Objective assessment of the NOAA global vegetation index data product

Abstract

For the last 10 years the U.S. National Oceanic and Atmospheric Administration has produced an experimental Global Vegetation Index (GVI) data set for terrestrial vegetation research. These data, sampled from advanced very high resolution radiometer (AVHRR) observations, have served as a primary stimulus for global-scale vegetation research but have, so far, not been adequately evaluated. This study reviews the GVI production procedures and compares the resultant observations with a more comprehensive compilation of the AVHRR data being produced at the NASA Goddard Space Flight Center. There are many aspects of the GVI production procedures which could be improved to achieve the desired objectives. In particular, the mapping and sampling procedures employed provide measurements which only approximate the observed GAC measurements. The GVI NDVI record varies more than ±NDVI units (～ 7 per cent of signal) from the GAC record and, in general, seriously underestimates the GAC NDVI measurements. The NDVI portion of the GVI record is compromised through use of digital numbers rather than calibrated reflectance. NDVI measurements from the calibrated channels of the GVI data set produces values that compare favourably with the GAC measurements, but with considerable residual variance. Calculation of a 3 by 3 pixel average of the GVI NDVI measurements reduces residual variance between the data sets to ±0.018 NDVI units (～3 per cent of signal). Decay of sensor calibration and orbital overpass time, experienced by all the AVHRR sensors, as well as differences in these parameters between the sensors are not addressed but the results suggest the importance of accounting for these factors. These results indicate that GVI data sets, following adequate reprocessing, provide reasonable estimates of major regional contrasts in vegetation activity but should not be employed to evaluate local or minor trends.     

NOAA AVHRR and its uses for rainfall and evapotranspiration monitoring

Abstract

NOAA-7 Advanced Very High Resolution Radiometer (AVHRR) Global Vegetation Indices (GVI) were used during the 1986 rainy season (June-September) over Senegal to monitor rainfall. The satellite data were used in conjunction with ground-based measurements so as to derive empirical relationships between rainfall and GVI. The regression obtained was then used to map the total rainfall corresponding to the growing season, yielding good results. Normalized Difference Vegetation Indices (NDVI) derived from High Resolution Picture Transmission (HRJT) data were also compared with actual evapotranspiration (ET) data and proved to be closely correlated with it with a time lapse of 20 days.     

Lowest-order correction to GVI data for solar zenith angle effect

Abstract

The aims of this presentation were (i) to simulate the solar zenith angle effect on the Global Vegetation Index (GVI), (ii) to derive an expression for removing such an effect from the GVI data by the above simulation procedure and (iii) to apply this relation to the GVI data obtained from the NOAA-AVHRR imagery.     

WinCC Global Script在数据存储中的应用

为了解决监控系统中过程数据的处理，通过编写WinCC的G16bal Script，提出了一种新的过程数据记录方法。将过程变量按记录时间生成日志文件和过程数据记录文件。使得对于过程数据的处理更为方便。通过自动生成的索引文件用户可以随时根据需要添加、删除记录的变量，而不需改动Global Script，使得该方法具有扩展性和可移植性。通过G1obal Script处理数据，使得数据采集周期由500ms缩短到250ms。该数据记录方法在实际工程应用中取得了良好效果．可以推广和移植到其他监控系统的数据记录中。     

Global data sets for land applications from the Advanced Very High Resolution Radiometer: an introduction

The Advanced Very High Resolution Radiometer (AVHRR) has become one of the most important sensors for monitoring the terrestrial environment at resolutions of 1 km to very coarse resolutions of 15 km and greater. To make these data suitable for scientific and other applications considerable effort has been devoted to the creation of global data sets. Experience has demonstrated that even for a relatively simple sensor such as the AVHRR, the task of creating global data set is fraught with difficulties and that a number of iterations have been necessary despite considerable efforts in the specification of users' requirements

Four types of data processing streams, overlapping in time, have occurred in the creation of global data sets from the AVHRR. The first three data processing streams were all based on the reduced resolution, Global Area Coverage (GAC) data set, which is collected globally every day. In the first data processing stream a much reduced data set was created in the form of the Global Vegetation Index (GVI) product: revised improved versions of the product have been produced. In the second data processing stream an improved product was created by workers at NASA's Goddard Space Flight Center with higher spatial resolution but which until recently has only been available by continent. This has resulted in the creation of a number of regional data sets. In the third data processing stream operational creation of global data sets at moderately coarse resolution (c. 8 km) is being initiated. The most notable example of this data processing stream is part of NASA's Pathfinder project and stems in large part directly from the second data processing stream: it will involved production of a reprocessed improved global data set for the period from 1982 to the present. In the fourth data processing stream the full potential of the AVHRR in terms of its spatial resolution is being realized, through the generation of a global data set at 1 1 km resolution data.     

