基于深度学习的欧洲农田土壤总碳的地理空间预测

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Dorijan Radočaj¹, Mateo Gašparović², Petra Radočaj³

¹Josip Juraj Strossmayer University of Osijek, Faculty of Agrobiotechnical Sciences Osijek, Chair of Geoinformation Technology and GIS, Vladimira Preloga 1, 31000 Osijek, Croatia.

The Science of the total environment

|December 27, 2023

概括

深度学习准确地预测了欧洲农业中的土壤碳,优于传统方法. 这一进步有助于数字化土壤绘制和了解气候变化对土壤健康的影响.

科学领域:

环境科学环境科学
土壤科学土壤科学
数据科学数据科学数据科学

背景情况:

准确地预测土壤参数对于数字土壤绘图至关重要.
实地土壤采样是昂贵和耗时的.
深度学习显示了改善地理空间土壤预测准确性的前景.

研究的目的:

提出和评估一个深度神经网络 (DNN),用于预测欧洲农田的土壤总碳 (TC).
将DNN性能与八种常见的机器学习方法进行比较.
确定影响TC预测的关键环境因素.

主要方法:

利用来自GEMAS数据集的6209个土壤样本,覆盖欧洲4,899,602平方公里.
采用了来自气候和遥感数据的96个环境共变量.
对所有模型进行了全面的超参数调整,包括DNN和量子随机森林 (QRF).

主要成果:

与所有评估的机器学习方法相比,DNN实现了更高的预测准确性 (R2 = 0.663,RMSE = 9.595,MAE = 5.565).
DNN在预测低TC值方面表现出色,导致较低的绝对残留值.
气候和陆地表面温度参数是最重要的预测因素 (23分之25).

结论:

深度神经网络在欧洲农业区的土壤总碳预测方面表现出优于机器学习的优势.
气候变量是土壤碳含量的关键驱动因素.
需要对最新的土壤数据进行进一步的研究,以评估气候变化对土壤碳的影响.

关键词:

气候气候气候气候气候深度神经网络是一个神经网络.环境共变量环境共变量在 GEMAS 数据集中,遥感是一种远程传感.

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