A thermal radiation directionality correction method for the surface upward longwave radiation of geostationary satellite based on a time-evolving kernel-driven model

Qin, Bangyong; Cao, Baoxiang; Roujean, Jean‐Louis; Gastellu-Etchegorry, Jean-Philippe; Ermida, Sofia L.; Bian, Zunjian; Du, Yang; Hu, Tian; Li, Hua; Xiao, Qing; Chen, Shuisen; Liu, Qinhuo

doi:10.1016/j.rse.2023.113599

Cited by 7 publications

(7 citation statements)

References 75 publications

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“…[35]. Recent SULR estimations have achieved an approximate accuracy with a RMSE from 15.72 to 18.75 W/m 2 , which is very close to the RMSE of our study [36][37][38].…”

Section: Point-by-point Validation Using the Sulr Measured In Situsupporting

confidence: 88%

“…These results indicate a favorable downscaling achievement, considering the acceptable instantaneous error of 20 W/m 2 [35]. Recent SULR estimations have achieved an approximate accuracy with a RMSE from 15.72 to 18.75 W/m 2 , which is very close to the RMSE of our study [36][37][38]. [35].…”

Section: Point-by-point Validation Using the Sulr Measured In Situsupporting

confidence: 87%

“…Looking to 24 July 2022 as an example (Figure 7(a3)), the downscaled SULR was obviously underestimated according to the MBE of −11.46 W/m 2 . In future work, methods for thermal radiation directionality corrections may be introduced to correct the SULR to a unified direction so that the downscaled results can be compared and evaluated more strictly [38,41].…”

Section: Conclusion and Discussionmentioning

confidence: 99%

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Estimation and Evaluation of 15 Minute, 40 Meter Surface Upward Longwave Radiation Downscaled from the Geostationary FY-4B AGRI

Zheng,

Cao,

et al. 2024

Remote Sensing

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Surface upward longwave radiation (SULR) is one of the four components of surface net radiation. Geostationary satellites can provide high temporal but coarse spatial resolution SULR products. Downscaling coarse SULR to a higher resolution is important for fine-scale thermal condition monitoring. Statistical regression downscaling is widely used due to its simplicity and is built on the assumption that the thermal parameter like land surface temperature (LST) or SULR has a relationship with the related surface factors like the normalized difference vegetation index (NDVI), and the relationship remains unchanged in any scales. In this study, to establish the relationship between SULR and the related surface factors, we chose the multiple linear regression (MLR) model and five surface factors (i.e., the modified normalized difference water index (MNDWI), normalized difference built-up and soil index (NDBSI), NDVI, normalized moisture difference index (NMDI), and urban index (UI)) to drive the downscaling process. Additionally, a step-by-step downscaling strategy was applied to reach the 100-fold increase in spatial resolution, transitioning the estimated SULR from 4 km of the advanced geostationary radiation imager (AGRI) onboard FengYun-4B (FY-4B) satellite to 40 m of the visual and infrared multispectral imager (VIMI) in infrared spectrum onboard GaoFen5-02 (GF5-02). Finally, we evaluated the downscaling results by comparing the downscaled SULR values with the in situ measured SULR and GF5-02-calculated SULR, and the root mean square errors (RMSEs) were 19.70 W/m2 and 24.86 W/m2, respectively. Throughout this MLR-based step-by-step downscaling method (high-frequency data from FY-4B and high spatial resolution data from GF5-02), high spatiotemporal SULR (15 min temporal resolution, 40 m spatial resolution) were successfully generated instead of coarse spatial resolution ones from the FY-4B satellite or a coarse temporal resolution one from the GF5-02 satellite, relieving the above-mentioned conflict to some extent.

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“…[35]. Recent SULR estimations have achieved an approximate accuracy with a RMSE from 15.72 to 18.75 W/m 2 , which is very close to the RMSE of our study [36][37][38].…”

Section: Point-by-point Validation Using the Sulr Measured In Situsupporting

confidence: 88%

Section: Point-by-point Validation Using the Sulr Measured In Situsupporting

confidence: 87%

Section: Conclusion and Discussionmentioning

confidence: 99%

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Estimation and Evaluation of 15 Minute, 40 Meter Surface Upward Longwave Radiation Downscaled from the Geostationary FY-4B AGRI

Zheng,

Cao,

et al. 2024

Remote Sensing

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“…(3) The TRD effect observed in sparse in situ sites can represent the global-scale TRD effect [14]. (4) The gap fraction effect can be ignored, i.e., discard the temperature difference between soil and vegetation surface components [22,23]. (5) The hotspot effect can also be ignored, i.e., discard the temperature difference between sunlit and sunshade surface components [24].…”

