Soil Data Cube and Artificial Intelligence Techniques for Generating National-Scale Topsoil Thematic Maps: A Case Study in Lithuanian Croplands

Abstract

There is a growing realization among policymakers that in order to pave the way for the development of evidence-based conservation recommendations for policy, it is essential to improve the capacity for soil-health monitoring by adopting multidimensional and integrated approaches. However, the existing ready-to-use maps are characterized mainly by a coarse spatial resolution (>200 m) and information that is not up to date, making their use insufficient for the EU’s policy requirements, such as the common agricultural policy. This work, by utilizing the Soil Data Cube, which is a self-hosted custom tool, provides yearly estimations of soil thematic maps (e.g., exposed soil, soil organic carbon, clay content) covering all the agricultural area in Lithuania. The pipeline exploits various Earth observation data such as a time series of Sentinel-2 satellite imagery (2018–2022), the LUCAS (Land Use/Cover Area Frame Statistical Survey) topsoil database, the European Integrated Administration and Control System (IACS) and artificial intelligence (AI) architectures to improve the prediction accuracy as well as the spatial resolution (10 m), enabling discrimination at the parcel level. Five different prediction models were tested with the convolutional neural network (CNN) model to achieve the best accuracy for both targeted indicators (SOC and clay) related to the R2 metric (0.51 for SOC and 0.57 for clay). The model predictions supported by the prediction uncertainties based on the PIR formula (average PIR 0.48 for SOC and 0.61 for clay) provide valuable information on the model’s interpretation and stability. The model application and the final predictions of the soil indicators were carried out based on national bare-soil-reflectance composite layers, generated by employing a pixel-based composite approach to the overlaid annual bare-soil maps and by using a combination of a series of vegetation indices such as NDVI, NBR2, and SCL. The findings of this work provide new insights for the generation of soil thematic maps on a large scale, leading to more efficient and sustainable soil management, supporting policymakers and the agri-food private sector.

Soil Loss Estimation by Water Erosion in Agricultural Areas Introducing Artificial Intelligence Geospatial Layers into the RUSLE Model

“…As presented, there is a correlation regarding the soil erosion values (with differences for various reasons, mentioned above); however, subsequent differences occurred in the spatial distribution of the soil layer and in the maps' spatial resolution. All three products present advantages and disadvantages; however, regarding the spatial resolution of the layers and considering also that about two-thirds (63.8%) of the EU's agricultural area consists of parcels less than 5 ha in size, it is a necessity to improve the spatial resolution of the maps [35], and the current work findings offer an alternative approach to this dimension.…”

“…To efficiently generate all the necessary layers as well as to handle and to process the large volume of EO data needed, the Soil Data Cube (SDC) was utilized. It is a self-hosted custom tool, powered by the Open Data Cube [33]; further details of the system may be found in [34,35]. Concerning the soil erosion process, the RUSLE formula was chosen, as it is among the most commonly used predictive erosion models and aligns perfectly with the objectives of this study.…”