用NOAA气象卫星的AVHRR遥感资料估算中国的净第一性生产力

重点研究用气象卫星ＡＶＨＲＲ遥感方法估算目前中国的净第一性生产力（ＮＰＰ）水平。ＡＶＨＲＲ可见光通道和近红外通道组合成的标准化差植被指数（ＮＤＶＩ）,既是监测植被生长好坏的参量,也是计算净第一性生产力的重要参量。介绍了用遥感统计模型计算ＮＰＰ的方法,获得了一系列结果。将遥感方法计算的ＮＰＰ与气候模型计算的ＮＰＰ进行了比较,证明两者有一定的可比性。计算净第一性生产力需要全年的ＮＤＶＩ,因此对卫星资料处理技术也作了介绍     

陆地植被净初级生产力计算模型研究进展

植被净初级生产力(NPP)研究是全球变化与陆地生态系统的核心内容之一。在回顾NPP模型研究的基础上,综合分析了气候模型、生态生理过程模型、光能利用率模型各自的优缺点,并对NPP模型研究做出展望。生态生理过程模型是当前陆地NPP估算研究的主要手段,而区域尺度转换则是它所面临的关键问题。近年来光能利用率模型已成为NPP估算的一种全新手段,它利用遥感所获得的全覆盖数据,使区域及全球尺度的NPP估算成为可能,但其生态学机理还有待于进一步研究。已有研究表明,“生态一遥感耦合模型”将是陆地NPP估算的主要发展方向,它融合了生态生理过程模型和光能利用率模型的优点,增强了NPP模型估算的可靠性和可操作性。     

植被光能利用率研究进展

光能利用率是表征植物固定太阳能效率的指标,指植物通过光合作用将所截获/吸收的能量转化为有机干物质的效率,是植物光合作用的重要概念,也是区域尺度以遥感参数模型监测植被生产力的理论基础。传统的研究方法是通过生物量收获法分别确定植物生长和辐射量,求年或生长季比值;涡度相关技术作为目前直接测定植被冠层与大气间的CO2和水热交换量的唯一方法,使从冠层到景观水平的光能利用率估计成为可能。由于植被类型的差异和气候环境的综合影响使光能利用率表现出显著的空间异质性和时间动态性。在全球尺度上,利用耦合大气CO2观测、卫星遥感和大气辐射传输模型的反演模拟,发现净初级生产力的光能利用率存在明显的地理分异。影响光能利用率时空变异性的因子包括植物内在因素(如叶形、叶羧化酶含量)和外在环境因素。针对光能利用率的时空特征及其波动,建立在通量观测及模型分析基础上的跨尺度模拟,将成为今后该领域的研究重点。     

中国潜在植被生产力的分布与模拟

利用中国植被净第一性生产力ＮＰＰ（ｎｅｔｐｒｉｍａｒｙｐｒｏｄｕｃｔｉｖｉｔｙ）的散点测量数据及中国一般气象站气候记录资料,并根据附近气象站数据进行地理位置相关插值,采用Ｐｅｎｍａｎ方法计算潜在蒸散,用Ｔｈｏｒｎｔｈｗａｉｔｅ的土壤水分处理方法,计算了ＮＰＰ测定位点的水分平衡。结果表明ＮＰＰ与生长季实际蒸散、总蒸散、潜在蒸散显著相关,与干燥度成负相关。从生长季的实际蒸散、干燥度指标两参数出发,建立了ＷＢＩＮＰＰ模型：ＮＰＰ＝２．５５·ＧＡＥ·ｅ－４．２０９２２－１．９６６５ＡＩ。该模型考虑了植被生产力形成机制,与ＮＰＰ实测值相关性高达０．８４２,高于国际上著名ＮＰＰ模型Ｍｉａｍｉ模型、ＴｈｏｒｎｔｈｗａｉｔｅＭｅｍｏｒｉａｌ模型和Ｃｈｉｋｕｇｏ模型（相关系数为０．６５５～０．７３２）。最后利用ＥＩＳ软件对全国潜在ＮＰＰ分布格局进行了分析,从东南到西北逐渐减少,最大值出现在海南和台湾,大于２２ｔ·ｈｍ－２·ａ－１,长江中下游地区１２～１６ｔ·ｈｍ－２·ａ－１,华北平原为８～１２ｔ·ｈｍ－２·ａ－１,东北地区为４～８ｔ·ｈｍ－２·ａ－１;草原地区为２ｔ·ｈｍ－２·ａ－１左右,干旱荒漠小于２ｔ·ｈｍ－２·ａ－１。     

