Spatiotemporal dynamics and drivers of bare soil albedo in European croplands
Abstract. Bare soil albedo plays a critical role in regulating surface energy balance and land-atmosphere interactions in agricultural systems, yet its spatiotemporal variability and controlling factors remain poorly quantified at the field scale across heterogeneous cropland landscapes. To address this, we investigate the spatial patterns and temporal dynamics of bare soil albedo in European croplands. We develop a method to reconstruct field-scale, spatiotemporally continuous bare soil albedo at a 5-day temporal resolution and 0.3 km spatial resolution using Sentinel-2 reflectance observations. Bare soil periods are identified by multiple spectral indices and the corresponding soil albedo values are derived for the period 2018–2020 using a novel machine learning framework. Two random forest models were employed to separately capture the long-term spatial structure and short-term temporal anomalies of bare soil albedo, allowing gaps caused by clouds, snow, and vegetation cover to be bridged. Model evaluation against independent site observations and existing products shows that the estimated bare soil albedo reproduces observed spatial gradients and seasonal variability across European croplands. Such variations in bare soil albedo are jointly controlled by soil properties, observation geometry and short-term soil moisture dynamics rather than by any single factor. Because these variations are of the same order of magnitude as radiation management solutions, soil radiative properties must be considered in their assessment. The resulting bare soil albedo offers a process-oriented basis for improving the representation of surface radiative properties and land-atmosphere coupling in agroecosystem and land surface models.
Authors
- David Makowski (ORCID: https://orcid.org/0000-0001-6385-3703)
- Shengbiao Wu (ORCID: https://orcid.org/0000-0003-4569-6366)
- Petra Sieber (ORCID: https://orcid.org/0000-0003-2626-9502)
- Yang Su (ORCID: https://orcid.org/0000-0002-4717-9971)
- Tianqi Shi (ORCID: https://orcid.org/0000-0003-4815-4175)
- Daniel Sebastian Goll (ORCID: https://orcid.org/0000-0001-9246-9671)
- Philippe Ciais (ORCID: https://orcid.org/0000-0001-8560-4943)
- Ke Yu (ORCID: https://orcid.org/0000-0002-4208-9301)
- Ronny Lauerwald (ORCID: https://orcid.org/0000-0001-5554-0897)
- Chuanlong Zhou (ORCID: https://orcid.org/0000-0001-8848-8247)
Institutions
- Centre National de la Recherche Scientifique (FR)
- International Institute for Applied Systems Analysis (AT)
- Université Libre de Bruxelles (BE)
- Université de Versailles Saint-Quentin-en-Yvelines (FR)
- AgroParisTech (FR)
- Commissariat à l'Énergie Atomique et aux Énergies Alternatives (FR)
- Université Paris Sciences et Lettres (FR)
- Université Paris-Saclay (FR)
- École Normale Supérieure - PSL (FR)
- ETH Zurich (CH)
- Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (FR)
- Écologie Fonctionnelle et Écotoxicologie des Agroécosystèmes (FR)
- Institut Pierre-Simon Laplace (FR)
- Laboratoire des Sciences du Climat et de l'Environnement (FR)
- Institute for Atmospheric and Climate Science (CH)
- University of Hong Kong (HK)
Publication Details
- Journal
- Biogeosciences
- Published
- 2026-11-09
- DOI
- https://doi.org/10.5194/bg-23-7973-2026
- Primary Topic
- Remote Sensing in Agriculture
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Horizon 2020