Question: How do meteorological variations at seasonal, interannual scales differentially affect the canopy dynamics of four contrasting landscape units within a region? Location: Flooding Pampa, Buenos Aires, Argentina. 5000 km². Central point: 35°15′S, 57°45′W. Methods: We used a 19‐year series of the normalized difference vegetation index (NDVI) derived from NOAA‐AVHRR PAL (Pathfinder AVHRR Land) images and meteorological data provided by a nearby weather station. The NDVI was used as surrogate of canopy photosynthetic status. The relationship between annually integrated NDVI and meteorological conditions was explored by stepwise multiple regressions for each defined unit. PC A was performed to compare units and growing seasons on a multivariate basis. Results: Mean seasonal NDVI curve was similarly shaped among landscapes. However, the absolute values differed widely. There was high interannual variation so that the mean seasonal pattern was seldom observed in any particular year. Annually integrated NDVI of all landscapes was negatively associated with summer temperature and positively with previous year precipitation. It was also directly related with current year winter precipitation in two landscapes and with summer precipitation in the others. NDVI response to September and March precipitation accounted for some of the differences in interannual variation among landscapes. Conclusions: Our results revealed a strong intra‐regional variation of canopy dynamics, closely linked to landscape (vegetation‐soil) and water availability (mainly in summer and during the previous year). These links may be used to predict forage production rates for livestock.     

Spatial variability of above‐ground net primary production in Uruguayan grasslands: a remote sensing approach

Question: How does above‐ground net primary production (ANPP) differ (estimated from remotely sensed data) among vegetation units in sub‐humid temperate grasslands? Location: Centre‐north Uruguay. Methods: A vegetation map of the study area was generated from LANDSAT imagery and the landscape configuration described. The functional heterogeneity of mapping units was analysed in terms of the fraction of photosynthetically active radiation absorbed by green vegetation (fPAR), calculated from the normalized difference vegetation index (NDVI) images provided by the moderate resolution imaging spectroradiometer (MODIS) sensor. Finally, the ANPP of each grassland class was estimated using NDVI and climatic data. Results: Supervised classification presented a good overall accuracy and moderate to good average accuracy for grassland classes. Meso‐xerophytic grasslands occupied 45% of the area, Meso‐hydrophytic grasslands 43% and Lithophytic steppes 6%. The landscape was shaped by a matrix of large, unfragmented patches of Meso‐xerophytic and Meso‐hydrophytic grasslands. The region presented the lowest anthropic fragmentation degree reported for the Rio de la Plata grasslands. All grassland units showed bimodal annual fPAR seasonality, with spring and autumn peaks. Meso‐hydrophytic grasslands showed a radiation interception 10% higher than the other units. On an annual basis, Meso‐hydrophytic grasslands produced 3800 kg dry matter (DM) ha^?1 yr^?1 and Meso‐xerophytic grasslands and Lithophytic steppes around 3400 kg·DM·ha^?1·yr^?1. Meso‐xerophytic grasslands had the largest spatial variation during most of the year. The ANPP temporal variation was higher than the fPAR variability. Conclusions: Our results provide valuable information for grazing management (identifying spatial and temporal variations of ANPP) and grassland conservation (identifying the spatial distribution of vegetation units).     

