Soil Mapping In Transition Region Of Plain And Hill Based On Remote Sensing Image

Soil survey is the basis of utilizing soil resources rationally and efficiently according to local conditions.The traditional soil survey method mainly relies on the field survey of soil experts,and interprets the distribution of soil types by combining topographic maps,aerial photographs and other data.This mapping method is not only time-consuming and laborious,but also requires a long renewal period.The mapping accuracy also needs to be improved.In recent years,with the rapid development of modern agriculture and other industries,traditional soil mapping was difficult to meet the requirements.How to use new technology to improve the efficiency of soil survey and mapping accuracy has become an inevitable trend.Based on the theory of soil-landscape,the knowledge of soil-environment is acquired by mathematical analysis,and the spatial distribution of soil types in the study area is inferred.In addition,remote sensing image data,due to its high resolution,fast imaging and abundant underlying surface details,has also been used in soil mapping,providing new technical means and data sources for soil survey.This study will carry Sheshui basin in Huajia River town,Hong'an county,Hubei province as the research area.Because the space scale is small,we only consider the effects of parent material,topography and biological factors on soil type formation and development.The environmental factor data set mainly includes the parent material type,the topographic factor of the contour data and the spectral index extracted by the remote sensing image data.Map area weighting method is used to design the sampling points,and soil-environment knowledge is obtained through data mining algorithms and perform inferential mapping,Using the field verification point data to evaluate the accuracy of the mappintg results.The research is divided into two levels: single-phase remote sensing and time-series remote sensing.1)Soil mapping based on single-temporal remote sensing image.In this study,geological map,contour data and high-resolution remote sensing image(GF-2)were used as data sources to extract environmental factors such as parent material type,elevation,slope,aspect,curvature,topographic moisture index,normalized vegetation index,first principal component and texture information.They were screened and dimension reduction processed.Through the support vector machine,decision tree C5.0 algorithm,random forest algorithm and other data mining algorithms,combined with environmental factor data sets to obtain soil-environment knowledge,reasoning to obtain the study area Spatial distribution of soil types.And field verification points are used to evaluate the mapping results of several algorithms.Comparing the mapping results of several data mining algorithms,whether it is the overall classification accuracy or Kappa coefficient,the random forest algorithm is superior to several other classification algorithms,and the mapping results are the best.2)Soil mapping based on time-series remote sensing images.This study mainly uses geological map,contour data and Sentinel-2A/B remote sensing image as the data source to extract parent material type,elevation,slope,aspect,and time series normalized vegetation index(12 months).And other environmental factors.Through the random forest algorithm to obtain soil-environment knowledge,reasoning to obtain the spatial distribution of soil types in the study area,and using field verification points to evaluate the results of the inference soil map.The accuracy evaluation results are as follows: the overall classification accuracy is 86%,the classification result is good,the Kappa coefficient is 0.83,the consistency level is significant,indicating that the degree of agreement between the two is better,and the spatial distribution of soil types can be more accurately reflected.3)Comparison of soil mapping based on single-temporal remote sensing images and time-series remote sensing images.In general,the mapping results based on time-series remote sensing images are better than those based on single-temporal remote sensing images.In terms of production precision,the classification accuracy of the latter in the shallow moist silt field and forest brown rendzina are greatly improved compared with the former;in terms of user precision,the classification accuracy of the brown rendzina in the latter is better than the former.The production accuracy or user accuracy of the remaining soil types are superior to or close to the latter.