Objectives When using airborne multi-baseline synthetic aperture radar (SAR) data for the inversion of stratified SAR forest parameters, the phase error caused by the orbit instability and other factors will distort the phase of the echo signal, which will lead to false peaks, target misjudgment, and defocusing of the stratified spectrum. Although the multi-aperture phase error correction method can remove most of the error phases, some errors will still remain after processing because it directly adopts the estimated phase error for correction. Although the self-focusing phase correction algorithm based on information entropy minimization can effectively correct the phase error and significantly improve the image focusing quality, it has the limitations of relying on the selection of the initial value and easily falling into the local optimal solution, which makes it difficult to realize the globally optimal phase correction effect. The aim is to develop an efficient airborne SAR phase error correction method to improve the accuracy and reliability of stratified SAR forest parameter inversion.

Methods A two-step phase error correction method combining the multi-aperture and self-focusing algorithms is proposed. First, the initial value of phase error is estimated by mining the redundant information of multi-baseline SAR data using the multi-aperture method. Second, the estimated initial value is used as the input of the self-focusing algorithm to iteratively optimize the phase error based on the principle of information entropy minimization to effectively eliminate the residual error.

Results Compared to the multi-aperture method, the on-board SAR phase error correction method utilizing the combined multi-aperture and self-focusing algorithms has significantly fewer error phases after correction. At the same time, the corrected chromatographic spectra have significantly fewer side flaps and offsets than the multi-aperture method, and the clarity is higher.

Conclusions The airborne SAR phase error correction algorithm combining multi-aperture and self-focusing overcomes the limitations of a single method, combining the robustness of multi-aperture correction with the high accuracy of self-focusing optimization. The method provides a reliable solution for airborne stratified SAR phase error correction, which is especially suitable for complex forest scenarios and lays a solid foundation for more accurate forest parameter inversion.