基于NDVI的中国天然森林植被净第一性生产力模型

根据叶面积指数、归一化植被指数（ＮＤＶＩ）建立了中国森林植被净第一性生产力（ＮＰＰ）模型：ＮＰＰ＝－０．６３９４－６７．０６４ｌｎ（１－ＮＤＶＩ）经我国１３组森林植被生产力数据的验证表明,该模型的预测结果与实测值相符较好。通过与Ｃｈｉｋｕｏ模型和综合模型（周广胜等,１９９６）预测结果的比较,该模型在总体上优于Ｃｈｉｋｕｇｏ模型和综合模型。表明基于ＮＤＶＩ的净第一生产力模型对我国森林植被有良好的适应     

塔里木盆地北部盐化草甸植被净第一性生产力模型研究

根据野外实测的植被生产力资料及计算所得的测定样地地下水潜水蒸发量数据。建立了估算塔里木盆地北部盐化草甸植被净第一性生产力（ＮＰＰ）的模型：Ｙ＝０．０３２４＋０．００３７Ｅ。（１－ｈ／ｈ。）＾２该模型表明：在一定的气候和土壤条件下,盐化草甸植被净第一性生产力随地下水埋深的增加而逐渐下降,对模型的灵敏度分析表明,当地下水埋深ｈ〈３．３ｍ时,盐化草甸植被净第一性生产力对地下埋深的变化不甚敏感,ｈ每变化１     

遥感GPP模型在中亚干旱区4个典型生态系统的适用性评价

总初级生产力（GPP）是全球生态系统碳循环的重要组成部分,对全球气候变化有重要影响。目前有多种遥感模型可以模拟总初级生产力,比较不同遥感模型在中亚干旱区上的适用性对推进全球干旱区碳收支估算具有重要意义。基于涡度协相关技术观测的四个地面站数据验证MOD17、VODCA2、VPM、TG、SANIRv五种模型的模拟精度。结果表明：（1）基于光能利用率理论的MOD17、VPM模型模拟咸海荒漠植被和阜康荒漠植被GPP的精度最高（R²分别为0.52和0.80）,但在模拟草地、农田生态系统生产力时存在较明显的低估（RE>20%）;基于植被指数的遥感模型TG模型、SANIRv模型模拟巴尔喀什湖草地生态系统和乌兰乌苏农田生态系统GPP的精度最高（R²分别为0.91和0.81）,同时模拟值与实测值的相对误差也较低;基于微波的VODCA2模型模拟各生态系统生产力的效果最差。（2）水分亏缺是限制植被GPP的主要因素,因此是否合理考虑水分胁迫是影响GPP模型在中亚干旱区适用性的重要因素。研究揭示了遥感GPP模型在中亚干旱区的应用潜力,为推进全球植被碳通量的准确估算提供参考。     

陆地生态系统净第一性生产力对全球变化的响应

陆地生态系统的年净第一性生产力是每年植物通过光合作用固定的碳总量。随着全球变化发生,ＮＰＰ发生相应的变化。传统的方法预测ＮＰＰ的变化是利用气候和植被之间的局地关系建立回归模型,但用此方法预测ＮＰＰ的变化是有条件的。目前国际上出现了一种陆地生态系统的动态模型,它考虑了植物营养元素如氮的有效性,同时利用不同ＧＣＭｓ模型预测的气候因子的变化值和全球变化模拟研究的实验数据,预测全球ＮＰＰ的可能变化及区域分     

江苏省农作物最大光能利用率时空特征及影响因子

基于遥感数据的光能利用率模型被广泛应用于计算陆地生态系统的生产力,但其结果对最大光能利用率(εmax)参数非常敏感。由于该参数存在明显的时空变异性,在区域尺度上难以确定。利用MODIS遥感数据、分县产量统计数据和VPM模型推算了2001—2010年江苏省各县逐年的农田εmax,分析了其时空变化特征及其可能原因。结果表明,在2001—2010年期间,江苏省61个县区农田εmax平均值的变化范围为0.757—3.435 g C/MJ,呈现北高南低、中间高四周较低的空间分布特征;各县区农田的εmax都呈现出上升趋势,但在2001—2006年期间存在明显的年际波动,2002年、2004年和2006年的εmax相对较低,2007年后全省农田εmax稳定上升;εmax的年际波动呈现由北向南递减的趋势;全省大部分地区εmax的年际变化与单位耕地面积农用化肥施用量呈正相关性,苏北北部尤为明显;同时也与C4作物产量所占比例相关。研究表明在利用光能利用率模型计算农田生产力时,需要发展能考虑εmax时空变化的参数化方案。     

