SOC content of global Mollisols at a 30 m spatial resolution from 1984 to 2021 generated by the novel ML-CNN prediction model

Abstract

Carbon cycle is influenced by agricultural soils, and accurately mapping the soil organic carbon (SOC) content of global Mollisols at a 30 m spatial resolution can contribute to clarifying the carbon sequestration capacity of each region, facilitate the quantification of agroecosystems and contribute to global food security. However, the high heterogeneity of environmental variables in global regions, coupled with the challenges posed by small-sample tasks, creates significant obstacles to producing reliable SOC content datasets. In this study, we collected 191,465 scenes of Landsat TM and OLI images and elevation model data to calculate spectral indices that can represent soil formation information based on a soil-pedogenic model. Second, a local strategy (LS) was proposed to reduce the influence of the high heterogeneity of SOC content and environmental variables on the prediction results. More importantly, the first meta-learning convolutional neural network (ML-CNN) model was proposed. It provides high prediction accuracy for small-sample tasks and was used to generate the first high-resolution global Mollisol region SOC content product (GMR-MCNN). Finally, we compared GMR-MCNN with the existing SoilGrids250m and Soil SubCenter products. The results showed that long-term, high-accuracy and high-resolution prediction of the SOC content in global Mollisol regions was achieved by the ML-CNN model (RMSE = 4.84 g kg−1, R2 = 0.75, RPIQ = 2.43). Compared with a CNN, ML-CNN can continuously optimize the meta-task, thus improving the performance of the model in small-sample tasks. Compared to the prediction model that combined the recursive feature elimination technique with the random forest model (RFE-RF), ML-CNN can efficiently extract high-level features from time-series data, thus improving the model performance. Compared with that of the global strategy, the RMSE of the LS decreased by 0.20 g kg−1, and R2 and RPIQ increased by 13.00% and 0.22, respectively. In addition, the GMR-MCNN results illustrated that the SOC content in the global Mollisol regions shows a decreasing trend, and the trend can be divided into significant decrease (1984–2000) and moderate decrease (2001−2021) phases. Different products were tested based on laboratory-measured SOC contents, and GMR-MCNN (RMSE = 6.13 g kg−1, R2 = 0.63) displayed better performance than SoilGrids250m (RMSE = 23.37 g kg−1, R2 = 0.28) and the Soil SubCenter map (RMSE = 8.59 g kg−1, R2 = 0.43). The developed methodology can provide a reference for the long-term observation of soil and crop properties at moderate and high resolutions globally.