Mapping the soil C:N ratio at the European scale by combining multi-year Sentinel radar and optical data via cloud computing

IF 6.1 1区 农林科学 Q1 SOIL SCIENCE
Xinyue Wang , Yajun Geng , Tao Zhou , Ying Zhao , Hongchen Li , Yanfang Liu , Huijie Li , Ruiqi Ren , Yazhou Zhang , Xiangrui Xu , Tingting Liu , Bingcheng Si , Angela Lausch
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引用次数: 0

Abstract

Spatial information on the soil carbon-to-nitrogen (C:N) ratio is essential for sustainable soil use and management. The unprecedented availability of Sentinel optical and radar data on cloud computing platforms, such as the Google Earth Engine (GEE), has created new possibilities for developing soil prediction models from the local scale to the planetary scale. However, there is a paucity of literature on the effects of Sentinel sensor selection and integration and radar data utilization strategies on mapping the C:N ratio. In this study, we explored the use of multiyear Sentinel-1 radar and Sentinel-2 optical data obtained from the GEE platform combined with the digital soil mapping (DSM) technique to map the soil C:N ratio at the European scale. The performance of soil prediction models, which were constructed using two modeling techniques (random forest and support vector machine), derived under multiple scenarios based on optical, radar and commonly used auxiliary data (climatic and topographic variables) combined with the LUCAS 2018 soil dataset, was evaluated by a cross-validation technique. The results showed that the modeling performance varied with the selection and integration of Sentinel observations, as well as the configuration of the radar data. Models based on single polarization performed the worst across all scenarios related to Sentinel-1, with cross-polarization performing better than copolarization. Models that utilized Sentinel-1 data from ascending orbits outperformed those that utilized data from descending orbits. The application of Sentinel-1 backscatter information derived from different orbits and polarization modes resulted in improved prediction accuracy. Our study also demonstrated the potential of integrating multiyear Sentinel satellite data via the GEE to map the continental-scale C:N ratio. The model based on Sentinel-1 data outperformed the one built on Sentinel-2 data, whereas combining Sentinel-2 optical data with Sentinel-1 radar data led to more accurate predictions. The variable importance results indicated that optical data and backscattering information from Sentinel observations are the most important groups of variables for soil C:N ratio mapping compared to the other variable groups (terrain and climate data). The digital soil maps generated under the different scenarios exhibited detailed patterns with significant spatial variation, with similar overall trends but slightly different details.
通过云计算结合多年哨兵雷达和光学数据绘制欧洲尺度的土壤碳氮比图
土壤碳氮比(C:N)的空间信息对于土壤的可持续利用和管理至关重要。在谷歌地球引擎(GEE)等云计算平台上前所未有地提供哨兵光学和雷达数据,为开发从本地到全球范围的土壤预测模型创造了新的可能性。然而,关于哨兵传感器的选择和集成以及雷达数据利用策略对绘制碳氮比图的影响的文献却很少。在这项研究中,我们探索了如何利用从 GEE 平台获得的多年哨兵-1 雷达和哨兵-2 光学数据,结合数字土壤制图(DSM)技术来绘制欧洲尺度的土壤碳氮比图。土壤预测模型采用两种建模技术(随机森林和支持向量机)构建,基于光学、雷达和常用辅助数据(气候和地形变量),结合 LUCAS 2018 土壤数据集,在多种情况下得出,通过交叉验证技术评估了模型的性能。结果表明,建模性能随哨兵观测数据的选择和整合以及雷达数据的配置而变化。在与哨兵-1 号有关的所有方案中,基于单极化的模型性能最差,而交叉极化的性能优于共极化。利用上升轨道哨兵-1 号数据的模型优于利用下降轨道数据的模型。应用从不同轨道和极化模式中获得的哨兵-1 号反向散射信息提高了预测精度。我们的研究还证明了通过 GEE 整合多年哨兵卫星数据绘制大陆尺度碳氮比图的潜力。基于哨兵-1 数据的模型优于基于哨兵-2 数据的模型,而将哨兵-2 光学数据与哨兵-1 雷达数据相结合则可获得更准确的预测结果。变量重要性结果表明,与其他变量组(地形和气候数据)相比,来自哨兵观测的光学数据和反向散射信息是土壤碳氮比制图最重要的变量组。在不同方案下生成的数字土壤图显示了具有显著空间差异的详细模式,总体趋势相似,但细节略有不同。
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来源期刊
Soil & Tillage Research
Soil & Tillage Research 农林科学-土壤科学
CiteScore
13.00
自引率
6.20%
发文量
266
审稿时长
5 months
期刊介绍: Soil & Tillage Research examines the physical, chemical and biological changes in the soil caused by tillage and field traffic. Manuscripts will be considered on aspects of soil science, physics, technology, mechanization and applied engineering for a sustainable balance among productivity, environmental quality and profitability. The following are examples of suitable topics within the scope of the journal of Soil and Tillage Research: The agricultural and biosystems engineering associated with tillage (including no-tillage, reduced-tillage and direct drilling), irrigation and drainage, crops and crop rotations, fertilization, rehabilitation of mine spoils and processes used to modify soils. Soil change effects on establishment and yield of crops, growth of plants and roots, structure and erosion of soil, cycling of carbon and nutrients, greenhouse gas emissions, leaching, runoff and other processes that affect environmental quality. Characterization or modeling of tillage and field traffic responses, soil, climate, or topographic effects, soil deformation processes, tillage tools, traction devices, energy requirements, economics, surface and subsurface water quality effects, tillage effects on weed, pest and disease control, and their interactions.
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