土壤盐渍化遥感监测信息提取与处理模型研究进展OA

Soil salinization is one of the primary causes leading to land desertification and reduced crop yields.Remote sensing monitoring technology,with its macro perspective and rich information content,provides a new approach for rapidly achieving dynamic monitoring of large areas affected by soil salinization.This paper reviews relevant literature on remote sensing monitoring of soil salinization over the past few years both domestically and internationally.It summarizes research progress and landmark achievements in information extraction and processing models for remote sensing monitoring of soil salinization,focusing on three types of applications:classification of saline-affected soils,inver-sion of soil salinity,and dynamic monitoring of soil salinization.The results indicate that in recent years,a research hotspot in this field has been the integration of multi-source remote sensing data from satellite,aerial,and ground-based platforms with deep learning techniques,aimed at enhancing the accuracy of remote sensing monitoring models for soil salinization;in the construction of different remote sensing moni-toring models,the selection of modeling factors shows a trend towards diversification and integration;remote sensing images exhibit a scale effect issue with soil salinity,necessitating the coordination of complementary relationships between images at different resolutions to deeply ex-plore their internal connections.