青藏高原植被生长季NDVI时空变化与影响因素

青藏高原是中国乃至亚洲的生态屏障,研究其植被对气候变化的响应对区域生态保护具有重要的现实意义。基于MOD09A1数据反演的生长季归一化植被指数(NDVI),分析2001—2018年青藏高原植被生长季NDVI时空特征和变化趋势,结合气象站点数据阐释NDVI与气候因子的关系。结果表明: 研究期间,青藏高原植被生长季NDVI呈缓慢上升趋势,不同气候区生长季NDVI年际变化差异明显,NDVI值波动幅度表现为高原湿润气候区>半湿润气候区>半干旱气候区>干旱气候区。青藏高原湿润气候区、半湿润气候区、干旱气候区、半干旱气候区NDVI显著升高和降低面积占比分别为1.4%和1.9%、4.9%和1.5%、16.4%和0.8%、7.0%和2.0%,干旱和半干旱气候区NDVI升高面积占比明显大于湿润和半湿润气候区。气温是影响湿润气候区和半湿润气候区NDVI变化的主导因子,而在干旱气候区,降水对NDVI的影响明显强于其他气候因子。气温对整个青藏高原植被生长季NDVI的驱动作用强于降水和相对湿度。     

近30年新疆植被生长异常值时空变化及驱动因子

作为陆地生态系统的主体,植被的时空变化深刻地影响着景观格局和生态功能,深入理解植被动态及其对气候变化的响应,对于提高对生态过程的认识、加强生态管理具有重要意义。在一致性检验的基础上,利用中分辨率成像光谱仪(moderateresolution imaging Spectroradiometer,MODIS)的归一化植被指数(normalized Difference Vegetation Index,NDVI)数据集将新疆地区全球检测与模型研究组(Global Inventory Modeling and Mapping Studies,GIMMS)开发的NDVI数据集的时间序列拓展到2012年,探讨了生长季和各季节植被绿度、气候异常值的动态变化,分析了植被对气候变化的响应。研究结果显示,区域尺度和像元尺度GIMMS与MODIS NDVI之间的一致性较强。1982—2012年,研究区域生长季和各季节植被绿度呈显著增加趋势,但生长季存在明显阶段性:1998年前后分别呈显著增加和显著减少,夏季与秋季与生长季类似,而春季则不存在变化趋势的逆转。NDVI呈正异常值的面积比例与区域尺度NDVI的变化趋势一致;极端异常值、较大异常值多呈明显减少趋势,而一般异常值多呈增加趋势,NDVI的变化倾向于逐渐平稳。区域变暖趋势显著,降水量略有增加,潜在蒸散发显著提高,而湿润指数变化不明显。气温、潜在蒸散发主要在春季、秋季促进植被生长,而夏季降水量、湿润指数对植被生长的调节作用更为突出。     

Regional scale relationships between ecosystem structure and functioning: the case of the Patagonian steppes

Aims 1. To characterize ecosystem functioning by focusing on above‐ground net primary production (ANPP), and 2. to relate the spatial heterogeneity of both functional and structural attributes of vegetation to environmental factors and landscape structure. We discuss the relationship between vegetation structure and functioning found in Patagonia in terms of the capabilities of remote sensing techniques to monitor and assess desertification. Location Western portion of the Patagonian steppes in Argentina (39°30′ S to 45°27′ S). Methods We used remotely‐sensed data from Landsat TM and AVHRR/NOAA sensors to characterize vegetation structure (physiognomic units) and ecosystem functioning (ANPP and its seasonal and interannual variation). We combined the satellite information with floristic relevés and field estimates of ANPP. We built an empirical relationship between the Landsat TM‐derived normalized difference vegetation index (NDVI) and field ANPP. Using stepwise regressions we explored the relationship between ANPP and both environmental variables (precipitation and temperature surrogates) and structural attributes of the landscape (proportion and diversity of different physiognomic classes (PCs)). Results PCs were quite heterogeneous in floristic terms, probably reflecting degradation processes. Regional estimates of ANPP showed differences of one order of magnitude among physiognomic classes. Fifty percent of the spatial variance in ANPP was accounted for by longitude, reflecting the dependency of ANPP on precipitation. The proportion of prairies and semideserts, latitude and, to a lesser extent, the number of PCs within an 8 × 8 km cell accounted for an additional 33% of the ANPP variability. ANPP spatial heterogeneity (calculated from Landsat TM data) within an 8 × 8 km cell was positively associated with the mean AVHRR/NOAA NDVI and with the diversity of physiognomic classes. Main conclusions Our results suggest that the spatial and temporal patterns of ecosystem functioning described from ANPP result not only from water availability and thermal conditions but also from landscape structure (proportion and diversity of different PCs). The structural classification performed using remotely‐sensed data captured the spatial variability in physiognomy. Such capability will allow the use of spectral classifications to monitor desertification.     

