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基于棉田原位高光谱数据的土壤pH值反演与制图研究
蔡海辉, 彭杰, 柳维扬, 罗德芳, 王玉珍, 白建铎, 白子金
塔里木大学 植物科学学院, 新疆 阿拉尔 843300
摘要:
[目的] 研究快速、准确大面积监测农田土壤pH值,为大面积土壤改良和实现农田精细化管理提供科学支持。[方法] 以南疆阿拉尔市十二团棉田为研究区,采用网格采样法采集231个样点的原位高光谱数据,并同步采集其中116个样点的土壤样品;分析了原位高光谱反射率数据经不同预处理模式后的光谱数据与土壤pH值的相关性;采用偏最小二乘回归、支持向量机回归和随机森林3种建模方法分别建立了土壤pH值的高光谱反演模型,根据模型评价指标优选出最优模型对未采集土壤样点的pH值进行反演制图。[结果] 反射率经微分处理后可有效改善其与土壤pH值的相关性;反射率二阶微分的随机森林模型为所有模型中的最优模型,其验证集的R2为0.87,RMSE为0.04,RPD为2.53;最优模型反演的pH值数据插值所得数字图与实测pH值插值图的空间分布特征高度吻合,能客观反映土壤碱化的空间分布状况。[结论] 随机森林模型为原位反演南疆棉田土壤pH值的最优模型,克里金插值能够客观可视化表达研究区土壤pH值的分布状况。
关键词:  土壤pH值  原位高光谱  随机森林  数字制图  棉田  新疆阿拉尔市
DOI:10.13961/j.cnki.stbctb.2021.04.027
分类号:S153.4
基金项目:国家重点研发计划项目“土壤综合观测与智能服务平台研发与应用”(2018YFE0107000);国家自然科学基金项目“盐分对南疆土壤有机质高光谱特征与定量反演的影响及方法”(41361048);兵团南疆重点产业创新发展支撑计划项目“北斗导航南疆枣园精细施肥关键技术与装备研发”(2020DB003)
Inversion and Mapping of Soil pH Valve Based on In-situ Hyperspectral Data in Cotton field
Cai Haihui, Peng Jie, Liu Weiyang, Luo Defang, Wang Yuzhen, Bai Jianduo, Bai Zijin
College of Plant Science, Tarim University, Alaer, Xinjiang 843300, China
Abstract:
[Objective] Rapid and accurate monitoring of farmland soil pH value were explored for large-scale soil improvement and achieving fine management of farmland. [Methods] The cotton fileds of the 12 th regiment at Alar City in the South of Xinjiang Uygur Autonomous Region were selected as the study area, in-situ hyperspectral data of 231 sample points were collected by grid sampling method, and soil samples at 116 sampling points were collected simultaneously. The correlation between in-situ hyperspectral reflectance data after different pretreatment modes and soil pH value was analyzed. Partial least squares regression, support vector machine regression and random forest were used to establish the hyperspectral inversion model of soil pH, respectively. According to the model evaluation indexes, the optimal model was selected and used for inversion and mapping of the pH value of the uncollected soil sample points. [Results] The reflectance after the differentia treatment could effectively improve its correlation with soil pH value. The random forest model with second-order derivative of reflectance was the optimal model among all models with R2 of 0.87, RMSE of 0.04, and RPD of 2.53. The digital map interpolated by the pH value of optimal model inversion was highly consistent with the spatial distribution characteristics of the actual measurement pH value, which could objectively reflect the spatial distribution of soil alkalinization. [Conclusion] The random forest model is the optimal model for in-situ inversion of soil pH value in cotton fields in South Xinjiang Uygur Autonomous Region, and Kriging interpolation could objectively visualize the soil pH value distribution in the study area.
Key words:  soil pH value  in-situ hyperspectral  random forest  digital mapping  cotton field  Alar City, Xingjiang Wei Autonomous Region