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利用 MODIS 数据和机器学习算法绘制农田土壤有机质动态图。

Mapping dynamics of soil organic matter in croplands with MODIS data and machine learning algorithms.

机构信息

Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China; Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture, Beijing 100081, China; Earth Systems Research Center, Institute for the Study of Earth, Oceans, and Space, University of New Hampshire, Durham, NH, 03824, USA.

Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China; Earth Systems Research Center, Institute for the Study of Earth, Oceans, and Space, University of New Hampshire, Durham, NH, 03824, USA.

出版信息

Sci Total Environ. 2019 Jun 15;669:844-855. doi: 10.1016/j.scitotenv.2019.03.151. Epub 2019 Mar 11.

Abstract

As an important indicator of soil quality, soil organic matter (SOM) significantly contributes to land productivity and ecosystem health. Accurately mapping SOM at 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 for multiple years and examined its temporal dynamics. We compared the performance of four machine learning algorithms: decision tree (DT), bagging decision tree (BDT), random forest (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) (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 × 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 SOM content increased in southern Hubei 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 (~0.45 × 10 ha) was lost due to land use change (e.g., urbanization).

摘要

作为土壤质量的重要指标,土壤有机质(SOM)对土地生产力和生态系统健康有着重要的贡献。准确地在区域尺度上绘制 SOM 地图对于可持续农业和土壤利用管理至关重要,这仍然是一个巨大的挑战。许多研究使用土壤采样数据和机器学习算法来预测特定年份的区域尺度上的 SOM,而很少有研究绘制多年的 SOM 并研究其时间动态。我们比较了四种机器学习算法(决策树(DT)、袋装决策树(BDT)、随机森林(RF)和梯度提升回归树(GBRT))在中国湖北省在 2000 年至 2017 年的 18 年期间的 SOM 制图性能。我们的结果表明,RF 和 DT 的决定系数(R)最高(0.61),潜在偏差最小(9.48 g/kg),而 GBRT 的平均误差(ME)最低(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)最低。基于 GBRT 的 SOM 图比基于 RF 的 SOM 图更好地捕捉了土壤样本数据的分布。我们使用训练好的 GBRT 模型和空间显式解释变量数据(如气候、地形、遥感)来预测 2000 年至 2017 年期间湖北省每个 500 m×500 m 网格单元的 SOM。我们的结果表明,耕地的 SOM 含量在东南部较高,在北部较低。表层土壤中的 SOM 含量从 0.89 到 58.86 g/kg,平均为 20.52 g/kg。全省耕地平均 SOM 含量从 2000 年到 2017 年呈增加趋势,增加了 0.26 g/kg,增长率为 1.28%。空间上,南部的 SOM 含量增加,而中部和北部的 SOM 含量减少。湖北省北部 SOM 含量下降的大部分地区是开垦的耕地,而东部 SOM 含量上升的高质量耕地(约 0.45×10 ha)的大部分由于土地利用变化(如城市化)而流失。

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