Salinization is a threat to global agricultural and soil resource allocation.Current investigations of global soil salinity are limited to coarse spatial resolution of the available datasets(>250 m)and semiqualitat...Salinization is a threat to global agricultural and soil resource allocation.Current investigations of global soil salinity are limited to coarse spatial resolution of the available datasets(>250 m)and semiqualitative classification rules(five ranks).Based on these two limitations,we proposed a framework to quantitatively estimate global soil salt content in five climate regions at 10 m by integrating Sentinel-1/2 remotely sensed images,climate,parent material,terrain data,and machine learning.In hyper-arid and arid region,models established using Sentinel-2 and other geospatial data showed the highest accuracy with R^(2) of 0.85 and 0.62,respectively.In semi-arid,dry sub-humid,and humid regions,models performed best using Sentinel-1,Sentinel-2,and other geospatial data with R^(2) of 0.87,0.80,and 0.87,respectively.The accuracy of the global models is considerable with field validation in Iran and Xinjiang,and compared with digitized salinity maps in California,Brazil,Turkey,South Africa,and Shandong.The proportion of extremely saline soils in Europe is 10.21%,followed by South America(5.91%),Oceania(5.80%),North America(4.05%),Asia(1.19%),and Africa(1.11%).Climatic conditions,groundwater,and salinity index are key covariates in global soil salinity estimation.Use of radar data improves estimation accuracy in wet regions.The map of global soil salinity at 10 m provides a detailed,high-precision basis for soil property investigation and resource management.展开更多
基金supported by the National Key Research and Development Program(grant numbers 2018YFE0107000 and 2023YFD1900102)the National Science Foundation of China(grant numbers 42261016 and 41061031)+4 种基金the Bingtuan Science and Technology Program(grant number 2020CB032)the Tarim University President’s Fund(grant number TDZKCX202205)the China Scholarship Council(CSC)the Academic Rising Star Program for Doctoral Students of Zhejiang Universitythe Outstanding Ph.D.Dissertation Funding of Zhejiang University.
文摘Salinization is a threat to global agricultural and soil resource allocation.Current investigations of global soil salinity are limited to coarse spatial resolution of the available datasets(>250 m)and semiqualitative classification rules(five ranks).Based on these two limitations,we proposed a framework to quantitatively estimate global soil salt content in five climate regions at 10 m by integrating Sentinel-1/2 remotely sensed images,climate,parent material,terrain data,and machine learning.In hyper-arid and arid region,models established using Sentinel-2 and other geospatial data showed the highest accuracy with R^(2) of 0.85 and 0.62,respectively.In semi-arid,dry sub-humid,and humid regions,models performed best using Sentinel-1,Sentinel-2,and other geospatial data with R^(2) of 0.87,0.80,and 0.87,respectively.The accuracy of the global models is considerable with field validation in Iran and Xinjiang,and compared with digitized salinity maps in California,Brazil,Turkey,South Africa,and Shandong.The proportion of extremely saline soils in Europe is 10.21%,followed by South America(5.91%),Oceania(5.80%),North America(4.05%),Asia(1.19%),and Africa(1.11%).Climatic conditions,groundwater,and salinity index are key covariates in global soil salinity estimation.Use of radar data improves estimation accuracy in wet regions.The map of global soil salinity at 10 m provides a detailed,high-precision basis for soil property investigation and resource management.
