Driving force and changing trends of vegetation phenology in the Loess Plateau of China from 2000 to 2010

Changes in vegetation phenology are key indicators of the response of ecosystems to climate change. Therefore, knowledge of growing seasons is essential to predict ecosystem changes, especially for regions with a fragile ecosystem such as the Loess Plateau. In this study, based on the normalized difference vegetation index (NDVI) data, we estimated and analyzed the vegetation phenology in the Loess Plateau from 2000 to 2010 for the beginning, length, and end of the growing season, measuring changes in trends and their relationship to climatic factors. The results show that for 54.84% of the vegetation, the trend was an advancement of the beginning of the growing season (BGS), while for 67.64% the trend was a delay in the end of the growing season (EGS). The length of the growing season (LGS) was extended for 66.28% of the vegetation in the plateau. While the temperature is important for the vegetation to begin the growing season in this region, warmer climate may lead to drought and can become a limiting factor for vegetation growth. We found that increased precipitation benefits the advancement of the BGS in this area. Areas with a delayed EGS indicated that the appropriate temperature and rainfall in autumn or winter enhanced photosynthesis and extended the growth process. A positive correlation with precipitation was found for 76.53% of the areas with an extended LGS, indicating that precipitation is one of the key factors in changes in the vegetation phenology in this water-limited region. Precipitation plays an important role in determining the phenological activities of the vegetation in arid and semiarid areas, such as the Loess Plateau. The extended growing season will significantly influence both the vegetation productivity and the carbon fixation capacity in this region.     

Sources of uncertainty in exploring rangeland phenology: A case study in an alpine meadow on the central Tibetan Plateau

Global climate change has been found to substantially influence the phenology of rangeland, especially on the Tibetan Plateau. However, there is considerable controversy about the trends and causes of rangeland phenology owing to different phenological exploration methods and lack of ground validation. Little is known about the uncertainty in the exploration accuracy of vegetation phenology. Therefore, in this study, we selected a typical alpine rangeland near Damxung national meteorological station as a case study on central Tibetan Plateau, and identified several important sources influencing phenology to better understand their effects on phenological exploration. We found man-made land use was not easily distinguished from natural rangelands, and therefore this may confound phenological response to climate change in the rangeland. Change trends of phenology explored by four methods were similar, but ratio threshold method (RTM) was more suitable for exploring vegetation phenology in terms of the beginning of growing season (BGS) and end of growing season (EGS). However, some adjustments are needed when RTM is used in extreme drought years. MODIS NDVI/EVI dataset was most suitable for exploring vegetation phenology of BGS and EGS. The discrimination capacities of vegetation phenology declined with decreasing resolution of remote sensing images from MODIS to GIMMS AVHRR datasets. Additionally, distinct trends of phenological change rates were indicated in different terrain conditions, with advance of growing season in high altitudes but delay of season in lower altitudes. Therefore, it was necessary to eliminate interference of complex terrain and man-made land use to ensure the representativeness of natural vegetation. Moreover, selecting the appropriate method to explore rangelands and fully considering the impact of topography are important to accurately analyze the effects of climate change on vegetation phenology.     

青藏高原典型植被生长季遥感模型提取分析

物候变化是衡量全球气候变化最直接、敏感的指示器,针对青藏高原这个独特地域单元上特殊的高寒植被进行关键物候期遥感提取模型及植被物候时空变化的研究具有重要的意义。本文首先以反距离加权空间插值算法与Savitzky-Golay滤波算法相结合的数据重建模型获得高质量2003-2012年青藏高原MODIS归一化植被指数(NDVI)数据。在此数据基础上,分别利用动态阈值法、最大变化斜率法、logistic曲线拟合法3种遥感植被生长季提取模型,对青藏高原地区两种典型植被的生长季(SOS生长季开始期,EOS生长季结束期,LOS生长季长度)进行提取。通过对3种模型提取结果的对比分析,并结合日均温模型对提取结果的验证发现,动态阈值法为青藏高原地区典型植被生长季的最优遥感提取模型。该模型对近10 a的高分辨率典型高寒植被物候参量的反演及时空变化特征分析表明,受青藏高原水热及海拔梯度的影响,青藏高原植被物候变化呈现出从东南向西北的空间分异规律,随春季温度的升高,近10 a来青藏高原高寒草地总体呈现生长季开始期(SOS)提前(0.248 d/a)的趋势。     