孟印缅地区农田生产力脆弱性变化及气候影响机制——基于1982-2015年GIMMS3g植被指数

农田生产力对气候变化的敏感性决定了其脆弱性,全球气候变暖及极端气候频发将严重影响农业粮食生产,进而将可能影响区域粮食安全。科学评估农田生产力脆弱性并分析其气候影响机制有助于积极应对气候变化,保障区域粮食安全,具有重要的现实和科学意义。以"一带一路"区域的"孟中印缅经济走廊"为研究区,基于1982-2015年卫星遥感数据的归一化植被指数,根据IPCC脆弱性定义,采用年际变率及其变化趋势计算农田生产力对气候变化的敏感性、适应性和脆弱性指数,分时段分析研究区农田生态系统脆弱性空间格局变化及气候影响机制。结果表明：（1）较之1982-2000年,2000-2015年期间研究区农田脆弱性程度略有提升,高度和极度脆弱面积略有增加（分别增加0.42%和1.12%）,但其分布格局发生北移。（2）年降水、年平均气温和年辐射与年累积NDVI间线性回归分析表明,孟加拉和缅甸地区与气候因素显著相关的区域面积在本国农田面积中的比例分别增加21.3%和16.7%,而印度地区减少10.5%,全区减少8.1%;（3）线性回归方程的复相关系数（R²）表征气候变化的解释能力,整个研究区增加12%,其中印度气候解释能力从48%提升至64%,增加16%。（4）农田生产力脆弱性受气候影响的范围略有减小,但影响程度增大,且存在较大的区域性差异;高温和降水季节不均引发的旱涝灾害是农田高脆弱度形成的两个关键气候因素。为该地区农业应对气候变化适应性管理措施的提出及决策提供了科学依据,有效支撑"一带一路"建设;也为其他地区应用卫星遥感开展脆弱性研究提供了方法参考,为生态系统对全球变化响应研究提供重要知识参考。     

Emergent climate and CO2 sensitivities of net primary productivity in ecosystem models do not agree with empirical data in temperate forests of eastern North America

Ecosystem models show divergent responses of the terrestrial carbon cycle to global change over the next century. Individual model evaluation and multimodel comparisons with data have largely focused on individual processes at subannual to decadal scales. Thus far, data‐based evaluations of emergent ecosystem responses to climate and CO₂ at multidecadal and centennial timescales have been rare. We compared the sensitivity of net primary productivity (NPP) to temperature, precipitation, and CO₂ in ten ecosystem models with the sensitivities found in tree‐ring reconstructions of NPP and raw ring‐width series at six temperate forest sites. These model‐data comparisons were evaluated at three temporal extents to determine whether the rapid, directional changes in temperature and CO₂ in the recent past skew our observed responses to multiple drivers of change. All models tested here were more sensitive to low growing season precipitation than tree‐ring NPP and ring widths in the past 30 years, although some model precipitation responses were more consistent with tree rings when evaluated over a full century. Similarly, all models had negative or no response to warm‐growing season temperatures, while tree‐ring data showed consistently positive effects of temperature. Although precipitation responses were least consistent among models, differences among models to CO₂ drive divergence and ensemble uncertainty in relative change in NPP over the past century. Changes in forest composition within models had no effect on climate or CO₂ sensitivity. Fire in model simulations reduced model sensitivity to climate and CO₂, but only over the course of multiple centuries. Formal evaluation of emergent model behavior at multidecadal and multicentennial timescales is essential to reconciling model projections with observed ecosystem responses to past climate change. Future evaluation should focus on improved representation of disturbance and biomass change as well as the feedbacks with moisture balance and CO₂ in individual models.     