Testing scale dependent assumptions in regional ecosystem simulations

Abstract. We present a Regional Ecosystem Simulation System (RESSys) which uses satellite data to define vegetation properties, topographic and soil data to define site characteristics, and a climate generator program to build a topographically sensitive microclimate map. We use a 150-km² mountainous forested watershed in Glacier National Park to test the consequences of modeling various ecosystems processes using different versions of RESSys with increasing simplification of the landscape: (1) spatial scaling generated using 30 m x 30 m Landsat Thematic Mapper data versus 1 km x 1 km Advanced Very High Resolution Radiometer data for vegetation definition; (2) modeling hydrologic dynamics produced by using a topographic routing model versus a simple soil ‘bucket’ model; (3) variable landscape partitioning based on patterns of topographic complexity; and (4) representation of annual net primary productivity (ANPP) using an absorbed photosynthetic active radiation (APAR) model. We evaluate results of these simulations by comparison with average values and areal distributions of photosynthesis, evapotranspiration, hydrologic outflow, and ANPP. Our primary goal is to test whether areal average flux of carbon and water can be scaled linearly over a complex landscape. We found that daily photosynthesis could be predictably estimated between modeling scales with correlation coefficients ranging between 0.89 to 0.99. ANPP was highly correlated among the modeling scales with maximum differences between ANPP prediction of ca. 0.5Mg C ha^-1 yr^-1. Evapotranspiration was similarly predictable between scales but was influenced by differences associated with hydrologic modeling. Hydrologic outflow was not highly correlated between different modeling scales as a function of the different hydrologic models used at different scales.     

天山北坡前山带降水分布型对荒漠植被的影响——基于逐日降水数据和NDVI分析

降水分布型对荒漠植被的影响规律的揭示是理解全球气候变化下干旱区地表植被覆盖变化过程的基础。基于新疆天山北坡前山带1999—2014年生长季MODIS 1B逐日遥感资料和7个气象站点降水观测数据,运用GIS软件提取出气象站点周围荒漠植被的归一化植被指数(Normalized Difference Vegetation Index,NDVI)值,利用相关分析、决策树判识分析等方法,系统分析天山北坡前山带生长季荒漠植被NDVI在不同量级降水和降水型下的时空变化及其持续性等特征,并探究两者的相关程度。研究表明:生长季NDVI在不同时间、空间尺度上对降水存在一定规律的响应特征。从时间尺度来看,NDVI对降水的响应延迟现象随时间越来越不明显,两者相关性先增后减,在生长季中期相关性最强;空间尺度上,各区域差异较大,NDVI对降水的响应快慢与研究区各地的降水量大小明显相关。不同地区的年内NDVI变化趋势也不相同,18天以上的干期长度影响显著。无论研究区域中任一地区年均降水量大小,都一致表现出在生长季期间降水较多的时期,植被覆盖普遍出现为基本不变或轻微改善的趋势,相反在降水较少的时期,植被覆盖普遍出现轻...     

