26 Oct 2023
 | 26 Oct 2023
Status: this preprint is currently under review for the journal ESSD.

HiQ-LAI: A High-Quality Reprocessed MODIS LAI Dataset with Better Spatio-temporal Consistency from 2000 to 2022

Kai Yan, Jingrui Wang, Rui Peng, Kai Yang, Xiuzhi Chen, Gaofei Yin, Jinwei Dong, Marie Weiss, Jiabin Pu, and Ranga B. Myneni

Abstract. Leaf Area Index (LAI) is a crucial parameter for characterizing vegetation canopy structure and energy absorption capacity. The Moderate Resolution Imaging Spectroradiometer (MODIS) LAI has played a significant role in landmark studies due to its clear theoretical basis, extensive historical time series, reliable validation results, and open accessibility. However, MODIS LAI retrievals are calculated independently for each pixel and a specific day, resulting in high noise levels in the time series and limiting its applications. Existing reprocessing studies for MODIS LAI predominantly rely on temporal information to achieve smoother LAI profiles, with little use of spatial information. This may not only easily ignore genuine LAI anomalies but also likely lead to certain overfitting issues. To address these problems, we designed the Spatio-Temporal Information Compositing Algorithm (STICA) for the reprocessing of MODIS LAI products. This method integrates information from multiple dimensions, including pixel quality information, spatio-temporal correlation, and original retrieval, and thus enables both "reprocessing" and "data value-added" of the existing MODIS LAI products, leading to the development of the High-Quality LAI (HiQ-LAI) dataset. Compared to ground measurements, HiQ-LAI shows better performance than the original MODIS product with Root-Mean-Square Error (RMSE) / Bias decreased from 0.87 / -0.17 to 0.78 / -0.06. This is due to the improvement of HiQ-LAI in capturing the seasonality of vegetation phenology and in reducing time-series abnormal fluctuations. The Time-series Stability (TSS) index which represents temporal stability, indicated that the area with smooth LAI time-series expanded from 31.8 % (MODIS) to 78.8 % (HiQ) globally, and this improvement is more obvious in equatorial regions where optical remote sensing usually cannot achieve good performance. We found that the HiQ-LAI demonstrates superior continuity and consistency compared to raw MODIS LAI from both spatial and temporal perspectives. We anticipate that the global HiQ-LAI time-series, generated by the STICA procedure on the Google Earth Engine (GEE) platform, will significantly enhance support for diverse global LAI time-series applications. The 500 m/5 km-8 days HiQ-LAI dataset from 2000 to 2022 is available at (Yan et al., 2023).

Kai Yan et al.

Status: open (until 09 Dec 2023)

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  • RC1: 'Comment on essd-2023-410', Anonymous Referee #1, 21 Nov 2023 reply

Kai Yan et al.

Data sets

A High-Quality Reprocessed MODIS Leaf Area Index Dataset (HiQ-LAI) Kai Yan, Jingrui Wang, Marie Weiss, Ranga B. Myneni

Kai Yan et al.


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Short summary
Variations in observation conditions led to poor spatiotemporal consistency in LAI curves. We introduced prior knowledge and leveraged high-quality observations and spatiotemporal correlation to reprocess MODIS LAI and generated the High-Quality Reprocessed LAI (HiQ-LAI) which exhibits fewer abnormal fluctuations in time series. The reprocessing is conducted on GEE, providing users with convenient access to this value-added data and facilitating large-scale research and applications.