高光谱遥感在农作物长势监测中的应用

该研究是加拿大Saskatchewan Scott农作物轮作系统(ACS)研究的一部分.研究始于1994年,历时18 a,评价9个可耕种农作物产量系统的可靠性.由3种处理水平(organic,reduced,high)和3种作物多样性水平(low,diversified annual grains,diversified annual perennials)结合而产生的9个农作物产量系统,被用于监测和评价加拿大牧场不同处理和不同作物种植轮作下可耕种农作物的产量.在2003年生长季共收集了3次叶面积指数和光谱反射率的数据:生长季前期(6月)、生长季旺盛期(7月)、生长季后期(8月).叶面积指数是由LAI-2000植物冠层分析仪监测的,光谱测量是由覆盖了350～2500 nm波长范围共2215个波段的ADS便携式高光谱仪完成的.结果显示,光学测量可以用于监测农作物生长状况的差异.从生长季的早期到中期,光谱和叶面积指数在不同处理下有显著差异.7月中期是用遥感资料监测农作物长势的最佳季节;红光波段与近红外波段反射率的比值和基于这两个波段构造的归一化植被指数,是检测农作物长势的最佳植被指数.     

夏玉米植被指数与叶面积指数的关系研究

利用ASD便携式地物光谱仪和SunScan冠层分析仪实测了陕西杨凌区和扶风县夏玉米关键生育期冠层光谱反射率及叶面积指数(LAI),对归一化植被指数(NDVI)、比值植被指数(RVI)和差值植被指数(DVI)与叶面积指数进行了相关性分析,建立了基于三种植被指数的LAI估算模型,并进行精度检验。结果表明:基于抽穗期和蜡熟期NDVI以及灌浆期RVI的LAI估算模型的均方根差和相对误差较低,模拟效果较好。结果对夏玉米生长状况及病虫害监测、产量预测以及田间管理具有参考价值。     

不同播期冬小麦叶面积指数高光谱遥感监测模型

叶面积指数(Leaf area index,LAI)与植物的光合能力密切相关,是评价作物长势和预测产量的重要农学参数,利用高光谱遥感能够实现农作物LAI快速无损监测。为了建立不同播期条件下冬小麦LAI反演的最佳高光谱监测模型,提高冬小麦LAI估算模型精度,将地面实测冬小麦LAI数据和冠层高光谱数据相结合,对4个播期及4个播期组合模拟的混合播期数据进行分析,选取8种植被指数,通过相关分析、回归分析等统计方法,构建不同播期冬小麦叶面积指数监测模型。结果表明,在4个播期处理和由一个所有播期组合下(即混合播期)建立的LAI光谱监测模型中,播期1和播期4分别以EVI2和mNDVI拟合效果较好,播期2、播期3及混合播期均与NDGI拟合效果最好。不同播期及混合播期的拟合方程决定系数(R~2)分别为0.803,0.823,0.907,0.819和0.798;通过试验田实测LAI与反演LAI数据进行拟合模型验证,均方根误差分别为0.81,0.78,0.63,0.82,0.91。通过分析可知,不同播期的分期监测模型比混合播期统一监测模型的拟合效果更好,精度更高。因此,播期1、播期2、播期3、播期4分别选用植被指数EVI2、NDGI、NDGI、mNDVI建立冬小麦LAI反演模型。该结果可为实现不同播期下冬小麦长势精确监测提供理论依据和技术支撑。     

作物长势评估指数的设计与应用

合理有效地开展作物长势评估,可以及时反映作物生长状况及其对天气气候条件的响应。由于WOFOST模型、ORYZA2000模型在模拟冬小麦、玉米和水稻生长发育过程具备较强机理性,研究基于2001年以来全国冬小麦、玉米、水稻主产区逐日模拟的作物发育进程、叶面积指数和地上总生物量,通过隶属函数构建评估指数,开展高时空分辨率的作物长势评估。结果表明:长势综合评估指数在作物生长前期以发育进程、叶面积指数和地上总生物量三要素加权集合表征,中后期以发育进程和地上总生物量与穗重相关性的加权集合表征;长势评估指数与常规地面观测和遥感长势监测一致性较好,可以反映天气气候条件影响。在作物生长季内,以日为单位构建了作物长势评估指数数据库;根据长势评估指数将作物长势分为长势好、长势偏好、长势持平、长势偏差、长势差,实现空间上的长势监测、对比;以空间集成的方式,开展省级作物长势对比分析;利用长势评估指数变化反映典型天气气候条件对作物生长发育的影响。上述基于作物模型的作物长势评估指数符合现代化农业气象科研与业务服务发展的需求。     

