Chinese Academy of Sciences

Institute of Forensic Science of China

Huayong Wu

Feng Liu

Ganlin Zhang

Xiaodong Song

Fei Yang

Zhao Zhao

Zhenwen Sun

Junya Wang

Decheng Li

Jinling Yang

Yuguo Zhao

Provided is a pedo-transfer function (PTF)-based method for predicting a target soil property. Based on the collection of a multi-source soil dataset and environmental variables, a dataset containing all measured information is divided. Second-level regions are obtained by zoning according to the spatial variation in soil properties. An optimal independent variable set of PTFs in different regions is obtained by screening. Then, linear fitting and nonlinear fitting of the PTFs are performed for different zones separately. By comparing the accuracy of different functions between different zones, optimal PTFs oriented toward sampling sites are selected, so as to build a database including soil sampling sites. Further, regional independent variable layers are constructed by means of machine learning, to establish region-oriented PTFs; and a spatial distribution map of the target soil property and content for a target region is produced.

PTF-based method for predicting target soil property and content

The disclosure provides a method for predictive soil mapping based on solar radiation in large flat area, comprising: Step 1, capturing the response process of the earth's surface heat: after an observation day or an observation period is selected, a curve graph depicting a dynamic response of the surface heat to solar radiation captured by using a remote sensor of a moderate-resolution imaging spectrometer; Step 2, constructing environmental covariates: quantitative analysis is performed for the dynamic response curve graph obtained in Step 1 by a mathematical method, and characteristic parameters are extracted and taken as environmental covariates; and Step 3: a machine learning model is established for the predictive soil attribute mapping. This method solves the challenge of effective soil mapping in flat areas, significantly improves the accuracy and efficiency of predictive soil mapping in flat areas, reduces the cost in time and economy of soil survey mapping.

Method for predictive soil mapping based on solar radiation in large flat area

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Nanjing, China

Huayong Wu