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

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. 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 multi-angle bands reflectance (R² = 0.90, RMSE = 0.03, MAPE = 8.91%). Under various CRC scenarios, the CRBR model consistently outperformed linear mixed models (R² ≥ 0.99, RMSE ≤ 0.02, MAPE ≤ 4.14% vs R² ≥ 0.97, RMSE ≤ 0.05, MAPE ≤ 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 radiative transfer 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.

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Publication Details

Journal
IEEE Transactions on Geoscience and Remote Sensing
Published
2025-01-01
DOI
https://doi.org/10.1109/tgrs.2025.3592199
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
0.00

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article

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

Xuan Fu, Wancheng Tao, Yi Dong, Xianda Huang et al.
IEEE Transactions on Geoscience and Remote Sensing
Remote Sensing in Agriculture
article

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

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

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. 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 multi-angle bands reflectance (R² = 0.90, RMSE = 0.03, MAPE = 8.91%). Under various CRC scenarios, the CRBR model consistently outperformed linear mixed models (R² ≥ 0.99, RMSE ≤ 0.02, MAPE ≤ 4.14% vs R² ≥ 0.97, RMSE ≤ 0.05, MAPE ≤ 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 radiative transfer 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.

IEEE Transactions on Geoscience and Remote SensingVol. 63
Fujian Normal University (CN), Aarhus University (DK), Slagelse Hospital (DK), Institute of Forest Resource Information Techniques (CN), Chinese Academy of Forestry (CN), China Agricultural University (CN)
National Natural Science Foundation of China
Zero hunger
Openalex Percentile: Top 82%
Remote Sensing in Agriculture
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