大范围的农作物长势监测可以为农业政策的制订和粮食贸易提供决策依据,也是农作物产量估测的必要前提。遥感估算的作物生物量是评价作物长势的主要群体特征指标,在大范围上开展作物长势监测时,不同区域的作物因为所处的物候阶段不同而导致生物量存在差异,这种差异与因作物长势状况差别而产生的差异混合在一起,增加了长势监测结果的不确定性。以中国河南、山东两省为研究区,以MODIS 250m NDVI产品数据为主要数据源,结合改进的CASA模型实现了区域内冬小麦生物量的估算,结合冬小麦生长过程特征进行了典型物候期的监测。在此基础上,分析了扬花期前后物候差异对冬小麦生物量估算的影响,研究其特定物候阶段的变化规律,从而实现了生物量的物候归一化,初步探索了如何消除大区域物候差异对作物长势监测与评估的影响。

大范围的可靠农情信息对粮食市场及相关政策的制定至关重要,是保障区域及全球粮食安全的重要依据,在全球气候变化、人口增长、土地利用/覆盖变化剧烈的背景下,对这一信息的需求也更加迫切。传统农情信息的获取依赖于庞大的调查队伍和大量的调查工作,信息的获取存在成本高、时效性差和结果受主观影响大的缺点。伴随着近30年遥感技术本身及其在农情信息获取领域能力的提升,一些国家与国际组织建设了各自的农情遥感监测系统,并开展了运行化的监测。对美国、欧盟、FAO、加拿大、巴西、阿根廷、俄罗斯、印度等主要的农情遥感监测系统进展进行了详细的介绍,并通过对这些系统的分析得到一些农情监测系统建设的启示。指出作物种植面积估算、单产预测、长势监测、旱情监测是农情遥感监测中最主要的4个主题。在面积估算方面,各个系统在遥感技术不断发展的同时对地面调查的依赖并没有减少,甚至得到了强化,这与遥感降低地面调查的初衷相违背,导致遥感技术在大范围农情监测中的潜力没有得到充分发挥,在单产预测方面,需要发展独立的遥感预测方法。提升遥感的作用是未来一段时间内农情遥感监测系统建设的主要方向。

为改善归一化植被指数(NDVI)作为遥感监测作物长势指标的性能,该文分析了归一化植被指数的内在设计缺陷,在不增加额外波段的情况下,以近红外波段和红色波段为基础引入一种新的作物长势遥感监测指标——GRNDVI。通过在像素和区域层次上同其他4种指数进行比较发现:GRNDVI能够改善归一化植被指数在低植被覆盖度时期/地区容易受到作物冠层土壤背景的影响,而在高植被覆盖度时期/地区又容易发生饱和现象的设计缺陷,可以作为遥感监测作物长势过程中替代归一化植被指数的指标。

The seasonal patterns of green leaf area index (GLAI) can be used to assess crop physiological and phenological status, to assess yield potential, and to incorporate in crop simulation models. This study focused on examining the potential capabilities and limitations of satellite data retrieved from the moderate resolution imaging spectroradiometer (MODIS) 8- and 16-day composite products to quantitatively estimate GLAI over maize ( Zea mays L.) fields. Results, based on the nine years of data used in this study, indicated a wide variability of temporal resolution obtained from MODIS 8- and 16-day composite periods and highlighted the importance of information about day of MODIS products pixel composite for monitoring agricultural crops. Due to high maize GLAI temporal variability, the inclusion of day of pixel composite is necessary to decrease substantial uncertainties in estimating GLAI. Results also indicated that maize GLAI can be accurately retrieved from the 250-m resolution MODIS products (MOD13Q1 and MOD09Q1) by a wide dynamic range vegetation index with root mean square error (RMSE) below 0.6002m 2 02m 612 or by the enhanced vegetation index with RMSE below 0.7002m 2 02m 612 .

