[1]王婷,刘振华,彭一平,等.华南地区土壤有机质含量高光谱反演[J].江苏农业学报,2020,(02):350-357.[doi:doi:10.3969/j.issn.1000-4440.2020.02.014]
　WANG Ting,LIU Zhen-hua,PENG Yi-ping,et al.Predicting soil organic matter content in South China based on hyperspectral reflectance[J].,2020,(02):350-357.[doi:doi:10.3969/j.issn.1000-4440.2020.02.014]
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华南地区土壤有机质含量高光谱反演()
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江苏农业学报[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2020年02期
页码:
350-357
栏目:
耕作栽培·资源环境
出版日期:
2020-04-30

文章信息/Info

Title:
Predicting soil organic matter content in South China based on hyperspectral reflectance
作者:
王婷1; 2; 3; 4; 刘振华1; 2; 3; 4; 彭一平1; 2; 3; 4; 胡月明1; 2; 3; 4; 5
(1.华南农业大学，广东广州510642;2.国土资源部建设用地再开发重点实验室，广东广州510642;3.广东省土地信息工程技术研究中心，广东广州510642;4.广东省土地利用与整治重点实验室，广东广州510642;5.青海大学农牧学院，青海西宁810016)
Author(s):
WANG Ting1; 2; 3; 4; LIU Zhen-hua1; 2; 3; 4; PENG Yi-ping1; 2; 3; 4; HU Yue-ming1; 2; 3; 4; 5
(1.South China Agricultural University， Guangzhou 510642， China;2.Key Laboratory of Construction Land Improvement， Ministry of Land and Resources， Guangzhou 510642， China;3.Guangdong Province Engineering Research Center for Land Information Technology， Guangzhou 510642， China;4.Guangdong Province Key Laboratory for Land Use and Consolidation， Guangzhou 510642， China;5.College of Agriculture and Animal Husbandry, Qinghai University， Xining 810016， China)
关键词:
土壤有机质含量; 高光谱; 估测模型; 华南地区
Keywords:
soil organic matter content; hyperspectrum; estimation model; South China
分类号:
S127
DOI:
doi:10.3969/j.issn.1000-4440.2020.02.014
文献标志码:
A
摘要:
为实现对土壤有机质含量的快速监测，在对土壤有机质含量作倒数变换的同时将土壤高光谱数据进行多种数据变换处理，筛选出与土壤有机质含量倒数变换后相关性最高的光谱指标，最后构建了土壤有机质含量高光谱反演的最佳模型，实现对土壤有机质含量的反演。结果表明：估算土壤有机质含量的最佳光谱指标为反射率一阶微分波段组合R(587，126*R(734，049)*R(1 095，892)，相关系数为0.769；在此基础上构建的土壤有机质含量高光谱反演模型最佳（Y=5×1016x3-5×1010x2+59 471.000 0x+0.101 1），其决定系数R2为0.65，均方根误差（RMSE）为0.040 mg/kg。将其验证样本预测值与实测值进行比较，平均相对误差为27.00%，RMSE为4.19 mg/kg。该验证结果证明利用该模型进行华南地区土壤有机质含量的快速监测是可行的。
Abstract:
In order to monitor soil organic matter content rapidly, the reciprocal transformation of soil organic matter content and a variety of data transformation processing on soil hyperspectral data were carried out. On this basis, the spectral index having the highest correlation with the content of soil organic matter after reciprocal transformation was selected to construct the best hyperspectral inversion model of soil organic matter content. The results indicated that the band combination R(587,126)×R(734,049)×R(1 095,892) was the best spectral index for estimating soil organic matter content, and the correlation coefficient was 0.769. The best hyperspectral inversion model constructed by the band combination was y = 5×1016x3 -5×1010x2+59 471.000 0x+0.101 1, with determination coefficient(R2) of 0.65 and root mean squared error (RMSE) of 0.040 mg/kg. In addition, the predicted value of the verified sample was compared with the measured value, the mean relative error (MRE) was 27.00%, and RMSE was 4.19 mg/kg. In conclusion, it is feasible to monitor the soil organic matter content in South China by using the model constructed in this study.

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备注/Memo

备注/Memo:
收稿日期：2019-06-04基金项目：广东省科技计划项目（2017A050501031）；青海省科技计划项目（2017-ZJ-730）；广州市科技计划项目（201807010048）；广东省林业科技创新项目（2015KJCX047）作者简介：王婷（1994-），女，广东阳江人，硕士研究生，主要从事定量遥感研究。(E-mail)18344263281@163.com通讯作者：刘振华，(E-mail)grassmountain@163.com
更新日期/Last Update: 2020-05-18