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    Thin-Plate Spline Interpolation Spatial Modelling of GNSS Retrieved Water Vapour Over Northeast Japan
    (2025-01-01)
    Ansari, Kutubuddin
    ;
    Walo, Janusz
    ;
    Wezka, Kinga
    ;
    Marjanska, Dorota
    ;
    Jamjareegulgarn, Punyawi
    The study investigated precipitable water vapour (PWV) variation over Northeast Japan by using more than two decades (2000 to 2022) of measurements. These data have been extracted from 36 Global Navigation Satellite System (GNSS) networks and interpolated with thin-plate spline interpolation to understand its spatial variation over the region. PWV annual and seasonal variation is studied and correlated with ERA5 reanalysis observations. These PWV variation shows that the northern part of Northeast Japan which is located at higher latitudes showed a smaller magnitude compared to PWV magnitude in the southern part of low latitudes. This is possible because the cold air is drier and the warm air holds more moisture in the lower southern part. The correlation coefficient variation based on geographical area is visible which quantifies the consistency between GNSS PWV and ERA5 and visualises the effects in sampling. The southern part, where the correlation coefficients are very high (more than 0.80), indicates good agreements between GNSS PWV and ERA5 values and can be considered well-sampled observations. Moreover, the correlation coefficients drop the values to approximately 0.40–0.60 for poorly sampled observations on the northern coast, pointing out the interannual inconsistency of PWV measurements, which are loosely related to ERA5 observations. Finally, the correlation coefficient between PWV with surface temperature and atmospheric pressure over Northeast Japan is investigated. This is clear from the results that the correlation coefficient between PWV and temperature is around 0.98 for each site, indicating that PWV has a very high dependency on surface temperature. The correlation coefficient between PWV and pressure is highly negative around −0.80, indicating their inverse relationship. We believe the outcomes from this study will contribute to a better understanding of PWV over Japan and the future refinements of atmospheric models over the East Asian region.
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    Performance of multi-source remote sensing soil moisture products over Punjab Pakistan during 2022–2023
    (2024-08-01)
    Hassan, Saba ul
    ;
    Shah, Munawar
    ;
    Shahzad, Rasim
    ;
    Ghaffar, Bushra
    ;
    Li, Bofeng
    The Global Navigation Satellite System Reflectometry (GNSS-R) has emerged as a valuable tool for terrestrial remote sensing applications, particularly in the context of land Surface Soil Moisture (SSM) detection. The high-resolution capability of GNSS-R complements traditional satellite-based active and passive missions but the product reliability and robustness evaluations are still absent due to an efficient retrieval algorithms. In this study, we addressed this lack of reliability and robustness by comprehensively assessing the SSM retrievals from CYclone Global Navigation Satellite System (CYGNSS) data with the satellite-based microwave radiometry products Soil Moisture Active Passive (SMAP) and Modern Era Retrospective-Analysis for Research and Applications (MERRA2) over Punjab in various seasons. ERA5 model-based products for the same period in 2022–2023. Our study reveals a distinct seasonal average SSM variation during autumn (0.20 cm<sup>3</sup>/cm<sup>3</sup>), followed by winter values of 0.19 cm<sup>3</sup>/cm<sup>3</sup>. Subsequently, the minimum SSM values are observed during summer (0.11 cm<sup>3</sup>/cm<sup>3</sup>) and an increase in spring to 0.13 cm<sup>3</sup>/cm<sup>3</sup>. Moreover, a strong positive linear relationship (0.74) is evident between SMAP and ERRA 5 in contrast to a low correlation (0.03) between MERRA2 and both the SMAP and ERRA 5. Additionally, SMAP demonstrates moderate and weak correlation of 0.53 and 0.03 with CYGNSS and MERRA2, respectively. The CYGNSS exhibits moderate correlations (0.46) with ERRA 5 and SMAP and a weaker association (0.14) with MERRA2. Our analysis concluded that MERRA2 (Bias = 0.20 cm³/cm³, ubRMSD = 0.25 cm³/cm³, RMSE = 0.12 cm³/cm³, SD = 0.13 cm³/cm³, MAE = 0.04 cm³/cm, R = 0.03) SSM product performs poorly as compared to SMAP (Bias = 0.03 cm³/cm³, ubRMSD = 0.03 cm³/cm³, RMSE = 0.04 cm³/cm³, SD = 0.05 cm³/cm³, MAE = 0.03 cm³/cm³, R = 0.74) and CYGNSS (Bias = -0.01 cm³/cm³, ubRMSD = 0.09 cm³/cm³, RMSE = 0.07 cm³/cm³, SD = 0.06 cm³/cm³, MAE = 0.05 cm³/cm³, R = 0.46) products. This study provides accurate future predictions of SSM with delineating the limitations of GNSS-R in comparison to remote sensing and model values. The findings from this study have also significant implications for the advancement of GNSS-R applications in agriculture and crop management.