Estimation of rubber yield on Hainan Island, China using multi-source data during 2021-2025

Rubber is both a strategic material and an essential industrial raw commodity, making accurate yield estimation critically important. Current remote sensing-based physical models require enhancement to converting potential yield into actual yield using limited field samples, while improving parameter applicability and estimation accuracy. Integrating DEM, Sentinel-2 imagery, and meteorological data, this study develops a multi-stage framework that couple the CASA model, SOLARFLUX, a conversion module, and a parameter calibration scheme. Calibrated and validated with field-measured data, the model enables high-resolution dynamic monitoring of dry rubber yield across Hainan Island. Validation demonstrates strong model performance (R2 > 0.95), with relative error (RE) reduced from 21.99 % to 16.62 % after refinement. The model substantially outperforms machine learning approaches. Results reveal a year-on-year decline in total rubber yield from 2021 to 2023, followed by a marked rebound during the 2023-2024. The proposed framework offers a robust and practical solution for data‑scarce regions, and its products provide valuable support for the sustainable development and ecological monitoring of Hainan’s rubber industry.