Announcements

ISMC/SOILPROM pollution working group meeting

On 24th of June the ISMC/SOILPROM pollution working group meeting took place and we had two excellent presentations. The first presentation was given by Thilo Hofmann from Vienna University entitled “Uptake and Transport of Plastics Additives in Soil ”. After a nice and comprehensive introduction of global plastic use and its fate, Thilo pointed to the importance of looking into the release of chemicals from plastics into the pore water and soils as well as plant uptake. The second presentation was given by Agatha Zamuner from Wageningen University entitled “Connecting the soil-atmosphere interface with MicroHH: Modelling the emission, transport & deposition of dust-bounded pollutants from the soil into the atmosphere”. Both presentations can be found on the ISMC Youtube Channel.

An Updated Global Daily Soil Moisture Product (1950-2025)

This dataset provides an improved global surface soil moisture product for the surface soil layer, named the adjusted ERA5-Land dataset. It was created by fusing ERA5-Land and SMAP L4 data using a mean-variance rescaling method optimized for long time-series alignment. The dataset offers daily temporal resolution at a spatial resolution of 0.1°, covering the continuous period from January 1950 to December 2025, and is provided as GeoTIFF files named by date (YYYYMMDD).
The dataset is validated against, to the best of our knowledge, one of the most extensive global in situ soil moisture compilation to date, comprising approximately 3.8 million records. These records are organized into a primary dataset for contemporary validation (2015-2020) and an independent historical dataset (1960-2015) for backward-extension assessment. The dataset can be further used for research and to support drought monitoring, weather prediction, and water resource management, contributing to global climate resilience and informed decision-making across diverse ecosystems. The paper to the data can be accessed here and the dataset itself can be downloaded here.

Featured Paper

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Pedotransfer functions for Peruvian soils: A web tool for dry bulk density estimation

Dry bulk density measurements are crucial in soil science for calculating soil mass and absolute contents of compounds such as carbon, nutrients, or contaminants. Despite its importance, bulk density is often omitted in soil survey due to the specialized equipment and time required for direct measurement. Pedotransfer functions provide an accurately and cost-effective alternative for estimating bulk density from readily available soil data. However, these equations face two key limitations: they lack universal applicability, requiring country-specific production or recalibration to account for national soil conditions and laboratory protocols, and their implementation remains challenging for end-users (e.g., farmers and agronomists), who need simplified tools to implement functions in field settings. Here we developed dry bulk density pedotransfer functions for Peruvian soil conditions and an open-access web tool to facilitate their application. A total of 15 pedotransfer functions were developed, 4 traditional and 11 machine learning-based, the latter including 3 models based on tabular deep learning. Model performance was evaluated based on the root mean square error (RMSE), goodness of fit (R2), and training time (TT). Statistical comparisons between the model predictions were performed with the Friedman test. Our results show that eXtreme Gradient Boosting machine (RMSE = 0.2215 Mg·m−3, R2 = 0.56, TT = 0.24 s) achieve the highest predictive performance. However, Friedman test revealed no statistically differences among most models, suggesting that traditional approaches, like the multiple linear regression (RMSE = 0.2475 Mg·m−3, R2 = 0.45, TT = 0.02 s), retain practical advantages due to their simplicity and practicality. Among tabular deep learning, only the Feature Tokenizer Transformer demonstrated competitive performance (RMSE = 0.2278 Mg·m−3, R2 = 0.54, TT = 223 s), other models showed limited predictive capability, likely due to constraints imposed by our training dataset size. The pedotransfer functions web tool enables end-users to access and utilize the developed models, thereby reducing the knowledge and application gaps. More can be found here.