Retrieval of Organic Matter Content in Black Soil Based on Airborne Hyperspectral Remote Sensing Data: Taking Jiansanjiang District in Heilongjiang Province as an Example
-
摘要: 掌握黑土地有机质含量对黑土资源利用与保护具有重要意义，而高光谱卫星影像的缺乏制约了区域尺度土壤有机质反演研究的开展.以黑龙江省建三江黑土区为例，采用CASI/SASI航空高光谱数据、ASD（analytical spectral devices）地面光谱数据和土壤样品有机质含量数据，基于有机质含量与光谱反射率的相关性和定量关系，构建最优的回归模型并开展研究区土壤有机质含量遥感反演.结果表明：偏最小二乘法回归模型比多元逐步回归模型更稳定（判定系数分别为0.885和0.653），且精度更高（均方根误差分别为0.424和0.744）；采用偏最小二乘模型反演的结果与地面化探结果基本一致.Abstract: The retrieval of organic matter content in black soil is of great significance for the utilization and conservation of black soil resources. However, the lack of hyperspectral satellite images has restricted the development of soil organic matter retrieval at the regional scale. In this paper, a typical black soil area of Heilongjiang Province was selected as the study area. Then, the CASI/SASI airborne hyperspectral data, ASD(analytical spectral devices) surface spectral data, and the soil organic matter content data are used to establish the optimal regression model based on the correlation and quantitative relationship between organic matter content and spectral reflectance values of soil samples. Finally, the retrieval of soil organic matter content of the study area is carried out. The results show that the partial least square regression model is more stable than the multivariate stepwise regression model (with determination coefficients of 0.885 and 0.653, respectively), and the accuracy is higher (with root mean square error of 0.424 and 0.744, respectively). The retrieval result derived from partial least square regression is basically consistent with the result of geochemical exploration on the ground.
-
Key words:
- remote sensing /
- soil organic matter /
- hyperspectral /
- black soil /
- regression model
-
图 4 大气校正前后土壤光谱曲线对比
Fig. 4. The comparison of soil spectral curves before and after atmospheric correction
图 7 偏最小二乘法主成分数量与模型精度对比
Fig. 7. Comparison of partial least square method for principal component number and model accuracy
图 8 验证样本实测与预测值对比
a.偏最小二乘法; b.多元逐步回归法
Fig. 8. Comparison diagrams of tested and predicted values
图 9 土壤有机质高光谱反演与地面化探对比
a.有机质高光谱反演结果；b.地面化探结果
Fig. 9. Comparison of hyperspectral retrieval of soil organic matter with geochemical exploration
表 1 CASI/SASI系列机载成像光谱仪参数
Table 1. Parameters of the airborne imaging spectrometers of the CASI/SASI series
参数 CASI-1500 SASI-600 光谱范围 380~1 050 nm 950~2 450 nm 每行像元数 1 470 640 连续光谱通道数 288 100 光谱带宽 2.3 nm 15 nm 帧频(全波段) 14 fps 100 fps 总视场角 40° 40° 瞬时视场角 0.028° 0.07° 信噪比(峰值) ＞1 100 ＞1 100 绝对辐射精度 ＜2% ＜2% 
下载: 导出CSV
表 2 土壤有机质统计特征
Table 2. Statistical characteristics of soil organic matter
样品 最小值 最大值 均值 方差 标准差 偏度 峰度 P 有机质 7.651 143.985 40.207 400.017 20.000 2.881 12.263 0.313 注：单位为g/kg；P为K-S正态检验概率值.
