| Hyper\|spectral estimation of soil organic matter content in black soil based on CR-CARS-RF |
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| DOI：10.7606/j.issn.1000-7601.2025.04.21 |
| Key Words: black soil soil organic matter hyper\|spectral competitive adaptive re\|weighting algorithm random forest model |
| Author Name | Affiliation | | LI Yuhong | Institute of Atmospheric Environment, China Meteorological Administration, Liaoning， Shenyang 110166, China
Key Laboratory of Agro\|Meteorological Disasters, Liaoning， Shenyang 110166, China
Liaoning Ecological Meteorology and Satellite Remote Sensing Center, Liaoning， Shenyang 110166, China
National Climate Observatory in Panjin, Liaoning， Shenyang 110166, China
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China | | FENG Rui | Institute of Atmospheric Environment, China Meteorological Administration, Liaoning， Shenyang 110166, China
Key Laboratory of Agro\|Meteorological Disasters, Liaoning， Shenyang 110166, China | | JI Ruipeng | Institute of Atmospheric Environment, China Meteorological Administration, Liaoning， Shenyang 110166, China
Key Laboratory of Agro\|Meteorological Disasters, Liaoning， Shenyang 110166, China | | ZHANG Xia | Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China | | WU Jinwen | Institute of Atmospheric Environment, China Meteorological Administration, Liaoning， Shenyang 110166, China
Key Laboratory of Agro\|Meteorological Disasters, Liaoning， Shenyang 110166, China | | YU Wenying | Institute of Atmospheric Environment, China Meteorological Administration, Liaoning， Shenyang 110166, China
Key Laboratory of Agro\|Meteorological Disasters, Liaoning， Shenyang 110166, China | | WANG Ting | Liaoning Ecological Meteorology and Satellite Remote Sensing Center, Liaoning， Shenyang 110166, China
National Climate Observatory in Panjin, Liaoning， Shenyang 110166, China
| | LI Jing | Liaoning Ecological Meteorology and Satellite Remote Sensing Center, Liaoning， Shenyang 110166, China
National Climate Observatory in Panjin, Liaoning， Shenyang 110166, China
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| Abstract: |
| This study, based on 149 black soil samples from Changtu and Sujiatun districts in Liaoning Province, combined ground hyperspectral data with laboratory chemical determination of soil organic matter (SOM) content, systematically analyzed the impacts of different hyperspectral preprocessing and modeling methods on SOM inversion accuracy, and aimed to construct a rapid and precise hyperspectral inversion model for SOM content. After denoising spectral data using the Savitzky\|Golay smoothing method, five spectral transformation methods were compared: reciprocal (1/R), logarithmic reciprocal (log(1/R)), first derivative (FDR), standard normal variate (SNV), and continuum removal (CR). Pearson correlation coefficient (PCC) analysis and competitive adaptive reweighted sampling (CARS) algorithm were used for feature band selection. Three modeling methods—partial least squares regression (PLSR), multiple linear regression (MLR), and random forest (RF)—were combined to construct 12 inversion models, with their prediction accuracies compared and analyzed. The results showed that: (1) CR transformation significantly enhanced the correlation between spectra and SOM, with the absolute values of correlation coefficients exceeding 0.5 for 128 bands after 2 166 nm, reaching up to 0.75. (2) The CARS algorithm effectively reduced the number of feature bands to below 6% of the full bands, and compared with PCC-PLSR and PCC-RF, the prediction accuracies of CARS-PLSR and CARS-RF models were significantly improved, with their coefficients of determination (R2) increasing by 13.4% and 14.5%, and root mean square errors (RMSE) decreasing by 12.8% and 11.9%, respectively. (3) The nonlinear RF model achieved the best prediction accuracy, with R2 increasing by 32.1% and 3.5%, and RMSE decreasing by 34.9% and 4.4% compared to MLR and PLSR, respectively. (4) Among the 12 prediction models, the CR-CARS-RF model performed optimally, with a modeling R2 of 0.91 and RMSE of 1.76 g·kg-1, and a prediction R2 of 0.79 and RMSE of 2.49 g·kg-1. These results indicate that the model has high prediction accuracy and reliability, providing a strong technical support for efficient and precise monitoring of SOM in black soil region of Liaoning. |
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