Maximizing Temporal Correlations in Long-Term Global Satellite Soil Moisture Data-Merging

Daniel Fiifi Tawia Hagan,Guojie Wang,Waheed Ullah,Buda Su,Xiaowen Ma,Robert M Parinussa,Seokhyeon Kim,Yi Liu,Asher Samuel Bhatti,Tong Jiang

doi:10.3390/rs12132164

Abstract

In this study, an existing combination approach that maximizes temporal correlations is used to combine six passive microwave satellite soil moisture products from 1998 to 2015 to assess its added value in long-term applications. Five of the products used are included in existing merging schemes such as the European Space Agency’s essential climate variable soil moisture (ECV) program. These include the Special Sensor Microwave Imagers (SSM/I), the Tropical Rainfall Measuring Mission (TRMM/TMI), the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) sensor on the National Aeronautics and Space Administration’s (NASA) Aqua satellite, the WindSAT radiometer, onboard the Coriolis satellite and the soil moisture retrievals from the Advanced Microwave Scanning Radiometer 2 (AMSR2) sensor onboard the Global Change Observation Mission on Water (GCOM-W). The sixth, the microwave radiometer imager (MWRI) onboard China’s Fengyun-3B (FY3B) satellite, is absent in the ECV scheme. Here, the normalized soil moisture products are merged based on their availability within the study period. Evaluation of the merged product demonstrated that the correlations and unbiased root mean square differences were improved over the whole period. Compared to ECV, the merged product from this scheme performed better over dense and sparsely vegetated regions. Additionally, the trends in the parent inputs are preserved in the merged data. Further analysis of FY3B’s contribution to the merging scheme showed that it is as dependable as the widely used AMSR2, as it contributed significantly to the improvements in the merged product.

Highlights

Surface soil moisture is a vital variable within the climate system to regulate terrestrial water and energy cycles
To focus on the complementarity from various passive microwave sensors, this study consistently uses soil moisture products retrieved with the land parameter retrieval model (LPRM) [27], which is one of the most commonly used soil moisture retrieval algorithm that links soil moisture to microwave brightness temperature from low frequencies observed by radiometers
An existing correlation-based merging approach has been used to combine six global passive microwave soil moisture retrievals over a long period

Summary

Introduction

Surface soil moisture is a vital variable within the climate system to regulate terrestrial water and energy cycles. Existing studies have demonstrated the impact of soil moisture anomalies on extreme events such as heatwaves [2], floods [3] and droughts [4], as well as its importance in operational services and climate monitoring [5,6] at both regional and global scales. While they demonstrate the significant role of soil moisture, most of these studies rely on model simulations for which large uncertainties remain unexplored, observations are necessary to understand the impact of soil moisture better. They are very useful when validating alternate sources such as satellite and model-based products even though the differences in spatial resolutions may pose a challenge because of the high spatial variability of the variable [7]

Methods

Discussion

Conclusion