A multi-year composite of GVI data as a clear reflectance database

A method is developed to generate a top of the atmosphere clear reflectance from the Global Vegetation Index (GVI) product. Our goal is to use this dataset as a threshold to be applied to the forthcoming POLDER observations, for operational cloud detection. The method is based on the hypothesis that clouds add a high frequency signal to the slow variations of the surface reflectance in clear conditions. The validity of our algorithm is verified through an analysis of the temporal profile of the reflectance that it generates. We show that these profiles are better than those resulting from the simpler Maximum Value Composite (MVC) method. The method is applied to five years of GVI products and the results are used to derive a reference database which accounts for the interannual variability of the surface reflectance.     

Monitoring global vegetation dynamics with ERS-1 wind scatterometer data

ERS-1 wind scatterometer (WSC) data is analysed over a wide range of terrain types for the period May 1992-April 1994. Comparison is made with Global Vegetation Index (GVI) data for the monitoring of vegetation dynamics. Results show that WSC data display a well-pronounced seasonality over most vegetated surfaces. The highest sensibility to vegetation dynamics is found over semi-arid regions and boreal zones. In these two cases, there is a marked seasonality in environmental parameters which is well depicted by σ⁰ temporal profiles. However, the sensibility of the ERS-1 response is much less pronounced over densely vegetated surfaces. In yspite of its low spatial resolution, the usefulness of a C-band scatterometer for monitoring vegetation dynamics is shown.     

Temporal stability of some global NDVI products derived from NOAA/AVHRR GVI

The Normalized DilTerence Vegetation Index (NDVI) derived from NOAA's Advanced Very High Resolution Radiometer (AVHRR) has been widely used in monitoring continental and global vegetation distribution and dynamics, drought severity and location, and environmental deterioration. Since 1982, NOAA has produced the Weekly Global Vegetation Index (GVI) product from AVHRR. The analyses of the GVI product have revealed many problems due to the simplified radiometric correction involved in the processing. Those limitations have inspired several elTorts to reprocess the NOAA GVI data sets to produce an improved representation of global NDVI patterns. In this paper, the quality of three Global NDVI products resulting from very simple to rather sophisticated reprocessing was examined by using a global approach. In general, the quality of data improves with increasing sophistication of radiometric correction. However, this study reveals some significant errors common in all three products assessed. The problems include a systematic annual increase in values computed from a single satellite and jumps between consecutive satellites. These errors are large enough to alTect results of the long term time-series analyses. This pattern suggests an additional radiometric distortion in NOAA/ AVHRR data. It is found that the values computed from data of the first year after satellite launch are roughly the same statistically for NOAA satellites. Thus, the discontinuity ofNDVls between satellites appears to be mainly caused by the systematic drift. Therefore, data collected in the first year of satellite launch might be considered as a baseline for correcting the systematic errors. By comparing NDVI from the first year of satellites in space, it is also found that NDVI increases at higher latitude and decreases or keeps constant at lower latitude. This change of NDVI with time might signal the change of global climate.     

Fourier analysis of multi-temporal AVHRR data applied to a land cover classification

A signal processing technique is presented and applied to annual patterns of the Global Vegetation Index (GVI) derived from the Advanced Very High Resolution Radiometer (AVHRR) to examine the frequency distribution of the multi-temporal signal. It is shown that frequencies of the signal are linked to integrated GVI, seasonal variability and subseasonal variability of the land cover type. These characteristics are used to derive a land cover classification.     

