Uncertainty assessment of the moderate resolution imaging spectroradiometer (MODIS) leaf area index (LAI) and the fraction of photosynthetically active radiation absorbed by vegetation (FPAR) retrieval algorithm can provide a scientific basis for the usage and improvement of this widely-used product. Previous evaluations generally depended on the intercomparison with other datasets as well as direct validation using ground measurements, which mix the uncertainties from the model, inputs, and assessment method. In this study, we adopted the evaluation method based on three-dimensional radiative transfer model (3D RTM) simulations, which helps to separate model uncertainty and other factors. We used the well-validated 3D RTM LESS (large-scale remote sensing data and image simulation framework) for a grassland scene simulation and calculated bidirectional reflectance factors (BRFs) as inputs for the LAI/FPAR retrieval. The dependency between LAI/FPAR truth and model estimation serves as the algorithm uncertainty indicator. This paper analyzed the LAI/FPAR uncertainty caused by inherent model uncertainty, input uncertainty (BRF and biome classification), clumping effect, and scale dependency. We found that the uncertainties of different algorithm paths vary greatly (−6.61% and +84.85% bias for main and backup algorithm, respectively) and the “hotspot” geometry results in greatest retrieval uncertainty. For the input uncertainty, the BRF of the near-infrared (NIR) band has greater impacts than that of the red band, and the biome misclassification also leads to nonnegligible LAI/FPAR bias. Moreover, the clumping effect leads to a significant LAI underestimation (−0.846 and −0.525 LAI difference for two clumping types), but the scale dependency (pixel size ranges from 100 m to 1000 m) has little impact on LAI/FPAR uncertainty. Overall, this study provides a new perspective on the evaluation of LAI/FPAR retrieval algorithms.

Leaf area index (LAI) and fraction of photosynthetically active radiation (FPAR) are critical biophysical parameters for the characterization of terrestrial ecosystems. Long-term global LAI/FPAR products, such as the moderate resolution imaging spectroradiometer (MODIS) and the Visible Infrared Imaging Radiometer Suite (VIIRS), provide the fundamental dataset for accessing vegetation dynamics and studying climate change. However, existing global LAI/FPAR products suffer from several limitations, including spatial–temporal inconsistencies and accuracy issues. Considering these limitations, this study develops a sensor-independent (SI) LAI/FPAR climate data record (CDR) based on Terra-MODIS/Aqua-MODIS/VIIRS LAI/FPAR standard products. The SI LAI/FPAR CDR covers the period from 2000 to 2022, at spatial resolutions of 500 m/5 km/0.05^∘, 8 d/bimonthly temporal frequencies and available in sinusoidal and WGS1984 projections. The methodology includes (i) comprehensive analyses of sensor-specific quality assessment variables to select high-quality retrievals, (ii) application of the spatial–temporal tensor (ST-tensor) completion model to extrapolate LAI and FPAR beyond areas with high-quality retrievals, (iii) generation of SI LAI/FPAR CDR in various projections and various spatial and temporal resolutions, and (iv) evaluation of the CDR by direct comparisons with ground data and indirectly through reproducing results of LAI/FPAR trends documented in the literature. This paper provides a comprehensive analysis of each step involved in the generation of the SI LAI/FPAR CDR, as well as evaluation of the ST-tensor completion model. Comparisons of SI LAI (FPAR) CDR with ground truth data suggest an RMSE of 0.84 LAI (0.15 FPAR) units with R² of 0.72 (0.79), which outperform the standard Terra/Aqua/VIIRS LAI (FPAR) products. The SI LAI/FPAR CDR is characterized by a low time series stability (TSS) value, suggesting a more stable and less noisy dataset than sensor-dependent counterparts. Furthermore, the mean absolute error (MAE) of the CDR is also lower, suggesting that SI LAI/FPAR CDR is comparable in accuracy to high-quality retrievals. LAI/FPAR trend analyses based on the SI LAI/FPAR CDR agree with previous studies, which indirectly provides enhanced capabilities to utilize this CDR for studying vegetation dynamics and climate change. Overall, the integration of multiple satellite data sources and the use of advanced gap filling modeling techniques improve the accuracy of the SI LAI/FPAR CDR, ensuring the reliability of long-term vegetation studies, global carbon cycle modeling, and land policy development for informed decision-making and sustainable environmental management. The SI LAI/FPAR CDR is open access and available under a Creative Commons Attribution 4.0 License at https://doi.org/10.5281/zenodo.8076540 (Pu et al., 2023a).

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, extensive 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 in the regions of optical remote sensing. Reprocessing MODIS LAI predominantly relies on temporal information to achieve smoother LAI profiles with little use of spatial information and may easily ignore genuine LAI anomalies. To address these problems, we designed the spatiotemporal information compositing algorithm (STICA) for the reprocessing of MODIS LAI products. This method integrates information from multiple dimensions, including pixel quality information, spatiotemporal correlation, and the original retrieval, thereby enabling both “reprocessing” and “value-added data” with respect to the existing MODIS LAI products, leading to the development of the high-quality LAI (HiQ-LAI) dataset. Compared with ground measurements, HiQ-LAI shows better performance than the original MODIS product with a root-mean-square error (RMSE) or bias decrease from 0.87 or −0.17 to 0.78 or −0.06, respectively. This is due to the improvement of HiQ-LAI with respect to capturing the seasonality in vegetation phenology and reducing abnormal time-series 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 cannot usually achieve good performance. We found that HiQ-LAI demonstrates superior continuity and consistency compared with raw MODIS LAI from both spatial and temporal perspectives. We anticipate that the global HiQ-LAI time series, generated using the STICA procedure on the Google Earth Engine (GEE) platform, will substantially enhance support for diverse global LAI time-series applications. The 5 km 8 d HiQ-LAI datasets from 2000 to 2022 are available at https://doi.org/10.5281/zenodo.8296768 (Yan et al., 2023).

As an essential vegetation structural parameter, leaf area index (LAI) is involved in many critical biochemical processes, such as photosynthesis, respiration, and precipitation interception. The MODerate resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imager Radiometer Suite (VIIRS) LAI sequence products have long supported various global climate, biogeochemistry, and energy flux research. These applications all rely on the accuracy of the product's long time series. However, uncontrolled interferences (e.g., adverse observation conditions and sensor uncertainties) potentially introduce substantial uncertainties to time series in product applications. As one of the most sensitive areas in response to global climate change, the Tibet Plateau (TP) has been treated as a crucial testing ground for thousands of studies on vegetation. To ensure the credibility of the studies arising from MODIS/VIIRS LAI products, the temporal quality uncertainties of data need to be clarified. This article proposed a method to revisit the temporal stability of the MODIS (MOD and MYD) and VIIRS (VNP) LAI in the TP, expecting to provide useful information for better accounting for the uncertainties in this area. Results show that the MODIS and VIIRS LAI were relatively stable in time series and available to be used continuously, among which the temporal quality of the MODIS LAI was the most stable. Moreover, the MODIS and VIIRS LAI products performed similarly in both time-series stability and time-series anomaly distribution, magnitudes and fluctuations. The time-series stability evaluation strategy applied to the MODIS and VIIRS LAI can also be employed to other remote sensing products.