Assessing the Spatiotemporal Variation in Distribution,Extent and NPP of Terrestrial Ecosystems in Response to Climate Change from 1911 to 2000

To assess the variation in distribution, extent, and NPP of global natural vegetation in response to climate change in the period 1911–2000 and to provide a feasible method for climate change research in regions where historical data is difficult to obtain. In this research, variations in spatiotemporal distributions of global potential natural vegetation (PNV) from 1911 to 2000 were analyzed with the comprehensive sequential classification system (CSCS) and net primary production (NPP) of different ecosystems was evaluated with the synthetic model to determine the effect of climate change on the terrestrial ecosystems. The results showed that consistently rising global temperature and altered precipitation patterns had exerted strong influence on spatiotemporal distribution and productivities of terrestrial ecosystems, especially in the mid/high latitudes. Ecosystems in temperate zones expanded and desert area decreased as a consequence of climate variations. The vegetation that decreased the most was cold desert (18.79%), while the maximum increase (10.31%) was recorded in savanna. Additionally, the area of tundra and alpine steppe reduced significantly (5.43%) and were forced northward due to significant ascending temperature in the northern hemisphere. The global terrestrial ecosystems productivities increased by 2.09%, most of which was attributed to savanna (6.04%), tropical forest (0.99%), and temperate forest (5.49%). Most NPP losses were found in cold desert (27.33%). NPP increases displayed a latitudinal distribution. The NPP of tropical zones amounted to more than a half of total NPP, with an estimated increase of 1.32%. The increase in northern temperate zone was the second highest with 3.55%. Global NPP showed a significant positive correlation with mean annual precipitation in comparison with mean annual temperature and biological temperature. In general, effects of climate change on terrestrial ecosystems were deep and profound in 1911–2000, especially in the latter half of the period.     

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.     

中国森林生产力及其对全球气候变化的响应（英文）

根据国内外关于森林生物生产力的研究资料及其对全球气候变化响应的预测结果,本文对中国的森林生产生产力进行了归纳和总结,对其与全球气候变化的关系作了初步分析。结果表明,中国森林的总生物量为４．０－７．１ＰｇＣ（１ＰｇＣ＝１０＾９ｔＣ）,均值为４．６ＰｇＣ,总生物生产力（不包括经济林和竹林）为０．４－０．６ＰｇＣ．ａ＾－１,均值为０．５ＰｇＣ．ａ＾－１;按已知的全球变化预测结果,ＣＯ２浓度倍增后,中国森     

遥感在植被净第一性生产力研究中的应用

植被净第一性生产力 (简称ＮＰＰ)是指绿色植物在单位面积、单位时间内所累计的有机物数量 ,是由光合作用所产生的有机质总量中扣除自养呼吸后的剩余部分。它直接反映了植物群落在自然环境条件下的生产能力。据研究 ,在假使生态系统的呼吸作用保持恒定的条件下 ,植被ＮＰＰ每增长 2 %就会净吸收 1Ｇｔ(10 15ｔ)的Ｃ(碳物质 ) [3 8] 。所以 ,近几年来各国学者对ＮＰＰ的研究倍受重视 ,尤其在国际生物学计划 (ＩＢＰ)期间进行了大量的植物ＮＰＰ的测定 ,并以测定资料为基础联系环境因子建立模型对植被ＮＰＰ的区域分布进行评估 ,极大地促进了…     

Sources of Variability in Net Primary Production Predictions at a Regional Scale: A Comparison Using PnET-II and TEM 4.0 in Northeastern US Forests

