Performance stability of the MODIS and VIIRS LAI algorithms inferred from analysis of long time series of products

Yan, Kai; Pu, Jiabin; Park, Taejin; Xu, Baodong; Zeng, Yelu; Yan, Guangjian; Weiss, Marie; Knyazikhin, Yuri; Myneni, Ranga B.

doi:10.1016/j.rse.2021.112438

Cited by 38 publications

(21 citation statements)

References 36 publications

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“…The LiSparse kernel models the reflectance of a sparse canopy scene by the areal proportions Appendix A.2. Other Forms of Kernel-Driven Models An accurate description of surface anisotropy is important for radiative transfer modeling and the retrieval of surface parameters [7,[46][47][48]. Additionally, due to due to the advantages of the kernel-driven models, scholars gradually started to focus on extending the models to more application areas.…”

Section: Discussionmentioning

confidence: 99%

Revisiting the Performance of the Kernel-Driven BRDF Model Using Filtered High-Quality POLDER Observations

Yan

Gao

et al. 2022

Forests

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The Bidirectional Reflectance Distribution Function (BRDF) is usually used to describe the reflectance anisotropy of a non-Lambertian surface and estimate surface parameters. Among the BRDF models, the kernel-driven models have been extensively used due to their simple form and powerful fitting ability, and their reliability has been validated in some studies. However, existing validation efforts used in situ measurements or limited satellite data, which may be subject to inadequate observational conditions or quality uncertainties. A recently released high-quality BRDF database from Polarization and Directionality of the Earth’s Reflectances (POLDER) provides an opportunity to revisit the performance of the kernel-driven models. Therefore, in order to evaluate the fitting ability of the kernel-driven models under different observational conditions and explore their application direction in the future, we use the filtered high-quality BRDF database to evaluate the fitting ability of the kernel-driven model represented by the RossThick-LiSparseR (RTLSR) kernels in this paper. The results show that the RTLSR model performs well, which shows small fitting residuals under most observational conditions. However, the applicability of the RTLSR model performed differently across land cover types; the RTLSR model exhibited larger fitting residuals, especially over non-vegetated surfaces. Under different sun-sensor geometries, the fitting residuals show a strong positive correlation with the Solar Zenith Angle. The above two factors cause the RTLSR model to exhibit a poorer fitting ability at high latitudes. As an exploration, we designed a model combination strategy that combines the advantages of different models and achieved a better performance at high latitudes. We believe that this study provides a better understanding of the RTLSR model.

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Section: Discussionmentioning

confidence: 99%

Revisiting the Performance of the Kernel-Driven BRDF Model Using Filtered High-Quality POLDER Observations

Yan

Gao

et al. 2022

Forests

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“…Xu et al analyzed the MODIS and VIIRS LAI products for Spatiotemporal consistency and uncertainty from 2012 to 2016, indicating that the consistency between the two sensors had met the stability requirement for long-term LAI ESDRs from multisensors suggested by GCOS [16]. Yan et al performed stability analysis on MODIS and VIIRS LAI product quality, concluding that the performance stability of their retrieval algorithm was sufficient to support the trend-related studies published so far [34]. Nevertheless, the current quality validations of MODIS/VIIRS LAI products have focused more on a given quality flag while ignoring temporal consistency.…”

Section: Introductionmentioning

confidence: 86%

“…The MK test is a common statistical tool for climate diagnostics and prediction (Eqs. ( 10)-( 12)) which determines whether there are significant trends in a timeseries [34,56]:…”

Section: ) Trend Analysismentioning

confidence: 99%

Revisit the Performance of MODIS and VIIRS Leaf Area Index Products from the Perspective of Time-Series Stability

Zou

Yan

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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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 paper 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.

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“…In addition, for testing the overall performance of these methods, the actual remote sensing data (Genhe) was split into training dataset (TR-dataset) and testing dataset (TE-dataset). Further, from one site to another site is challenging [47,73,74], thus the developed models were further validated on the independent dataset (IN-dataset). The IN-dataset was collected in a geographically different site, Chengde and never used to train the model.…”

Section: Methodsmentioning

confidence: 99%

Combining 3D Radiative Transfer Model and Convolutional Neural Network to Accurately Estimate Forest Canopy Cover From Very High-Resolution Satellite Images

Jin¹,

Qi²,

Huang³

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Forest canopy cover (FCC) plays an important role in many ecological, hydrological and forestry applications. For large-scale applications, FCC is usually estimated from remotely sensed data by inverting radiative transfer models (RTMs) or using data-driven regressions. In this study, we proposed a hybrid model, which combines a 3D RTM and transfer learning-based convolutional neural network (T-CNN), to estimate FCC from very high-resolution satellite images (e.g., Chinese GaoFen-2, 1 m resolution with 4 bands). Unlike common hybrid models that are purely trained with simulation data, T-CNN combines simulation data-based pre-training and actual data-based transfer learning, which is a widely used technique in artificial intelligence for finetuning models. The performance of T-CNN was compared with a random forest (RF) model and two general CNN models, including CNN trained with actual dataset only (Data-CNN) and CNN trained with RTM simulation data only (RTM-CNN). Results on the independent validation dataset (not used in training stage) showed that T-CNN had higher accuracy (RMSE = 0.121, R 2 = 0.83), compared with RF (RMSE = 0.26, R 2 = 0.61), Data-CNN (RMSE = 0.142, R 2 = 0.81), and RTM-CNN (RMSE = 0.144, R 2 = 0.73), which indicates that T-CNN has a strong transferability. Tests on different training sizes showed that T-CNN (0.084 < RMSE < 0.108) provided constantly better performances than RF (0.116 < RMSE < 0.122) and Data-CNN (0.103 < RMSE < 0.128), which demonstrates the potential of T-CNN as an alternative to RTM-based inversion and data-driven regressions to estimate FCC, especially when training data is imbalanced and inadequate.

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Performance stability of the MODIS and VIIRS LAI algorithms inferred from analysis of long time series of products

Cited by 38 publications

References 36 publications

Revisiting the Performance of the Kernel-Driven BRDF Model Using Filtered High-Quality POLDER Observations

Revisiting the Performance of the Kernel-Driven BRDF Model Using Filtered High-Quality POLDER Observations

Revisit the Performance of MODIS and VIIRS Leaf Area Index Products from the Perspective of Time-Series Stability

Combining 3D Radiative Transfer Model and Convolutional Neural Network to Accurately Estimate Forest Canopy Cover From Very High-Resolution Satellite Images

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