下载: 导出CSV
表 3 土壤反射率光谱及各种变换光谱与有机质含量相关系数绝对值最大的波段
Table 3. The band of soil reflectance spectrum and the absolute value of the correlation coefficient between various transformation spectra and organic matter contents
R R' R" (1/R)' (1/R)" ln(R) (ln(R))' 波段 r 波段 r 波段 r 波段 r 波段 r 波段 r 波段 r 352 -0.54 1 746 0.66 993 0.50 1 773 -0.71 509 0.67 352 -0.56 1 585 0.73 802 -0.50 1 747 0.66 603 0.49 1 252 -0.70 354 -0.65 793 -0.54 1 568 0.73 801 -0.50 1 744 0.65 489 -0.48 1 251 -0.70 770 -0.64 794 -0.54 1 249 0.73
下载: 导出CSV
表 4 多元逐步回归与偏最小二乘法反演模型对比
Table 4. Comparison between multiple stepwise regression and partial least squares inversion model
样品 偏最小二乘法 多元逐步回归法 RMSE R2 RMSE R2 有机质 0.424 0.885 0.744 0.653
下载: 导出CSV
-
[1] Ben-Dor, E., Banin, A., 1995.Near-Infrared Analysis as a Rapid Method to Simultaneously Evaluate Several Soil Properties.Soil Science Society of America Journal, 59(2):364-372.doi: 10.2136/sssaj1995.03615995005900020014x [2] Cheng, X.F., Song, T.T., Chen, Y., et al., 2017.Retrieval and Analysis of Heavy Metal Content in Soil Based on Measured Spectra in the Lanping Zn-Pb Mining Area, Western Yunnan Province.Acta Petrologica et Mineralogica, 36(1):60-69(in Chinese with English abstract). http://www.en.cnki.com.cn/Article_en/CJFDTotal-YSKW201701005.htm [3] Cui, M.Z., 2013.Research on Evaluation of Cultivated Land Resource Security in Jiansanjiang Reclamation Area Based on RS and GIS (Dissertation).Northeast Agricultural University, Harbin (in Chinese with English abstract). [4] Fox, G.A., Sabbagh, G.J., 2002.Estimation of Soil Organic Matter from Red and Near-Infrared Remotely Sensed Data Using a Soil Line Euclidean Distance Technique.Soil Science Society of America Journal, 66(6):1922-1929.doi: 10.2136/sssaj2002.1922 [5] Galvão, L.S., Pizarro, M.A., 2001, Variations in Reflectance of Tropical Soils-Spectral-Chemical Composition Relationships from AVIRIS Data.Remote Sensing of Environment, 75(2):245-255. doi: 10.1016/S0034-4257(00)00170-X [6] Gong, S.Q., Wang, X., Shen, R.P., et al., 2010.Study on Heavy Metal Element Content in the Coastal Saline Soil by Hyperspectral Remote Sensing.Remote Sensing Technology and Application, 25(2):169-177 (in Chinese with English abstract). http://en.cnki.com.cn/Article_en/CJFDTOTAL-YGJS201002000.htm [7] Henderson, T.L., Baumgardner, M.F., Franzmeier, D.P., et al., 1992.High Dimensional Reflectance Analysis of Soil Organic Matter.Soil Science Society of America Journal, 56(3):865-872.doi: 10.2136/sssaj1992.03615995005600030031x [8] Lian, G., Guo, X.D., Fu, B.J., et al., 2006.Spatial Variability and Prediction of Soil Organic Matter at County Scale on the Loess Plateau.Progress in Geography, 25(2):112-122, 142(in Chinese with English abstract). doi: 10.11820/dlkxjz.2006.02.013 [9] Liu, H.J., Pan, Y., Dou, X., et al., 2018.Soil Organic Matter Content Inversion