Effect of Mathematical Expression of Vegetation Indices on the Estimation of Phenology Trends from Satellite Data

Vegetation indices(VIs) from satellite remote sensing have been extensively applied to analyze the trends of vegetation phenology. In this paper, the NDVI(normalized difference vegetation index) and SR(simple ration), which are calculated from the same spectral bands of MODIS data with different mathematical expressions, were used to extract the start date(SOS) and end date(EOS) of the growing season in northern China and Mongolia from 2000 to 2015. The results show that different vegetation indices would lead to differences in vegetation phenology especially in their trends. The mean SOS from NDVI is 15.5 d earlier than that from SR, and the mean EOS from NDVI is 13.4 d later than that from SR. It should be noted that 16.3% of SOS and 17.2% of EOS derived from NDVI and SR exhibit opposite trends. The phenology dates and trends from NDVI are also inconsistent with those of SR among various vegetation types. These differences based on different mathematical expressions in NDVI and SR result from different resistances to noise and sensitivities to spectral signal at different stage of growing season. NDVI is prone to be effected more by low noise and is less sensitive to dense vegetation. While SR is affected more by high noise and is less sensitive to sparse vegetation. Therefore, vegetation indices are one of the uncertainty sources of remote sensing-based phenology, and appropriate indices should be used to detect vegetation phenology for different growth stages and estimate phenology trends.     

融合植被遥感数据的北京市次日花粉浓度预测

中国国土绿化状况公报指出,2010—2020年中国许多城市的绿化面积增加、绿化质量提高,可随之而来的影响人体健康的致敏性花粉风险也逐渐提高。本文利用遥感手段获得北京市乔木和草地生长区域平均植被叶面积指数（LAI）时间序列作为植被物候信息,并将其作为花粉浓度预测因子之一,结合日气象数据,使用具有外部输入的非线性自回归神经网络模型（NARXnet）,进行北京市次日花粉浓度的预测。结果显示：① 通过逐步回归计算,对于春季数据,日均气温3日平滑,积温,叶面积指数（LAI）和叶面积指数一阶导为次日花粉浓度预测的关键变量;对于秋季数据,日均气温、平均风速、最低日气温、日均气温3日平滑、积温和叶面积指数（LAI）为次日花粉浓度预测的关键变量;② 加入遥感物候信息可显著地提高NARXnet模型的春秋时段的花粉浓度的预测精度。使用本文提出的结合叶面积指数的NARX模型后,预测模型的总体精度为71%。由此,本研究认为在原有气象因子的基础上,辅之以用遥感技术手段获取的大面积植被物候信息,如叶面积指数动态,可作为预测次日花粉浓度的一种有效手段。     

京津冀地区植被时空动态及定量归因

作为气候变化的敏感指示器,植被的物候、生长、空间分布格局等特征及其动态变化主要取决于气候环境中的水热条件,因此在气候变化背景下,气候-植被关系成为了全球变化研究的前沿和热点问题。本文综合平均温度、降水、水汽压、湿度、日照时数、SPEI等气候因子,坡度、坡向海拔等地形因子及人为活动因子,应用地理探测器方法针对2006-2015年京津冀地区不同季节NDVI、不同地貌类型区、不同植被类型区生长季NDVI的定量归因研究,揭示了过去10年间植被时空分布格局,及植被对气候、非气候因素响应的季节差异与区域差异,以期为生态工程的建设与修复提供参考意义。趋势分析表明：①2006-2015年京津冀地区NDVI呈现增加趋势,但存在显著的空间差异,如山地生长季NDVI的增长速率大于平原、台地、丘陵等地;②基于地理探测器的定量归因结果表明,降水是年尺度上NDVI空间分布的主导因子（解释力39.4%）,土地利用与降水的交互作用对NDVI的影响最为明显（q=58.2%）;③NDVI对气候因子的响应存在季节性及区域性差异,水汽压是春季NDVI空间分布的主导因子,湿度是夏、秋两季的主导因子,土地利用是冬季的主导因子;④影响因子对生长季NDVI的解释力因不同地貌类型区、不同植被类型区而差异显著。     