提出农作物长势综合监测方法,利用卫星遥感得到的NDVI时间序列数据,综合采用实时监测、过程监测和时间序列聚类监测方法,明确不同方法适用的监测尺度及监测目的,对不同范围农作物长势进行监测。改进了Crop Watch全球农情遥感速报系统运行化作物长势监测方法,克服了原有作物长势监测中实时监测方法无法反映相同区域苗情在整个生长过程中的连续变化情况的缺点。实现对相同区域作物长势连续变化的定量描述,可对作物长势进行更准确的判断。利用官方发布的作物单产变幅数据,对单产变幅较大的12个作物主产省区作物长势监测结果的准确性进行判断,结果表明:6个邦的实时监测和聚类监测方法所得结果一致,都符合作物单产变化的实际状况;4个邦的聚类监测方法所得结果对作物长势监测更为准确,更符合该区域作物单产的实际变化;1个邦实时监测结果对作物长势监测比聚类监测方法更为准确;只有1个邦采用两种方法对作物长势的监测存在误差,聚类监测方法在对农作物生长过程的连续监测及空间分布的定量化表述方面,比实时监测更为准确。3种方法可以综合使用,实现业务化运行的农作物长势监测。

In Brazil there is a need for less subjective, more efficient and less expensive methodologies for crop yield forecast. Owing to the continental dimensions of the country, orbital images have been used to estimate the productive potential of crops. In this study, NDVI (Normalized Difference Vegetation Index) time-series, derived from AVHRR/NOAA (Advanced Very High Resolution Radiometer/National Oceanic and Atmospheric Administration) imagery were used for the soybean crop monitoring in a large production region in Brazil in the 2002/2003 and 2003/2004 cropping seasons. NDVI temporal profiles describing the biomass condition of crops throughout the phenological stages were generated in 18 municipalities. Quantitative parameters were measured from the temporal profiles, based on the full time or partial phenological cycle. Linear regressions between the quantitative parameters and the municipal average yields in both seasons have shown that the most significant correlations occurred when the full time period was considered. When considering periods prior to harvest, the correlations showed a tendency to decline. The NDVI monitoring during these two cropping seasons, which presented different weather conditions, could explain a major part of the soybean yield variability at the municipal level. Results showed the potential of the NDVI time-series analysis in generating parameters to be employed by agrometeorological pectral models for soybean yield estimations. The automatic system for temporal profiles generation developed in this study sped up the analysis and can be used for further studies at a regional scale.

Wheat is one of the key cereal crops grown worldwide, providing the primary caloric and nutritional source for millions of people around the world. In order to ensure food security and sound, actionable mitigation strategies and policies for management of food shortages, timely and accurate estimates of global crop production are essential. This study combines a new BRDF-corrected, daily surface reflectance dataset developed from NASA's Moderate resolution Imaging Spectro-radiometer (MODIS) with detailed official crop statistics to develop an empirical, generalized approach to forecast wheat yields. The first step of this study was to develop and evaluate a regression-based model for forecasting winter wheat production in Kansas. This regression-based model was then directly applied to forecast winter wheat production in Ukraine. The forecasts of production in Kansas closely matched the USDA/NASS reported numbers with a 7% error. The same regression model forecast winter wheat production in Ukraine within 10% of the official reported production numbers six weeks prior to harvest. Using new data from MODIS, this method is simple, has limited data requirements, and can provide an indication of winter wheat production shortfalls and surplus prior to harvest in regions where minimal ground data is available.

Although the Normalized Difference Vegetation Index (NDVI) time-series data, derived from NOAA/AVHRR, SPOT/VEGETATION, TERRA or AQUA/MODIS, has been successfully used in research regarding global environmental change, residual noise in the NDVI time-series data, even after applying strict pre-processing, impedes further analysis and risks generating erroneous results. Based on the assumptions that NDVI time-series follow annual cycles of growth and decline of vegetation, and that clouds or poor atmospheric conditions usually depress NDVI values, we have developed in the present study a simple but robust method based on the Savitzky olay filter to smooth out noise in NDVI time-series, specifically that caused primarily by cloud contamination and atmospheric variability. Our method was developed to make data approach the upper NDVI envelope and to reflect the changes in NDVI patterns via an iteration process. From the results obtained by applying the newly developed method to a 10-day MVC SPOT VGT-S product, we provide optimized parameters for the new method and compare this technique with the BISE algorithm and Fourier-based fitting method. Our results indicate that the new method is more effective in obtaining high-quality NDVI time-series.

