[1]陈文娇,翁永玲,范兴旺,等.基于光谱转换的土壤盐分反演与动态分析[J].东南大学学报(自然科学版),2017,47(6):1233-1238.[doi:10.3969/j.issn.1001-0505.2017.06.024]
 Chen Wenjiao,Weng Yongling,Fan Xingwang,et al.Soil salinity retrieval and dynamic analysis based on spectral band inter-calibration[J].Journal of Southeast University (Natural Science Edition),2017,47(6):1233-1238.[doi:10.3969/j.issn.1001-0505.2017.06.024]
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基于光谱转换的土壤盐分反演与动态分析()
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
47
期数:
2017年第6期
页码:
1233-1238
栏目:
自动化
出版日期:
2017-11-20

文章信息/Info

Title:
Soil salinity retrieval and dynamic analysis based on spectral band inter-calibration
作者:
陈文娇1翁永玲1范兴旺2曹一茹1
1东南大学交通学院, 南京 210096; 2中国科学院南京地理与湖泊研究所, 南京 210008
Author(s):
Chen Wenjiao1 Weng Yongling1 Fan Xingwang2 Cao Yiru1
1School of Transportation, Southeast University, Nanjing 210096, China
2Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China
关键词:
盐渍土 黄河三角洲 光谱转换 偏最小二乘回归 动态分析
Keywords:
saline soil Yellow River Delta spectral band inter-calibration partial linear square regression(PLSR) dynamic analysis
分类号:
TP237
DOI:
10.3969/j.issn.1001-0505.2017.06.024
摘要:
为了对黄河三角洲地区进行大范围、长期的盐渍土监测,以研究区2000—2016年间4景Landsat-5 TM,EO-1 ALI,Landsat-8 OLI时间序列影像及Hyperion高光谱数据为基础开展土壤盐分定量反演分析.将Hyperion数据按照光谱响应函数分别重采样为TM,ALI,OLI模拟数据,采用数值回归方法计算TM,ALI与OLI对应波段间的光谱转换系数,从而将TM,ALI影像转换为OLI时序影像.分别采用偏最小二乘回归模型与多元线性回归模型建立土壤光谱与盐分参量间的预测关系,并将最优预测模型应用于OLI时序影像进行盐分反演制图,通过叠置方法进行盐渍土演化分析.结果表明,光谱转换方法提高了多传感器间数据一致性.偏最小二乘回归-电导率(PLSR-EC)模型的相关系数为0.700,采用2012年电导率实测值检验该模型反演结果,相关系数为0.690.研究区内高盐分土壤减少并向低盐分土壤转化.
Abstract:
Soil salinity quantitative retrieval were investigated based on four Landsat-5 TM(thematic mapper), EO-1(earth observing-one), ALI(advanced land imager), Landsat-8 OLI(operational land imager)time series images and hyper-spectral Hyperion data from 2000 to 2016 for long-term and extensive soil salinity monitoring over the Yellow River Delta. The Hyperion data were resampled to simulate TM, ALI, OLI data according to the corresponding spectral response functions, respectively. The spectral band inter-calibration coefficients were calculated from homologous TM, ALI and OLI bands by the statistical regression method. Then, the TM, ALI images were transformed into the OLI time series images. The PLSR(partial least square regression)model and the MLR(multiple linear regression)model were used to quantify the relationships between the soil spectra and the soil salinity parameters. The optimal predictive model was applied to OLI time series images to map soil salinity. The temporal variations in soil salinity were detected by overlay analysis. The results show that the band inter-calibration method improves the consistency of the multi-senor data. The PLSR-EC(electrical conductivity)model exhibits a correlation of 0.700 and the model validation yields a correlation of 0.690 with the soil samples collected in 2012. High salinity soil in the range of research area reduces and converts to low salinity soil.