Because model predictions at continental and global scales are necessarily based on broad characterizations of vegetation, soils, and climate, estimates of carbon stocks and fluxes made by global terrestrial biosphere models may not be accurate for every region. At the regional scale, we suggest that attention can be focused more clearly on understanding the relative strengths of predicted net primary productivity (NPP) limitation by energy, water, and nutrients. We evaluate the sources of variability among model predictions of NPP with a regional-scale comparison between estimates made by PnET-II (a forest ecosystem process model previously applied to the northeastern region) and TEM 4.0 (a terrestrial biosphere model typically applied to the globe) for the northeastern US. When the same climate, vegetation, and soil data sets were used to drive both models, regional average NPP predictions made by PnET-II and TEM were remarkably similar, and at the biome level, model predictions agreed fairly well with NPP estimates developed from field measurements. However, TEM 4.0 predictions were more sensitive to regional variations in temperature as a result of feedbacks between temperature and belowground N availability. In PnET-II, the direct link between transpiration and photosynthesis caused substantial water stress in hardwood and pine forest types with increases in solar radiation; predicted water stress was relieved substantially when soil water holding capacity (WHC) was increased. Increasing soil WHC had little effect on TEM 4.0 predictions because soil water storage was already sufficient to meet plant demand with baseline WHC values, and because predicted N availability under baseline conditions in this region was not limited by water. Because NPP predictions were closely keyed to forest cover type, the relative coverage of low- versus high-productivity forests at both fine and coarse resolutions was an important determinant of regional NPP predictions. Therefore, changes in grid cell size and differences in the methods used to aggregate from fine to coarse resolution were important to NPP predictions insofar as they changed the relative proportions of forest cover. We suggest that because the small patches of high-elevation spruce-fir forest in this region are substantially less productive than forests in the remainder of the region, more accurate NPP predictions will result if models applied to this region use land cover input data sets that retain as much fine-resolution forest type variability as possible. The differences among model responses to variations in climate and soil WHC data sets suggest that the models will respond quite differently to scenarios of future climate. A better understanding of the dynamic interactions between water stress, N availability, and forest productivity in this region will enable models to make more accurate predictions of future carbon stocks and fluxes. Received 19 June 1998; accepted 25 June 1999.     

Net primary productivity and rain‐use efficiency as affected by warming,altered precipitation,and clipping in a mixed‐grass prairie

Grassland productivity in response to climate change and land use is a global concern. In order to explore the effects of climate change and land use on net primary productivity (NPP), NPP partitioning [f_BNPP, defined as the fraction of belowground NPP (BNPP) to NPP], and rain‐use efficiency (RUE) of NPP, we conducted a field experiment with warming (+3 °C), altered precipitation (double and half), and annual clipping in a mixed‐grass prairie in Oklahoma, USA since July, 2009. Across the years, warming significantly increased BNPP, f_BNPP, and RUE_BNPP by an average of 11.6%, 2.8%, and 6.6%, respectively. This indicates that BNPP was more sensitive to warming than aboveground NPP (ANPP) since warming did not change ANPP and RUE_ANPP much. Double precipitation stimulated ANPP, BNPP, and NPP but suppressed RUE_ANPP, RUE_BNPP, and RUE_NPP while half precipitation decreased ANPP, BNPP, and NPP but increased RUE_ANPP, RUE_BNPP, and RUE_NPP. Clipping interacted with altered precipitation in impacting RUE_ANPP, RUE_BNPP, and RUE_NPP, suggesting land use could confound the effects of precipitation changes on ecosystem processes. Soil moisture was found to be a main factor in regulating variation in ANPP, BNPP, and NPP while soil temperature was the dominant factor influencing f_BNPP. These findings suggest that BNPP is critical point to future research. Additionally, results from single‐factor manipulative experiments should be treated with caution due to the non‐additive interactive effects of warming with altered precipitation and land use (clipping).     

2000—2015年中国东北森林生产力和碳素利用率的时空变异

中国东北森林生态系统是重要的碳汇功能区,也是对环境变化响应的敏感区,分析其植被生产力和碳素利用效率的变化特征及其对气候变化的响应对于区域碳收支的准确评估和预测具有重要意义.本研究利用MODIS的长期监测数据,结合植被类型分布数据,对中国东北森林生态系统2000—2015年生产力(净初级生产力NPP、总初级生产力GPP)和碳素利用率(NPP/GPP)时空变化特征进行分析.结果表明: 研究期间,东北森林生态系统平均NPP和GPP分别为346.4和773 g C·m^-2·a^-1,平均NPP/GPP为0.45.不同森林类型的NPP和GPP依次为针阔混交林>落叶阔叶林>针叶林,NPP/GPP在不同森林类型间无显著差异.NPP和GPP呈现出东南高、西北低的空间分布特点.2000—2015年间,东北森林生态系统NPP、GPP和NPP/GPP呈波动增加趋势,固碳能力逐步增强.NPP、GPP和NPP/GPP的变化趋势和变化速率表现出空间差异性,在大兴安岭南部地区显著增加,在大兴安岭北部地区显著下降,其余区域呈微弱增加趋势.与气候因子的相关性分析表明,年降水量的增加是驱动东北森林生态系统NPP、GPP和NPP/GPP波动增加的主要因素.