中国陆地1km AVHRR数据集

介绍了中国陆地范围的长序列AVHRR数据集及处理方法。数据处理链包括辐射标定、导航定位、几何精纠正、云检测、大气纠正、双向反射纠正以及多时相数据合成等一系列过程。大气校正采用SMAC方法．利用每日的大气参数对臭氧、瑞利散射、气溶胶和水汽柱等4个主要大气因子的影响进行了纠正。利用地面能见度和水汽压信息反演气溶胶光学厚度，利用最大植被指数法合成旬数据集。完成了1991-2003年的AVHRR数据集处理，形成了标准的数据集。     

Effect of NOAA satellite orbital drift on AVHRR-derived phenological metrics

The U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center routinely produces and distributes a remote sensing phenology (RSP) dataset derived from the Advanced Very High Resolution Radiometer (AVHRR) 1-km data compiled from a series of National Oceanic and Atmospheric Administration (NOAA) satellites (NOAA-11, −14, −16, −17, −18, and −19). Each NOAA satellite experienced orbital drift during its duty period, which influenced the AVHRR reflectance measurements. To understand the effect of the orbital drift on the AVHRR-derived RSP dataset, we analyzed the impact of solar zenith angle (SZA) on the RSP metrics in the conterminous United States (CONUS). The AVHRR weekly composites were used to calculate the growing-season median SZA at the pixel level for each year from 1989 to 2014. The results showed that the SZA increased towards the end of each NOAA satellite mission with the highest increasing rate occurring during NOAA-11 (1989–1994) and NOAA-14 (1995–2000) missions. The growing-season median SZA values (44°–60°) in 1992, 1993, 1994, 1999, and 2000 were substantially higher than those in other years (28°–40°). The high SZA in those years caused negative trends in the SZA time series, that were statistically significant (at α = 0.05 level) in 76.9% of the CONUS area. A pixel-based temporal correlation analysis showed that the phenological metrics and SZA were significantly correlated (at α = 0.05 level) in 4.1–20.4% of the CONUS area. After excluding the 5 years with high SZA (>40°) from the analysis, the temporal SZA trend was largely reduced, significantly affecting less than 2% of the study area. Additionally, significant correlation between the phenological metrics and SZA was observed in less than 7% of the study area. Our study concluded that the NOAA satellite orbital drift increased SZA, and in turn, influenced the phenological metrics. Elimination of the years with high median SZA reduced the influence of orbital drift on the RSP time series.     

Assessing land cover change resulting from large surface mining development

A remote sensing based land cover change assessment methodology is presented and applied to a case study of the Oil Sands Mining Development in Athabasca, Alta., Canada. The primary impact was assessed using an information extraction method applied to two LANDSAT scenes. The analysis based on derived land cover maps shows a decrease of natural vegetation in the study area (715,094 ha) for 2001 approximately −8.64% relative to 1992. Secondary assessment based on a key resources indicator (KRI), calculated using normalized difference vegetation index (NDVI measurements acquired by NOAA–AVHRR satellites), air temperature and global radiation was performed for a time period from 1990 to 2002. KRI trend analysis indicates a slightly decreasing trend in vegetation greenness in close proximity to the mining development. A good agreement between the time series of inter-annual variations in NDVI and air temperature is observed increasing the confidence of NDVI as an indicator for assessing vegetation productivity and its sensitivity to changes in local conditions.     

Observed biases in the conterminous U.S. AVHRR satellite data set: A North Carolina case study

The USGS EROS Data Center has produced a national data set for long‐term ecological monitoring termed the “Conterminous U.S. AVHRR (Advanced Very High Resolution Radiometer) Data Set” that includes biweekly maximum‐value composite (MVC) images of NDVI (Normalized Difference Vegetation Index) values, original five channels of calibrated AVHRR satellite data, image viewing and illumination geometry, date of observation, and ancillary data sets pertaining to landcover and political boundaries (Loveland et al., 1991; Eidenshink, 1992).

The basic intent of the study was to evaluate the potential of the data set for broad‐scale, multitemporal landscape mapping by assessing the quality and sensitivity of the data set to support such applications. Potential biases existing in the data set were identified and analytical procedures suggested to deal with such biases. Results from analyses within the State of North Carolina suggest that the time series of the NDVI values is influenced by sensitivities to residual cloud contamination, preceeding climatic events, temporal and spatial scales of analyses, and the composition and spatial organization of the study area. Spatial and temporal discontinuities within and between images, irregular space‐time semivariograms, and statistical summaries of the data show the existence of biases in the data set for North Carolina. Possible adjustments to reduce this level of uncertainty include the generation of NDVI composites over longer time periods, exclusion of suspected contaminated data, or the use of spatial and temporal interpolations of contaminated values to reduce their relative impact on each composite image. Regional variations in NDVI responses to viewing and illumination geometry may also be important factors for users to consider.     

