Evaluation of Synthetic-Temporal Imagery as an Environmental Covariate for Digital Soil Mapping: A Case Study in Soils under Tropical Pastures

IF 4.3 3区 材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
F. A. Avalos, M. D. de Menezes, Fausto Weimar Acerbi Júnior, N. Curi, J. C. Avanzi, M. L. Silva
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引用次数: 0

Abstract

Digital soil maps are paramount for supporting environmental process analysis, planning for the conservation of ecosystems, and sustainable agriculture. The availability of dense time series of surface reflectance data provides valuable information for digital soil mapping (DSM). A detailed soil survey, along with a stack of Landsat 8 SR data and a rainfall time series, were analyzed to evaluate the influence of soil on the temporal patterns of vegetation greenness, assessed using the normalized difference vegetation index (NDVI). Based on these relationships, imagery depicting land surface phenology (LSP) metrics and other soil-forming factors proxies were evaluated as environmental covariates for DSM. The random forest algorithm was applied as a predictive model to relate soils and environmental covariates. The study focused on four soils typical of tropical conditions under pasture cover. Soil parent material and topography covariates were found to be similarly important to LSP metrics, especially those LSP images related to the seasonal availability of water to plants, registering significant contributions to the random forest model. Stronger effects of rainfall seasonality on LSP were observed for the Red Latosol (Ferralsol). The results of this study demonstrate that the addition of temporal variability of vegetation greenness can be used to assess soil subsurface processes and assist in DSM.
将合成时空图像作为数字土壤制图的环境协变量进行评估:热带牧场土壤案例研究
数字土壤地图对于支持环境过程分析、生态系统保护规划和可持续农业至关重要。密集的地表反射率数据时间序列为数字土壤制图(DSM)提供了宝贵的信息。通过对详细的土壤调查、Landsat 8 SR 数据堆栈和降雨时间序列进行分析,评估了土壤对植被绿度时间模式的影响,并使用归一化差异植被指数(NDVI)进行评估。基于这些关系,描述地表物候(LSP)指标的图像和其他土壤形成因子代用指标被评估为 DSM 的环境协变量。应用随机森林算法作为预测模型,将土壤和环境协变量联系起来。研究重点是牧草覆盖下的四种典型热带土壤。研究发现,土壤母质和地形协变量对长势敏感性指标同样重要,特别是那些与植物季节性供水有关的长势敏感性图像,对随机森林模型有显著贡献。降雨季节性对红壤(Ferralsol)LSP 的影响更大。这项研究的结果表明,增加植被绿度的时间变异性可用于评估土壤地下过程和协助 DSM。
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来源期刊
CiteScore
7.20
自引率
4.30%
发文量
567
期刊介绍: ACS Applied Electronic Materials is an interdisciplinary journal publishing original research covering all aspects of electronic materials. The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrate knowledge in the areas of materials science, engineering, optics, physics, and chemistry into important applications of electronic materials. Sample research topics that span the journal's scope are inorganic, organic, ionic and polymeric materials with properties that include conducting, semiconducting, superconducting, insulating, dielectric, magnetic, optoelectronic, piezoelectric, ferroelectric and thermoelectric. Indexed/​Abstracted: Web of Science SCIE Scopus CAS INSPEC Portico
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