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Many researches have been conducted to understand the scale-location specific variations of soil properties in one-dimensional soil samples. However, these results did not enable us to properly understand the 2D (two-dimensional) distributions of soil characteristics, especially in the coal mining areas. Therefore, the objective of this study is to reveal the scale-location dependent variation of two-dimensional patterns for soil properties and to develop a soil organic matter (SOM) prediction based on those scale-location effects in the coal mining area. To this end, SOM, porosity, and silt were measured in the top (0-20 cm), middle (20-40 cm), and bottom (40-60 cm) layers of soil in Changhe watershed, China. The scale-specific spatial patterns of soil properties were extracted using two-dimensional empirical mode decomposition (2DEMD), and SOM was predicted using stepwise multiple linear regression (SMLR) and 2DEMD GS. It was found that the spatial distributions of SOM residue (>24 km) at middle layer and porosity residue at top and bottom layers could reveal the differentiation between coal mining and non-coal mining areas. The scale-effects on the relationships between SOM and its covariates was stronger than that of land uses, and the relationships of SOM with the covariates in different land use types were distinctive at top layer, and were similar at bottom layer. 2DEMD GS performed better than SMLR on SOM prediction for its involvement of scale and location effects. Therefore, the relationship between SOM and influencing factors is a function of scale, location, and land uses activities, and the scale-and location-dependent method should be considered for SOM prediction. K E Y W O R D S bidimensional intrinsic mode function (BIMF), coal mining area, land use type, soil organic matter (SOM), two-dimensional empirical mode decomposition (2DEMD) 1 | INTRODUCTION Soil is one of the most valuable nonrenewable resources (Lal, 2015) and serves as an nexus for the atmosphere, hydrosphere, biosphere, and lithosphere (Alcañiz, Outeiro, Francos, & Úbeda, 2018). Previous studies indicated that minesoils undergo rapid changes in chemical, physical, and biological properties as a result of mining activities and pedogenic weathering processes (Shrestha & Lal, 2007; Skousen, 1995), and their spatial variation differed greatly compared with those in the non

1) Background: Coal mining operations caused severe land subsidence and altered the distributions of soil nutrients that influenced by multiple environmental factors at different scales. However, the prediction performances for soil nutrients based on their scale-specific relationships with influencing factors remains undefined in the coal mining area. The objective of this study was to establish prediction models of soil nutrients based on their scale-specific relationships with influencing factors in a coal mining area. (2) Methods: Soil samples were collected based on a 1 × 1 km regular grid, and contents of soil organic matter, soil available nitrogen, soil available phosphorus, and soil available potassium were measured. The scale components of soil nutrients and the influencing factors collected from remote sensing and topographic factors were decomposed by two-dimensional empirical mode decomposition (2D-EMD), and the predictions for soil nutrients were established using the methods of multiple linear stepwise regression or partial least squares regression based on original samples (MLSR Ori or PLSR Ori ), partial least squares regression based on bi-dimensional intrinsic mode function (PLSR BIMF ), and the combined method of 2D-EMD, PLSR, and MLSR (2D-EMD PM ).(3) Results: The correlation types and correlation coefficients between soil nutrients and influencing factors were scale-dependent. The variances of soil nutrients at smaller scale were stochastic and non-significantly correlated with influencing factors, while their variances at the larger scales were stable. The prediction performances in the coal mining area were better than those in the non-coal mining area, and 2D-EMD PM had the most stable performance. (4) Conclusions: The scale-dependent predictions can be used for soil nutrients in the coal mining areas.

Vegetation dynamic is sensitive to climatic warming, and is affected by individual or combined climatic factors at different temporal scale with different intensity. Previous studies have unraveled the relationships between vegetation condition and individual climatic factors; however, it is unclear whether the effects of single or combined climatic factors on vegetation dynamic was dominant for different temporal scales, vegetation types, and climatic regions. The objective of this study was to explore the scale-specific univariate and multivariate controls on vegetation over the period 1982-2015 using bivariate wavelet coherency (BWC), multiple wavelet coherence (MWC), and multiple empirical model decomposition (MEMD). The results indicated that the significant vegetation dynamics were mainly located at scales of 1, 0.5, and 0.3 years. The combined explanatory power of the seven climatic factors on the vegetation were greater at the short-term and long-term scales, while the individual climatic factor might affect vegetation dynamic in the seasonal and medium-term scales at some climatic regions. The combined effect of climatic factors in grassland of Tibetan Plateau (TP) and Tempera grassland of Inner Mongolia (TGIM) regions were the greatest, which were 65.06% and 59.53%, respectively. The explanatory powers of climate for crop dynamics between temperate humid & subhumid Northeast China (THSNC) and TP, warmtemperate humid & subhumid North China (WHSNC) and subtropical humid Central & South China (SHCSC), and TGIM and temperate & warm-temperate desert of Northwest China (TWDNC) were equivalent, which were around 47%, 45%, and 39%, respectively. Farming practices in cropland could alleviate the spatial variation of the relationships between climate and vegetation, while enhance the temporal difference of their relationships. Additionally, the dominant influencing factor among different regions varied greatly in the medium-term scale. Collectively, the results might provide alternative perspective for understanding vegetation evolution in response to climatic changes in China.