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Climate and Environmental Remote Sensing @TU Wien GEO

@climers.bsky.social

🛰️ Earth observation data and applications for a sustainable future 🌍 🔗 https://www.tuwien.at/en/mg/geo/climers

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Climate and Environmental Remote Sensing @TU Wien GEO@climers.bsky.social · 11mo ago

🛠️ Turning uncertainty into a tool for better climate data 🛰️📊 Our colleague Alexander Gruber, with Adam Povey & Claire Bulgin, co-authored a new Surveys in Geophysics paper on how to make the best use of uncertainty in essential climate variables. 🔗 link.springer.com/article/10.1...

A Practical Introduction to Utilising Uncertainty Information in the Analysis of Essential Climate Variables - Surveys in Geophysics

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...

link.springer.com

Climate and Environmental Remote Sensing @TU Wien GEO@climers.bsky.social · 11mo ago

📢 New publication 💧 Wolfgang Preimesberger, Pietro Stradiotti & @wouterdorigo.bsky.social present the @esa.int CCI Soil Moisture GAPFILLED record 🌍🛰️ (1991–2023), based on 19 satellites w/ uncertainty estimates & validation. 🔗 Paper: doi.org/10.5194/essd... 🔗 Dataset: doi.org/10.48436/hcm...

ESA CCI Soil Moisture GAPFILLED: an independent global gap-free satellite climate data record with uncertainty estimates

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...

doi.org

Climate and Environmental Remote Sensing @TU Wien GEO@climers.bsky.social · 12mo ago

🚨📄 Paper alert! Our latest paper in Surveys in Geophysics provokes a paradigm shift for the way we think about remote sensing data uncertainties. Our innovative approach can help decision-makers to make better-informed decisions based on Earth observation data. doi.org/10.1007/s107...

Making Sense of Uncertainties: Ask the Right Question - Surveys in Geophysics

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...

doi.org

Climate and Environmental Remote Sensing @TU Wien GEO@climers.bsky.social · last yr.

🚨📄 Paper alert! 🎉 Congrats to long-term collaborator Richard de Jeu on his new paper unlocking Ka-band microwave for land-surface temp & vegetation monitoring. 🌍📡 @wouterdorigo.bsky.social @ruxizotta.bsky.social 🔗 doi.org/10.3389/frsen.2025.1574072 #RemoteSensing #KaBand #VOD

Frontiers | Analyzing satellite and airborne Ka-band passive microwave observations over land for temperature and vegetation monitoring

doi.org

Climate and Environmental Remote Sensing @TU Wien GEO@climers.bsky.social · 3y ago

📢Paper Alert📷Check out our latest publication about an error-characterized global long-term root-zone soil moisture product created from @copernicus C3S soil moisture data: https://t.co/dAWnEImhcH%E2%80%A6 funded by @H2020Projects

Uncertainty estimation for a new exponential-filter-based long-term root-zone soil moisture dataset from Copernicus Climate Change Service (C3S) surface observations

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.

doi.org