Model with Remote Sensing Image in Field Scale of Blacksoil Area.Transactions of the Chinese Society of Agricultural Engineering, 34(1):127-133(in Chinese with English abstract). http://en.cnki.com.cn/Article_en/CJFDTotal-NYGU201801017.htm [10] Liu, H.J., Zhao, C.J., Wang, J.H., et al., 2011.Soil Organic Matter Predicting with Remote Sensing Image in Typical Blacksoil Area of Northeast China.Transactions of the Chinese Society of Agricultural Engineering, 27(8):211-215 (in Chinese with English abstract). http://dl.sciencesocieties.org/publications/tcsae/abstracts/2011/8/2011.8.036 [11] Mao, X., Liu, L.J., Li, C.A., et al., 2017.Elemental Composition Features of Loess-Paleosol Profile in Fengning, Hebei Province.Earth Science, 42(10):1750-1759(in Chinese with English abstract). http://en.cnki.com.cn/Article_en/CJFDTotal-DQKX201710010.htm [12] McCarty, G.W., Reeves, J.B., Reeves, V.B., et al., 2002.Mid-Infrared and Near-Infrared Diffuse Reflectance Spectroscopy for Soil Carbon Measurement.Soil Science Society of America Journal, 66(2):640-646.doi: 10.2136/sssaj2002.6400 [13] Palmer, C.J., Smith, W.D., Conkling, B.L., 2002.Development of a Protocol for Monitoring Status and Trends in Forest Soil Carbon at a National Level.Environmental Pollution, 116:S209-S219.doi: 10.1016/s0269-7491(01)00253-6 [14] Qiu, H., Chen, H.Y., Xing, S.H., et al., 2017.Soil Organic Matter Estimation Models Based on Hyperion Data.Journal of Fujian Agriculture and Forestry University(Natural Science Edition), 46(4):460-467(in Chinese with English abstract). http://en.cnki.com.cn/Article_en/CJFDTotal-FJND201704017.htm [15] Shuai, Q., Huang, S., Li, Z.Z., et al., 2015.The Metal Element Information Extraction from Hyperion Data Based on the Vegetation Stress Spectra.Earth Science, 40(8):1319-1324(in Chinese with English abstract). http://en.cnki.com.cn/Article_en/CJFDTotal-DQKX201508006.htm [16] Tan, K., Zhang, Q.Q., Cao, Q., et al., 2015.Hyperspectral Retrieval Model of Soil Organic Matter Content Based on Particle Swarm Optimization-Support Vector Machines.Earth Science, 40(8):1339-1345(in Chinese with English abstract). http://en.cnki.com.cn/Article_en/CJFDTotal-DQKX201508009.htm [17] Wang, R.C., Su, H.P., Wang, S.F., 1986.Studies on Spectral Reflectance of Typicai Soils and Its Fuzzy Category in Soil Classification in Zhejiang Province.Journal of Zhejiang University, 12(4):464-471(in Chinese with English abstract). http://en.cnki.com.cn/Article_en/CJFDTOTAL-ZJNY198604017.htm [18] Wu, C.W., Xia, J.X., Duan, Z, R., 2015.Technologies of Predictive Mapping for Soil Organic Matter.Chinese Journal of Soil Science, 46(1):239-247(in Chinese with English abstract). http://en.cnki.com.cn/Article_en/CJFDTotal-TRTB201501040.htm [19] Wu, S., 2016.Study on the Inversion of Soil Organic Matter Content in Typical Black Soil