2001-2020年中国东北区域土壤水蚀数据集  

A dataset of soil water erosion of Northeast China from 2001 to 2020

作　　者：吴瀚逸 熊俊峰 侯渲[4,5] 林晨 许金朵[4,5] 马荣华[4,5] WU Hanyi;XIONG Junfeng;HOU Xuan;LIN Chen;XU Jinduo;MA Ronghua(International Research Center of Big Data for Sustainable Development Goals,Beijing 100094,P.R.China;State Key Laboratory of Remote Sensing Science,Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences,Beijing 100875,P.R.China;Key Laboratory of Environmental Change and Natural Disaster,Ministry of Education,Beijing Normal University,Beijing 100875,P.R.China;Nanjing Institute of Geography and Limnology,Chinese Academy of Sciences,Nanjing 210008,P.R.China;Lake-Watershed Science Data Center,National Earth System Science Data Center,National Science&Technology Infrastructure of China,Nanjing 210008,P.R.China)

机构地区：[1]可持续发展大数据国际研究中心,北京100094 [2]北京师范大学遥感科学国家重点实验室,北京100875 [3]北京师范大学环境演变与自然灾害教育部重点实验室,北京100875 [4]中国科学院南京地理与湖泊研究所,南京210008 [5]国家科技资源共享服务平台,国家地球系统科学数据中心湖泊-流域分中心,南京210008

出　　处：《中国科学数据（中英文网络版）》2023年第4期283-297,共15页China Scientific Data

基　　金：可持续发展大数据国际研究中心主任青年基金(CBAS2022DF014);国家自然科学基金青年科学基金项目(42201400);江苏省自然科学基金资助项目(BK20221058);全球变化背景下中国区域湖泊响应数据库(CAS-WX2021SF-0306);中国科学院南京地理与湖泊研究所科学数据中心(CAS-WX2022SDC-SJ05)。

摘　　要：东北黑土区是我国重要的粮食产区和生态保障区,近年来由于气候变化和过度垦殖,水土流失加剧,严重威胁东北地区粮食安全和生态环境。高时空分辨率土壤水蚀模拟对实现东北地区农业持续发展和土地退化监测等可持续发展目标有着重要意义。本数据集基于Google Earth Engine(GEE)云平台,融合多源遥感数据,解构RUSLE模型,对模型各因子进行算法组合优选。结合水利部公开的东北区域主要水文监测站的年输沙量和输沙模数数据与RUSLE模型估算的土壤侵蚀模数对比验证,以时序相关性、均方根误差以及平均绝对误差三个精度作为评价指标选取最优因子算法组合,得到东北区域250 m土壤水蚀模数估算结果。本数据集可以较好地表征2001–2020年东北区域土壤水蚀模数空间分布及时间序列变化,为东北地区的水土流失防治以及土壤侵蚀评估提供有效参考。The black soil area in Northeast China plays an important role in food production and ecological security in China.However,over-cultivation practices have led to severe soil erosion,which seriously threatens food security and ecological environment in Northeast China.Accurate simulations of water erosion with high spatio-temporal resolution are important for advancing sustainable development goals,such as promoting sustainable agriculture and monitoring land degradation in Northeast China.This dataset is based on Google Earth Engine(GEE)cloud platform,integrating multi-source remote sensing data,deconstructing RUSLE model,and optimizing algorithm combinations for each factor of the model.We compared and verified the annual sand transport volume and sand transport modulus data from the main hydrological monitoring stations in Northeast China with the soil erosion modulus estimated by the RUSLE model.Then,we selected the optimal factor algorithm combination based on three accuracy metrics(time series correlation,root mean square error and mean absolute error),and obtained the estimation results of soil water erosion modulus with the resolution of 250 m.This dataset can better depict the spatial distribution and temporal changes of soil erosion modulus in Northeast China from 2001 to 2020.It can serve as an effective reference for soil erosion control and assessments in Northeast China.

关 键 词：土壤水蚀模数 模型优选 东北区域 RUSLE 

分 类 号：S157[农业科学—土壤学]