Application of machine learning-based pedotransfer functions to produce large-scale maps of particle size fractions using big legacy data

Spatially detailed quantitative data regarding soil physical/hydraulic properties is in high demand for a range of modeling applications. EU-SoilHydroGrids has demonstrated its utility at the European level. HU-SoilHydroGrids, has been developed for the whole area of Hungary at 100 m spatial resolution with several enhancements in its elaboration process. A further step toward larger spatial resolution is based on NATASA (Hungarian acronym for Profile-level Database of Hungarian Large-Scale Soil Mapping) initiative to produce large-scale 3D Soil Hydraulic Databases (LS-HU-SoilHydroGrids).

Digitial processing of the soil observation records of the still available soil observation legacy data originating from large-scale surveys carried out in Hungary between the 60s and 90s was firstly finalized for the watershed of the Lake Balaton in order to support hydrological modelling studies on the catchment. The digitized soil observations are firstly used in digital mapping of primary soil properties at a scale of 25 meters, which DSM products then will be similarly adapted as the 100 m resolution DOSoReMI.hu products for the derivation of soil hydraulic property predictions down to 2 meters for six standard GSM soil depth layers, thus providing the “Balaton watershed LS-HU-SoilHydroGrids”.

Prior to step forward particle size fractions (i.e., sand, silt, and clay contents) were targeted to be mapped in a case study based since NATASA includes information on soil taxonomy and basic soil chemical and physical properties, but no direct information on sand, silt and clay content, only an indirect parameter, namely, the upper limit of soil plasticity. Since particle size distribution is not only crucial for assessing soil degradation, hydrology and fertility, but also a basic information to model the planned hydraulic properties, we developed pedotransfer functions (PTFs) to compute the particle size distribution from the soil properties available in the NATASA dataset (1,372 soil profiles). The PTFs were trained and tested on the Hungarian Detailed Soil Hydrophysical Database (3,970 soil profiles) using random forest method. For the prediction model, i) additive log-ratio transformed clay, silt and sand content were used as the dependent variables, and ii) the upper limit of soil plasticity, soil type, calcium carbonate content, organic matter content and pH were included as independent variables. The results indicate that the R² values of the PTFs are 0.69 for clay, 0.58 for silt, and 0.74 for sand content. Since the NATASA database contains soil information from different depths, we splined the data into six standard depth layers (0–5, 5–15, 15–30, 30–60, 60–100 and 100–200 cm depths). The spatial modelling was performed by random forest kriging (RFK) using environmental auxiliary variables. The R² values of the RFK models range from 0.19 to 0.67 for clay content, from 0.49 to 0.62 for silt content and from 0.69 to 0.74 for sand content.

 

Acknowledgement: This work has been carried out within the framework of the Széchenyi Plan Plus program with the support of the RRF 2.3.1 21 2022 00008 project and the Sustainable Development and Technologies National Programme of the Hungarian Academy of Sciences.