The important role of soil moisture for the environment and climate system is well known. Soil moisture influences hydrological and agricultural processes, runoff generation, drought development and many other processes. It also impacts on the climate system through atmospheric feedback. Soil moisture is a source of water for evapotranspiration over the continents, and is involved in both the water and the energy cycles. Soil moisture was recognized as an Essential Climate Variable (ECV) in 2010.
The Soil Moisture_cci project is part of the ESA Programme on Global Monitoring of Essential Climate Variables (ECV), better known as the Climate Change Initiative (CCI), initiated in 2010 and producing an updated soil moisture product every year. The ESA CCI Soil Moisture product has contributed to hundreds of hydrological and climatological studies worldwide, as well as the annual BAMS "State of the Climate" reports.
The CCI Soil Moisture project produces:
- Annually algorithmically updated global climate data record of soil moisture spanning over 40 years.
- 3 separate soil moisture products derived from active, passive and combined (active + passive).
- 14 public releases to date, each updated with new sensors and extended time series.
The dataset ingests soil moisture datasets derived from the sensors listed in the figures below. In particular, the following datasets are used (at ESA CCI SM v08.1):
- ASCAT-A, -B and -C data are generated through the HSAF soil moisture project which can be accessed on the EUMETSAT HSAF soil moisture website. At v08.1, version H119/120 of the data record is ingested.
- ERS data is processed by the Microwave Remote Sensing Group at TU Wien.
- All passive datasets are processed using the LPRMv7 algorithm, designed and implemented by Planet.
Root-Zone SM Study (ongoing)
While the surface soil moisture products delivered by ESA CCI have proven valuable contributions to many climate applications, particularly carbon and vegetation modellers have expressed a strong interest in long-term satellite-based root-zone soil moisture products for linking vegetation phenology and biomass carbon to moisture availability in the soil (Dorigo et al. 2017).
The goal of this study is to develop such a product based on the exponential filter method (Wagner et al. 1999; Albergel et al. 2008). More specifically, a global dataset spanning 1991-2022 period with daily temporal, and 0.25o spatial resolution, describing the moisture content in the soil column based on the effective plant rooting depth (see e.g., Fan et al. 2017), up to a maximum depth of 2 meters. Furthermore, this study aims to provide grid-scale uncertainty estimates using an uncertainty estimation scheme for the exponential filter method (Pasik et al., in review).
The resulting dataset will be evaluated against in situ measurements from the International Soil Moisture Network (Dorigo et al., 2021) as well as modelled (Munoz Sabater et al. 2021) and satellite-based (e.g., Al Bitar and Mahmoodi 2020) root-zone soil moisture products.
Al Bitar Ahmad, & Mahmoodi Ali. (2020, November 30). Algorithm Theoretical Basis Document (ATBD) for the SMOS Level 4 Root Zone Soil Moisture (Version v30_01). Zenodo. http://doi.org/10.5281/zenodo.4298572
Albergel, C., Rüdiger, C., Pellarin, T., Calvet, J.-C., Fritz, N., Froissard, F., Suquia, D., Petitpa, A., Piguet, B., and Martin, E.: From near-surface to root-zone soil moisture using an exponential filter: an assessment of the method based on in-situ observations and model simulations, Hydrology and Earth System Sciences, 12, 1323–1337, https://doi.org/10.5194/hess-12-1323-2008, 2008.
Dorigo, W., et al.: ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions, Remote Sensing of Environment, 203, 185–215, https://doi.org/https://doi.org/10.1016/j.rse.2017.07.001, 2017
Dorigo, W., et al.: The International Soil Moisture Network: serving Earth system science for over a decade, Hydrology and Earth System Sciences, 25, 5749–5804, https://doi.org/10.5194/hess-25-5749-2021, 2021.
Fan, Y., Miguez-Macho, G., Jobbagy, E., Jackson, R. and Otero-Casal, C.: Hydrologic regulation of plant rooting depth, PNAS, 114 (40), 10572-15077, https://doi.org/10.1073/pnas.1712381114, 2017.
Muñoz Sabater, J., at al.: ERA5-Land: a state-of-the-art global reanalysis dataset for land applications, Earth System Science Data, 13, https://doi.org/10.5194/essd-13-4349-2021, 2021.
Wagner, W., Lemoine, G., and Rott, H.: A Method for Estimating Soil Moisture from ERS Scatterometer and Soil Data, Remote Sensing of Environment, 70, 191–207, https://doi.org/10.1016/S0034-4257(99)00036-X, 1999.
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