MA Chi.Quantitative Inversion of Soil Total Phosphorus Content Based on Sentinel-2A Remote Sensing Data[J].Northern Horticulture,2022,(15):74-80.[doi:10.11937/bfyy.20220234]
基于Sentinel-2A遥感数据的土壤全磷含量定量反演研究
- Title:
- Quantitative Inversion of Soil Total Phosphorus Content Based on Sentinel-2A Remote Sensing Data
- 关键词:
- Sentinel-2A; 定量反演; 遥感; 全磷含量
- 文献标志码:
- A
- 摘要:
- 以Sentinel-2A遥感影像为试材，采用决策树分类、相关性分析和逐步回归分析的方法，研究了扶余市裸土的全磷含量，以期为区域精细农业的实施提供数据支持，也为Sentinel-2A遥感数据在土壤成分探测方面的研究提供参考依据。结果表明：利用决策树分析的方法可以有效分离出研究区的裸土；Sentinel-2A遥感影像的反射率经过波段求商、波段反射率倒数的一阶微分变换后，与研究区裸土全磷含量的相关性较好，相关系数分别达到-0.826和0.831；利用Sentinel-2A波段求商建立的裸土全磷含量反演模型Y=-613.5X（3/2）-545.0X（12/2）+3 679.1X（8A/7）+494.9X（11/8）-3 609.2，模型决定系数达到0.780，均方根误差为113.1 mg?kg-1，表明利用Sentinel-2A遥感数据反演扶余市裸土全磷含量的方法是可行的。
- Abstract:
- Taking Sentinel-2A remote sensing images as test materials,the total phosphorus content of bare soil in Fuyu city was studied by using decision tree classification,correlation analysis and stepwise regression analysis,in order to provide data support for the implementation of regional fine agriculture and provide reference for the study of Sentinel-2A remote sensing data in soil composition detection. The results showed that the decision tree analysis method could effectively separate the bare soil in the study area; the reflectance of Sentinel-2A remote sensing image had a good correlation with the total phosphorus content of bare soil in the study area after the first-order differential transformation of band quotient and band reflectance reciprocal,and the correlation coefficients reached -0.826 and 0.831 respectively; the inversion model of total phosphorus content of bare soil established by quotient of sentinel-2A band was Y=-613.5X(3/2)-545.0X(12/2) +3 679.1X（8A/7）+494.9X(11/8)-3 609.2,the determination coefficient of the model reached 0.780,and the root mean square error was 113.1 mg?kg-1,indicated that the method of retrieving total phosphorus content of bare soil in Fuyu City by using Sentinel-2A remote sensing data was feasible.
参考文献/References:
[1]ALONGI D,TROTT L,WATTAYAKORN G,et al.Below-ground nitrogen cycling in relation to net canopy production in mangrove forests of southern Thailand[J].Marine Biology,2002,140(4)：855-864．[2]王维奇,徐玲琳,曾从盛,等.河口湿地植物活体-枯落物-土壤的碳氮磷生态化学计量特征[J].生态学报,2011,31(23):7119-7124.[3]卢艳丽,白由路,王磊,等.黑土土壤中全氮含量的高光谱预测分析[J].农业工程学报,2010,26(1):256-261．[4]马驰.基于Sentinel-2A 遥感影像土壤有机质含量的反演研究[J].北方园艺,2020(2):94-100.[5]宋雪,张民,周洪印,等.基于土壤优化光谱参数估测太湖地区土壤全氮含量[J].农业资源与环境学报,2020,37(1):43-50.[6]顾晓鹤,王堃,潘瑜春,等.基于 HJ-1A HSI超光谱影像的耕地有机质遥感定量反演[J].地理与地理信息科学,2011,27(6):69-73.[7]陈鹏飞,刘良云,王纪华,等.近红外光谱技术实时测定土壤中总氮及磷含量的初步研究[J].光谱学与光谱分析,2008,28(2):285-298.[8]王莉雯,卫亚星.湿地土壤全氮和全磷含量高光谱模型研究[J].生态学报,2016,36(6)：5116-5125.[9]万余庆,谢涛,许石,等.土壤氮、磷、钾含量的高光谱遥感探测方法[J].中国煤炭地质,2018,30(1)：19-23.[10]王磊,顾晓鹤,王宝山,等.基于HJ-1A超光谱影像的县域尺度耕地土壤速效磷含量遥感制图研究[J].土壤通报,2015,46(6):1314-1320.[11]栾海军,章欣欣,田庆久,等.Landsat 7-ETM+归一化差分植被指数影像真实性检验[J].遥感信息,2016,31(6):138-146.[12]杨江燕,吴田,潘肖燕,等.基于遥感生态指数的雄安新区生态质量评估[J].应用生态学报,2019,30(1):277-284.[13]马宇龙,林志垒.基于面向对象和CART决策树方法的遥感影像湿地变化检测研究:以龙祥岛地区为例[J].福建师范大学学报(自然科学版),2017,33(6):69-81.[14]徐良将,黄昌春,李云梅,等.基于高光谱遥感反射率的总氮总磷的反演[J].遥感技术与应用,2013,28(4):681-688.[15]栾福明,张小雷,熊黑钢,等.基于ＴＭ影像的荒漠-绿洲交错带土壤有机质含量反演模型[J].中国沙漠,2014,34(4):1080-1086.[16]蔡东全,吴泉源,曹学江,等.基于HJ1A-HSI的龙口污水灌溉区土壤重金属含量反演[J].安全与环境工程,2015,22(5):33-39.
相似文献/References:
[1]马驰.基于Sentinel-2A遥感影像土壤有机质含量的反演研究[J].北方园艺,2020,44(02):94.[doi:10.11937/bfyy.20191174]
MA Chi.Inversion of Soil Organic Matter Content Based on Sentinel-2A Remote Sensing Image[J].Northern Horticulture,2020,44(15):94.[doi:10.11937/bfyy.20191174]
备注/Memo
作者简介：马驰(1975-),男,博士,副教授,现主要从事RS与GIS应用等研究工作。E-mail:7552442@qq.com.基金项目：国家自然科学基金资助项目(41371332)；辽宁地矿职业教育集团资助项目（Ldk2102，Ldk2106）；辽宁省交通高等专科学校科研资助项目（lnccjykyz202101）。收稿日期：2022-01-18