冬小麦地面光谱监测

从小麦反射光谱的特征，探讨了小麦不同物候期反射光谱特征和小麦日变化、季节变化特征。建立了大田冬小麦光谱植被指数与叶面积指数的关系模型和冬小麦农学参数与植被指数之间的关系模型，为开展冬小麦大田生长状况的遥感动态监测和产量预报打下基础。     

农作物长势遥感监测业务化应用与研究进展

农作物长势监测可为田间管理提供及时的决策支持信息和早期估产提供依据。为了更好地研究作物生长过程中不同遥感监测作物长势方法的适用性,从多光谱遥感数据、高光谱遥感数据和微波遥感数据的应用及遥感监测指标与模型模拟方面综述了国内外农作物长势遥感监测研究及业务化应用的最新进展,指出了未来拟重点加强的研究任务,包括高时空分辨率和高光谱分辨率遥感数据的业务化技术、多遥感反演参数协同监测作物长势技术研发、基于遥感信息与作物过程模型的集合预报技术研究、全球尺度作物长势监测业务运行系统研发。     

宁夏灌区春小麦LAI与生长性状和产量的关系

利用宁夏永宁农试站1994—2012年2个品种小麦观测资料,对2个小麦品种的生长性状、产量和产量结构进行方差分析,然后选取2个品种间差异不显著的样本,建立小麦不同发育期叶面积指数(LAI)与株高、密度、产量、穗粒数、结实小穗数及千粒重的关系。结果表明,不同发育期的LAI能反映小麦株高、密度和单位面积结实穗数。抽穗至乳熟阶段的LAI可估测株高,三叶至乳熟阶段的LAI均可监测同期密度,以拔节至乳熟阶段最好。抽穗至乳熟阶段的LAI可估测单位面积有效穗数,以抽穗期效果最好。抽穗至乳熟阶段LAI能较好地反映小麦产量的变化,可利用小麦冠层高光谱测定值构建的植被指数或MODIS植被指数反演LAI,利用LAI与产量的关系估产。如果在小麦不同生育阶段用遥感植被指数估测LAI,就可通过LAI与小麦株高、密度的关系监测小麦长势。抽穗至灌浆前期的LAI相对稳定,利用MODIS植被指数反演的LAI可估测小麦密度、产量和产量结构。     

华北平原不同生长发育期作物长势对气候因子的响应研究

利用卫星遥感归一化植被指数(NDVI)时间序列数据和站点气象数据,从农作物生长发育过程的角度,分析了1981~2008年华北平原农田在12个生长发育期(冬小麦8个、夏玉米4个)对降水和温度不同的响应特征。研究区农田植被指数对降水响应的滞后性强于对温度的滞后性,其中对降水最为敏感的是前1和前2个生长发育期,对温度最为敏感的是同期和前1个生长发育期。不同种类作物在不同时期对气候因子响应不同:冬小麦发育中后期、夏玉米发育中期,绝大多数站点植被指数与降水呈正相关;冬小麦生长发育前中期植被指数与温度呈显著甚至极显著正相关。冬小麦出苗期温度、返青期温度和返青期降水分别与不同时期植被指数显著相关,出苗期和返青期为研究区农田长势对气候因子响应的敏感期。     