Comparison of SPOT cloud cover quality index against meteorological surface observations

A quality index, based on cloud contamination within the image, is assigned to each SPOT scene. In this study, the information content, of such a quality index, in terms of cloud cover, was tested by comparing it with coincident meteorological surface observation. Global observations during the period June-October 1995 were analysed, starting with the investigation of the spatial and temporal variability of cloud covers from both kinds of observations. Comparisons for a series of 'dependent' datasets generated by varying the time (from 0.5 to 1.5 h) and space (from 5 to 100 km) matching criteria were performed successively. Results were reported as 5 x 9 cloud-class contingency tables and relevant statistics evaluating the correlation between the two observations. The analyses showed an overall good agreement between the two cloud-cover estimations, the correlation was slightly lower (10%) than that obtained by comparing SYNOP against SYNOP. Compared with SYNOP observations, SPOT tended to slightly underestimate cases of broken cloud-cover. One of the most important sources of disagreement was the lack of quantitative information in the three-class cloud quality index code found in 30% of the SPOT images used in this study. When processed as described here, the information contained in the SPOT cloud cover quality index is consistent with the surface observation of cloud cover.     

Effects of atmospheric variation on AVHRR NDVI data

The AVHRR (Advanced Very High Resolution Radiometer) series of instruments has frequently been used for vegetation studies. The 25+ year record has enabled important time-series studies. Many applications use NDVI (Normalized Difference Vegetation Index), or derivatives of it, as their operational variable. However, most AVHRR datasets have incomplete atmospheric correction, because of which there is considerable, but largely unknown, uncertainty in the significance of differences in NDVI and other short wave observations from AVHRR instruments.The purpose of this study was to gain better understanding of the impact of incomplete or lack of atmospheric correction in widely-used, publicly available processed AVHRR-NDVI long-term datasets. This was accomplished by comparison with atmospherically corrected AVHRR data at AERONET (AErosol RObotic NETwork) sunphotometer sites in 1999. The datasets included in this study are: TOA (Top Of Atmosphere) that is with no atmospheric correction; PAL (Pathfinder AVHRR Land); and an early version of the new LTDR (Long Term Data Record) NDVI. The other publicly available datasets like GIMMS (Global Inventory Modeling and Mapping studies) and GVI (Global Vegetation Index) have atmospheric error budget similar to that of TOA, because no atmospheric correction is used in either processing stream. Of the three datasets, LTDR was found to have least errors (accuracy = 0.0064 to − 0.024, precision = 0.02 to 0.037 for clear and average atmospheric conditions) followed by PAL (accuracy = − 0.145 to − 0.035, precision = 0.0606 to 0.0418), and TOA (accuracy = − 0.0791 to − 0.112, precision = 0.0613 to 0.0684). It was also observed that temporal maximum value compositing technique does not cause significant improvement of precision in regions experiencing persistently high AOT (Aerosol Optical Thickness).     

The availability of cloud-free Landsat ETM+ data over the conterminous United States and globally

The U.S. Landsat satellite series provide the longest dedicated land remote sensing data record with a balance between requirements for localized high spatial resolution studies and global monitoring. As with any other optical wavelength satellite sensor, cloud contamination greatly compromises image usability for land surface studies. Additionally, selective scene acquisition due to payload, ground station and mission cost constraints further reduces Landsat image availability. Since the 1999 launch of the Landsat Enhanced Thematic Mapper Plus (ETM+) a Long-term Acquisition Plan (LTAP) has been used to anticipate user requests with the goal of annually refreshing a global daytime archive of cloud-free ETM+ data. This research evaluates the availability of cloud-free Landsat ETM+ data over the conterminous U.S. and globally using 3 years of ETM+ cloud fraction metadata archived by the U.S. Landsat project. Landsat application requirements including obtaining at least one cloud-free observation in a year, a season, and two different seasons, or at least a pair of cloud-free observations occurring no more than 16, 32, 48, 64, and 80 days apart within a year and season are considered. Probabilistic analyses indicate that over the conterminous U.S., land applications requiring at least one cloud-free observation in a year, a season, two different seasons, or at least two cloud-free observations occurring within any period of the year, are on average largely unaffected by cloud cover, except for certain Winter applications and cloudy scenes near the U.S.-Canada border and the Great Lakes. Cloud becomes a constraint when at least two cloud-free observations are required from the same season over the conterminous U.S., especially when the separation between observations is restricted to short time intervals. Global applications requiring at least one cloud-free observation in a season, in two different seasons, and applications requiring at least two cloud-free observations in a year, are all severely affected by cloud and data availability constraints; and globally it is generally not practical to consider land applications that require at least two cloud-free observations in any season. Globally, only land applications requiring at least one cloud-free observation per year are largely unaffected by cloud cover and the reduced global ETM+ data availability. These results are specific only to the U.S. Landsat ETM+ archive; they suggest the need for an increased global Landsat acquisition rate for the current and future Landsat missions and/or the development of new approaches to mitigating cloud contamination in the U.S. global Landsat ETM+ archive.     

