Saline-Sodic Soil EC Retrieval Based on Box-Cox Transformation and Machine Learning

Electrical conductivity (EC) is an important index of soil salinity level and an essential factor in judging whether improvement can be made and assessing the improvement, if any. However, saline soils of different types show different spectral characteristic bands, which affects the retrieval precision to a certain extent. To tackle this issue, we present a novel way of modeling. Specifically, we compare the correlation of sensitive bands, multiplication of sensitive bands, and spectral indexes with EC data to determine the optimal input parameters, and take the EC data after Box-Cox transformation as the output. We then try out several machine-learning algorithms to establish retrieval models for soil EC. Validation results show that the model precision increases by 3.23–85.71% after EC data transformation and machine learning models produce 29.41–86.67% higher precision than the linear regression model. Finally, using the optimal model, we retrieve pixel-level (10 m × 10 m) EC for the soil in our study areas. This provides necessary data support for working out soil desalinization initiatives and evaluating the effectiveness of such initiatives.