## 3 Results
### Importance of Environmental Covariates  
Feature selection and modeling evaluation were used to estimate the importance of environmental covariates. Firstly, Boruta was used to reduce environmental covariates from 141 to 40 (soil and parent material: 16-7, climate: 32-15, organism: 54-8, relief: 29-4, position: 10-6).  
Figure 4: Spatial distribution pattern at 90 m spatial resolution of uncertainty at the compacted layer in (**a**) Northeast Plain, (**b**) Sanjiang Plain, (**c**) Songnen Plain, and (**d**) Liaohe Plain.  
Figure 3: Spatial distribution pattern at 90 m spatial resolution of uncertainty at the plough layer in (**a**) Northeast Plain, (**b**) Sanjiang Plain, (**c**) Songnen Plain, and (**d**) Liaohe Plain.  
In Boruta, the Z score varied from 3.19 to 11.91 on average. The variable interpretability based on the average Z score can be divided into three levels, i.e., low (<5), medium (5-10), and high relevance (>10). There were four soil and parent material variables (Silt, Sand, BSI, GI), four climate variables (BIO03, BIO07, BIO13, Tmax), three organism variables (CroCANIa: the average of CANI during the growth period of crops, NCroFPARa and NCroLAIa: the average of FPAR and LAI during the non-growth period of crops), three relief variables (ELE, VD, TWI), and one position variable (OGC60: OGC at 60\({}^{\circ}\)) in the low relevance level. A total of 20 variables in the medium relevance level, including three soil and parent material variables (SE, BI, Carl), nine climate variables (BIO04, BIO08, BIO09, BIO10, LSTD, LSTD, Sol, Tmin, VP), five organism variables (NCroNDRIa, NCroGPPa and NCroNPPa: the average of NDRI, GPP and NPP during the non-growth period of crops, NCroGPPv, and NCroNPPv: the variance of GPP and NPP during the non-growth period of crops), 1 relief variable (CNBL), and two position variables (OGC30 and OGC120: OGC at 30\({}^{\circ}\) and 120\({}^{\circ}\). The left two climate variables (BIO11, Tavg) and three position variables (OGC45, OGC105 and OGC165: OGC at 45\({}^{\circ}\), 105\({}^{\circ}\), and 165\({}^{\circ}\)) were in the high relevance level.  
Secondly, the %IncMSE of variables in the QRF model was used to subsequently explain the contributions of environmental covariates (Figures 5 and 6). There were significant differences of relative importance in five environmental covariates. Climate was the main controlling factor, with a relative importance of 42.56% and 48.02% in two layers, followed by position (29.27% and 23.91%), organism (16.88% and 16.88%), soil and parent material (8.74% and 6.56%), and relief (2.55% and 4.63%). Compared with the plough layer, the relative importance of the climate and relief was higher in the compacted layer, which was derived from the reduction in soil and parent material and the position. The change of relative importance in detailed variables is shown in Figure 6. To be more specific, Tavg (plough layer: 13.84%, compacted layer: 15.87%), LSTD (6.82%, 2.73%), LSTN (2.62%, 4.92%), and BIO09 (4.29%, 3.62%) were the dominant sub-climate variables. The NCroNPPa (3.95%, 5.16%) and NCroNPPv (3.71%, 4.69%) could explain most of the organism information, especially in the compacted layer. The position contributed more to the model in the plough layers, especially in OGC105 (11.05%, 5.80%), OGC165 (7.11%, 6.36%), and OGC45 (5.79%, 5.88%).  
Figure 5: Relative importance of environmental covariates by QRF at the plough layer and the compacted layer, S&P is the soil and parent material covariate, C is the climate covariate, O is the organism covariate, R is the relief covariate, and N is the location covariate.