亚热带福建省森林生长季与气温、降水相关性的遥感分析

植物生长季的变化反映了全球气候变化对生态环境的影响。本研究以2000-2006年间MODIS-NDVI影像数据集,使用TIMESAT软件从归一化植被指数（NDVI）时间序列中,分别提取福建省不同森林植被的生长季开始日期（Start of Season,SOS）、生长季结束日期（End of Season,EOS）和生长季长度（Length of season,LOS）等物候参数,并与全省尺度的气温与降水量进行相关分析。结果表明：不同森林类型NDVI与当月月均气温之间具有较显著的相关性（R²为0.72-0.79,p<0.01）,同期温度变化对植被生长的影响相对于降水量更重要;而植被生长对降水量的响应存在大约2个月的时滞效应（R²为0.54-0.75,p<0.01）,说明前期的降水累积对于后续植被生长有较显著影响。福建省森林植被生长季持续时间约213~223 d,开始于每年4月初到4月中旬（第98~103 d）,结束于11月中旬前后（第316~321 d）。其中,南亚热带森林生长季长于中亚热带森林,相同气候条件下的阔叶林生长季时间略长于针叶林。另外,春季（2-4月）气温变化是导致福建省内2个气候带森林生长季开始时间、生长季结束时间及生长季长度变化的关键因素,而伴随春季温度升高,植被生长季开始时间提前（R²为0.83,p<0.01）,同时生长季长度延长（R²为0.80,p<0.01）。7 a间,生长季持续时间呈现微弱延长趋势,总体延长幅度为2.4~3.1 d。     

2000-2013年三江源植被NDVI变化趋势及影响因素分析

基于2000-2013年三江源MODIS NDVI数据,本文系统地分析了三江源植被生长季累计NDVI的时空变化特征,并结合三江源生态保护与建设工程实施的相关统计数据,探讨了人类活动对三江源植被变化的影响,最后通过气候因子与生长季累计NDVI的相关性分析,揭示了影响三江源不同地区植被变化的主要气候限制因素。结果表明,2000-2013年三江源植被NDVI整体上呈增加趋势,NDVI明显增加的区域面积比例达17.84%,主要分布于研究区的西部和北部;明显减少的区域仅占0.78%,多零星分布于研究区中部;NDVI变化稳定或没有显著变化趋势的区域面积比例为59.64%,主要位于研究区东部和南部。三江源生态保护与建设工程的实施虽然促进了植被恢复,但对区域植被整体变化的影响有限,研究时段内区域植被整体好转主要受气候因素控制。西部长江源区的植被生长主要受气温影响,东北部黄河源区主要受降水制约,南部澜沧江源区降水和气温的限制性均不明显。     

Vegetation cover variation in the Qilian Mountains and its response to climate change in 2000–2011

An understanding 0f variati0ns in vegetati0n c0ver in resp0nse t0 climate change is critical f0r predicting and managing future terrestrial ec0system dynamics. Because scientists anticipate that m0untain ec0systems will be m0re sensitive t0 future climate change c0mpared t0 0thers, 0ur 0bjectives were t0 investigate the impacts 0f climate change 0n variati0n in vegetati0n c0ver in the Qilian M0untains （QLM）, China, between 2000 and 2011. T0 acc0mplish this, we used linear regressi0n techniques 0n 250-m MODIS N0rmalized Difference Vegetati0n Index （NDVI） datasets and mete0r0l0gical rec0rds t0 determine spati0temp0ral variability in vegetati0n c0ver and climatic fact0rs （i.e. temperature and precipitati0n）. Our results sh0wed that temperatures and precipitati0n have increased in this regi0n during 0ur study peri0d. In additi0n, we f0und that gr0wing seas0n mean NDVI was mainly distributed in the vertical z0ne fr0m 2,700 m t0 3,600 m in elevati0n. In the study regi0n, we 0bserved significant p0sitive and negative trends in vegetati0n c0ver in 26.71% and 2.27% 0f the vegetated areas. C0rrelati0n analyses indicated that rising precipitati0n fr0m May t0 August was resp0nsible f0r increased vegetati0n c0ver in areas with p0sitive trends in gr0wing seas0n mean NDVI. H0wever, there was n0 similar significant c0rrelati0n between gr0wing seas0n mean NDVI and precipitati0n in regi0ns where vegetati0n c0ver declined thr0ugh0ut 0ur study peri0d. Using spatial statistics, we f0und that veeetati0n c0ver freauentlvdeclined in areas within the 2,500-3,100 m vertical z0ne, where it has steep sl0pe, and is 0n the sunny side 0f m0untains. Here, the p0sitive influences 0f increasing precipitati0n c0uld n0t 0ffset the drier c0nditi0ns that 0ccurred thr0ugh warming trends. In c0ntrast, in higher elevati0n z0nes （3,900-4,500 m） 0n the shaded side 0f the m0untains, rising temperatures and increasing precipitati0n impr0ved c0nditi0ns f0r vegetati0n gr0wth. Increased precipitati0n als0 facilitated vegetati0n gr0wth in areas experiencing warming trends at l0wer elevati0ns （2,000-2,400 m） and 0n l0wer sl0pes where water was m0re easily c0nserved. We suggest that spatial differences in variati0n in vegetati0n as the result 0f climate change depend 0n l0cal m0isture and thermal c0nditi0ns, which are mainly c0ntr0lled by t0p0graphy （e.g. elevati0n, aspect, and sl0pe）, and 0ther fact0rs, such as l0cal hydr0l0gy.     

