Fine Resolution Mapping of Soil Organic Carbon in Croplands with Feature Selection and Machine Learning in Northeast Plain China  
[PERSON]  
1 Institute of Digital Agriculture, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China; [EMAIL_ADDRESS]  
[PERSON]  
2 Institute of Applied Remote Sensing and Information Technology, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China; [EMAIL_ADDRESS]  
[PERSON]  
4 ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou 3101215, China; [EMAIL_ADDRESS]  
[PERSON]  
2 Institute of Applied Remote Sensing and Information Technology, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China; [EMAIL_ADDRESS]  
[PERSON]  
2 Institute of Applied Remote Sensing and Information Technology, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China; [EMAIL_ADDRESS]  
[PERSON]  
2 Institute of Applied Remote Sensing and Information Technology, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China; [EMAIL_ADDRESS]  
[PERSON]  
2 Institute of Applied Remote Sensing and Information Technology, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China; [EMAIL_ADDRESS]  
[PERSON]  
2 Institute of Applied Remote Sensing and Information Technology, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China; [EMAIL_ADDRESS]  
[PERSON]  
6 College of Land Science and Technology, China Agricultural University, Beijing 100193, China; [EMAIL_ADDRESS]  
[PERSON]  
1 Institute of Digital Agriculture, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China; [EMAIL_ADDRESS]  
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the climate change problem [2]. Additionally, SOC affects nutrient mineralization and aggregate stability, which is vital to crop growth and food production [3]. Having a cropland area of about 2.99 \(\times\) 10\({}^{5}\) km\({}^{2}\), the Northeast China Plain is the central basement of marketable grain [4]. The flat terrain and high SOC make it possible to perform mechanized operations which greatly increase food production capabilities but cause the risk of subsoil compaction [5]. Compaction can destroy the stability of soil aggregates and stress the growth of plant roots, which can finally cause a decrease in water and fertilizer retention, as well as massive loss of nutrients, land erosion, and degradation [6]. Thus, to acquire up-to-date soil health conditions and effectively assess agricultural production capacity in the area, it is important to dynamically investigate SOC in the plough and compacted layer after long-term cultivation. Based on the soil-landscape theory, Digital soil mapping (DSM) is a predictive technology of spatial soil information, which can rapidly predict soil information [7]. With the application of machine learning (ML) algorithms in DSM since the 2010s, there is a great increase in the predicted accuracy and calculated efficiency of DSM, broadening DSM application prospects in various soil investigation [8; 9]. At the global scale, using the recursive feature elimination feature selection algorithm and Quantile Random Forest (QRF), [PERSON] et al. [10] produced a series of global texture, coarse fragments, bulk density (BD), SOC content, pH, total nitrogen (TN), and cation exchange capacity (CEC) maps at six depths with 240,000 soil profiles and 400 environmental covariates. At the country scale, combining Earth observation data (i.e., Landsat bare soil spectral reflectance composite) and environmental auxiliary information (e.g., terrain attributes), [PERSON] et al.