Can interannual land surface signal be discerned in composite AVHRR data?

Cihlar, J.; Chen, J. M.; Li, Z.; Huang, Fu-Ying; Latifovic, Rasim; Dixon, Royden

doi:10.1029/98jd00050

Cited by 64 publications

(38 citation statements)

References 32 publications

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“…A steady increase in annual maximum LAI during 1981-1991 was associated with a slight advance of spring budburst and delay of autumn abscission (1). Subsequent work spanning 1981-1999 has confirmed these findings (2,9), but doubts about the validity of the trend have persisted because of the need for data corrections for instrumental and navigational drift, intercalibration of successive instruments, and consideration of aerosol effects (10). Such doubts could be dispelled if the interannual variations in greenness and growing season length were shown to be quantitatively consistent with independent expectations on the basis of climate variability and/or with independent reconstructions of changes in regional CO 2 balance.…”

mentioning

confidence: 66%

Climatic Control of the High-Latitude Vegetation Greening Trend and Pinatubo Effect

Lucht

Prentice

Myneni

et al. 2002

Science

689

508

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A biogeochemical model of vegetation using observed climate data predicts the high northern latitude greening trend over the past two decades observed by satellites and a marked setback in this trend after the Mount Pinatubo volcano eruption in 1991. The observed trend toward earlier spring budburst and increased maximum leaf area is produced by the model as a consequence of biogeochemical vegetation responses mainly to changes in temperature. The post-Pinatubo decline in vegetation in 1992-1993 is apparent as the effect of temporary cooling caused by the eruption. High-latitude CO 2 uptake during these years is predicted as a consequence of the differential response of heterotrophic respiration and net primary production.Satellite observations over the past two decades indicate a trend toward longer growing seasons and greater annual net primary production (NPP) in high latitudes (1-3). Using a dynamic vegetation model that predicts leaf area index (LAI), primary production, and net ecosystem carbon exchange from first principles (4 -7), we show that the trend and variability in the satellite observations are consistent quantitatively with independent climate data and qualitatively with net ecosystem carbon exchange, which was independently calculated from atmospheric CO 2 concentration measurements (8).We analyzed data for a 1982-1998 interval for which we had access to both climate and satellite data. A previous analysis of 10 years of data from the Advanced Very High Resolution Radiometer (AVHRR) indicated a progressive greening of the boreal zone. A steady increase in annual maximum LAI during 1981-1991 was associated with a slight advance of spring budburst and delay of autumn abscission (1). Subsequent work spanning 1981-1999 has confirmed these findings (2, 9), but doubts about the validity of the trend have persisted because of the need for data corrections for instrumental and navigational drift, intercalibration of successive instruments, and consideration of aerosol effects (10). Such doubts could be dispelled if the interannual variations in greenness and growing season length were shown to be quantitatively consistent with independent expectations on the basis of climate variability and/or with independent reconstructions of changes in regional CO 2 balance. This analysis uses a climate-driven terrestrial carbon cycle model capable of simulating the interannual variability of LAI and the components of the terrestrial carbon balance.We compare monthly LAI anomalies for the boreal zone, derived from a recent version of the AVHRR data, with monthly LAI anomalies independently predicted by the LPJ Dynamic Global Vegetation Model (LPJ-DGVM), a biogeochemical process model driven by monthly climate observations and by the global mean CO 2 concentration increase (see Materials and Methods in supporting online material). Both simulated and observed LAI anomalies show an overall increasing trend, but periods lasting several years with positive or negative deviations from the trend can be discerned in the ob...