京津冀地区NDVI变化及气候因子驱动分析

植被覆盖动态监测及与气候变化的响应,是陆地生态系统研究的重要内容。本文以2001-2013年间京津冀地区MOD13A 3月合成NDVI数据,结合生长季的降水和气温资料,运用偏相关和复相关分析、趋势分析方法,研究了该区域NDVI的变化特征和空间分布,以及其区域植被覆盖变化的气候驱动力。结果表明,该区域NDVI最大值在13a间缓慢增加,植被覆盖呈现改善趋势;NDVI和生长季降雨量及平均气温的平均偏相关系数分别为0.20和-0.14,表明在年际变化水平上,京津冀地区NDVI总体与降水量呈正相关,与平均气温呈负相关,且降水对NDVI的影响大于温度对NDVI的影响。对植被覆盖驱动分区得出,降水和气温驱动型占区域面积的5.68%;单独降水驱动型和气温驱动型分别占4.51%、0.18%;区域内植被覆盖变化主要受非气候因子驱动型为主,所占比例为89.63%,表明人类活动对植被变化的影响巨大。     

Spatio-temporal variation of spring phenology in Tibetan Plateau and its linkage to climate change from 1982 to 2012

The influence of climate change on vegetation phenology is a heated issue in current climate change study. We used GIMMS-3g NDVI data to detect the spatio-temporal dynamics of the start of the growing season (SGS) over the Tibetan Plateau (TP) from 1982 to 2012 and to analyze its relationship with temperature and precipitation. No significant trend was observed in the SGS at the regional scale during the study period (R ² = 0.03, P = 0.352). However, there were three time periods (1982-1999, 1999-2008 and 2008-2012) with identifiable, distinctly different trends. Regions with a significant advancing trend were mainly scattered throughout the humid and semi-humid areas, whereas the regions with a significant delaying trend were mostly distributed throughout the semi-arid areas. Statistical analysis showed that the response of the SGS to climate change varies spatially. The SGS was significantly correlated with the spring temperature and the start of the thermal growth season (STGS) in the relatively humid area. With increasing aridity, the importance of the spring temperature for the SGS gradually decreased. However, the influences of precipitation and winter temperature on the SGS were complicated across the plateau.     

南方亚热带季风区将乐县森林植被动态变化及其对气候变化的响应

在全球气候变化背景下,植被动态变化以及植被对气候变化的响应方式已经成为生态学和地理学领域的热点。本文对比分析了南方亚热带季风区将乐县不同类型森林植被对不同时间尺度的干旱响应的差别。基于2000-2017年MODIS-EVI数据及气象站点数据,用最大值合成法、趋势分析法以及相关分析法,分析了森林植被及气象因子的动态变化特征,并对比不同森林植被对气候变化响应的差别。研究表明：① 2000-2017年,研究区植被覆盖度、EVI和降水均显著增加,区域内湿度增加,森林长势渐趋良好;② EVI在生长季初期和末期与同期的降水、温度均显著正相关（P<0.1）,初期森林受降水因子的影响更大,末期受温度因子的影响大;③ 1-3月和周年的气候变化对森林的生长至关重要,长时间尺度的湿度增加对森林生长具有显著的促进作用,SPEI的时间尺度越长与EVI的相关性也越大;④ 针阔混交林与同期温度、降水的相关系数最高,并且与不同时间尺度的SPEI相关性均比较高,属于气候敏感型林型,在生产经营中要谨慎预防气候变化对该林型带来的伤害;⑤ 森林覆盖度变化与降水和SPEI_24的相关性极显著,长时间尺度的降水变化是影响森林植被覆盖率变化的重要因素之一。     