Identification of current ecosystem functional types in the Iberian Peninsula

Aim To examine the geographical patterns of the interception of photosynthetically active radiation by vegetation and to describe its spatial heterogeneity through the definition of ecosystem functional types (EFTs) based on the annual dynamics of the Normalized Difference Vegetation Index (NDVI), a spectral index related to carbon gains. Location The Iberian Peninsula. Methods EFTs were derived from three attributes of the NDVI obtained from NOAA/AVHRR sensors: the annual integral (NDVI‐I), as a surrogate of primary production, an integrative indicator of ecosystem functioning; and the intra‐annual relative range (RREL) and month of maximum NDVI (MMAX), which represent key features of seasonality. Results NDVI‐I decreased south‐eastwards. The highest values were observed in the Eurosiberian Region and in the highest Mediterranean ranges. Low values occurred in inner plains, river basins and in the southeast. The Eurosiberian Region and Mediterranean mountains presented the lowest RREL, while Eurosiberian peaks, river basins, inner‐agricultural plains, wetlands and the southeastern part of Iberia presented the highest. Eurosiberian ecosystems showed a summer maximum of NDVI, as did high mountains, wetlands and irrigated areas in the Mediterranean Region. Mediterranean mountains had autumn–early‐winter maxima, while semi‐arid zones, river basins and continental plains had spring maxima. Based on the behaviour in the functional traits, 49 EFTs were defined. Main conclusions The classification, based on only the NDVI dynamics, represents the spatial heterogeneity in ecosystem functioning by means of the interception of radiation by vegetation in the Iberian Peninsula. The patterns of the NDVI attributes may be used as a reference in evaluating the impacts of environmental changes. Iberia had a high spatial variability: except for biophysically impossible combinations (high NDVI‐I and high seasonality), almost any pattern of seasonal dynamics of radiation interception was represented in the Peninsula. The approach used to define EFTs opens the possibility of monitoring and comparing ecosystem functioning through time.     

Remotely sensed vegetation phenology for describing and predicting the biomes of South Africa

Questions: What are the patterns of remotely sensed vegetation phenology, including their inter‐annual variability, across South Africa? What are the phenological attributes that contribute most to distinguishing the different biomes? How well can the distribution of the recently redefined biomes be predicted based on remotely sensed, phenology and productivity metrics? Location: South Africa. Method: Ten‐day, 1 km, NDVI AVHRR were analysed for the period 1985 to 2000. Phenological metrics such as start, end and length of the growing season and estimates of productivity, based on small and large integral (SI, LI) of NDVI curve, were extracted and long‐term means calculated. A random forest regression tree was run using the metrics as the input variables and the biomes as the dependent variable. A map of the predicted biomes was reproduced and the differentiating importance of each metric assessed. Results: The phenology metrics (e.g. start of growing season) showed a clear relationship with the seasonality of rainfall, i.e. winter and summer growing seasons. The distribution of the productivity metrics, LI and SI were significantly correlated with mean annual precipitation. The regression tree initially split the biomes based on vegetation production and then by the seasonality of growth. A regression tree was used to produce a predicted biome map with a high level of accuracy (73%). Main conclusion: Regression tree analysis based on remotely sensed metrics performed as good as, or better than, previous climate‐based predictors of biome distribution. The results confirm that the remotely sensed metrics capture sufficient functional diversity to classify and map biome level vegetation patterns and function.     

Comparing global models of terrestrial net primary productivity (NPP): analysis of differences in light absorption and light-use efficiency

Twelve global net primary productivity (NPP) models were compared: BIOME3, CASA, CARAIB, FBM, GLO-PEM, HYBRID, KGBM, PLAI, SDBM, SIB2, SILVAN and TURC. These models all use solar radiation as an input, and compute either absorbed solar radiation directly, or the amount of leaves used to absorb solar radiation, represented by the leaf area index (LAI). For all models, we obtained or estimated photosynthetically active radiation absorbed by the canopy (APAR). We then computed the light use efficiency for NPP (LUE) on an annual basis as the ratio of NPP to APAR. We analysed the relative importance for NPP of APAR and LUE. The analyses consider the global values of these factors, their spatial patterns represented by latitudinal variations, and the overall grid cell by grid cell variability. Spatial variability in NPP within a model proved to be determined by APAR, and differences among models by LUE. There was a compensation between APAR and LUE, so that global NPP values fell within the range of ‘generally accepted values’. Overall, APAR was lower for satellite driven models than for the other models. Most computed values of LUE were within the range of published values, except for one model.     

