Journal of Integrative Agriculture ›› 2023, Vol. 22 ›› Issue (9): 2882-2892.DOI: 10.1016/j.jia.2023.02.038

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基于土壤转换函数的中国土壤温度状况预测及划分

  

  • 收稿日期:2022-10-13 接受日期:2022-11-26 出版日期:2023-09-20 发布日期:2023-09-14

Predicting and delineating soil temperature regimes of China using pedotransfer function

BAO Wan-kui1, 2*, LEI Qiu-liang2*, JIANG Zhuo-dong1#, SUN Fu-jun1, ZHANG Tian-peng2, HU Ning3, WANG Qiu-bing1#   

  1. 1 College of Land and Environment, Shenyang Agricultural University, Shenyang 110866, P.R.China

    2 Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 10081, P.R.China

    3 School of Food and Biological Engineering, Hezhou University, Hezhou 542899, P.R.China

  • Received:2022-10-13 Accepted:2022-11-26 Online:2023-09-20 Published:2023-09-14
  • About author:BAO Wan-kui, E-mail: baowankui@caas.cn; LEI Qiu-liang, E-mail: leiqiuliang@caas.cn; #Correspondence JIANG Zhuo-dong, E-mail: zhuodongjiang@163.com; WANG Qiu-bing, E-mail: qbwang@syau.edu.cn * These authors contributed equally to this study.
  • Supported by:
    The study was funded by the National Key Basic Research Special Foundation of China (2021FY100405), the National Natural Science Foundation of China (U20A20114, 42201069 and 42077002), and the Fundamental Research Funds for Central Non-profit Scientific Institution, China (1610132018012).

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摘要：

土壤温度状况对土壤分类和土地利用至关重要。按照中国土壤系统分类，土壤温度状况通常是依据土表下50 cm深度处的年均土壤温度（MAST50）来确定的。然而，由于缺乏多年实测数据且难以准确估算MAST50，中国土壤温度状况的预测和划分目前仍然是土壤学研究的关键问题。本研究通过探究MAST50空间分布与环境因素之间的关系，预测MAST50在全国范围内的空间分布，并生成中国土壤温度状况空间分布图。本研究基于全国386个国家气象站1971-2000年的MAST50，以及全国2048个国家气象站1971-2000年的年均气温（MAAT），利用集成线性回归克里格模型，建立了用以预测我国各个区域MAST50的分区土壤转换函数模型。研究结果表明，MAAT是影响MAST50最重要的环境因素。本研究进一步评估所建立模型的精度，基于验证数据集的分区模型平均绝对误差（MAE）和均方根误差（RMSE）分别是0.66 oC和0.78 oC，而全国统一模型的MAE和RMSE分别为0.83 oC和0.96 oC。结果表明，本研究提出的分区土壤转换函数模型精度较高，可以明显提高MAST50预测的准确性。基于此，本研究进一步预测并划分了全国土壤温度状况空间分布。研究结果不仅进一步完善了中国土壤系统分类，还为促进分类成果广泛应用，以及土地可持续利用和管理提供了数据支撑。

Abstract: Soil temperature regime (STR) is important for soil classification and land use.  Generally, STR is delineated by estimating the mean annual soil temperature at a depth of 50 cm (MAST50) according to the Chinese Soil Taxonomy (CST).  However, delineating the STR of China remains a challenge due to the difficulties in accurately estimating MAST50.  The objectives of this study were to explore environmental factors that influence the spatial variation of MAST50 and generate an STR map for China.  Soil temperature measurements at 40 and 80 cm depth were collected from 386 National Meteorological Stations in China during 1971–2000.  The MAST50 was calculated as the average mean annual soil temperature (MAST) from 1971–2000 between 40 and 80 cm depths.  In addition, 2 048 mean annual air temperature (MAAT) measurements from 1971 to 2000 were collected from the National Meteorological Stations across China.  A zonal pedotransfer function (PTF) was developed based on the ensemble linear regression kriging model to predict the MAST50 in three topographic steps of China.  The results showed that MAAT was the most important variable related to the variation of MAST50.  The zonal PTF was evaluated with a 10% validation dataset with a mean absolute error (MAE) of 0.66°C and root mean square error (RMSE) of 0.78°C, which were smaller than the unified model with MAE of 0.83°C and RMSE of 0.96°C, respectively.  This study demonstrated that the zonal PTF helped improve the accuracy of the predicted MAST50 map.  Based on the prediction results, an STR map across China was generated to provide a consistent scientific base for the improvement and application of CST and land use support.

Key words: soil temperature ,  soil temperature regimes ,  Soil Taxonomy ,  pedotransfer function