| 地面高光谱耦合可解释性集成机器学习的农田土壤含盐量和pH反演 |
| 摘要点击 425 全文点击 27 投稿时间：2024-12-27 修订日期：2025-04-03 |
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| 中文关键词 河套平原 地面高光谱 土壤含盐量 竞争性自适应重加权采样（CARS） 环境变量 夏普利加性解释 |
| 英文关键词 Hetao Plain ground-based hyperspectral soil salinity competitive adaptive reweighted sampling （CARS） environmental variables Shapley additive explanations |
| DOI 10.13227/j.hjkx.20260235 |
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| 中文摘要 |
| 土壤盐碱化是制约农业可持续发展的关键因素. 盐碱信息的及时获取对土壤改良与地力长效提升至关重要. 以河套平原地面高光谱和实测土壤含盐量（SSC）及pH值为数据源，对高光谱反射率进行正交信号校正（OSC）变换后，利用竞争性自适应重加权采样（CARS）筛选盐碱信息的特征波段，并引入环境变量和微波遥感数据，基于极端梯度提升（XGBoost）、自适应提升（AdaBoost）和随机森林（RF）等6种集成机器学习算法，构建SSC和pH值的反演模型，并利用夏普利加性解释（SHAP）对模型进行可视化分析. 结果表明：①河套平原农田土壤盐碱化等级整体呈轻、中程度，且盐化和碱化表现出较强的空间异质性. ②OSC变换优化了光谱数据结构，使其在复杂背景下的解析能力显著增强；CARS有效筛选出与盐碱信息相关的特征波段，SSC特征波段包括450、470和600 nm等13个波长，pH特征波段包括680、730和740 nm等15个波长. ③AdaBoost算法对SSC反演表现最优，验证集Rp2、均方根误差（RMSE）和相对分析误差（RPD）分别为0.852、1.352和2.88，而pH值则以XGBoost模型效果最佳，其Rp2、RMSE和RPD分别为0.908、0.151和3.31. ④SHAP分析表明，SSC和pH值的预测模型体现了多因子协同作用. 波段和气候因子为SSC建模的主导因素，累计贡献率达80.8%. 土壤属性（24.88%）对pH值的建模贡献率最高，波段数据贡献率最小，为15.13%，微波遥感数据对盐碱信息建模贡献有限，多源数据组合为土壤盐碱化的精准监测提供了有力支撑. 研究结论有助于推动土地可持续管理和农业高效生产. |
| 英文摘要 |
| Soil salinity and alkalinity are key factors limiting sustainable agricultural development. Timely acquisition of salinity and alkalinity information is crucial for soil improvement and long-term fertility enhancement. After orthogonal signal correction （OSC） transformation of the hyperspectral reflectance， competitive adaptive reweighted sampling （CARS） was used to screen the characteristic bands of salinity and alkalinity information using the ground hyperspectral and measured soil salinity （SSC） and pH values of the Hetao Plain as data sources. Then， environmental variables and microwave remote sensing data were introduced to build the inversion models based on six integrated machine learning algorithms， including extreme gradient boosting （XGBoost）， adaptive boosting （AdaBoost）， and random forest （RF）， and six integrated machine learning algorithms were used to build inversion models of SSC and pH. The models were visualized and analyzed using Shapley additive explanations （SHAP）. The results showed that： ① The salinity and alkalinity grades of farmland soils in the Hetao Plain were generally mild to moderate， with strong spatial heterogeneity in salinity and alkalinity. ② The OSC transform optimized the structure of the spectral data， which greatly improved the resolution ability under the complex background. CARS effectively screened out the characteristic bands related to salinity and alkalinity information， and the SSC characteristic bands included 13 bands such as 450， 470， and 600 nm. The pH characteristic bands included 15 bands such as 680， 730， and 740 nm. ③ The AdaBoost algorithm performed optimally for SSC inversion with validation set Rp2， root mean square error （RMSE）， and relative analysis error （RPD） of 0.852， 1.352， and 2.88， respectively， whereas pH was best with the XGBoost model， which had an Rp2， RMSE， and RPD of 0.908， 0.151， and 3.31， respectively. ④ SHAP analysis showed that the prediction models for SSC and pH reflected multifactorial synergies. Waveband and climate factors were the dominant factors in SSC modeling with a cumulative contribution of 80.8%. Soil attributes （24.88%） had the highest contribution to pH modeling， waveband data had the smallest contribution of 15.13%， microwave remote sensing data had limited contribution to salinity and alkalinity modeling， and the combination of multi-source data provided a strong support for the accurate monitoring of soil salinization and alkalization. The study conclusions help to promote sustainable land management and efficient agricultural production. |