2000—2018年黄河流域生长季植被指数变化及其对气候因子的响应

黄河流域位于我国干旱、半干旱地区,生态环境脆弱,探究其植被指数变化和对气候因子的响应对该地区生态建设具有重要意义。基于黄河流域2000—2018年MODIS归一化植被指数、增加型植被指数和气象数据,利用最大值合成法、趋势分析和相关分析等方法,分析了两种植被指数的时空变化特征及受气候因子的影响机制,探讨了NDVI与EVI在反映植被变化和对气候因子响应的差异。结果表明：2000—2018年,黄河流域地区植被NDVI、EVI分别以0.059/10a、0.038/10a的变化率增加,空间上以显著改善为主,面积占比分别为77.13%和75.27%,大多分布在1000—1500 m海拔处,中游地区改善较为良好,林地改善率最高。显著退化区域较小,主要分布在巴颜喀拉山西北部、西宁市、银川市、包头市、呼和浩特市、太原市、西安市及关中盆地和洛阳市周边,建设用地退化率最高。在生长季期间,植被指数变化与气温和降水以正相关为主,气温滞后时间为1个月,降水滞后时间为3个月,都为草地最为相关;与辐射之间为负相关,滞后时间为3个月,其中林地最为相关。在0.05显著性检验水平下,驱动黄河流域生长季植被变化的主要气候因子...     

Protected areas buffer the Brazilian semi‐arid biome from climate change

The Caatinga is a botanically unique semi‐arid ecosystem in northeast Brazil whose vegetation is adapted to the periodic droughts that characterize this region. However, recent extreme droughts events caused by anthropogenic climate change have challenged its ecological resilience. Here, we evaluate how deforestation and protection status affect the response of the Caatinga vegetation to drought. Specifically, we compared vegetation responses to drought in natural and deforested areas as well as inside and outside protected areas, using a time‐series of satellite‐derived Normalized Difference Vegetation Index (NDVI) and climatic data for 2008–2013. We observed a strong effect of deforestation and land protection on overall vegetation productivity and in productivity dynamics in response to precipitation. Overall, deforested areas had significantly lower NDVI and delayed greening in response to precipitation. By contrast, strictly protected areas had higher productivity and considerable resilience to low levels of precipitation, when compared to sustainable use or unprotected areas. These results highlight the importance of protected areas in protecting ecosystem processes and native vegetation in the Caatinga against the negative effects of climate change and deforestation. Given the extremely small area of the Caatinga currently under strict protection, the creation of new conservation areas must be a priority to ensure the sustainability of ecological processes and to avoid further desertification.     

A cross-biome comparison of daily light use efficiency for gross primary production

Vegetation light use efficiency is a key physiological parameter at the canopy scale, and at the daily time step is a component of remote sensing algorithms for scaling gross primary production (GPP) and net primary production (NPP) over regional to global domains. For the purposes of calibrating and validating the light use efficiency ( ε _g) algorithms, the components of ε _g– absorbed photosynthetically active radiation (APAR) and ecosystem GPP – must be measured in a variety of environments. Micrometeorological and mass flux measurements at eddy covariance flux towers can be used to estimate APAR and GPP, and the emerging network of flux tower sites offers the opportunity to investigate spatial and temporal patterns in ε _g at the daily time step. In this study, we examined the relationship of daily GPP to APAR, and relationships of ε _g to climatic variables, at four micrometeorological flux tower sites – an agricultural field, a tallgrass prairie, a deciduous forest, and a boreal forest. The relationship of GPP to APAR was close to linear at the tallgrass prairie site but more nearly hyperbolic at the other sites. The sites differed in the mean and range of daily ε _g, with higher values associated with the agricultural field than the boreal forest. ε_g decreased with increasing APAR at all sites, a function of mid‐day saturation of GPP and higher ε _g under overcast conditions. ε _g was generally not well correlated with vapor pressure deficit or maximum daily temperature. At the agricultural site, a ε _g decline towards the end of the growing season was associated with a decrease in foliar nitrogen concentration. At the tallgrass prairie site, a decline in ε _g in August was associated with soil drought. These results support inclusion of parameters for cloudiness and the phenological status of the vegetation, as well as use of biome‐specific parameterization, in operational ε _g algorithms.     

