Summary Results from:

Improving the Quality of MODIS LAI Products by Exploiting Spatiotemporal Correlation Information
As they relate to the validation of MOD15

Authors: Wang, J., Yan, K., Gao, S., Pu, J., Liu, J., Park, T., Bi, J., Maeda, E.E., Heiskanen, J., Knyazikhin, Y., Myneni, R.

Source: IEEE Transactions on Geoscience and Remote Sensing

Link to: Access Publication


The Moderate Resolution Imaging Spectroradiometer (MODIS) leaf area index (LAI) product is critical for global terrestrial carbon monitoring and ecosystem modeling. However, MODIS LAI is calculated on a pixel-by-pixel and day-by-day basis without using spatial or temporal correlation information, which leads to its high sensitivity of LAI to uncertainties in observed reflectance, resulting in an increased noise level in time series. While exploiting prior knowledge is a common practice to fill gaps in observations, little research has been conducted on reducing noisy fluctuations and improving the overall quality of the MODIS LAI product. To address this issue, we proposed a spatiotemporal information composition algorithm (STICA), which directly introduces prior spatiotemporal correlation and multiple quality assessment (MQA) information into the existing MODIS LAI product. STICA reduces the noise level and improves the quality of the product while maintaining the original physically based (radiative transfer model, RTM) LAI production process. In our analysis, the R2 increased from 0.79 to 0.81 and the root-mean-square error (RMSE) decreased from 0.81 to 0.68 compared to the ground-based LAI reference. The improvement was more pronounced with the degradation of the data quality. STICA reduced noisy fluctuations in the LAI time series to varying degrees among eight biome types. In the Amazon forest, STICA significantly improved the time-series stability of LAI. Moreover, STICA can effectively eliminate abnormal declines in time series and correct for extreme outliers in LAI. We expect that the MODIS LAI reanalyzed product generated by this method will better support the application of high-quality LAI datasets.