Abstract

Reliable soil moisture retrievals from passive microwave satellite sensors are limited during certain conditions, e.g., snow coverage, radio-frequency interference, and dense vegetation. In these cases, the retrievals can be masked using flagging algorithms. Currently available single- and multi-sensor soil moisture products utilize different flagging approaches. However, a clear overview and comparison of these approaches and their impact on soil moisture data are still lacking. For long-term climate records such as the soil moisture products of the European Space Agency (ESA) Climate Change Initiative (CCI), the effect of any flagging inconsistency resulting from combining multiple sensor datasets is not yet understood. Therefore, the first objective of this study is to review the data flagging system that is used within multi-sensor ESA CCI soil moisture products as well as the flagging systems of two other soil moisture datasets from sensors that are also used for the ESA CCI soil moisture products: The level 3 Soil Moisture and Ocean Salinity (SMOS) and the Soil Moisture Active/Passive (SMAP). The SMOS and SMAP soil moisture flagging systems differ substantially in number and type of conditions considered, critical flags, and data source dependencies. The impact on the data availability of the different flagging systems were compared for the SMOS and SMAP soil moisture datasets. Major differences in data availability were observed globally, especially for northern high latitudes, mountainous regions, and equatorial latitudes (up to 37%, 33%, and 32% respectively) with large seasonal variability. These results highlight the importance of a consistent and well-performing approach that is applicable to all individual products used in long-term soil moisture data records. Consequently, the second objective of the present study is to design a consistent and model-independent flagging strategy to improve soil moisture climate records such as the ESA CCI products. As snow cover, ice, and frozen conditions were demonstrated to have the biggest impact on data availability, a uniform satellite driven flagging strategy was designed for these conditions and evaluated against two ground observation networks. The new flagging strategy demonstrated to be a robust flagging alternative when compared to the individual flagging strategies adopted by the SMOS and SMAP soil moisture datasets with a similar performance, but with the applicability to the entire ESA CCI time record without the use of modelled approximations.

Highlights

  • Recognized as an Essential Climate Variable in 2010, soil moisture is one of the primary drivers of water, energy, and carbon cycles [1,2]

  • The first objective of this study is to review the data flagging system that is used within multi-sensor European Space Agency (ESA) Climate Change Initiative (CCI) soil moisture products as well as the flagging systems of two other soil moisture datasets from sensors that are used for the ESA CCI soil moisture products: The level 3 Soil Moisture and Ocean Salinity (SMOS) and the Soil Moisture Active/Passive (SMAP)

  • The flagging systems at the single sensor level for SMOS, SMAP, and at the multi-sensor level for ESA CCI SM were compared in the number of flags and types of data source used for these flags

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Summary

Introduction

Recognized as an Essential Climate Variable in 2010, soil moisture is one of the primary drivers of water, energy, and carbon cycles [1,2]. Climate change-induced changes in these cycles may have a more profound effect on human and nature than global warming itself [3]. For this reason, consistent, long-term soil moisture records are key in fully understanding the impact of climate change. Long-term data records (from 1978 onwards) of soil moisture, such as the soil moisture (SM) datasets of the Climate Change Initiative (CCI) of the European Space Agency (ESA), are provided on a global scale by remote sensing satellites using passive and/or active microwave sensors [3,4,5,6,7]. Differences in mission design, sensor system, and retrieval algorithms have led to inconsistencies between satellite SM products [9,10,11]

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