Global vegetation dynamics: satellite observations over Asia

Abstract

The weekly global vegetation index (GVI) derived from the NOAA AVHRR instrument has been analysed for the 1982-1985 period over a wide range of vegetation formations of Asia. Temporal development curves of the index are presented for environments ranging from the desert of central Asia to the tropical forest of Borneo. The paper shows that, despite the coarse resolution of the GVI product, a large set of useful information on ecosystem dynamics and cropping practices can be consistently derived from time series of such data. In addition, it is shown that the impact of the 1982-1983 El Nino Southern Oscillation-related drought can be detected in the GVI data through an analysis of anomalies in the development of selected vegetation formations. The relevance of such analysis for global vegetation monitoring and change detection is then underlined.     

Identification of carbon quantifiable regions in the former Soviet Union using unsupervised classification of AVHRR global vegetation index images

Global Vegetation Index (GVI) data from the Advanced Very High Resolution Radiometer (AVHRR) was used to identify macro-scale vegetation/ land cover regions in the former Soviet Union (FSU). These regions are a better representation of surface vegetation and land cover than can be obtained from existing thematic maps of the FSU. Image classes were identified through cluster analysis using the ISODATA clustering algorithm and a maximum likelihood classifier. Qualitative analysis of the image variants produced with different input parameters indicated that an image with 42 classes best represented significant details in vegetation and land cover patterns without producing uninterpretable levels of details that represent artefacts of the clustering algorithm. Initial identification of image classes has been made by considering the weight of evidence provided by quantitative and qualitative analysis of existing maps, analytical tools from class statistics, ancillary data from a variety of sources and expert assessment by Russian scientists with extensive field experience in the FSU. Overall, this method of image classification using GVI data appears to describe accurately regions with similar vegetation and hind cover across the FSU. Some questions regarding the identification of wetlands and potential problems with classification in the Russian high arctic are discussed. The products of this research will help improve carbon budget estimates of the FSU by providing accurate delineation and definition of carbon quantifiable regions.     

Total ozone depletion over Greece as deduced from satellite observations

The reprocessed (version 7) daily total ozone observations made by the Total Ozone Mapping Spectrometer (TOMS) on the Nimbus-7 satellite over Athens (37.6 N, 23.4 E) for the period from November 1978 until April 1993 have been used to investigate total ozone depletion. To estimate the trends in total ozone content a linear fitting to the data has been applied, given that the other components like the quasi-biennial oscillation, the El Nino/Southern Oscillation and the solar cycle have a very small contribution to the total ozone depletion effects over that geographical region. The total ozone depletion over the 15-year period was derived from version 7 shows a strong seasonal variation from more than 6% in winter and early spring to about 1.5% in summer. The total ozone depletion over Greece is found to be about 1% (per decade) less using version 7 than using version 6.     

An improved in-situ bio-optical data set for ocean color algorithm development and satellite data product validation

Global satellite ocean color instruments provide the scientific community a high-resolution means of studying the marine biosphere. Satellite data product validation and algorithm development activities both require the substantial accumulation of high-quality in-situ observations. The NASA Ocean Biology Processing Group maintains a local repository of in-situ marine bio-optical data, the SeaWiFS Bio-optical Archive and Storage System (SeaBASS), to facilitate their ocean color satellite validation analyses. Data were acquired from SeaBASS and used to compile a large set of coincident radiometric observations and phytoplankton pigment concentrations for use in bio-optical algorithm development. This new data set, the NASA bio-Optical Marine Algorithm Data set (NOMAD), includes over 3400 stations of spectral water-leaving radiances, surface irradiances, and diffuse downwelling attenuation coefficients, encompassing chlorophyll a concentrations ranging from 0.012 to 72.12 mg m^− 3. Metadata, such as the date, time, and location of data collection, and ancillary data, including sea surface temperatures and water depths, accompany each record. This paper describes the assembly and evaluation of NOMAD, and further illustrates the broad geophysical range of stations incorporated into NOMAD.     

