Spatial Scaling Effects to Enhance the Prediction for the Temporal Changes of Soil Nitrogen Density From 2007 to 2017 in Different Climatic Basins - Ding, Haoxi; Hu, Wei; Zhu, Hongfen; Bi, Rutian (Frontiers in Ecology and Evolution, 2022)
Soil total nitrogen stock (STNS) is the main source of crop nitrogen requirement and the greenhouse gas nitrous oxide, and soil total nitrogen density (STND) can be used to assess STNS across space. The spatio-temporal distribution of top-layer STND, which differs widely due to the heterogeneity of environmental factors and human activities, are poorly understood. Thus, temporal change of STND (STNDTC) was created to evaluate the spatial and temporal variation of STND. In the study, three sampling transects under different climate-zone basins were established to explore the spatial-dependent STNDTC, construct the predicting models based on its scale-specific relations with environmental factors, and validate the models in each basin or in other climate-zone basins. The results indicated that the STNDTC is positive in the three basins, and the increment of STND was the greatest in the mid-temperate basin, and the variation of STNDTC ranked as cool-temperate > mid-temperate > warm-temperate basin. Under different soil types of different climate-zone basins, the spatial characteristics of STNDTC were different. Considering the land types, the averaged STNDTC under cropland was the greatest, and its variation was the lowest among different land types in each basin. Environmental factors, including climatic, topographic, and vegetation factors, had controls on STNDTC at the original or decomposed scales, and their effects were unstable at different scales and locations. Additionally, the optimum number of latent variable (ONLV) of partial least square regression (PLSR) calculated in the calibration procedure was more suitable for dependent STNDTC prediction in the same basin, and could not be validated for the independent validation data in other basins. Therefore, the temporal dynamics of STND was directly related to the spatial scales and locations, its spatial scaling relations with environmental factors could provide more information, and the combination of the extract information decomposed by wavelet transform and the method of PLSR could enhance the STNDTC prediction for dependent dataset. These findings are of great significance for future studies in the STNS dynamic modelling under the influence of environmental changes and human activities.