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首页> 外文期刊>The Science of the Total Environment >Mapping dynamics of soil organic matter in croplands with MODIS data and machine learning algorithms
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Mapping dynamics of soil organic matter in croplands with MODIS data and machine learning algorithms

机译:利用MODIS数据和机器学习算法绘制农田土壤有机质的动态图。

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摘要

As an important indicator of soil quality, soil organicmatter (SOM) significantly contributes to land productivity and ecosystemhealth. Accuratelymapping SOMat regional scales is of critical importance for sustainable agriculture and soil utilization management and remains a grand challenge. Many studies used soil sampling data and machine learning algorithms to predict SOM at regional scales for a given year, while few studies mapped SOM formultiple years and examined its temporal dynamics. We compared the performance of fourmachine learning algorithms: decision tree (DT), bagging decision tree (BDT), randomforest (RF), and gradient boosting regression trees (GBRT) in mapping SOM in Hubei province, China over the 18-year period from 2000 to 2017. Our results showed that RF and DT had the highest coefficient of determination (R-2) (0.61) and the lowest potential bias (9.48 g/kg), respectively, while GBRT had the lowest mean error (ME) (1.26 g/kg), root mean squared error (RMSE) (5.41 g/kg) and Lin's concordance correlation coefficient (LCCC) (0.72). The SOM map based on GBRT better captured the distribution of the soil sample data than that based on RF. The trained GBRT model and the spatially explicitly data on explanatory variables (e.g., climate, terrain, remote sensing) were used to predict SOM for each 500 m x 500 m grid cell in Hubei for the period from 2000 to 2017. Our results showed that the SOM content of cropland was relatively high in the southeast and relatively low in the north. The SOM content in the topsoil varied from 0.89 to 58.86 g/kg and was averaged at 20.52 g/kg. The mean cropland SOM content of the province exhibited an increasing trend from 2000 to 2017 with an increase of 0.26 g/kg and a growth rate of 1.28%. Spatially, the SOMcontent increased in southernHubei and decreased in central and northern parts of the province. A large portion of the areas with decreasing SOM content in northern Hubei was reclaimed cropland, while a large part of the high-quality cropland with rising SOM content in the east (similar to 0.45 x 10(4) ha) was lost due to land use change (e.g., urbanization). (C) 2019 Elsevier B.V. All rights reserved.
机译:作为土壤质量的重要指标,土壤有机质(SOM)极大地促进了土地生产力和生态系统健康。在区域范围内准确映射SOM对于可持续农业和土壤利用管理至关重要,并且仍然是一个巨大挑战。许多研究使用土壤采样数据和机器学习算法来预测给定年份在区域范围内的SOM,而很少有研究绘制SOM多年并检查其时间动态。我们比较了四种机器学习算法在18年期间在中国湖北省绘制SOM中的性能:决策树(DT),装袋决策树(BDT),随机森林(RF)和梯度提升回归树(GBRT) 2000年至2017年。我们的结果显示,RF和DT分别具有最高的测定系数(R-2)(0.61)和最低的潜在偏倚(9.48 g / kg),而GBRT的最低平均误差(ME)( 1.26 g / kg),均方根误差(RMSE)(5.41 g / kg)和林氏一致性相关系数(LCCC)(0.72)。与基于RF的SOM图相比,基于GBRT的SOM图可以更好地捕获土壤样本数据的分布。经过训练的GBRT模型和有关解释变量(例如,气候,地形,遥感)的空间显式数据被用于预测湖北省从2000年到2017年每个500 mx 500 m网格单元的SOM。我们的结果表明东南部农田的SOM含量较高,而北部则相对较低。表土中的SOM含量从0.89到58.86 g / kg不等,平均为20.52 g / kg。从2000年到2017年,全省农田平均SOM含量呈上升趋势,增加0.26 g / kg,增长率为1.28%。在空间上,湖北南部的SOM含量增加,而该省中部和北部的SOM含量下降。湖北北部大部分SOM含量降低的地区为开垦耕地,而东部SOM含量较高(约0.45 x 10(4)公顷)的大部分优质耕地因土地流失使用变化(例如,城市化)。 (C)2019 Elsevier B.V.保留所有权利。

著录项

  • 来源
    《The Science of the Total Environment》 |2019年第15期|844-855|共12页
  • 作者单位

    Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, Beijing 100081, Peoples R China|Minist Agr, Key Lab Agr Remote Sensing, Beijing 100081, Peoples R China|Univ New Hampshire, Earth Syst Res Ctr, Inst Study Earth Oceans & Space, Durham, NH 03824 USA;

    Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, Beijing 100081, Peoples R China|Univ New Hampshire, Earth Syst Res Ctr, Inst Study Earth Oceans & Space, Durham, NH 03824 USA;

    Univ New Hampshire, Earth Syst Res Ctr, Inst Study Earth Oceans & Space, Durham, NH 03824 USA;

    Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, Beijing 100081, Peoples R China|Minist Agr, Key Lab Agr Remote Sensing, Beijing 100081, Peoples R China;

    Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, Beijing 100081, Peoples R China|Minist Agr, Key Lab Agr Remote Sensing, Beijing 100081, Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Digital soil mapping; Multi-year; Soil organic carbon; MODIS; Machine learning algorithms; Cropland;

    机译:数字土壤测绘;多年;土壤有机碳;MODIS;机器学习算法;农田;

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