Detecting Field-level Crop Residue Cover across the EU Using Multi-source Satellite Data and Explainable Machine Learning

Abstract

Conservation tillage practices with crop residues covering soils can improve soil health to increase agronomic and environmental benefits for croplands. Accurate information of field-level crop residue cover is highly important for evaluating the implementation of government conservation programs and voluntary ecosystem service markets, as well as supporting agroecosystem modeling to quantify cropland biogeochemical processes. Remote sensing has been demonstrated to detect crop residue cover cost-effectively, yet existing regional-scale studies in Europe are rare. To fill this data gap, our study developed an explainable machine learning algorithm to integrate multi-source satellite data (Sentinel-2, Sentinel-1, and SMAP soil moisture) to quantify crop residue presence for the EU croplands. Specifically, we utilized satellite time series data of optical spectral tillage index, soil background reflectance, soil moisture, and SAR backscattering information to detect field-level crop residue cover. With 41,325 ground records of 10 major crops, we developed highly robust and explainable machine learning models with unbalanced label correction approaches to predict residue presence. Results show that models achieved high accuracy of 0.78 and F1-score of 0.70 to detect crop residue presence. We also aggregated field-level estimates to the regional level, which shows high match with regional census data. Among crop types, wheat and barley got higher prediction performance than other crop types. Our work highlights the feasibility of integrating multi-source satellite data with machine learning for detecting field-level crop residue cover at continental scale across the EU. These crop residue datasets can support analyzing the spatiotemporal variability of tillage practices across the EU and their potential impact on agroecosystem productivity and sustainability.