Abstract
Soil moisture plays an essential role in the land-atmosphere interface. It has become necessary to develop quality large-scale soil moisture data from satellite observations for relevant applications in climate, hydrology, agriculture, etc. Specifically, microwave-based observations provide more consistent land surface records because they are unhindered by cloud conditions. The recent microwave radiometers onboard FY-3B, FY-3C and FY-3D satellites launched by China’s Meteorological Administration (CMA) extend the number of available microwave observations, covering late 2011 up until the present. These microwave observations have the potential to provide consistent global soil moisture records to date, filling the data gaps where soil moisture estimates are missing in the existing records. Along these lines, we studied the FY-3C to understand its added value due to its unique time of observation in a day (ascending: 22:15, descending: 10:15) absent from the existing satellite soil moisture records. Here, we used the triple collocation technique to optimize a benchmark retrieval model of land surface temperature (LST) tailored to the observation time of FY3C, by evaluating various soil moisture scenarios obtained with different bias-imposed LSTs from 2014 to 2016. The globally optimized LST was used as an input for the land parameter retrieval model (LPRM) algorithm to obtain optimized global soil moisture estimates. The obtained FY-3C soil moisture observations were evaluated with global in situ and reanalysis datasets relative to FY3B soil moisture products to understand their differences and consistencies. We found that the RMSEs of their anomalies were mostly concentrated between 0.05 and 0.15 m3 m−3, and correlation coefficients were between 0.4 and 0.7. The results showed that the FY-3C ascending data could better capture soil moisture dynamics than the FY-3B estimates. Both products were found to consistently complement the skill of each other over space and time globally. Finally, a linear combination approach that maximizes temporal correlations merged the ascending and descending soil moisture observations separately. The results indicated that superior soil moisture estimates are obtained from the combined product, which provides more reliable global soil moisture records both day and night. Therefore, this study aims to show that there is merit to the combined usage of the two FY-3 products, which will be extended to the FY-3D, to fill the gap in existing long-term global satellite soil moisture records.
Highlights
This article is an open access articleThe introduction of soil moisture (SM), known as soil water content, is the per unit volume expressed as the dimensionless ratio of soil and water, which forms only a fraction of the world’s freshwater resources [1,2]
The results show that the skill of FY-3B in capturing daily SM dynamics is better in descending products, while FY-3C is better in the ascending product
The results demonstrate that the merged product has accurately captured the variability of in situ soil moisture
Summary
This article is an open access articleThe introduction of soil moisture (SM), known as soil water content, is the per unit volume expressed as the dimensionless ratio of soil and water, which forms only a fraction of the world’s freshwater resources [1,2]. SM is an distributed under the terms and conditions of the Creative Commons. 2022, 14, 1225 essential parameter for developing land–climate models, which is important for improving meteorological forecasts [8], estimating crop yields [9], investigating ecological challenges [10], and water resource management [11,12]. The in situ observations of SM are relatively sparse and unevenly distributed around the globe and, unable to provide a reliable large-scale picture of soil moisture conditions [13,14]. Satellite remote sensing technology provides a periodic, global coverage, and multi-temporal earth observation framework, revolutionizing scientific studies and operational services that depend on soil moisture information [15]. Usually less affected by atmospheric conditions, allow for large-scale and near-real-time monitoring of SM estimation [16–20]. Passive microwave sensors measure the soil microwave emission intensity (i.e., the observed surface brightness temperatures) linked to the dielectric constant
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