Revisiting the consistency of MODIS LAI products from a new perspective of spatiotemporal variability

Leaf Area Index (LAI) is a critical vegetation structural parameter for characterizing vegetation canopy structure. Multiple LAI products derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) observations have made a noteworthy impact on global energy fluxes, climate change, and bio...

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Veröffentlicht in:International journal of digital earth 2024-12, Vol.17 (1)
Hauptverfasser: Peng, Rui, Yan, Kai, Liu, Jinxiu, Gao, Si, Pu, Jiabin, Maeda, Eduardo Eiji, Zhu, Peng, Knyazikhin, Yuri, Myneni, Ranga B.
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Sprache:eng
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Zusammenfassung:Leaf Area Index (LAI) is a critical vegetation structural parameter for characterizing vegetation canopy structure. Multiple LAI products derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) observations have made a noteworthy impact on global energy fluxes, climate change, and biogeochemistry research. It is essential to examine the long-term performance of global LAI products and quantitatively evaluate their quality. However, traditional methods, which often rely on single overall accuracy metrics, fail to assess the ability to capture vegetation's internal heterogeneity and temporal dynamics. This study introduces a new approach by combining spatial heterogeneity and temporal stability to assess product consistency, using MOD15A2H and MYD15A2H as examples. We observed that the local spatial heterogeneity (within 5 × 5 pixels) of the two products exhibited similar distribution pattern. The MYD15A2H LAI exhibits better time-series stability and lower spatially heterogeneous anomalous fluctuations in the tropical forest region. Both products demonstrate sustainable usability in the time series, and their spatial heterogeneity exhibit consistent trends. In conclusion, this study shows the comparability and stability of MOD15A2H and MYD15A2H in terms of the spatiotemporal variability of LAI. For differences stemming from unavoidable reflectivity issues, it is recommended to filter for high-quality inversion results or to utilize reliable reanalysis datasets.
ISSN:1753-8947
1753-8955
DOI:10.1080/17538947.2024.2407045