桂西南喀斯特-北部湾海岸带生态环境脆弱性时空分异与驱动机制研究

为揭示生态环境脆弱性的时空分异和驱动因子,本研究在山江海视角下,以桂西南喀斯特-北部湾海岸带为典型研究区,运用空间主成分分析法,地理探测器模型,结合生态环境脆弱性综合指数,系统分析桂西南喀斯特-北部湾海岸带生态环境脆弱性的时空分异特征及驱动机制。结果表明：① 研究区2008、2013、2018年脆弱性指数分别为0.54、0.61、0.69,多年平均值为0.61,整体处于中度脆弱,在空间上,由城市中心向四周逐渐降低的趋势;在时间上,生态环境脆弱等级呈微恶化趋势; ② 在单因子作用中6个驱动因子对生态环境脆弱性的解释力强度为汛期降雨量（0.457）>植被覆盖度（0.384）>高温季节温度（0.311）>废水入海量（0.248）>NPP（0.184）>人口密度（0.036）。在多因子交互中,只有汛期降水量和NPP, NPP和高温季节温度、废水入海量和NPP呈非线性增强,其余的交互作用均为双线性增强,而且汛期降水量和植被覆盖度的单因子影响较强,交互作用后影响也是最强（0.679）,说明了汛期降水量和植被覆盖度为该区域的主要驱动因子。     

Different Responses of Vegetation to Frozen Ground Degradation in the Source Region of the Yellow River from 1980 to 2018

Frozen ground degradation under a warming climate profoundly influences the growth of alpine vegetation in the source region of the Qinghai-Tibet Plateau. This study investigated spatiotemporal variations in the frozen ground distribution, the active layer thickness(ALT) of permafrost(PF) soil and the soil freeze depth(SFD) in seasonally frozen soil from 1980 to 2018 using the temperature at the top of permafrost(TTOP) model and Stefan equation. We compared the effects of these variations on vegetation growth among different frozen ground types and vegetation types in the source region of the Yellow River(SRYR). The results showed that approximately half of the PF area(20.37% of the SRYR) was projected to degrade into seasonally frozen ground(SFG) during the past four decades; furthermore, the areal average ALT increased by 3.47 cm/yr, and the areal average SFD decreased by 0.93 cm/yr from 1980 to 2018. Accordingly, the growing season Normalized Difference Vegetation Index(NDVI) presented an increasing trend of 0.002/10 yr, and the increase rate and proportion of areas with NDVI increase were largest in the transition zone where PF degraded to SFG(the PF to SFG zone). A correlation analysis indicated that variations in ALT and SFD in the SRYR were significantly correlated with increases of NDVI in the growing season. However, a rapid decrease in SFD(-1.4 cm/10 yr) could have reduced the soil moisture and, thus, decreased the NDVI. The NDVI for most vegetation types exhibited a significant positive correlation with ALT and a negative correlation with SFD. However, the steppe NDVI exhibited a significant negative correlation with the SFD in the PF to SFG zone but a positive correlation in the SFG zone, which was mainly limited by water condition because of different change rates of the SFD.     

