Improving the Quality of MODIS LAI Products by Exploiting Spatiotemporal Correlation Information

Wang, Jian; Yan, Kai; Gao, Si; Pu, Jiabin; Liu, Jinxiu; Park, Taejin; Bi, Jian; Maeda, Eduardo Eiji; Heiskanen, Janne; Knyazikhin, Yuri; Myneni, Ranga B.

doi:10.1109/tgrs.2023.3264280

Cited by 6 publications

(4 citation statements)

References 74 publications

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“…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].…”

Section: Impact Of Quality Control On Vegetation Monitoringmentioning

confidence: 99%

“…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].…”

Section: Introductionmentioning

confidence: 99%

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

Yan,

Zhang,

Peng

et al. 2024

Forests

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Monitoring vegetation dynamics (VD) is crucial for environmental protection, climate change research, and understanding carbon and water cycles. Remote sensing is an effective method for large-scale and long-term VD monitoring, but it faces challenges due to changing data uncertainties caused by various factors, including observational conditions. Previous studies have demonstrated the significance of implementing proper quality control (QC) of remote sensing data for accurate vegetation monitoring. However, the impact of different QC methods on VD results (magnitude and trend) has not been thoroughly studied. The fraction of absorbed photosynthetically active radiation (FPAR) characterizes the energy absorption capacity of the vegetation canopy and is widely used in VD monitoring. In this study, we investigated the effect of QC methods on vegetation monitoring using a 20-year MODIS FPAR time series. The results showed several important findings. Firstly, we observed that the Mixed-QC (no QC on the algorithm path) generally produced a lower average FPAR during the growing season compared to Main-QC (only using the main algorithm). Additionally, the Mixed-QC FPAR showed a very consistent interannual trend with the Main-QC FPAR over the period 2002–2021 (p < 0.05). Finally, we found that using only the main algorithm for QC generally reduced the trend magnitude (p < 0.1), particularly in forests. These results reveal differences in FPAR values between the two QC methods. However, the interannual FPAR trends demonstrate greater consistency. In conclusion, this study offers a case study on evaluating the influence of different QC methods on VD monitoring. It suggests that while different QC methods may result in different magnitudes of vegetation dynamics, their impact on the time series trends is limited.

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Section: Impact Of Quality Control On Vegetation Monitoringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Yan,

Zhang,

Peng

et al. 2024

Forests

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“…The LAI over this region remains consistently high throughout the year [47]. The differences in the MODIS LAI over two different landcover types during the same period of time often produces outliers in the data [34,48]. In the light of these challenges, the interannual and seasonal variation data for various land types can serve as effective information to identify and remove the outliers in the MODIS LAI dataset.…”

Section: Introductionmentioning

confidence: 99%

Identifying Outliers of the MODIS Leaf Area Index Data by Including Temporal Patterns in Post-Processing

Ma,

2023

Remote Sensing

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The moderate resolution imaging spectroradiometer (MODIS) calculates the leaf area index (LAI) for each pixel without incorporating the temporal correlation information, leading to a higher sensitivity for the LAI that produces uncertainties in observed reflectance. As a result, an increased noise level is observed in the timeseries, making the data discontinuous and inconsistent in space and time. Therefore, it is important to identify and handle the outliers during the post-processing of MODIS data. This study proposed a method to identify the MODIS LAI outliers based on the analyses of temporal patterns, including the interannual and seasonal changes in the LAI. The analysis was carried out utilizing the data from 278 global MODIS LAI sites and the results were verified against the measurement obtained from 52 ground stations. The results from the analyses detected 50 and 92 outliers based on 1.5σ and 1.0σ standard deviations, respectively, of the difference between the MODIS LAI and ground measurements; correspondingly, 46 and 65 outliers, respectively, were identified by incorporating temporal patterns during the post-processing of the data. The validation results exhibited improved values of the coefficient of determination (R2) after eliminating the MODIS LAI outliers identified through the interannual and seasonal patterns. Specifically, the R2 between the ground measurement LAI and MODIS LAI increased from 0.51 to 0.54, 0.88, and 0.90 after eliminating MODIS LAI outliers when considering the interannual patterns, seasonal patterns, and both the interannual and seasonal patterns, respectively. The results from the study provided valuable information and theoretical support to improve MODIS LAI post-processing.

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“…These include 1) identifying areas with a high fraction of water area in the satellite pixel and removing their impact on the retrieval using a mixed pixel correction method (Xu et al, 2020;Dong et al, 2023); 2) integration of prior knowledge of reflectance variations into the generation of the image composite (Pu et al, 2023); 3) accounting for the canopy hot spot effect in the retrieval technique (Yan et al, 2021b). These methods would increase product spatial coverage and developing various gap filling techniques to extrapolate retrievals beyond areas with valid satellite observations such as 1) cubic splines (Mitášová and Hofierka, 1993); 2) spatial linear, bilinear and kriging interpolations (Xu et al, 2015;Smith, 1981;Oliver and Webster, 1990), 3) various temporal extrapolation techniques (Holben, 1986;Lange et al, 2017;Roerink et al, 2000;Zhu et al, 2011;Das and Ghosh, 2017;Chu et al, 2021;Wang et al, 2023). However, most of the approaches are characterized by high computational costs and/or lack of information necessary for their implementation at the global scale.…”

Section: Introductionmentioning

confidence: 99%

Sensor-Independent LAI/FPAR CDR: Reconstructing a Global Sensor-Independent Climate Data Record of MODIS and VIIRS LAI/FPAR from 2000 to 2022

Pu,

Yan,

Roy

et al. 2023

Preprint

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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 MODIS&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 500m/5km/0.05 degrees, 8-day/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, spatial and temporal resolutions, and (iv) evaluation of the CDR by direct comparisons to ground data and indirectly through reproducing results of LAI/FPAR trends documented in 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) with ground truth data suggest a RMSE of 0.84 LAI (0.15 FPAR) units with R2 of 0.72 (0.79), which are improvements of the standard Terra/Aqua/VIIRS LAI (FPAR) products by 0.02~0.19 LAI (0.01~0.02 FPAR) units with the R2 decreased by 0.02~0.16 (0.05~0.09). The SI LAI/FPAR CDR is characterized by a low time series stability (TSS) value, suggesting a more stable and less noisy data set than their 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 with high-quality retrievals. LAI/FPAR trend analyses based on the SI LAI/FPAR CDR agrees 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 modelling techniques improve the accuracy of the SI LAI/FPAR CDR, ensuring the reliability of long-term vegetation studies, global carbon cycle modelling and land policy development for informed decision-making and sustainable environmental management.

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Improving the Quality of MODIS LAI Products by Exploiting Spatiotemporal Correlation Information

Cited by 6 publications

References 74 publications

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

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

Identifying Outliers of the MODIS Leaf Area Index Data by Including Temporal Patterns in Post-Processing

Sensor-Independent LAI/FPAR CDR: Reconstructing a Global Sensor-Independent Climate Data Record of MODIS and VIIRS LAI/FPAR from 2000 to 2022

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