西北地区陆地生态系统植被状态参数业务化遥感研究

植被指数(NDVI)和叶面积指数(LAI)是两个非常重要的陆地生态系统植被状态参数.我们首先利用最大值(MVC)合成方法使用先进遥感数据如MODIS、AVHRR3等得到旬合成植被指数(NDVI),然后利用最新的经验方法针对不同的陆地生态系统类型反演得到叶面积指数,重点研究了我国沙尘暴发生频率较高的我国西北地区植被覆被状态及其变化情况.植被指数能够反映区域,乃至全球范围植被年季状态,用于监测陆地生态系统植物光合作用活动及其变化.植被指数作为一个基础参数能够用于计算反演更高级别的陆地生态系统状态参数.叶面积指数直接影响植被的光合作用,蒸腾作用的变化和陆面过程的能量平衡状态.在沙尘暴预测研究中使用的起沙过程模型需要将叶面积指数作为一个关键输入变量,另外,绝大多数生态过程模型模拟碳、水循环时也都需要将叶面积指数作为一个非常重要的输入变量.我们总结了最新的叶面积指数经验反演方法,针对6钟不同的陆地生态系统类型应用不同经验模型计算得到了叶面积指数.     

比利时作物长势监测系统在黑龙江省的应用研究

比利时作物长势监测系统(B—CGMS)已被移植成黑龙江省作物长势监测系统(H—CGMS)。这里介绍其中第二部分——移植和提高系统(由黑龙江省气象科学研究所研制)。文中列出了应用该系统作出的农作物产量预报。并利用气象指示仪成功描绘出遭到气候限制的地区。     

An annual cycle of vegetation in a GCM. Part II: global impacts on climate and hydrology

The Met Office Hadley Centre Unified Model (HadAM3) with the tiled version of the Met Office Surface Exchange Scheme (MOSES2) land surface scheme is used to assess the impact of a comprehensive imposed vegetation annual cycle on global climate and hydrology. Two 25-year numerical experiments are completed: the first with structural vegetation characteristics (Leaf Area Index, LAI, canopy height, canopy water capacity, canopy heat capacity, albedo) held at annual mean values, the second with realistic seasonally varying vegetation characteristics. It is found that the seasonalities of latent heat flux and surface temperature are widely affected. The difference in latent heat flux between experiments is proportional to the difference in LAI. Summer growing season surface temperatures are between 1 and 4 K lower in the phenology experiment over a majority of grid points with a significant vegetation annual cycle. During winter, midlatitude surface temperatures are also cooler due to brighter surface albedo over low LAI surfaces whereas during the dry season in the tropics, characterized by dormant vegetation, surface temperatures are slightly warmer due to reduced transpiration. Precipitation is not as systematically affected as surface temperature by a vegetation annual cycle, but enhanced growing season precipitation rates are seen in regions where the latent heat flux (evaporation) difference is large. Differences between experiments in evapotranspiration, soil moisture storage, the timing of soil thaw, and canopy interception generate regional perturbations to surface and sub-surface runoff annual cycles in the model.     

1982~1999年中国地区叶面积指数变化及其与气候变化的关系

利用1982～1999年AVHRR Pathfinder卫星遥感观测的植被叶面积指数（leaf area index,LAI）资料和中国730个气象台站的温度、降水观测资料,研究了中国不同地区（东北地区、华北地区、长江流域、华南地区和西南地区）LAI的季节、生长季和年变化,及其与气候变化（温度、降水）的关系。结果表明,在中国大部分地区,年平均LAI和生长季平均LAI均是增加的。由于区域和季节气候的差异,LAI变化趋势具有明显的空间和季节非均一性。从区域平均的角度来看,不同地区年和生长季平均LAI都有增加趋势,并且在华南地区增加最快。因而,在全球变化背景下,华南地区可能是潜在的碳汇。在季节尺度上,各地区区域平均LAI基本上都是增加的,并且都在春季增加最快。温度变化是LAI变化的主要原因。但是人类活动如农业活动、城市化等对华北平原、长江三角洲和珠江三角洲等地区LAI变化的作用不容忽视。     

Long-term variations of annual and growing season rainfalls in Nigeria

Summary Evidence for changes in the annual and growing season rainfall series for the period 1919 to 1985 in Nigeria are examined on a regional basis, using power-spectral and lowpass filter techniques, and the Mann-Kendall rank statistic. Four regions, the Coastal Zone, the Guinea-Savanna Zone, the Midland area and the Sahel, are used in the investigation of rainfall variation from south to north across the country.Quasi-periodic oscillations in the annual and growing season rainfall series are found to be concentrated in four spectral bands: 2.0–2.4, 2.7–2.9, 3.2–3.6 and 5.6–6.3 years. The spatial coherence of the fluctuations in annual and growing season rainfall is found to be limited to Nigeria south of 11 degrees north latitude. Evidence also emerges of a progressive decline in annual and growing season rainfall for northern Nigeria, north of nine degrees north latitude, for the period 1939–1985.With 5 Figures     