Mapping deciduous broad-leaved forested swamps using ALOS/Palsar data

Accurate, updated information on the distribution of wetlands is essential for estimating net fluxes of greenhouse gases and for effectively protecting and managing wetlands. Because of their complex community structure and rich surface vegetation, deciduous broad-leaved forested swamps are considered to be one of the most difficult types of wetland to classify. In this research, with the support of remote sensing and geographic information system, multi-temporal radar images L-Palsar were used initially to extract the forest hydrological layer and phenology phase change layer as two variables through image analysis. Second, based on the environmental characteristics of forested swamps, three decision tree classifiers derived from the two variables were constructed to explore effective methods to identify deciduous broad-leaved forested swamps. Third, this study focused on analyzing the classification process between flat-forests, which are the most severely disturbed elements, and forested swamps. Finally, the application of the decision tree model will be discussed. The results showed that: 1) L-HH band(a L band with wavelength of 0–235 m in HH polarization mode; HH means Synthetic Aperture Radars transmit pulses in horizontal polarization and receive in horizontal polarization) in the leaf-off season is shown to be capable of detecting hydrologic conditions beneath the forest; 2) the accuracy of the classification(forested swamp and forest plat) was 81.5% based on hydrologic features, and 83.5% was achieved by combining hydrologic features and phenology response features, which indicated that hydrological characteristics under the forest played a key role. The HHOJ(refers to the band created by the subtraction with HH band in October and HH band in July) achieved by multi-temporal radar images did improve the classification accuracy, but not significantly, and more leaf-off radar images may be more efficient than multi-seasonal radar images for inland forested swamp mapping; 3) the lower separability between forested swamps dominated by vegetated surfaces and forest plat covered with litter was the main cause of the uncertainty in classification, which led to misleading interpretations of the pixel-based classification. Finally, through the analysis with kappa coefficients, it was shown that the value of the intersection point was an ideal choice for the variable.     

Impacts of warming on root biomass allocation in alpine steppe on the north Tibetan Plateau

Biomass is an important component of global carbon cycling and is vulnerable to climate change. Previous studies have mainly focused on the responses of aboveground biomass and phenology to warming, while studies of root architecture and of root biomass allocation between coarse and fine roots have been scarcely reported in grassland ecosystems. We conducted an open-top-chamber warming experiment to investigate the effect of potential warming on root biomass and root allocation in alpine steppe on the north Tibetan Plateau. The results showed that Stipa purpurea had significantly higher total root length, root surface area and tips than Carex moocroftii. However,there were no differences in total root volume, mean diameter and forks for the two species. Warming significantly increased total root biomass(27.60%), root biomass at 0–10 cm depth(27.84%) and coarse root biomass(diameter 0.20 mm, 57.68%) in the growing season(August). However, warming had no significant influence on root biomass in the non-growing season(April). Root biomass showed clear seasonalvariations: total root biomass, root biomass at 0–10 cm depth and coarse root biomass significantly increased in the growing season. The increase in total root biomass was due to the enhancement of root biomass at 0–10 cm depth, to which the increase of coarse root biomass made a great contribution. This research is of significance for understanding biomass allocation, carbon cycling and biological adaptability in alpine grassland ecosystems under future climate change.     

Effect of topography and accessibility on vegetation dynamic pattern in Mountain-hill Region

Knowledge of both vegetation distribution pattern and phenology changes is very important.Their complicated relationship with elevation and accessibility were explored through a geographically weighted regression(GWR) framework in Fujian province,China.The 16-day time series of 250 m Moderate Resolution Imaging Spectroradiometer(MODIS) Enhanced Vegetation Index(EVI) dataset from 2000 to 2010 was applied.Wavelet transform method was adopted to decompose the original time series and construct the annual maximum EVI and amplitude of the annual phenological cycle(EVI).Candidate explaining factors included topographic conditions,accessibility variables and proportions of primary vegetation types.Results revealed very strong positive influence from parameters of elevation and accessibility to big rivers and negative effect from accessibility to resident on both maximum EVI and phenological magnitude through ordinary linear least square(OLS) regression analysis.GWR analysis revealed that spatially,the parameters of topography and accessibility had a very complex relationship with both maximum EVI and phenology magnitude,as a result of the various combinations of environmental factors,vegetation composition and also intensive anthropogenic impact.Apart from the continuously increasing trend of phenology magnitude with increasing altitude,the influence of topography and accessibility on maximum EVI and phenological magnitude generally decreased,even from strongly positive to negative,with increasing altitude or distance.Specially,the most rapid change of correlation coefficient between them was observed within a low elevation or close distance;less variation was discovered within a certain range of medium altitude or distance and their relationship might change above this range.Non-stationary approaches are needed to better characterize the complex vegetation dynamic pattern in Mountain-hill Region.     