Spatial and Temporal Relationships among NDVI,Climate Factors,and Land Cover Changes in Northeast Asia from 1982 to 2009

This study uses a multiple linear regression method to composite standard Normalized Difference Vegetation Index (NDVI) time series (1982-2009) consisting of three kinds of satellite NDVI data (AVHRR, SPOT, and MODIS). This dataset was combined with climate data and land cover maps to analyze growing season (June to September) NDVI trends in northeast Asia. In combination with climate zones, NDVI changes that are influenced by climate factors and land cover changes were also evaluated. This study revealed that the vegetation cover in the arid, western regions of northeast Asia is strongly influenced by precipitation, and with increasing precipitation, NDVI values become less influenced by precipitation. Spatial changes in the NDVI as influenced by temperature in this region are less obvious. Land cover dynamics also influence NDVI changes in different climate zones, especially for bare ground, cropland, and grassland. Future research should also incorporate higher-spatial-resolution data as well as other data types (such as greenhouse gas data) to further evaluate the mechanisms through which these factors interact.     

中国陆地区域陆表温度业务化遥感反演算法及产品运行系统

地表温度反演的裂窗算法已成功应用于NOAA系列卫星热红外遥感数据。目前，裂窗算法中应用较为广泛的一种是Becker等人于1990年提出的局地裂窗算法，主要是通过辐射传输模型模拟不同地表条件和大气状况下，地表温度和发射率对红外辐射亮温的影响，从而发展出一个利用AVHRR4，5通道亮温数据反演地表温度的线性模型。在晴空无云和地表比辐射率能精确估算的情况下，Becker算法反演地表温度的精度在1K以内。Becker算法用Lowtran程序模拟计算地表辐射量，且模型中参数主要针对NOAA-9传感器特性得到。本文在Becker算法的基础上，针对NOAA-16／17传感器热红外通道光谱响应函数特性，利用最新的、计算光谱分辨率更高的MODTRAN程序模拟不同大气状况下，不同地表温度和发射率对NOAAAVHRR4，5通道辐射亮温响应特性的影响，改进Becker算法中模型参数，使之能适用于NOAA-16／17热红外数据。同时，本文利用植被指数NDVI，在中国陆地区域lkm分辨率最新地表分类数据的基础上，得到模型中需要的地表比辐射率参数，将改进的模型应用于1km分辨率NOAA17数据，得到了旬合成中国陆地区域范围地表温度，通过地面气象台站实测数据对比验证．取得了较好的结果。     

NOAA AVHRR云标识技术的应用分析

中国农情遥感速报系统对每日的NOAA AVHRR进行定量处理，其中用CLAVR方法进行云标识。本文选择三景不同地区的NOAA AVHRR影像，分析评价CLAVR方法云标识的效果和各个步骤的标识能力，以及空间上的差异，并根据运行经验，对CLAVR方法的部分参数进行了调整，使其更加适应中国大陆的情况。总体上，CLAVR方法标识干净像元和云污染像元的准确性较高，而在标识混合像元时稍差。其中RGCT、RUT、TUT和C3AT的检出率占总检出率89．2％以上，并在不同的地区，不同步骤的贡献度不一致，也说明了云相变化随区域不同而变化。     

Spatial-temporal changes of NDVI and their relations with precipitation and temperature in Yangtze River basin from 1981 to 2001

Multitemporal NOAA/AVHRR NDVI images and monthly temperature and precipitation data were obtained across Yangtze River basin covering the period 1981–2001. The spatial and temporal patterns of NDVI are the same, while spatial analysis shows that the NDVI is influenced by the vegetation types growing in the study regions, and NDVI presents an increasing trend during the study period in the whole basin. The climate indicators play an important role in the changes of vegetation cover in the river basin. In the two Indicators, temperature has a significant effect on the NDVI values than precipitation in the whole basin. However, in the 11 subbasins, the different rules are shown in different subbasins.     