Section: Introductionmentioning

confidence: 99%

Correcting an Off-Nadir to a Nadir Land Surface Temperature Using a Multitemporal Thermal Infrared Kernel-Driven Model during Daytime

Na,

Cao,

Qin

et al. 2024

Remote Sensing

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Land surface temperature (LST) is a fundamental parameter in global climate, environmental, and geophysical studies. Remote sensing is an essential approach for obtaining large-scale and frequently updated LST data. However, due to the wide field of view of remote sensing sensors, the observed LST with diverse view geometries suffers from inconsistency caused by the thermal radiation directionality (TRD) effect, which results in LST products being incomparable, especially during daytime. To address this issue and correct current off-nadir LSTs to nadir LSTs, a semi-physical time-evolved kernel-driven model (TEKDM) is proposed, which depicts multitemporal TRD patterns during the daytime. In addition, we employ a Bayesian optimization method to calibrate seven unknown parameters in the TEKDM. Validation results using the U.S. Climate Reference Network (USCRN) sites show that the RMSE (MBE) for GOES-16 and MODIS off-nadir LST products is reduced from 3.29 K (−2.0 K) to 2.34 K (−0.02 K), with an RMSE reduction of 0.95 K (29%) and a significant reduction in systematic bias. Moreover, the proposed method successfully eliminates the angular and temporal dependence of the LST difference between the satellite off-nadir LST and in situ nadir LST. In summary, this study presents a feasible approach for estimating the high-accuracy nadir LST, which can enhance the applicability of LST products in various domains.

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“…Currently, the conventional approach for obtaining vegetation indices is through calculations based on satellite images bands. Due to the differences in solar incidence angles and observation geometries during imaging caused by differences in satellite orbits, differences arise in the calculation results of different sensors [18][19][20]. The Sentinel-2A multispectral instrument (MSI) obtains multispectral reflectance wavelength observations with directional effects due to surface reflectance anisotropy and variations in solar and observation geometries [18].…”

Section: Introductionmentioning

confidence: 99%

Bias Correction of Sentinel-2 MSI Vegetation Indices in a Desert Steppe With Original Assembled Field Online Multiangle Spectrometers

Tubuxin

Bao

2023

IEEE Trans. Geosci. Remote Sensing

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In desert steppe regions with sparse vegetation, there are discrepancies between vertical and oblique observations made by satellite-based sensors. In this study, we developed and deployed an online multiangle spectrometer in the desert steppe area of Inner Mongolia, China, to calibrate satellite-based vegetation indices. One of the key components of the device is a specially designed quarter-arc iron track that holds fixed view angles of 30°, 45°, 60°, 75° and 90° are fixed. These observation positions equipped with high-efficiency multichannel sensors can capture the reflectance of ground objects at visible and nearinfrared wavelengths. Real-time experiments were conducted with multiple observation angles and an error-based view angle correction model was constructed to reconcile the differences between angular and vertical observations. The calculated results were subsequently applied to the bias-correction process of the Sentinel-2 vegetation index. Across all view angles, the daily distributions of the Normalized Difference Vegetation Index (NDVI) and Ratio Vegetation Index (RVI) exhibited a U-shaped pattern with the nadir occurring at noon. Among the data, RVI demonstrated superior overall stability compared to NDVI. However, with vegetation growth, the NDVI showed less sensitivity, resulting in a decrease in its coefficient of variation (CV) from 32.3% to 18.2%. To correct the bias in Sentinel-2 products, we initially applied path length correction (PLC) to eliminate the topographic influence on the Band4 and Band8 band reflectance. Our findings revealed that Band8 performed better than Band4 in mitigating the effects of topography, as evidenced by a decrease in the determination coefficient from 0.448 to 0.09. Additionally, we corrected the view angle error of the NDVI of Sentinel-2 by constructing a view angle correction model (R² =0.84, RMSE=0.03). The corrected images exhibited significant variation characteristics in areas with relatively large view angles. The results of this study provide valuable scientific support for the correction of satellite image products and the accurate detection of vegetation through remote sensing in regions with low vegetation coverage.

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A thermal radiation directionality correction method for the surface upward longwave radiation of geostationary satellite based on a time-evolving kernel-driven model

Cited by 7 publications

References 75 publications

Estimation and Evaluation of 15 Minute, 40 Meter Surface Upward Longwave Radiation Downscaled from the Geostationary FY-4B AGRI

Estimation and Evaluation of 15 Minute, 40 Meter Surface Upward Longwave Radiation Downscaled from the Geostationary FY-4B AGRI

Correcting an Off-Nadir to a Nadir Land Surface Temperature Using a Multitemporal Thermal Infrared Kernel-Driven Model during Daytime

Bias Correction of Sentinel-2 MSI Vegetation Indices in a Desert Steppe With Original Assembled Field Online Multiangle Spectrometers

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