Simulating Bidirectional Reflectance in Croplands With Various Crop Residue Cover by a Geometric Optical–Radiative Transfer Model

Wancheng Tao, Wei Su, Yelu Zeng, Xiaoyao Li, Jing M. Chen, Sheng Wang, Xianda Huang, Yi Dong, Fu Xuan, Jianxi Huang

Abstract: The accurate simulation of bidirectional reflectance distribution function (BRDF) across varied crop residue cover (CRC) scenarios is pivotal for crop residue monitoring and management. Addressing the limitations of prior research in simulating BRDF for cropland with CRC, we have developed the novel crop residue-covered bidirectional reflectance (CRBR) model. This model couples geometric optical (GO) and radiative transfer (RT) model, which involves adding a clumping index and crop residue tilt angle (CRTA) distribution function through terrestrial laser scanning to parameterize the spatial distribution of covered crop residue. The validation of the CRBR model was conducted using corn residue cover data from Lishu County, Jilin Province, China, collected in April 2023. The results demonstrated strong alignment between the simulated and measured multiangle bands reflectance [ $R^{2} =0.90$ , root-mean-square error (RMSE) = 0.03, and mean absolute percentage error (MAPE) = 8.91%]. Under various CRC scenarios, the CRBR model consistently outperformed linear mixed models ( $R^{2} \ge 0.99$ , RMSE $\le 0.02$ , MAPE $\le 4.14$ % versus $R^{2} \ge 0.97$ , RMSE $\le 0.05$ , and MAPE $\le 23.69$ %). Sensitivity analysis revealed the impact of key model parameters on reflectance simulation. Furthermore, we also examined the adaptability of our model under different moisture conditions and CRC scenarios, confirming its robustness and flexibility. The CRBR model not only helps our understanding of RT in crop residue-soil scenarios but also offers a promising approach for efficient and precise CRC estimation on a regional scale. Such advancements in the CRBR model hold significant implications for conservation tillage monitoring, biomass energy reserve estimation, and cropland carbon storage capacity assessment.

External IDs:doi:10.1109/tgrs.2025.3592199