van der Schalie, R., Preimesberger, W., Stradiotti, P., Rodriguez-Fernandez, N., Madelon, R., Hirschi, M., van der Vliet, M., de Jeu, R., Kidd, R., & Dorigo, W. A. (2022, May 27). ESA CCI+ Soil Moisture project - Scientific Evolution [Conference Presentation]. ESA Living Planet Symposium 2022, Bonn, Germany.
ESA Living Planet Symposium 2022
The ESA CCI Soil Moisture dataset (ESA CCI SM, Dorigo et al., 2017) is a well-established Essential Climate Variable dataset within the scientific climate community, providing global merged surface soil moisture that is highly regarded for its quality and long historical coverage, ranging from November 1978 to December 31st 2021. For example in 2020 alone, over 100+ scientific publications were recorded that included the use of the ESA CCI SM, and this number has been continuously growing in 2021. The dataset has already been used for more than 10 years as the baseline for the annual evaluation and interpretation of global SSM conditions as reported in the leading BAMS' "State of the Climate" reports (Van der Schalie et al., 2021).
The ESA CCI SM consists of three products: “ACTIVE” and “PASSIVE” were created by fusing scatterometer and radiometer soil moisture products, respectively; the “COMBINED” product is a blended product based on the former two data sets. Early 2022, the latest version of the dataset will be released, which is the ESA CCI SM version 7. Next to an extension of the dataset up to December 2021, there are several other improvements that have been implemented into the latest version of the products, of which we will give an overview.
Observations from two new passive microwave satellites have been added to the merged PASSIVE product, which are FengYun-3C and FengYun-3D, making a total of 14 satellites in the complete record. The temporal coverage of the PASSIVE dataset has been strongly improved further by the inclusion of daytime retrievals, which is based on a brightness temperature dataset that is calibrated to best match the nighttime/early-morning observations, before running the Land Parameter Retrieval Model (LPRM; Owe et al., 2008; Van der Schalie et al., 2017) for retrieving SM. LPRM has also been further fine-tuned, among others introducing a new barren soils flag based on passive microwave observations (follow-up of Van der Vliet et al., 2020) that can help remove false retrievals over dry and barren soils, like in deserts. The ACTIVE dataset has also seen the integration of a new satellite, i.e. ASCAT-C on MetOp-C, and its consistency has been improved by the new rescaling of ASCAT-B to ASCAT-A. ASCAT data within this project is generated through the EUMETSAT HSAF soil moisture project, (H-SAF, 2019). The CDF-Matching, as used in the merging of all three products, is updated with a dynamic CDF-parameter estimation that minimises inner-annual biases after scaling. The effect of these changes on the quality of the dataset are internally evaluated and its results will be briefly presented.
Besides data production, there are continued research efforts made to improve our understanding of the SM data and to ensure that new science and improvements are available for future product evolutions. Some examples:
(1) Assessing the possibility of using remote sensing data from an L-band sensor as the reference in order to remove model dependency in the final scaling of the COMBINED product (Madelon et al., in review).
(2) A global, long term (2002-2020), root-zone soil moisture product is being developed and evaluated, which focuses on the assimilation of both soil moisture and (microwave based) vegetation information.
(3) Develop and evaluate an operational methodology to retrieve a 1-km-scale spatial resolution soil moisture product from Sentinel data and assess the quality of this high-resolution soil moisture product.
Concerning future research directions, based on the obtained knowledge from these and related research activities, we have defined an updated roadmap for the ESA CCI SM datasets. Main improvements that are foreseen include a spatial resolution increase from 0.25° to 0.10°, sub-daily SM with ~6 hourly timesteps, improved (model-independent) scaling, improved uncertainty estimates and more.