Hyperspectral Estimation of Soil Nutrient Content in the Black Soil Region Based on BA-Adaboost
LIN Nan1,2, LIU Hai-qi3, YANG Jia-jia4, WU Meng-hong1, LIU Han-lin1
1. College of Surveying and Prospecting Engineering, Jilin Jianzhu University, Changchun 130118, China
2. College of Earth Science, Jilin University, Changchun 130026, China
3. College of Resources and Civil Engineering, Northeastern University, Shenyang 110819, China
4. Shenyang Geological Survey Center, China Geological Survey, Shenyang 110034, China
Abstract：Organic matter (OM), phosphorus (P) and potassium (K) in black soil play a crucial role during crop growth. Studying the distribution of nutrient elements in black soil and carrying out quantitative calculation of element content is of great significance to the scientific management of black soil and environmental protection. Based on 80 black soil samples collected from Nehe city, Heilongjiang province and hyperspectral measured data, the correlation between 4 forms of spectral reflectance including original, first differential, absorbance transformation, and first differential of absorbance and soil OM, P, K contents were calculated, and the correlation coefficient method was used to extract the sensitive bands. For the optimization of parameter values in the machine learning model, the bat algorithm (BA) was introduced and combined with Adaboost model. Using BA to optimize the two important parameters of Adaboost, namely, maximum iterations n and weak learner weight reduction factor v, selecting CART decision tree and determination coefficient as the weak regression learner of the model and objective function value of the parameter optimization respectively, the BA-Adaboost model was constructed for estimating soil OM, P, K contents. The results showed that the BA-Adaboost model could quickly search the optimal global parameters, and the accuracy and reliability of the Adaboost were improved significantly after BA optimization, where the prediction accuracy of OM was the highest among the three nutrient elements. And determination coefficient and root mean square error were 0.864 and 0.152 g·kg-1 respectively, which were improved by 14.2% and 25.4% compared with before optimization. Therefore, the constructed BA-Adaboost model has potential in the hyperspectral estimation of soil element content and is an efficient estimation method.
Key words：Black soil region; Soil nutrient content; Hyperspectral estimation; Bat algorithm; Adaboost model
林 楠，刘海琪，杨佳佳，吴梦红，刘翰霖. BA-Adaboost模型的黑土区土壤养分含量高光谱估测[J]. 光谱学与光谱分析, 2020, 40(12): 3825-3831.
LIN Nan, LIU Hai-qi, YANG Jia-jia, WU Meng-hong, LIU Han-lin. Hyperspectral Estimation of Soil Nutrient Content in the Black Soil Region Based on BA-Adaboost. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(12): 3825-3831.
[1] WU Long-guo, WANG Song-lei, HE Jian-guo(吴龙国, 王松磊, 何建国). Spectroscopy and Spectral Analysis(光谱学光谱分析), 2018, 38(8): 2563.
[2] LIU Xiu-ying, SHI Zhao-yong, CHANG Qing-rui, et al(刘秀英, 石兆勇, 常庆瑞, 等). Acta Pedologica Sinica(土壤学报), 2018, 55(2): 325.
[3] NIE Zhe, LI Xiu-fen, LÜ Jia-xin, et al(聂 哲, 李秀芬, 吕家欣, 等). Chinese Journal of Soil Science(土壤通报), 2019, 303(6): 1285.
[4] Kemper T, Sommer S. Environmental Science & Technology, 2002, 36(12): 2742.
[5] TAN Kun, ZHANG Qian-qian, CAO Qian, et al(谭 琨, 张倩倩, 曹 茜, 等). Earth Science-Journal of China University of Geosciences(地球科学—中国地质大学学报), 2015, 40(8): 1339.
[6] ZHOU Peng, YANG Wei, LI Min-zan, et al(周 鹏，杨 玮，李民赞，等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报)，2017，48(S1)：271.
[7] Tan K, Ma W, Wu F, et al. Environmental Monitoring and Assessment, 2019, 191(7): 1.
[8] Chen Y, Wu W, Zhao Q. Minerals, 2019, 9(5): 317.
[9] LU Ru-kun(鲁如坤). Methods of Soil Agricultral Chemical Analysis(土壤农业化学分析方法). Beijing: China Agriculture and Science Press(北京: 中国农业科技出版社), 2000. 638.
[10] WU Chen-wen, LIU Xiao-guang, WEI Li-xin(吴辰文, 刘小光, 魏立鑫). Journal of Northwest University·Natural Science Edition(西北大学学报·自然科学版), 2019, 49(4): 597.
[11] MENG Kai-lu, YUE Ke-qiang, SHANG Jun-na(孟凯露, 岳克强, 尚俊娜). Computer Engineering(计算机工程), 2019, 45(4): 189.
[12] XU Ting, ZHANG Xiang, ZHANG Ya-kun, et al(徐 婷, 张 香, 张亚坤, 等). Journal of Safety and Environment(安全与环境学报), 2019, 19(4): 1273.
[13] YANG Xiao, WANG Zhi-zhang, ZHOU Zi-yong, et al(杨 笑, 王志章, 周子勇, 等). Acta Petrolei Sinica(石油学报), 2019, 40(4): 457.