An annual cycle of vegetation in a GCM. Part I: implementation and impact on evaporation

The sensitivity of evaporation to a prescribed vegetation annual cycle is examined globally in the Met Office Hadley Centre Unified Model (HadAM3) which incorporates the Met Office Surface Exchange Scheme (MOSES2) as the land surface scheme. A vegetation annual cycle for each plant functional type in each grid box is derived based on satellite estimates of Leaf Area Index (LAI) obtained from the nine-year International Satellite Land Surface Climatology Project II dataset. The prescribed model vegetation seasonality consists of annual cycles of a number of structural vegetation characteristics including LAI as well as canopy height, surface roughness, canopy water capacity, and canopy heat capacity, which themselves are based on empirical relationships with LAI. An annual cycle of surface albedo, which in the model is a function of soil albedo, surface soil moisture, and LAI, is also modelled and agrees reasonably with observed estimates of the surface albedo annual cycle. Two 25-year numerical experiments are completed and compared: the first with vegetation characteristics held at annual mean values, the second with prescribed realistic seasonally varying vegetation. Initial analysis uncovered an unrealistically weak relationship between evaporation and vegetation state that is primarily due to the insensitivity of evapotranspiration to LAI. This weak relationship is strengthened by the adjustment of two MOSES2 parameters that together improve the models LAI-surface conductance relationship by comparison with observed and theoretical estimates. The extinction coefficient for photosynthetically active radiation, k _par , is adjusted downwards from 0.5 to 0.3, thereby enhancing the LAI-canopy conductance relationship. A canopy shading extinction coefficient, k _sh , that controls what fraction of the soil surface beneath a canopy is directly exposed to the overlying atmosphere is increased from 0.5 to 1.0, which effectively reduces soil evaporation under a dense canopy. When the experiments are repeated with the adjusted parameters, the relationship between evaporation and vegetation state is strengthened and is more spatially consistent. At nearly all locations, the annual cycle of evaporation is enhanced in the seasonally varying vegetation experiment. Evaporation is stronger during the peak of the growing season and, in the tropics, reduced transpiration during the dry season when LAI is small leads to significantly lower total evaporation.     

The Warming and Wetting Ecological Environment Changes over the Qinghai-Tibetan Plateau and the Driving Effect of the Asian Summer Monsoon

The impact of warming and wetting on the ecological environment of the Qinghai-Tibet Plateau (TP) under the background of climate change has been a concern of the global scientific community. In this paper, the optimized interpolation variational correction approach is adopted for the analysis of monthly high-resolution satellite precipitation products and observations from meteorological stations during the past 20 years. As a result, the corrected precipitation products can not only supplement the “blank area” of precipitation observation stations on the TP, but also improve the accuracy of the original satellite precipitation products. The precipitation over the TP shows different spatial changes in the vegetation growing season, known as the time from May to September. The precipitation in the vegetation growing season and leaf area index (LAI) in the following month show a similar change pattern, indicating a “one-month lag” response of LAI to precipitation on the TP. Further analysis illustrates the influence of water vapor transport driven by the Asian summer monsoon. Water vapor derived from trans-equatorial air flows across the Indian Ocean and Arabian Sea is strengthened, leading to the increase of precipitation in the central and northern TP, where the trend of warming and wetting and the increase of vegetation tend to be more obvious. By contrast, as a result of the weakening trend of water vapor transport in the middle and low levels in southern TP, the precipitation decreases, and the LAI shows a downtrend, which inhibits the warming and wetting ecological environment in this area.     

