Mapping dynamic soil properties at high spatial resolution using spatio-temporal Machine Learning: towards a consistent framework for monitoring soil health across borders

There is an increasing need for dynamic soil information, especially focused on monitoring soil health indicators such as soil organic carbon, soil chemical properties, soil pollution / soil degradation and status of soil micro-, meso- macro-fauna. To detect change over time, AI4SoilHealth project (https://cordis.europa.eu/project/id/101086179) is developing a spatiotemporal Machine Learning framework based on large EO data cubes (Witjes et al., 2023) to model soil health indicators e.g. to map them at high spatial resolution (30-m) with annual increments (2000–2022+) and for standard depth intervals (e.g. 0–30 cm, 30–60 cm, 60–100 cm). Produced time-series of predictions are then analyzed for trends and slope and similar coefficients are derived (per pixel) showing positive and negative changes in soil health indicators over longer periods of time (25+ years) (for the time-series method see: Hackländer et al. 2024). Areas where the trends are especially negative (e.g. significant decrease in soil carbon, significant salinization, significant loss of land cover / FAPAR etc) are flagged as requiring further soil sampling and detection of drivers of soil degradation, which should be ideally done jointly with national soil monitoring system in Europe.

The difference between spatiotemporal vs purely 2D / 3D mapping is in the three main aspects: (1) points and covariate layers are matched in spacetime (usually month-year period or at least year), (2) covariate layers are based on time-series data and include also accumulative indices (e.g. cumulative rainfall, cumulative snow cover, cumulative cropping fraction and similar) and derivatives, (3) during model training and validation, points are subset in both spacetime to avoid overfitting and bias in predictions. The rationale for using spatiotemporal machine learning is fitness of data for reliable time-series analysis: the predictions for anywhere in the spacetime cube need to be unbiased, with objectively quantified prediction errors (uncertainty), so that hence changes can be derived without a risk for serious over-/under-estimation. This framework has been tested on local and regional data sets (e.g. LUCAS soil samples covering 2009, 2012, 2015, 2018 for Europe) and can be now potentially applied using global compilations of soil points (https://opengeohub.github.io/SoilSamples/). Spatiotemporal machine learning could also potentially be used for predicting future states of soil, e.g. by extrapolating models to future climate scenarios and future land use systems (Bonannella et al., 2023). We are currently building a Soil Health Data Cube for Europe that will include some 15–20 biophysical indices (annual tillage index, bare surface cover, bimonthly FAPAR, NDWI, SAVI), climatic, terrain and parent material covariates and including the time-series of predictions of the key soil properties. This data will be made available under open data license through https://EcoDataCube.eu.

Cited references:

• Bonannella, C., et al. (2023). Biomes of the world under climate change scenarios: increasing aridity and higher temperatures lead to significant shifts in natural vegetation. PeerJ, 11, e15593. https://doi.org/10.7717/peerj.15593 
• Hackländer, J., et al. (2023). Land potential assessment and trend-analysis using 2000–2021 FAPAR monthly time-series at 250 m spatial resolution. PeerJ, in review. https://doi.org/10.21203/rs.3.rs-3415685/v1
• Witjes, M., et al. (2023). Ecodatacube. eu: Analysis-ready open environmental data cube for Europe. PeerJ, 11, e15478. https://doi.org/10.7717/peerj.15478