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Microbial moribunds after microbial biomass turnover (microbial residues) contribute to the formation and stabilization of soil carbon pools; however, the factors influencing their accumulation on a global scale remain unclear. Here, we synthesized data for 268 amino sugar concentrations (biomarkers of microbial residues) in grassland and forest ecosystems for meta-analysis. We found that soil organic carbon, soil carbon-to-nitrogen ratio, and aridity index were key factors that predicted microbial residual carbon accumulation. Threshold aridity index and soil carbon-to-nitrogen ratios were identified (~0.768 and ~9.583, respectively), above which microbial residues decreased sharply. The aridity index threshold was associated with the humid climate range. We suggest that the soil carbon-to-nitrogen ratio threshold may coincide with a sharp decrease in fungal abundance. Although dominant factors vary between ecosystem and climate zone, with soil organic carbon and aridity index being important throughout, our findings suggest that climate and soil environment may govern microbial residue accumulation.

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Leaf area index (LAI) is a fundamental indicator of crop growth status, timely and non-destructive estimation of LAI is of significant importance for precision agriculture. In this study, a multi-rotor UAV platform equipped with CMOS image sensors was used to capture maize canopy information, simultaneously, a total of 264 ground‐measured LAI data were collected during a 2-year field experiment. Linear regression (LR), backpropagation neural network (BPNN), and random forest (RF) algorithms were used to establish LAI estimation models, and their performances were evaluated through 500 repetitions of random sub-sampling, training, and testing. The results showed that RGB-based VIs derived from UAV digital images were strongly related to LAI, and the grain-filling stage (GS) of maize was identified as the optimal period for LAI estimation. The RF model performed best at both whole period and individual growth stages, with the highest R2 (0.71–0.88) and the lowest RMSE (0.12–0.25) on test datasets, followed by the BPNN model and LR models. In addition, a smaller 5–95% interval range of R2 and RMSE was observed in the RF model, which indicated that the RF model has good generalization ability and is able to produce reliable estimation results.