Methodologies for Integrating Multiple High-Level Remotely Sensed Land Products

Liang, Shunlin; Wang, D.; Tao, Xin; Cheng, Jiahui; Yao, Yunjun; Zhang, X.; He, Tao

doi:10.1016/b978-0-12-409548-9.10342-2

Cited by 9 publications

(7 citation statements)

References 112 publications

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“…Data integration or data fusion is not novel in remote sensing fields. Previous studies have integrated multiple high-level satellite data products to estimate parameters with higher accuracy in geoscience [21,22]. For example, Chatterjee et al [23] applied a simple geostatistical data fusion approach to merge multiple aerosol optical thickness (AOT) datasets and obtained an optimal fused AOT dataset.…”

Section: Introductionmentioning

confidence: 99%

“…As noted, many algorithms have been proposed to integrate satellite data products. However, most of them are based on the assumption that satellite products have white noise that follows a normal distribution [21], which is rarely satisfied by regional and global forest AGB data. Additionally, the complex structures of these fusion algorithms affect the computational efficiency for calculating the weightings of individual datasets, which limits their application on a large scale [35].…”

Section: Introductionmentioning

confidence: 99%

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Fusion of Multiple Gridded Biomass Datasets for Generating a Global Forest Aboveground Biomass Map

Zhang

Liang

2020

Remote Sensing

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Many advanced satellite estimation methods have been developed, but global forest aboveground biomass (AGB) products remain largely uncertain. In this study, we explored data fusion techniques to generate a global forest AGB map for the 2000s at 0.01-degree resolution with improved accuracy by integrating ten existing local or global maps. The error removal and simple averaging algorithm, which is efficient and makes no assumption about the data and associated errors, was proposed to integrate these ten forest AGB maps. We first compiled the global reference AGB from in situ measurements and high-resolution AGB data that were originally derived from field data and airborne lidar data and determined the errors of each forest AGB map at the pixels with corresponding reference AGB values. Based on the errors determined from reference AGB data, the pixel-by-pixel errors associated with each of the ten AGB datasets were estimated from multiple predictors (e.g., leaf area index, forest canopy height, forest cover, land surface elevation, slope, temperature, and precipitation) using the random forest algorithm. The estimated pixel-by-pixel errors were then removed from the corresponding forest AGB datasets, and finally, global forest AGB maps were generated by combining the calibrated existing forest AGB datasets using the simple averaging algorithm. Cross-validation using reference AGB data showed that the accuracy of the fused global forest AGB map had an R-squared of 0.61 and a root mean square error (RMSE) of 53.68 Mg/ha, which is better than the reported accuracies (R-squared of 0.56 and RMSE larger than 80 Mg/ha) in the literature. Intercomparison with previous studies also suggested that the fused AGB estimates were much closer to the reference AGB values. This study attempted to integrate existing forest AGB datasets for generating a global forest AGB map with better accuracy and moved one step forward for our understanding of the global terrestrial carbon cycle by providing improved benchmarks of global forest carbon stocks.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fusion of Multiple Gridded Biomass Datasets for Generating a Global Forest Aboveground Biomass Map

Zhang

Liang

2020

Remote Sensing

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“…This situation is more pronounced in regions where multiple climate modes coexist. A promising alternative is to blend multiple ET products and in situ observations to obtain: 1) more reliable estimates under variable climatic conditions, and 2) a benchmark for multiple ET products, i.e., a surrogate indicator for uncertainties in climate forecasts (Aires 2014;Liang et al 2018).…”

Section: Introductionmentioning

confidence: 99%

DNN-MET: A deep neural networks method to integrate satellite-derived evapotranspiration products, eddy covariance observations and ancillary information

Shang

Yao

Liang

et al. 2021

Agricultural and Forest Meteorology

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“…Therefore, the TIR LST data and the PMW LST data are complementary in terms of spatiotemporal completeness and data accuracy. Therefore, the fusion of TIR and PMW LST data has become a promising method for obtaining high-quality all-weather RS products [17,18].…”

Section: Introductionmentioning

confidence: 99%

A Random Forest-Based Data Fusion Method for Obtaining All-Weather Land Surface Temperature with High Spatial Resolution

Cheng

Zhang

2021

Remote Sensing

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Land surface temperature (LST) is an important parameter for mirroring the water–heat exchange and balance on the Earth’s surface. Passive microwave (PMW) LST can make up for the lack of thermal infrared (TIR) LST caused by cloud contamination, but its resolution is relatively low. In this study, we developed a TIR and PWM LST fusion method on based the random forest (RF) machine learning algorithm to obtain the all-weather LST with high spatial resolution. Since LST is closely related to land cover (LC) types, terrain, vegetation conditions, moisture condition, and solar radiation, these variables were selected as candidate auxiliary variables to establish the best model to obtain the fusion results of mainland China during 2010. In general, the fusion LST had higher spatial integrity than the MODIS LST and higher accuracy than downscaled AMSR-E LST. Additionally, the magnitude of LST data in the fusion results was consistent with the general spatiotemporal variations of LST. Compared with in situ observations, the RMSE of clear-sky fused LST and cloudy-sky fused LST were 2.12–4.50 K and 3.45–4.89 K, respectively. Combining the RF method and the DINEOF method, a complete all-weather LST with a spatial resolution of 0.01° can be obtained.

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Methodologies for Integrating Multiple High-Level Remotely Sensed Land Products

Cited by 9 publications

References 112 publications

Fusion of Multiple Gridded Biomass Datasets for Generating a Global Forest Aboveground Biomass Map

Fusion of Multiple Gridded Biomass Datasets for Generating a Global Forest Aboveground Biomass Map

DNN-MET: A deep neural networks method to integrate satellite-derived evapotranspiration products, eddy covariance observations and ancillary information

A Random Forest-Based Data Fusion Method for Obtaining All-Weather Land Surface Temperature with High Spatial Resolution

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