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mentioning

confidence: 66%

Climatic Control of the High-Latitude Vegetation Greening Trend and Pinatubo Effect

Lucht

Prentice

Myneni

et al. 2002

Science

689

508

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“…However, in the NDVI time-series, there are always disturbances created by cloud contamination, atmospheric variability, bi-directional effects and sensor malfunctions [43,44]. Although Maximum Value Composite (MVC) products can partly eliminate some disturbances [22,45], the time-series NDVI data still includes many of these deviations (see Figure 1).…”

Section: Data and Processingmentioning

confidence: 99%

Investigation on the Patterns of Global Vegetation Change Using a Satellite-Sensed Vegetation Index

Deng

Liang

et al. 2010

Remote Sensing

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Abstract:The pattern of vegetation change in response to global change still remains a controversial issue. A Normalized Difference Vegetation Index (NDVI) dataset compiled by the Global Inventory Modeling and Mapping Studies (GIMMS) was used for analysis. For the period 1982-2006, GIMMS-NDVI analysis indicated that monthly NDVI changes show homogenous trends in middle and high latitude areas in the northern hemisphere and within, or near, the Tropic of Cancer and Capricorn; with obvious spatio-temporal heterogeneity on a global scale over the past two decades. The former areas featured increasing vegetation activity during growth seasons, and the latter areas experienced an even greater amplitude in places where precipitation is adequate. The discussion suggests that one should be cautious of using the NDVI time-series to analyze local vegetation dynamics because of its coarse resolution and uncertainties.

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“…They also have a strong impact on the consistency of satellite data, both within and among years. For example, Cihlar et al (1998a) found that depending on the measurement of interest (AVHRR channel 1, 2 or NDVI) and land cover type, the most important correction is the removal of contaminated pixels, atmospheric correction, or correction for bidirectional re ectance e ects caused by di erences in the source-target-sensor geometry. Thus, further pre-processing operations are necessary.…”

Section: Coarse Resolution Datamentioning

confidence: 99%

Land cover mapping of large areas from satellites: Status and research priorities

Cihlar¹

2000

International Journal of Remote Sensing

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538

220

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Abstract. Although land cover mapping is one of the earliest applications of remote sensing technology, routine mapping over large areas has only relatively recently come under consideration. This change has resulted from new information requirements as well as from new developments in remote sensing science and technology. In the near future, new data types will become available that will enable marked progress to be made in land cover mapping over large areas at a range of spatial resolutions. This paper is concerned with mapping strategies based on 'coarse' and ' ne' resolution satellite data as well as their combinations. The status of land cover mapping is discussed in relation to requirements, data sources and analysis methodologies-including pixel or scene compositing, radiometric corrections, classi cation and accuracy assessment. The overview sets the stage for identifying research priorities in data pre-processingand classi cation in relation to forthcoming improvements in data sources as well as new requirements for land cover information. Introduction and objectiveLand cover, i.e. the composition and characteristics of land surface elements, is key environmental information. It is important for many scienti c, resource management and policy purposes and for a range of human activities. It is an important determinant of land use and thus of value of land to the society. Land cover varies at a range of spatial scales from local to global, and at temporal frequencies of days to millennia. As the need for environmental planning and management became important, an accompanying call for land cover information emerged in parallel.Land cover mapping is a product of the development of remote sensing, initially through aerial photography (Colwell 1960 ). This is because 'viewing' large areas repeatedly is necessary for acquiring information about land cover. For the same reason, land cover mapping has been perhaps the most widely studied problem employing satellite data, beginning with Landsat 1. However, most of the studies using ' ne' resolution data (i.e. 20-100 m) were methodological in nature, exploring various information extraction techniques and applying these over limited areas. Applications over large areas were hampered by the lack of suitable technology, an absence of a user community with a strong need for such information, a lack of appropriate analysis methodologies, and the cost of data. Thus, at the global level, land cover data sets compiled from ground surveys or various national sources (Mathews 1983, Olson et al. 1983 were, for a number of years, the major source of information.

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Can interannual land surface signal be discerned in composite AVHRR data?

Cited by 64 publications

References 32 publications

Climatic Control of the High-Latitude Vegetation Greening Trend and Pinatubo Effect

Climatic Control of the High-Latitude Vegetation Greening Trend and Pinatubo Effect

Investigation on the Patterns of Global Vegetation Change Using a Satellite-Sensed Vegetation Index

Land cover mapping of large areas from satellites: Status and research priorities

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