Evaluation of the soil profile quality of subsided land in a coal mining area backfilled with river sediment based on monitoring wheat growth biomass with UAV systems.

Underground coal mining leads to land subsidence, and the situation is particularly serious in the Coal-Grain Complex in eastern China, causing the crop production to be reduced or to be taken out. Backfilling with Yellow River sediment is one of the effective methods to solve the land subsidence in this area, but a key issue is how to select the optimal soil reconstruction profile so that the crop yield after backfilling and reclamation is unaffected. The main purpose of this study is to verify the feasibility of selecting the optimal soil reconstruction profile by rapid monitoring of crop growth and judging soil quality with the aid of unmanned aerial vehicle systems (UAVs). A control treatment and 13 experimental treatments were established for the study area. The control treatment consisted of using 30 cm topsoil and 90 cm subsoil and the topsoil is a proxy for native (undisturbed) soil from the study sites. All other treatments consisted of using varying combinations of subsoil and sediment overlain by 30 cm of topsoil. The vegetation indices from the UAV multispectral images, and the plant height and vegetation coverage from the UAV RGB images were used for estimation of the winter wheat biomass in a random forest regression. The results showed that the random forest regression model yielded accurate estimation of the aboveground biomass. Furthermore, knowledge of plant height and vegetation coverage improved the accuracy of prediction such that crop growth was well characterized. The optimal soil profile consisted of 0.3 m topsoil + 0.2 m subsoil + 0.2 m sediment + 0.2 m subsoil + 0.3 m sediment. A fast and effective airborne monitoring method for soil quality was established, thus providing greatly improved monitoring efficiency.