参考文献/References:

[1] Rozema J, Flowers T. Crops for a salinized world[J]. Science, 2008, 322(5907): 1478-1480. DOI:10.1126/science.1168572.
[2] Yang J S. Development and prospect of the research on salt-affected soils in China[J]. Acta Pedologica Sinica, 2008, 45(5): 837-845.
[3] Allbed A, Kumar L, Sinha P. Mapping and modelling spatial variation in soil salinity in the Al Hassa Oasis based on remote sensing indicators and regression techniques[J]. Remote Sensing, 2014, 6(2): 1137-1157. DOI:10.3390/rs6021137.
[4] Wu W, Mhaimeed A S, Al-Shafie W M, et al. Mapping soil salinity changes using remote sensing in Central Iraq[J]. Geoderma Regional, 2014, 2-3: 21-31. DOI:10.1016/j.geodrs.2014.09.002.
[5] Fan X, Pedroli B, Liu G, et al. Soil salinity development in the yellow river delta in relation to groundwater dynamics[J]. Land Degradation & Development, 2011, 23(2): 175-189. DOI:10.1002/ldr.1071.
[6] Fan X, Weng Y, Tao J. Towards decadal soil salinity mapping using Landsat time series data[J]. International Journal of Applied Earth Observation and Geoinformation, 2016, 52: 32-41. DOI:10.1016/j.jag.2016.05.009.
[7] Chander G, Mishra N, Helder D L, et al. Applications of spectral band adjustment factors(SBAF)for cross-calibration[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(3): 1267-1281. DOI:10.1109/tgrs.2012.2228007.
[8] Miura T, Huete A, Yoshioka H. An empirical investigation of cross-sensor relationships of NDVI and red/near-infrared reflectance using EO-1 Hyperion data[J]. Remote Sensing of Environment, 2006, 100(2): 223-236. DOI:10.1016/j.rse.2005.10.010.
[9] 关元秀,刘高焕,刘庆生,等.黄河三角洲盐碱地遥感调查研究[J].遥感学报,2001,5(1):46-52. DOI:10.3321/j.issn:1007-4619.2001.01.009.
Guan Yuanxiu, Liu Gaohuan, Liu Qingsheng, et al. The study of salt-affected soils in the yellow river delta based on remote sensing[J]. Journal of Remote Sensing, 2001, 5(1): 46-52. DOI:10.3321/j.issn:1007-4619.2001.01.009. (in Chinese)
[10] Weng Y, Gong P, Zhu Z. Soil salt content estimation in the Yellow River delta with satellite hyperspectral data[J]. Canadian Journal of Remote Sensing, 2008, 34(3): 259-270.
[11] 鲍士旦.土壤农化分析[M].3版.北京:中国农业出版社,2000:178-198.
[12] 徐涵秋,唐菲.新一代Landsat系列卫星:Landsat 8遥感影像新增特征及其生态环境意义[J].生态学报,2013,33(11):3249-3257.
  Xu Hanqiu, Tang Fei. Analysis of new characteristics of the first Landsat 8 image and their eco-environmental significance[J]. Acta Ecologica Sinica, 2013, 33(11): 3249-3257.(in Chinese)
[13] Nawar S, Buddenbaum H, Hill J. Digital mapping of soil properties using multivariate statistical analysis and ASTER data in an arid region[J]. Remote Sensing, 2015, 7(2): 1181-1205. DOI:10.3390/rs70201181.
[14] 樊亚辉,塔西甫拉提·特依拜,王宏,等.艾比湖地区土地沙漠化遥感动态监测[J].干旱区资源与环境,2011,25(7):161-167.
  Fan Yahui, Tashpolat Tiyip, Wang Hong, et al. Dynamic monitoring of land desertification of the area of lake Ebinur[J]. Journal of Arid Land Resources and Environment, 2011, 25(7): 161-167.(in Chinese)
[15] 张海燕,樊江文,邵全琴.2000—2010年中国退牧还草工程区土地利用/覆被变化[J].地理科学进展,2015,34(7):840-853. DOI:10.18306/dlkxjz.2015.07.006.
Zhang Haiyan, Fan Jiangwen, Shao Quanqin. Land use/land cover change in the grassland restoration program areas in china, 2000-2010[J]. Progress in Geography, 2015, 34(7): 840-853. DOI:10.18306/dlkxjz.2015.07.006. (in Chinese)

备注/Memo

备注/Memo:
收稿日期: 2017-06-03.
作者简介: 陈文娇(1993—),女,硕士生;翁永玲(联系人),女,博士,教授,博士生导师,wengyongling@seu.edu.cn.
基金项目: 国家自然科学基金资助项目(41471352).
引用本文: 陈文娇,翁永玲,范兴旺,等.基于光谱转换的土壤盐分反演与动态分析[J].东南大学学报(自然科学版),2017,47(6):1233-1238. DOI:10.3969/j.issn.1001-0505.2017.06.024.
更新日期/Last Update: 2017-11-20