[Remote sensing inversion of surface soil organic matter at jointing stage of winter wheat based on unmanned aerial vehicle multispectral].

The rapid monitoring of soil organic matter (SOM) content in large-scale salinized wheat fields can provide data for promoting research in saline soils and carbon cycle. Based on field sampling and remote sensing images of unmanned aerial vehicle, we established remote sensing prediction models of regional SOM using three methods, , multiple linear regression (MLR), partial least squares (PLSR), and support vector machine regression (SVR) for bare land and wheat field, respectively. The models were validated and compared to identify the optimal inversion model of SOM.