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中国高分辨率国家土壤信息格网基本属性数据集_90米土壤全氮(2010-2018)

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国家地球系统科学数据中心2022-03-15 更新2024-04-21 收录
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https://www.geodata.cn/data/datadetails.html?dataguid=96200646971743&docId=9840
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资源简介:
本数据集是全国范围的土壤全氮高分辨率空间分布图,水平空间分辨率为90m,垂直方向包括六个土层深度:0-5, 5-15, 15-30, 30-60, 60-100, 100-200cm。该数据集是基于近年“我国土系调查与《中国土系志》编制项目”获得的土壤剖面样点,采用预测性土壤制图范式,利用地理信息与遥感技术对成土环境条件进行精细刻画和空间分析,研发自适应深度函数拟合方法,集成先进的集合式机器学习方法,生成了中国高分辨率系列土壤属性三维数字分布图及其不确定性分布。与现有土壤图和相关土壤数据集相比,显著提高了准确性和精细度,更好地表征了我国土壤属性的空间变异特征,可广泛应用于土壤、农业、生态、水文、气候、环境等多个学科领域,也是对“全球土壤制图科学计划”(GlobalSoilMap.net)的重要贡献。

This dataset is a high-resolution spatial distribution map of total soil nitrogen across China, with a horizontal spatial resolution of 90 meters. The vertical dimension covers six soil layer depths: 0–5, 5–15, 15–30, 30–60, 60–100, and 100–200 cm. This dataset was developed based on soil profile sampling points collected from the recent project "National Soil Series Survey and Compilation of Soil Series Annals of China". Adopting the predictive soil mapping paradigm, it utilizes geographic information and remote sensing technologies to conduct fine characterization and spatial analysis of soil-forming environmental conditions. By developing an adaptive deep function fitting method and integrating advanced ensemble machine learning approaches, this work generated high-resolution 3D digital distribution maps of serial soil properties across China and their corresponding uncertainty distributions. Compared with existing soil maps and related soil datasets, this product significantly enhances both accuracy and spatial resolution, and better characterizes the spatial variability of soil properties in China. It can be widely applied across multiple disciplines including soil science, agriculture, ecology, hydrology, climatology, environmental science and other related fields, and represents an important contribution to the GlobalSoilMap.net Global Soil Mapping Science Initiative.
提供机构:
中国科学院南京土壤研究所
创建时间:
2022-01-20
搜集汇总
数据集介绍
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背景与挑战
背景概述
该数据集是中国全国范围的土壤全氮高分辨率空间分布图,覆盖2010年至2018年,水平空间分辨率为90米,垂直方向包括六个土层深度(0-5cm至100-200cm)。它基于土壤剖面调查数据,采用预测性土壤制图和机器学习方法生成,显著提高了土壤属性空间变异的表征精度,适用于土壤学、农业、生态学等多个学科的研究和应用。
以上内容由遇见数据集搜集并总结生成
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