玉米叶面积指数的CHRIS/PROBA数据反演分析

叶面积指数(LAI)是衡量植被生态状况和估算作物产量的一个重要指标。LAI的反演是定量遥感研究的重要内容。传统的经验统计反演方法基于单一观测角度的遥感数据进行,忽略了地物反射率的方向性。若在反演中加入多观测角度的信息,则有可能提升LAI反演的精度。以2008年甘肃省张掖市玉米实验区为研究区,利用欧空局的CHRIS/PROBA多角度高光谱数据对比分析了传统植被指数NDVI、RVI、EVI的变化规律及其反演玉米叶面积指数LAI的精度,并根据NDVI随观测角度的变化规律,构造出新型多角度归一化植被指数MNDVI,分别对实测叶面积指数进行线性回归并利用实测数据对估算LAI进行精度验证,结果表明：新型MNDVI指数相比于传统NDVI、RVI、EVI对LAI的反演精度有了显著提升,估算模型决定系数R²达到0.716,精度验证均方根误差为0.127,平均减小了33.3%。     

Evaluating Spatial Heterogeneity of Land Surface Hydrothermal Conditions in the Heihe River Basin

Land surface hydrothermal conditions(LSHCs) reflect land surface moisture and heat conditions, and play an important role in energy and water cycles in soil-plant-atmosphere continuum. Based on comparison of four evaluation methods(namely, the classic statistical method, geostatistical method, information theory method, and fractal method), this study proposed a new scheme for evaluating the spatial heterogeneity of LSHCs. This scheme incorporates diverse remotely sensed surface parameters, e.g., leaf area index-LAI, the normalized difference vegetation index-NDVI, net radiation-Rn, and land surface temperature-LST. The LSHCs can be classified into three categories, namely homogeneous, moderately heterogeneous and highly heterogeneous based on the remotely sensed LAI data with a 30 m spatial resolution and the combination of normalized information entropy(S') and coefficient of variation(CV). Based on the evaluation scheme, the spatial heterogeneity of land surface hydrothermal conditions at six typical flux observation stations in the Heihe River Basin during the vegetation growing season were evaluated. The evaluation results were consistent with the land surface type characteristics exhibited by Google Earth imagery and spatial heterogeneity assessed by high resolution remote sensing evapotranspiration data. Impact factors such as precipitation and irrigation events, spatial resolutions of remote sensing data, heterogeneity in the vertical direction, topography and sparse vegetation could also affect the evaluation results. For instance, short-term changes(precipitation and irrigation events) in the spatial heterogeneity of LSHCs can be diagnosed by energy factors, while long-term changes can be indicated by vegetation factors. The spatial heterogeneity of LSHCs decreases when decreasing the spatial resolution of remote sensing data. The proposed evaluation scheme would be useful for the quantification of spatial heterogeneity of LSHCs over flux observation stations toward the global scale, and also contribute to the improvement of the accuracy of estimation and validation for remotely sensed(or model simulated) evapotranspiration.     

Evapotranspiration Estimation Based on MODIS Products and Surface Energy Balance Algorithms for Land （SEBAL） Model in Sanjiang Plain, Northeast China

In this study,the Surface Energy Balance Algorithms for Land(SEBAL) model and Moderate Resolution Imaging Spectroradiometer(MODIS) products from Terra satellite were combined with meteorological data to estimate evapotranspiration(ET) over the Sanjiang Plain,Northeast China.Land cover/land use was classified by using a recursive partitioning and regression tree with MODIS Normalized Difference Vegetation Index(NDVI) time series data,which were reconstructed based on the Savitzky-Golay filtering approach.The MODIS product Quality Assessment Science Data Sets(QA-SDS) was analyzed and all scenes with valid data covering more than 75% of the Sanjiang Plain were selected for the SEBAL modeling.This provided 12 overpasses during 184-day growing season from May 1st to October 31st,2006.Daily ET estimated by the SEBAL model was misestimaed at the range of-11.29% to 27.57% compared with that measured by Eddy Covariance system(10.52% on average).The validation results show that seasonal ET from the SEBAL model is comparable to that from ground observation within 8.86% of deviation.Our results reveal that the time series daily ET of different land cover/use increases from vegetation on-going until June or July and then decreases as vegetation senesced.Seasonal ET is lower in dry farmland(average(Ave):491 mm) and paddy field(Ave:522 mm) and increases in wetlands to more than 586 mm.As expected,higher seasonal ET values are observed for the Xingkai Lake in the southeastern part of the Sanjiang Plain(Ave:823 mm),broadleaf forest(Ave:666 mm) and mixed wood(Ave:622 mm) in the southern/western Sanjiang Plain.The ET estimation with SEBAL using MODIS products can provide decision support for operational water management issues.