Determination of the bulk temperature from NOAA/AVHRR satellite data in a midlatitude lake

The spatial and temporal variability of the bulk temperature gives important insights into biological and hydrodynamic processes. However, standard algorithms for satellite data only provide information of the surface temperature. The comparison of current and new split-window coefficients applied on NOAA-14/AVHRR brightness temperatures of Lake Constance showed that a regional adaption was most promising. To derive the bulk temperature information, a priori progression from a weather station was included into the AVHRR analysis. Among the weather is data, the mean temperature of the three preceding days and the day of the year were the most relevant additional information. By a multiple regression approach the bulk temperature in the upper 4 m of Lake Constance could be determined with an accuracy of ±1.20 °C. The training of a neural network improved the determination of the bulk temperature to ±1.04 °C.An extended field campaign demonstrated that the algorithm is also applicable to other sensors with the same spectral band settings (in this case NOAA-16/AVHRR) with an acceptable error and that it is equally accurate over the entire lake.     

Development of a 1-km landcover dataset of China using AVHRR data

This paper describes the development of a 1-km landcover dataset of China by using monthly NDVI data spanning April 1992 through March 1993. The method used combined unsupervised and supervised classification of NDVI data from AVHRR. It is composed of five steps: (a) unsupervised clustering of monthly AVHRR NDVI maximum value composites is performed using the ISOCLASS algorithm; (b) preliminary identification is carried out with the addition of digital elevation models, eco-region data and a collection of other landcover/vegetation reference data to identify the clusters with single landcover classes; (c) re-clustering is performed of clusters with size greater than a given threshold value and containing two or more disparate landcover classes; (d) cluster combining is performed to combine all clusters with a single landcover class in one cluster, and all other clusters into one mixed cluster; and (e) supervised classification is used to carry out post-classification of the mixed cluster generated in the previous step by using the maximum likelihood algorithm and the identified single landcover classes of the previous step as training data. The classification is based on extensive use of computer-assisted image processing and tools, as well as the skills of the human interpreter to take the final decisions regarding the relationship between spectral classes defined using unsupervised methods and landscape characteristics that are used to define landcover classes.     

NOAA18/AVHRR与FY-3A/VIRR的OLR产品一致性和差异性分析

卫星的射出长波辐射OLR(Outgoing Long-wave Radiation)数据具有不同程度的误差。为满足业务和科研工作的需要,对国产卫星FY-3 A/VIRR的OLR产品与其他同类卫星产品进行一致性和差异性分析是非常必要的。采用风云三号A星(FY-3A)扫描辐射计(VIRR)的OLR日平均产品作为被检验数据,美国大气海洋局NOAA18卫星上搭载的甚高分辨率扫描辐射计(AVHRR)OLR日平均产品作为检验源数据,使用相关系数、平均偏差、均方根误差、相对误差等检验方法,对两种产品进行一致性和差异性分析。结果表明,两种OLR资料大部分相关系数较大,平均偏差、均方根误差和相对误差较小,个别资料相关系数较小,平均偏差、均方根误差和相对误差较大。如2010年4月23日、7月13日和10月13日,相关系数、平均偏差、均方根误差、相对误差分别为(0.63,7 Wm^-2,31 Wm^-2,0.12)、(0.5,-5 Wm^-2,45 Wm^-2,0.125)和(0.3,-200 Wm^-2,225 Wm^-2,0.85)。这些偏差主要发生在高山和海洋地区,并且暖季相对冷季偏差较大,可能是由于高山、海洋地区和暖季较强的对流活动造成两种资料的对比结果存在一定的差异;此外,由于FY-3A为上午星,NOAA18为下午星,过境时间存在一定的差异,也给二者的对比结果带来一定的差异。     

Analysis of China vegetation dynamics using NOAA-AVHRR data from 1982 to 2001

The authors derived the normalized difference vegetation index (NDVI) from the NOAA/AVHRR Land dataset, at a spatial resolution of 8km and 15-day intervals, to investigate the vegetation variations in China during the period from 1982 to 2001. Then, GIS is used to examine the relationship between precipitation and the Normalized Difference Vegetation Index (NDVI) in China, and the value of NDVI is taken as a tool for drought monitoring. The results showed that in the study period, China’s vegetation cover had tended to increase, compared to the early 1980s; mean annual NDVI increased 3.8%. The agricultural regions (Henan, Hebei, Anhui and Shandong) and the west of China are marked by an increase, while the eastern coastal regions are marked by a decrease. The correlation between monthly NDVI and monthly precipitation/temperature in the period 1982 to 2001 is significantly positive (R2=0.80, R2=0.84); indicating the close coupling between climate conditions (precipitation and temperature) and land surface response patterns over China. Examination of NDVI time series reveals two periods: (1) 1982–1989, marked by low values below average NDVI and persistence of drought with a signature large-scale drought during the 1982 and 1989; and (2) 1990–2001, marked by a wetter trend with region-wide high values above average NDVI and a maximum level occurring in 1994 and 1998.     

