An estimate of uncertainty is essential to understanding what information is conveyed by data and how it relates to the wider context of what one intended to measure. It can be difficult to know how t...

Abstract. The ESA CCI Soil Moisture multi-satellite climate data record is a widely used dataset for large-scale hydrological and climatological applications and studies. However, data gaps in the rec...

Earth observation data should inform decision making, but good decisions can only be made if the uncertainties in the data are taken into account. Making sense of uncertainty information can be diffic...

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Explore ESA's latest and ground-breaking global soil moisture dataset, spanning 40 years (1978-2023). Developed by the CCI Soil Moisture team, this trusted dataset is cited by the IPCC's 6th Assessmen...

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Abstract. Soil moisture is a key variable in monitoring climate and an important component of the hydrological, carbon, and energy cycles. Satellite products ameliorate the sparsity of field measurements but are inherently limited to observing the near-surface layer, while water available in the unobserved root-zone controls critical processes like plant water uptake and evapotranspiration. A variety of approaches exist for modelling root-zone soil moisture (RZSM), including approximating it from surface layer observations. While the number of available RZSM datasets is growing, they usually do not contain estimates of their uncertainty. In this paper we derive a long-term RZSM dataset (2002–2020) from the Copernicus Climate Change Service (C3S) surface soil moisture (SSM) COMBINED product via the exponential filter (EF) method. We identify the optimal value of the method's model parameter T, which controls the level of smoothing and delaying applied to the surface observations, by maximizing the correlation of RZSM estimates with field measurements from the International Soil Moisture Network (ISMN). Optimized T-parameter values were calculated for four soil depth layers (0–10, 10–40, 40–100, and 100–200 cm) and used to calculate a global RZSM dataset. The quality of this dataset is then globally evaluated against RZSM estimates of the ERA5-Land reanalysis. Results of the product comparison show satisfactory skill in all four layers, with the median Pearson correlation ranging from 0.54 in the topmost to 0.28 in the deepest soil layer. Temporally dynamic product uncertainties for each of the RZSM product layers are estimated by applying standard uncertainty propagation to SSM input data and by estimating structural uncertainties in the EF method from ISMN ground reference measurements taken at the surface and at varying depths. Uncertainty estimates were found to exhibit both realistic absolute magnitudes and temporal variations. The product described here is, to the best of our knowledge, the first global, long-term, uncertainty-characterized, and purely observation-based product for RZSM estimates up to 2 m depth.

Irrigation accounts for a substantial proportion of global water usage and can have biophysical and biogeochemical impacts on Earth systems. This Review outlines key irrigation–Earth system interactions, and discusses the effect of future climate and socioeconomic changes on irrigation patterns and their interaction.

Abstract. The moisture content of vegetation canopies controls various ecosystem processes such as plant productivity, transpiration, mortality, and flammability. Leaf moisture content (here defined as the ratio of leaf water mass to leaf dry biomass, or live-fuel moisture content, LFMC) is a vegetation property that is frequently used to estimate flammability and the danger of fire occurrence and spread, and is widely measured at field sites around the globe. LFMC can be retrieved from satellite observations in the visible and infrared domain of the electromagnetic spectrum, which is however hampered by frequent cloud cover or low sun elevation angles. As an alternative, vegetation water content can be estimated from satellite observations in the microwave domain. For example, studies at local and regional scales have demonstrated the link between LFMC and vegetation optical depth (VOD) from passive microwave satellite observations. VOD describes the attenuation of microwaves in the vegetation layer. However, neither were the relations between VOD and LFMC investigated at large or global scales nor has VOD been used to estimate LFMC. Here we aim to estimate LFMC from VOD at large scales, i.e. at coarse spatial resolution, globally, and at daily time steps over past decadal timescales. Therefore, our objectives are: (1) to investigate the relation between VOD from different frequencies and LFMC derived from optical sensors and a global database of LFMC site measurements; (2) to test different model structures to estimate LFMC from VOD; and (3) to apply the best-performing model to estimate LFMC at global scales. Our results show that VOD is medium to highly correlated with LFMC in areas with medium to high coverage of short vegetation (grasslands, croplands, shrublands). Forested areas show on average weak correlations, but the variability in correlations is high. A logistic regression model that uses VOD and additionally leaf area index as predictor to account for canopy biomass reaches the highest performance in estimating LFMC. Applying this model to global VOD and LAI observations allows estimating LFMC globally over decadal time series at daily temporal sampling. The derived estimates of LFMC can be used to assess large-scale patterns and temporal changes in vegetation water status, drought conditions, and fire dynamics.

The southern Pacific Ocean has been locked in its La Niña phase for three winters running. That is unusual, and dangerous

The data descriptor paper can be accessed here: https://doi.org/10.5194/essd-14-1063-2022----------------------------------------------------------------------------------------------------------------------------------------------------------------------Gross Primary Productivity (GPP) describes the amount of carbohydrates that is produced by vegetation's synthesis of CO2 and is therefore crucial in the assessment of the global carbon cycle. VODCA2GPP represents the first microwave remote sensing derived GPP dataset and covers the period between 1988-2020. The data is sampled on a regular 0.25°x 0.25° grid and is based on the novel sink-driven GPP estimation approach introduced by Teubner et al. (2019) and Teubner et al. (2021). It utilizes the new merged-frequency Vegetation Optical Depth Climate Archive (VODCA CXKu; Zotta et al., in preparation) in combination with ERA5-Land air temperature data to produce a coherent long-term data record of global GPP. The dataset also includes an uncertainty metric (var_name: 'Uncertainties') which indicates regions where VODCA2GPP estimates tend to be less robust. We advise users to take these uncertainties into account when analyzing the VODCA2GPP data. For more details concerning the production of VODCA2GPP and its accuracy assessment please be referred to our dataset paper which was published in Earth System Science Data Wild et al. (2022).