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Effect of orbital drift and sensor changes on the time series of AVHRR vegetation index data

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6 Author(s)
R. K. Kaufmann ; Dept. of Geogr., Boston Univ., MA, USA ; L. Zhou ; Y. Knyazikhin ; V. Shabanov
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This paper assesses the effect of changes in solar zenith angle (SZA) and sensor changes on reflectances in channel 1, channel 2, and normalized difference vegetation index (NDVI) from the advanced very high resolution radiometer (AVHRR) Pathfinder land data set for the period July 1981 through September 1994. First, the effect of changes in SZA on channel reflectances and NDVI is derived from equations of radiative transfer in vegetation media. Starting from first principles, it is rigorously shown that the NDVI of a vegetated surface is a function of the maximum positive eigenvalue of the radiative transfer equation within the framework of the theory used and its assumptions. A sensitivity analysis of this relation indicates that NDVI is minimally sensitive to SZA changes, and this sensitivity decreases as leaf area increases. Second, statistical methods are used to analyze the relationship between SZA and channel reflectances or NDVI. It is shown that the use of ordinary least squares can generate spurious regressions because of the nonstationary property of time series. To avoid such a confusion, the authors use the notion of cointegration to analyze the relation between SZA and AVHRR data. Results are consistent with the conclusion of theoretical analysis from equations of radiative transfer. NDVI is not related to SZA in a statistically significant manner except for biomes with relatively low leaf area. From the theoretical and empirical analysis, they conclude that the NDVI data generated from the AVHRR Pathfinder land data set are not contaminated by trends introduced from changes in solar zenith angle due to orbital decay and changes in satellites (NOAA-7, 9, 11). As such, the NDVI data can be used to analyze interannual variability of global vegetation activity.

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IEEE Transactions on Geoscience and Remote Sensing  (Volume:38 ,  Issue: 6 )