海河流域NDVI对气候变化的响应研究

以海河流域为研究区,利用8 km分辨率AVHRR/NDVI数据和气象资料,逐像元对1981-2000年时段的流域NDVI值、年降水量和年均气温的变化率进行分析,计算了NDVI和年降水量、年均气温的相关关系.结果表明,1981-2000年时段内,海河流域年降水量变化总体呈现北部和南部增加,中部减少的趋势,其变化范围在-8...     

Deriving Crop Phenology Metrics and Their Trends Using Times Series NOAA-AVHRR NDVI Data

In this study, an attempt has been made to derive the spatial patterns of temporal trends in phenology metrics and productivity of crops grown, at disaggregated level in Indo-Gangetic Plains of India (IGP), which are helpful in understanding the impact of climatic, ecological and socio-economic drivers. The NOAA-AVHRR NDVI PAL dataset from 1981 to 2001 was stacked as per the crop year and subjected to Savitzky-Golay filtering. For crop pixels, maximum and minimum values of normalized difference vegetation index (NDVI), their time of occurrence and total duration of kharif (June-October) and rabi (November–April) crop seasons were derived for each crop year and later subjected to pixel-wise regression with time to derive the rate and direction of change. The maximum NDVI value showed increasing trends across IGP during both kharif and rabi seasons indicating a general increase in productivity of crops. The trends in time of occurrence of peak NDVI during kharif dominated with rice showed that the maximum vegetative growth stage was happening early with time during study period across most of Punjab, North Haryana, Parts of Central and East Uttar Pradesh and some parts of Bihar and West Bengal. Only central parts of Haryana showed a delay in occurrence of maximum vegetative stage with time. During rabi, no significant trends in occurrence of peak NDVI were observed in most of Punjab and Haryana except in South Punjab and North Haryana where early occurrence of peak NDVI with time was observed. Most parts of Central and Eastern Uttar Pradesh, North Bihar and West Bengal showed a delay in occurrence of peak NDVI with time. In general, the rice dominating system was showing an increase in duration with time in Punjab, Haryana, Western Uttar Pradesh, Central Uttar Pradesh and South Bihar whereas in some parts of North Bihar and West Bengal a decrease in the duration with time was also observed. During rabi season, except Punjab, the wheat dominating system was showing a decreasing trend in crop duration with time.     

Mapping crop phenology using NDVI time-series derived from HJ-1 A/B data

With the availability of high frequent satellite data, crop phenology could be accurately mapped using time-series remote sensing data. Vegetation index time-series data derived from AVHRR, MODIS, and SPOT-VEGETATION images usually have coarse spatial resolution. Mapping crop phenology parameters using higher spatial resolution images (e.g., Landsat TM-like) is unprecedented. Recently launched HJ-1 A/B CCD sensors boarded on China Environment Satellite provided a feasible and ideal data source for the construction of high spatio-temporal resolution vegetation index time-series. This paper presented a comprehensive method to construct NDVI time-series dataset derived from HJ-1 A/B CCD and demonstrated its application in cropland areas. The procedures of time-series data construction included image preprocessing, signal filtering, and interpolation for daily NDVI images then the NDVI time-series could present a smooth and complete phenological cycle. To demonstrate its application, TIMESAT program was employed to extract phenology parameters of crop lands located in Guanzhong Plain, China. The small-scale test showed that the crop season start/end derived from HJ-1 A/B NDVI time-series was comparable with local agro-metrological observation. The methodology for reconstructing time-series remote sensing data had been proved feasible, though forgoing researches will improve this a lot in mapping crop phenology. Last but not least, further studies should be focused on field-data collection, smoothing method and phenology definitions using time-series remote sensing data.     

基于HJ-1A-B卫星影像的污水灌区植被指数研究

污水灌区植被指数的研究对污水排放、生态保护等方面具有指导意义。利用山东省龙口市2008-2010年21个时相的中国环境与灾害监测预报小卫星(HJ-1A-B)CCD影像数据,选取易于比较的典型污水灌区和清水灌区,提取NDVI平均值,得到3年同期的数据,进行横向和纵向的量化比较。结果表明,NDVI能够动态监测植被变化,污水灌区与清水灌区内的植被指数随时间的变化趋势基本一致,但污水灌区内的植被指数呈现逐年递减的变化趋势;生活污水灌区内的植被表现出早熟现象,工业废水灌区内的植被指数是所有研究区内最低的。     

Remote Sensing of Spatial and Temporal Vegetation Dynamics in Hispaniola: A Comparison of Haiti and the Dominican Republic

