Multisource Data Integration of Sentinel-1 and Sentinel-2 for Above Ground Biomass Inversion

Remote sensing data provides several advantages for vegetation parameter inversion, including non-invasive measurement, high spatiotemporal resolution, and extensive coverage. However, as multisource remote sensing data continues to advance, challenges emerge in data fusion, particularly in ensuring consistency across various data sources and addressing physical differences among them. To address such challenges, this paper employs multisource re mote sensing data as inputs for Above Ground Biomass (AGB) inversion. As a scattering model, the Water Cloud Model (WCM) is widely adopted for vegetation parameter inversion due to its simplicity and ability to describe both vegetation and soil scattering mechanisms. In this study, Sentinel-1 data is utilized to extract backscatter coefficients and incidence angle information, while Sentinel-2 multispectral data is used to calculate soil moisture through the tasseled cap transformation. These variables are employed to calibrate the coefficients of the WCM, facilitating the bio mass inversion process. The approach effectively integrates multiple sources of remote sensing data, overcoming limitation of data saturation typically associated with single-source data, and achieving accurate biomass inversion across different polarization modes.