Sensor-independent LAI/FPAR CDR: reconstructing a global sensor-independent climate data record of MODIS and VIIRS LAI/FPAR from 2000 to 2022

Abstract

Abstract. 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).

A global dataset of the fraction of absorbed photosynthetically active radiation for 1982–2022

“…GIMMS FPAR4g shows a systematic overestimation for low FPAR values in in-situ observation datasets, while it displays a systematic underestimation for high FPAR values. Similar issues are observed in the SI FPAR dataset that we used as the target variable for the BPNN models 46 . Overall, our product demonstrates good consistency with ground reference data post-2000, with the RMSE closely approaching the GCOS accuracy requirement for FPAR (10%).…”

“…We used four sets of global FPAR satellite-derived products (i.e., SI FPAR CDR, GIMMS FPAR3g, GLASS AVHRR FPAR, and Terrestrial CDR [TCDR]). The SI FPAR CDR served as the benchmark in this study 46 . The dataset demonstrates notable advantages: (1) Stringent filtering principles are applied to exclude poor-quality retrievals, ensuring the retention of high-quality samples and the reliability of the CDR.…”

The Impact of Quality Control Methods on Vegetation Monitoring Using MODIS FPAR Time Series

“…However, despite the limited influence of different QC methods on the monitoring of long time series vegetation trends, we still recommend referring to the QC documents of remote sensing products and selecting high-quality inversion results for the study, which can enhance the reliability and accuracy of the study to a limited extent. In addition, studies can be carried out using reanalyzed datasets with higher product quality, better spatial and temporal continuity, and more internal consistency compared to the original ones [60,61,93].…”

“…The QC information provides information about the relative reliability and accuracy of the remote sensing products. Researchers can use this information to filter the inversion results with higher quality, thereby improving the reliability of the research results or developing improved re-analysis datasets based on QC [49,[56][57][58][59][60][61]. However, as an important source of uncertainty in remote sensing products, differences in QC may lead to less reliable vegetation monitoring results or even contradictory conclusions [18,[62][63][64][65].…”