Abstract

Spatial and temporal vegetation contrasts between the nations of Haiti and the Dominican are analyzed using NDVI data derived from 30m resolution Landsat imagery and 8km resolution AVHRR imagery from the NOAA / NASA Pathfinder database. Analysis of vegetation dynamics in the Hispaniola border region indicates denser vegetation cover and a stronger correlation between elevation, slope, and NDVI on the Dominican side of the frontier. Temporal patterns of NDVI dynamics along the frontier suggest that changes in biomass are both more homogeneous and more extreme on the Haitian side. Analysis of 17 years of 8km resolution AVHRR imagery for the entire island of Hispaniola reveals consistently higher NDVI values for the Dominican Republic and a distinct intra‐annual pattern of mean monthly NDVI deviations that have important implications for future studies of vegetation dynamics in the region.     

资料误差对大尺度NDVI-气温关系的影响

大量研究利用PathfinderAVHRR NDVI资料分析植被状况与气温、降水等气候要素之间的关系。许多分析指出Pathfinder资料包含误差 ,并分析这些资料误差对大尺度NDVI 气温耦合关系检测结果的影响。利用奇异值分解方法 (SVD) ,通过比较不同NDVI资料误差情况下北半球春季NDVI对气温变化响应的时空特征的差异 ,对资料误差造成的分析结果的可靠性进行判断。考虑了 4种误差形式 ,分别是不同强度的连续误差、不连续误差、强火山喷发造成的误差及趋势误差。分析结果表明 ,利用SVD分析大尺度的NDVI 气温耦合特征时 ,允许的NDVI资料误差的最大上限阈值大致在 0 5σ左右。PathfinderAVHRR NDVI原始资料包含的误差很可能低于此阈值 ,得到的分析结果有较高的可信度。此外 ,在不知道NDVI原始资料误差的情况下进行植被对气候变化响应的检测时 ,可以借鉴此方法对结果的可靠性进行检查和验证。     

多时相MODIS影像水田信息提取研究

水稻种植及其分布信息是土地覆被变化、作物估产、甲烷排放、粮食安全和水资源管理分析的重要数据源。基于遥感的水田利用监测中,通常采用时序NDVI植被指数法和影像分类法分别进行AVHRR和TM影像的水田信息获取。针对8天合成MODIS陆地表面反射比数据的特点和水稻生长特征,选取水稻种植前的休耕期、秧苗移植期、秧苗生长期和成熟期等多时相MODIS地表反射率影像数据,通过归一化植被指数、增强植被指数及利用对土壤湿度和植被水分含量较敏感的短波红外波段计算得到的陆表水指数进行水田信息获取。将提取结果与基于ETM＋影像的国土资源调查水田数据,通过网格化计算处理并进行对比分析,结果表明,利用MODIS影像的8天合成地表反射率数据,进行区域甚至全国的水田利用监测是可行的。     

Comparison of eMODIS and MOD/MYD13A2 NDVI products during 2012–2014 spring green-up periods in Alaska and northwest Canada

Accurate monitoring of vegetation dynamics is required to understand the inter-annual variability and long term trends in terrestrial carbon exchange in tundra and boreal ecoregions. In western North America, two Normalized Vegetation Index (NDVI) products based on spectral reflectance data from the Moderate Resolution Imaging Spectroradiometer (MODIS) are available. The MOD/MYD13A2 NDVI product is available as a 16-day composite product in a sinusoidal projection as global hdf tiles. The eMODIS Alaska NDVI product is available as a 7-day composite geotif product in a regional equal area conic projection covering Alaska and the entire Yukon River Basin. These two NDVI products were compared for the 2012–2014 late May–late June spring green-up periods in Alaska and the Yukon Territory. Relative to the MOD/MYD13A2 NDVI product, it is likely that the eMODIS NDVI product contained more cloud-contaminated NDVI values. For example, the MOD/MYD13A2 product flagged substantially fewer pixels as “good quality” in each 16-day composite period compared to the corresponding MODIS Alaska NDVI product from a 7-day composite period. During the spring green-up period, when field-based NDVI increases, the eMODIS NDVI product averaged 43 percent of pixels that declined by at least 0.05 NDVI between 2 composite periods, consistent with cloud-contamination problems, while the MOD/MYD13A2 NDVI averaged only 6 percent of pixels. Based on a cloudy Landsat-8 scene, the eMODIS compositing process selected 23 percent pixels, while the MOD/MYD13A2 compositing process selected less than 0.003 percent pixels. Based on the results, it appears that the MOD/MYD13A2 NDVI product is superior for scientific applications based on NDVI phenology in the tundra and boreal regions of northwestern North America.