<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This study delves into the synergy between remote sensing and satellite gravimetry, focusing on the utilization of Advanced Microwave Scanning Radiometer (AMSR-E) data for modeling delta Total Water Storage (ΔTWS) values derived from the GRACE mission. Various machine learning algorithms were employed to investigate the concordance between Gravity Recovery and Climate Experiment (GRACE) and AMSR-E observations. Despite the limited correlation in circumpolar permafrost areas, ΔTWS was successfully modeled with an accuracy of a Root Mean Square Error (RMSE) of 3.5 cm. The Amazon region exhibited a notable model error, attributed to significant ΔTWS amplitude; the overall model quality was affirmed by Normalized Root Mean Square Error (NRMSE) and Nash-Sutcliffe Efficiency (NSE) metrics. Importantly, the effectiveness of AMSR-E Soil Moisture (SM) data, encompassing C (frequency of 4–8 GHz) and X (frequency of 8–12 GHz) ranges (~0.04 m and ~0.03 m wavelength, respectively) in modeling ΔTWS, even in heavily forested equatorial regions, was demonstrated.</p>
</abstract>

This study delves into the synergy between remote sensing and satellite gravimetry, focusing on the utilization of Advanced Microwave Scanning Radiometer (AMSR-E) data for modeling delta Total Water Storage (ΔTWS) values derived from the GRACE mission. Various machine learning algorithms were employed to investigate the concordance between Gravity Recovery and Climate Experiment (GRACE) and AMSR-E observations. Despite the limited correlation in circumpolar permafrost areas, ΔTWS was successfully modeled with an accuracy of a Root Mean Square Error (RMSE) of 3.5 cm. The Amazon region exhibited a notable model error, attributed to significant ΔTWS amplitude; the overall model quality was affirmed by Normalized Root Mean Square Error (NRMSE) and Nash-Sutcliffe Efficiency (NSE) metrics. Importantly, the effectiveness of AMSR-E Soil Moisture (SM) data, encompassing C (frequency of 4–8 GHz) and X (frequency of 8–12 GHz) ranges (~0.04 m and ~0.03 m wavelength, respectively) in modeling ΔTWS, even in heavily forested equatorial regions, was demonstrated.

<bold>Using machine learning techniques</bold> to reconstruct the signal observed by the GRACE mission based on AMSR-E microwave data

Department of Geodesy and Geodetic Astronomy, Faculty of Geodesy and Cartography

Department of Photogrammetry, Remote Sensing and Spatial Information Systems, Faculty of Geodesy and Cartography

Miscellanea Geographica

This work is licensed under the Creative Commons Attribution 4.0 International License.

 to reconstruct the signal observed by the GRACE mission based on AMSR-E microwave data

This study delves into the synergy between remote sensing and satellite gravimetry, focusing on the utilization of Advanced Microwave Scanning Radiometer...

Using machine learning techniques to reconstruct the signal observed by the GRACE mission based on AMSR-E microwave data

Published Online: Apr 30, 2024

Page range: 80 - 86

Received: Jan 20, 2024

Accepted: Apr 21, 2024

DOI: https://doi.org/10.2478/mgrsd-2023-0033

Keywords
GRACE, AMSR-E, total water storage anomalies, soil moisture, remote sensors

© 2024 Viktor Szabó et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Using machine learning techniques to reconstruct the signal observed by the GRACE mission based on AMSR-E microwave data

Published Online: Apr 30, 2024

Page range: 80 - 86

Received: Jan 20, 2024

Accepted: Apr 21, 2024

DOI: https://doi.org/10.2478/mgrsd-2023-0033

KeywordsGRACE, AMSR-E, total water storage anomalies, soil moisture, remote sensors

© 2024 Viktor Szabó et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Keywords
GRACE, AMSR-E, total water storage anomalies, soil moisture, remote sensors