Pedotransfer functions (PTFs) are widely used to estimate soil hydraulic properties (SHPs) from easily measurable characteristics. However, most existing PTFs rely on unimodal hydraulic models, which fail to accurately represent the bimodal SHPs caused by soil structure common in field conditions. In this study, we developed new PTFs using two bimodal soil hydraulic models and introduced soil physics-informed neural networks (SPINN) to embed the models into PTF training. The results showed that the new PTFs effectively captured bimodality in hydraulic conductivity curves, achieving an RMSE of 0.578 in the test set, compared to 0.709 for unimodal models. The PTFs also improved soil water retention curve (SWRC) predictions but struggled with bimodal SWRCs for some samples, likely due to the limited number of bimodal SWRCs in the dataset. An independent dataset evaluation revealed that the RMSE for hydraulic conductivity predicted by the new PTFs was approximately one-third of that of classic PTFs. This underscores the significant role of soil structure in SHPs, which classic PTFs fail to capture. Additionally, PTFs developed using the SPINN method outperformed those optimized fitted hydraulic parameters via machine learning, a common approach in the literature. We also found that separate versus simultaneous optimization of water retention and hydraulic conductivity greatly affects PTF performance. Finally, we provided global 1 km-resolution maps of soil hydraulic parameters for the bimodal model.

The Krakatau volcano erupted in 1883 and created a unique ecosystem where the surrounding islands were completely sterilised. While volcanic activity and plant succession have been extensively studied in the Krakatau islands, the soils received less attention. As the age of the parent material is known precisely, and the islands are isolated, soils of Krakatau islands could provide insights on the first stages of weathering in tropical volcanic regions. This study aims to characterize soils from the Krakatau islands. In 2015, ten sampling sites were selected from Mt. Anak Krakatau, Rakata, Panjang, and Sebesi islands, all making part of the Krakatau island complex. Field morphology was observed from representative profiles on each island. Soil samples were collected and analysed for physical and chemical properties. The geochemical analysis was carried out using the X-ray fluorescence (XRF). Linear discriminant analysis was used to separate materials from the four islands based on their chemical and geochemical concentrations. While the four islands were nearby and influenced by the 1883 eruption of Mt. Krakatau, the analysis showed that the chemical and geochemical characteristics of volcanic ash for each island are distinct. Discriminant analysis of chemical and geochemical properties differentiated soils of Anak Krakatau as the youngest ones, and soils of Sebesi are the most developed ones. The following sequence of the soil weathering degree was established: Sebesi > Rakata > Panjang > Anak Krakatau.