Northern hemisphere photosynthetic trends 1982–99

We examined trends in the averaged May–September AVHRR normalized difference vegetation index (NDVI) from 1982 to 1999 for the northern hemisphere. NDVI is closely related to the amount of absorbed photosynthetically active radiation; hence, trends in NDVI reflect trends in photosynthetic activity of land‐surface vegetation. Linear and nonlinear trend analysis techniques were applied to four differently processed and corrected Advanced Very High Resolution Radiometer (AVHRR) NDVI data sets. The results were compared in order to evaluate the effects of trends in NDVI unrelated to vegetation activity. We consistently found significant positive trends in averaged NDVI for latitude bands above 35°N in all but one data set; this one data set lacked corrections for sensor drift and instrument calibration. An impressive improvement in data quality was achieved by applying calibration and corrections for atmospheric effects. Conservative estimates of the trends over the 1982–99 period range from 0.0015 to 0.0045 NDVI units year^?1 for global latitude bands from 35 to 75°N, with trends generally higher in the 1990s than in the 1980s; trends in NDVI were larger than trends explained by artefacts. In the 1980s, North American and Eurasian trends were roughly comparable, whereas in the 1990s the North American trends were generally higher. A pixel‐level analysis shows the trends to be widespread, with large areas of Canada, Europe and northern Asia experiencing significant positive increases across all vegetated landcovers.     

1982—2012年新疆植被NDVI的动态变化及其对气候变化和人类活动的响应

植被在调节陆地碳平衡、气候系统中发挥了重要作用,并在生态系统服务功能提供方面占据主导地位,因此,监测植被生长变化意义重大.基于AVHRR GIMMS NDVI和MODIS NDVI数据集,在区域、像元两个空间尺度,研究了中国典型干旱区新疆1982—2012年间植被生长的动态变化,探讨了气候变化和人类活动对植被生长的影响.结果表明: 区域尺度,1982—2012年生长季植被NDVI呈极显著增加趋势(4.09×10^-4·a^-1);NDVI变化趋势存在明显阶段性,1998年前后分别呈极显著增加(10×10^-4·a^-1)和显著减少(-3×10^-4·a^-1);生长季NDVI变化趋势的逆转主要发生在夏季,其次是秋季,而春季不存在逆转.像元尺度上,农业区NDVI增加趋势显著;NDVI变化呈两极分化现象,剧烈变化区域多随时段长度延长而增加,尤其是显著减少区域范围快速扩张,导致区域尺度NDVI增加的停滞或放缓.研究区域植被生长受水热条件、人类活动共同控制.春、秋季的气温发挥主导作用,而夏季主要受到降水量的影响.大量施肥、灌溉面积增加等生产活动提高了农田植被覆盖,种植结构、灌溉方式等的改变降低了春季农田NDVI值,载畜量的增加则降低了部分草地的NDVI.     