南水北调华北受水区植被与降水的关系研究

华北地区年际归一化植被指数(NDⅥ)的变化与降水的年际变化有相当强的正相关,降水量增加会显著的改善植被覆盖.华北的NDⅥ变化显示了很强的季节变化特征,6月是华北农作物种植和生长的关键时期,但该月的需水量并不大,农作物生长旺季在7～8月.北京、邢台和潍坊的7月份农作物增长最快,月平均相对NDⅥ增长速度为0.4,8月的为0.2,因此,在7～8月农作物生长需水量最大,相当降水量接近180 mm,因此,在调配农业用水时应充分考虑这些因子.       

Modeling Gross Primary Production by Integrating Satellite Data and Coordinated Flux Measurements in Arid and Semi-Arid China

Assessing large-scale patterns of gross primary production (GPP) in arid and semi-arid (ASA) areas is important for both scientific and practical purposes. Remote sensing-based models, which integrate satellite data with input from ground-based meteorological measurements and vegetation characteristics, improve spatially extended estimates of vegetation productivity with high accuracy. In this study, the authors simulated GPP in ASA areas by integrating moderate resolution imaging spectral radiometer (MODIS) data with eddy covariance and meteorological measurements at the flux tower sites using the Vegetation Photosynthesis Model (VPM), which is a remote sensing-based model for analyzing the spatial pattern of GPP in different land cover types. The field data were collected by coordinating observations at nine stations in 2008. The results indicate that in the region during the growing season GPP was highest in cropland sites, second highest in woodland sites, and lowest in grassland sites. VPM captured the temporal and spatial characteristics of GPP for different land covers in ASA areas. Further, Enhanced Vegetation Index (EVI) had a strong liner relationship with GPP in densely vegetated areas, while the Normalized Difference Vegetation Index (NDVI) had a strong liner relationship with GPP over less dense vegetation. This study demonstrates the potential of satellite-driven models for scaling-up GPP, which is a key component for studying the carbon cycle at regional and global scales.     

Climate change and implications for agriculture in Niger

Five-year moving averages of annual rainfall for 21 locations in Niger showed a decline in the annual rainfall after 1960. Correlation coefficients of the moving averages of monthly rainfall with annual rainfall showed significant correlations between the decline in the annual rainfall with decreased rainfall in August. Analysis of daily rainfall data for rainy season parameters of interest to agriculture suggested that from 1965 there was a significant decrease in the amount of rainfall and in the number of rainy days in the months of July and August, resulting in a decreased volume of rainfall for each rainstorm. In comparison to the period 1945–64, major shifts have occurred in the average dates of onset and ending of rains during 1965–88. The length of the growing season was reduced by 5–20 days across different locations in Niger. The standard deviation for the onset and ending of the rains as well as the length of the growing season has increased, implying that cropping has become more risky. Water balance calculations also demonstrated that the probability of rainfall exceeding potential evapotranspiration decreased during the growing season. The implications of these changes for agriculture in Niger are discussed using field data.     

Climate and Vegetation Drivers of Terrestrial Carbon Fluxes:A Global Data Synthesis

The terrestrial carbon(C) cycle plays an important role in global climate change, but the vegetation and environmental drivers of C fluxes are poorly understood. We established a global dataset with 1194 available data across site-years including gross primary productivity(GPP), ecosystem respiration(ER), net ecosystem productivity(NEP), and relevant environmental factors to investigate the variability in GPP, ER and NEP, as well as their covariability with climate and vegetation drivers.The results indicated that both GPP and ER increased exponentially with the increase in mean annual temperature(MAT)for all biomes. Besides MAT, annual precipitation(AP) had a strong correlation with GPP(or ER) for non-wetland biomes.Maximum leaf area index(LAI) was an important factor determining C fluxes for all biomes. The variations in both GPP and ER were also associated with variations in vegetation characteristics. The model including MAT, AP and LAI explained 53%of the annual GPP variations and 48% of the annual ER variations across all biomes. The model based on MAT and LAI explained 91% of the annual GPP variations and 92.9% of the annual ER variations for the wetland sites. The effects of LAI on GPP, ER or NEP highlighted that canopy-level measurement is critical for accurately estimating ecosystem–atmosphere exchange of carbon dioxide. The present study suggests a significance of the combined effects of climate and vegetation(e.g.,LAI) drivers on C fluxes and shows that climate and LAI might influence C flux components differently in different climate regions.