归一化植被指数（NDVI）时间序列数据拟合目的是降低时序数据的噪声水平，重建高质量的NDVI时序数据，有利于多种参数反演和信息提取。针对国际上普遍应用的两种NDVI时间序列数据拟合方法，即Savitzky-Golay滤波法和非对称性高斯函数拟合法，该文在介绍两种方法基本概念的基础上，利用直接比较法和间接比较法在中国对两种拟合方法进行了比较分析。结果表明，Savitzky—Golay滤波法和非对称性高斯函数拟合法的拟合效果总体上一致，但二者之间还是存在区域差异性，这种区域差异与两种方法的自身特点和中国区域自然条件紧密相关。不同数据拟合方法的比较研究可以弄清每种方法的优缺点和区域适宜性，有助于研究人员针对不同研究目的和研究区域选择适宜的NDVI数据拟合方法，减少遥感数据处理中的误差，提高研究精度。

One of the primary interests of the NASA Earth Observing System (EOS) program is to study the role of vegetation in biospheric processes. Currently, the normalized difference vegetation index (NDVI) is utilized to study and monitor vegetation activity on an operational basis at global scales. Recently developed variants to the NDVI equation, based on improved knowledge of atmosphere, canopy background, and sensor-view geometry, are being considered for use with the EOS Moderate-Resolution Imaging Spectrometer (MODIS) sensor. A set of criteria is established to evaluate their performance with respect to the vegetation signal and atmospheric and soil sources of noise. These include the vegetation signal-to-noise ratio (S/N), % relative error, and vegetation equivalent noise (VEN), which form the basis for the evaluation and comparison of vegetation indices (VIs) across a wide range in vegetation covers. The NDVI variant equations outperformed the NDVI by minimizing atmospheric and/or soil background sources of contamination as well as increasing vegetation signal sensitivity. Preliminary estimates, based on the data sets tested here, indicate an overall level of uncertainty of 0.4 LAI (15% cover) with the use of the soil adjusted and atmospherically resistant vegetation index (SARVI), compared with 0.8 LAI (30% cover) for the NDVI. Eventually, the final evaluation and validation of VIs for MODIS will include a field campaign strategy, simulation studies, and a worldwide establishment of test sites that collectively cover a diverse range of biomes. Lastly, the retrieval of canopy biophysical information from these VIs are discussed.

ABSTRACT Spectra of Spartina alterniflora were measure under different salinity, growth form and moisture conditions.

Continuous observation of leaf area index (LAI) is needed in order to interpret and model carbon, water and energy fluxes measured at Fluxnet tower sites. Although remote sensing LAI products can be used in regional and global scale modelling with reasonable performance, the site level modelling of ecophysiological processes needs more accurate LAI time series than those provided by global LAI products. Here we present a semi-empirical approach using satellite measured modified soil-adjusted vegetation index (MSAVI) and sparsely sampled LAI time series measurements at 7 Canadian Carbon Program (CCP) flux tower sites to produce continuous observations of site level LAI. The results indicate that accurate and continuous observation of site level LAI time series is possible using a few ground measurements and remotely sensed MSAVI observations. In order for the semi-empirical model to work correctly, the ground LAI measurements should represent all seasons, preferably including extreme values in winter and the peak of growing season.

ABSTRACT The Normalized Difference Vegetation Index (NDVI) equation has a simple, open loop structure (no feedback), which renders it susceptible to large sources of error and uncertainty over variable atmospheric and canopy background conditions. In this study, a systems analysis approach is used to examine noise sources in existing vegetation indices (VIs) and to develop a stable, modified NDVI (MNDVI) equation. The MNDVI, a closed-loop version of the NDVI, was constructed by adding 1) a soil and atmospheric noise feedback loop, and 2) an atmospheric noise compensation forward loop. The coefficients developed for the MNDVI are physically-based and are empirically related to the expected range of atmospheric and background “boundary” conditions. The MNDVI can be used with data uncorrected for atmosphere, as well as with Rayleigh corrected and atmospherically corrected data. In the field observational and simulated data sets tested, the MNDVI was found to considerably reduce noise for any complex soil and atmospheric situation. The resulting uncertainty, expressed as vegetation equivalent noise, was ±0.11 leaf area index (LAI) units, which was 7 times less than encountered with the NDVI (±0.8 LAI). These results indicate that the MNDVI may be satisfactory in meeting the need for accurate, long term vegetation measurements for the Earth Observing System (EOS) program

利用2013年12月-2014年2月山东省气温、降水资料以及500hPa 月平均位势高度场、距平场等资料，结合逐日高空环流形势，分析了山东省2013年冬季天气特点、环流特征和主要天气过程，对季内的主要天气及其影响进行了评述。