全球气候变暖胁迫下的雅鲁藏布江流域植被覆盖度变化驱动机制探讨

为了解雅鲁藏布江流域内植被变化对气候变化响应的时空差异性,引入重心模型,分析和探讨了2002-2014年雅鲁藏布江流域植被的变化特点与气候因子的相关性。结果表明,植被的NDVI(归一化植被指数,Normalized difference vegetation index)重心与降水重心年际迁移方向具有正相关性。雅鲁藏布江流域的月植被NDVI受前0-1月降水影响最大,而不同季节植被的NDVI对降水影响表现出一定的滞后性,其中春季和冬季的植被NDVI均与前一季的降水呈现正相关性。该流域中乔木、灌木对降水反应的滞后性比草本植物要大;生长季的温度变化与植被的生长具有相关性。植被NDVI与月均温的正相关性达到最大的时间段差异较大。因此,植被NDVI和气候因子间的时空异质性研究对于雅鲁藏布江流域的生态环境保护具有重要意义。     

Seasonally different response of photosynthetic activity to daytime and night‐time warming in the Northern Hemisphere

Over the last century the Northern Hemisphere has experienced rapid climate warming, but this warming has not been evenly distributed seasonally, as well as diurnally. The implications of such seasonal and diurnal heterogeneous warming on regional and global vegetation photosynthetic activity, however, are still poorly understood. Here, we investigated for different seasons how photosynthetic activity of vegetation correlates with changes in seasonal daytime and night‐time temperature across the Northern Hemisphere (>30°N), using Normalized Difference Vegetation Index (NDVI) data from 1982 to 2011 obtained from the Advanced Very High Resolution Radiometer (AVHRR). Our analysis revealed some striking seasonal differences in the response of NDVI to changes in day‐ vs. night‐time temperatures. For instance, while higher daytime temperature (T_max) is generally associated with higher NDVI values across the boreal zone, the area exhibiting a statistically significant positive correlation between T_max and NDVI is much larger in spring (41% of area in boreal zone – total area 12.6 × 10⁶ km²) than in summer and autumn (14% and 9%, respectively). In contrast to the predominantly positive response of boreal ecosystems to changes in T_max, increases in T_max tended to negatively influence vegetation growth in temperate dry regions, particularly during summer. Changes in night‐time temperature (T_min) correlated negatively with autumnal NDVI in most of the Northern Hemisphere, but had a positive effect on spring and summer NDVI in most temperate regions (e.g., Central North America and Central Asia). Such divergent covariance between the photosynthetic activity of Northern Hemispheric vegetation and day‐ and night‐time temperature changes among different seasons and climate zones suggests a changing dominance of ecophysiological processes across time and space. Understanding the seasonally different responses of vegetation photosynthetic activity to diurnal temperature changes, which have not been captured by current land surface models, is important for improving the performance of next generation regional and global coupled vegetation‐climate models.     

Variations in <Emphasis Type="Italic">Anarthrophyllum rigidum</Emphasis> radial growth,NDVI and ecosystem productivity in the Patagonian shrubby steppes

The lack of long-term records of productivity is a critical limitation to the study of ecosystem dynamics. Annual rings, a measure of growth in woody species, are a useful tool to document ecosystem dynamics. Time series of the Normalized Difference Vegetation Index (NDVI) provide estimates of ecosystem productivity through satellite-derived data on the fraction of photosynthetic active radiation absorbed by vegetation. In the Patagonian steppes, we relate changes in NDVI to interannual variations in the radial growth of the shrub Anarthrophyllum rigidum. A widely distributed network of 15 ring-width chronologies of A. rigidum was used to estimate changes in NDVI across the Patagonia steppe (35°–50°S). In most sites, interannual variations in shrub growth and NDVI are regulated by winter precipitation. The water accumulated in the soil during winter is used by A. rigidum during the growing season, concurrent with the maximum NDVI values. At 10 from the 15 selected sites, variations in the radial growth of A. rigidum explained between 23 and 62% of the total variance in seasonal NDVI, suggesting that the A. rigidum growth at some sites provides good estimates of productivity in the Patagonian shrubby steppes during the growing season. However, we were unable to determine clear relationships between radial growth and NDVI at high-elevation mountainous sites or where intensive grazing by sheep masked the effect of climate variability on shrub growth. We conclude that dendrochronological methods can complement other estimates to reconstruct variations of productivity, supplementing and extending the few short records available in the Patagonian steppe.