Biomass estimation on grazed and ungrazed rangelands using spectral indices

Aboveground biomass was estimated on the shortgrass steppe of Eastern Colorado using Landsat TM Tasseled Cap green vegetation index (GVI), brightness index (BI), and wetness index (WI), the normalized difference vegetation index (NDVI) and the red waveband (RED), for two grazing treatments (moderately grazed or ungrazed). Field measurements of standing crop were obtained on six sites per grazing treatment. Ordinary least squares regression models of biomass as a function of one or more indices were tested for grazed, ungrazed, and combined grazed and ungrazed data. Biomass from grazed sites was linearly related to GVI, NDVI, WI, and RED indices (R2 0.62-0.67). Ungrazed sites produced no significant relations. With combined ungrazed and grazed data, biomass was not significantly related to GVI, NDVI, WI, or BI, and was poorly related to the RED index (R2 0.35). When grazing treatments were treated as dummy variables for the combined data, the RED index was moderately related to biomass (R2 0.70). These results suggest that information about grazing utilization is useful for estimating aboveground biomass in rangelands. The RED index appears to be sensitive to biomass variations for green vegetation and to a lesser extent dry or senescent vegetation when relatively bright soil backgrounds are present which is often the case for semi-arid environments such as the shortgrass steppe.     

Land cover mapping of North and Central America—Global Land Cover 2000

The Land Cover Map of North and Central America for the year 2000 (GLC 2000-NCA), prepared by NRCan/CCRS and USGS/EROS Data Centre (EDC) as a regional component of the Global Land Cover 2000 project, is the subject of this paper. A new mapping approach for transforming satellite observations acquired by the SPOT4/VGTETATION (VGT) sensor into land cover information is outlined. The procedure includes: (1) conversion of daily data into 10-day composite; (2) post-seasonal correction and refinement of apparent surface reflectance in 10-day composite images; and (3) extraction of land cover information from the composite images. The pre-processing and mosaicking techniques developed and used in this study proved to be very effective in removing cloud contamination, BRDF effects, and noise in Short Wave Infra-Red (SWIR). The GLC 2000-NCA land cover map is provided as a regional product with 28 land cover classes based on modified Federal Geographic Data Committee/Vegetation Classification Standard (FGDC NVCS) classification system, and as part of a global product with 22 land cover classes based on Land Cover Classification System (LCCS) of the Food and Agriculture Organisation. The map was compared on both areal and per-pixel bases over North and Central America to the International Geosphere-Biosphere Programme (IGBP) global land cover classification, the University of Maryland global land cover classification (UMd) and the Moderate Resolution Imaging Spectroradiometer (MODIS) Global land cover classification produced by Boston University (BU). There was good agreement (79%) on the spatial distribution and areal extent of forest between GLC 2000-NCA and the other maps, however, GLC 2000-NCA provides additional information on the spatial distribution of forest types. The GLC 2000-NCA map was produced at the continental level incorporating specific needs of the region.     

Oracle数据库日志文件损坏时修复方法的实验研究

Oracle数据库日志文件记录了对数据库进行的所有操作,而日志文件又分为重做日志文件和归档日志文件.重做日志可用于进行实例恢复,但如果数据文件意外丢失或损坏,则必须要用到归档日志.针对归档或非归档日志文件损坏或丢失时,数据库发生故障的情况,用模拟故障的方法研究了不同情况下的修复方法.     

Retrospective analysis of Suomi NPP VIIRS radiometric bias for reflective solar bands due to operational calibration changes

A comprehensive analysis of Visible Infrared Imaging Radiometer Suite (VIIRS) radiometric bias relative to Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) collection 6 data has been performed since early 2012 for selected reflective solar bands. The study suggests that VIIRS bias trends changes over time mainly due to calibration updates and anomalies. Results show nearly consistent biases of 1.7% for M5 (0.672 µm) and 2% for M7 (0.865 µm) throughout the mission. However, M1 (0.412 µm) and M4 (0.555 µm) biases are less consistent. While biases for both M1 and M4 fluctuates mostly around 0%, M1 shows most frequent short-term changes in bias trends, as high as 4%. When the bias trends are compared with VIIRS on- board-calibration-based gain trends, there exists a high correlation. In addition, the operational VIIRS data product of NOAA and the reprocessed NASA Land Product Evaluation and Test Element (PEATE) data were compared by trending the radiance ratio. The ratio trends show calibration differences that agree well with bias trends. The comparison of bias with F-factors and ratio trends indicates that the frequent changes observed in VIIRS bias trends are primarily caused by calibration updates and anomalies in VIIRS operational calibration. The study suggests that even though the operational VIIRS data archive meets the specification of ±2% radiometric uncertainty, reprocessing can improve data quality needed for rigorous scientific applications.