Zone(Dissertation).Jilin University, Changchun(in Chinese with English abstract). [20] Ye, F.W., Liu, D.C., Zhao, Y.J., 2011.Airborne Hyper-Spectral Survey System CASI/SASI and Its Preliminary Application in Uranium Exploration.World Nuclear Geoscience, 28(4):231-236(in Chinese with English abstract). http://en.cnki.com.cn/Article_en/CJFDTOTAL-GWYD201104010.htm [21] Yu, L., Hong, Y.S., Geng, L., et al., 2015.Hyperspectral Estimation of Soil Organic Matter Content Based on Partial Least Squares Regression.Transactions of the Chinese Society of Agricultural Engineering, 31(14):103-109 (in Chinese with English abstract). http://en.cnki.com.cn/Article_en/CJFDTotal-NYGU201514015.htm [22] Zhou, T., Shi, P.J., Luo, J.Y., et al., 2007.Estimation of Soil Organic Carbon Based on Remote Sensing and Process Model.Journal of Remote Sensing, 11(1):127-136 (in Chinese with English abstract). doi: 10.1007/s11461-008-0038-3 [23] 程先锋, 宋婷婷, 陈玉, 等, 2017.滇西兰坪铅锌矿区土壤重金属含量的高光谱反演分析.岩石矿物学杂志, 36(1):60-69. http://www.cnki.com.cn/Article/CJFDTotal-NHBH201401014.htm [24] 崔明哲, 2013.基于RS和GIS的建三江垦区耕地资源安全评价研究(硕士学位论文).哈尔滨:东北农业大学. [25] 龚绍琦, 王鑫, 沈润平, 等, 2010.滨海盐土重金属含量高光谱遥感研.遥感技术与应用, 25(2):169-177. doi: 10.11873/j.issn.1004-0323.2010.2.169 [26] 连纲, 郭旭东, 傅伯杰, 等, 2006.黄土丘陵沟壑区县域土壤有机质空间分布特征及预测.地理科学进展, 25(2):112-122, 142. doi: 10.11820/dlkxjz.2006.02.013 [27] 刘焕军, 潘越, 窦欣, 等, 2018.黑土区田块尺度土壤有机质含量遥感反演模型.农业工程学报, 34(1):127-133. doi: 10.11975/j.issn.1002-6819.2018.01.17 [28] 刘焕军, 赵春江, 王纪华, 等, 2011.黑土典型区土壤有机质遥感反演.农业工程学报, 27(8):211-215. http://www.cnki.com.cn/Article/CJFDTotal-NYGU201108039.htm [29] 毛欣, 刘林敬, 李长安, 等, 2017.丰宁黄土-古土壤剖面常量元素地球化学特征.地球科学, 42(10):1750-1759. http://earth-science.net/WebPage/Article.aspx?id=3662 [30] 邱壑, 陈瀚阅, 邢世和, 等, 2017.基于Hyperion数据的耕地土壤有机质含量遥感反演.福建农林大学学报(自然科学版), 46(4):460-467. http://cdmd.cnki.com.cn/Article/CDMD-10300-2008091989.htm [31] 帅琴, 黄爽, 李志忠, 等, 2015.基于植被胁迫光谱的高光谱遥感植被元素富集信息提取.地球科学, 40(8):1319-1324. http://earth-science.net/WebPage/Article.aspx?id=3135 [32] 谭琨, 张倩倩, 曹茜, 等, 2015.基于粒子群优化支持向量机的矿区土壤有机质含量高光谱反演.地球科学, 40(8):1339-1345. http://earth-science.net/WebPage/Article.aspx?id=3136 [33] 王人潮, 苏海萍, 王深法, 1986.浙江省主要土壤光谱反射特性及其模糊分类在土壤分类中的应用研究.浙江大学学报, 12(4):464-471. http://www.cnki.com.cn/Article/CJFDTotal-STTB201302038.htm [34] 吴才武, 夏建新, 段峥嵘, 2015.土壤有机质预测性制图方法研究进展.土壤通报, 46(1):239-247. http://mall.cnki.net/magazine/Article/TRTB201501040.htm [35] 吴嵩, 2016.典型黑土区土壤有机质含量反演研究(博士学位论文).长春:吉林大学. [36] 叶发旺, 刘德长, 赵英俊, 2011.CASI/SASI航空高光谱遥感测量系统及其在铀矿勘查中的初步应用.世界核地质科学, 28(4):231-236. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=sjhdzkx201104008 [37] 于雷, 洪永胜, 耿雷, 等, 2015.基于偏最小二乘回归的土壤有机质含量高光谱估算.农业工程学报, 31(14):103-109. doi: 10.11975/j.issn.1002-6819.2015.14.015 [38] 周涛, 史培军, 罗巾英, 等, 2007.基于遥感与碳循环过程模型估算土壤有机碳储量.遥感学报, 11(1):127-136. doi: 10.11834/jrs.20070117 -
点击查看大图
图(9) / 表(4)
计量
- 文章访问数: 4401
- HTML全文浏览量: 1782
- PDF下载量: 40
- 被引次数: 0
