LI Jianguo, PU Lijie, ZHU Ming, DAI Xiaoqing, XU Yan, CHEN Xinjian, ZHANG Lifang, ZHANG Runsen. Monitoring Soil Salt Content Using HJ-1A Hyperspectral Data: A Case Study of Coastal Areas in Rudong County, Eastern China[J]. Chinese Geographical Science, 2015, 25(2): 213-223. doi: 10.1007/s11769-014-0693-2
Citation: LI Jianguo, PU Lijie, ZHU Ming, DAI Xiaoqing, XU Yan, CHEN Xinjian, ZHANG Lifang, ZHANG Runsen. Monitoring Soil Salt Content Using HJ-1A Hyperspectral Data: A Case Study of Coastal Areas in Rudong County, Eastern China[J]. Chinese Geographical Science, 2015, 25(2): 213-223. doi: 10.1007/s11769-014-0693-2

Monitoring Soil Salt Content Using HJ-1A Hyperspectral Data: A Case Study of Coastal Areas in Rudong County, Eastern China

doi: 10.1007/s11769-014-0693-2
Funds:  Under the auspices of National Natural Science Foundation of China (No. 41230751, 41101547), Scientific Research Foundation of Graduate School of Nanjing University (No. 2012CL14)
More Information
  • Corresponding author: PU Lijie
  • Received Date: 2013-03-04
  • Rev Recd Date: 2013-07-23
  • Publish Date: 2015-01-27
  • Hyperspectral data are an important source for monitoring soil salt content on a large scale. However, in previous studies, barriers such as interference due to the presence of vegetation restricted the precision of mapping soil salt content. This study tested a new method for predicting soil salt content with improved precision by using Chinese hyperspectral data, HuanJing-Hyper Spectral Imager (HJ-HSI), in the coastal area of Rudong County, Eastern China. The vegetation-covered area and coastal bare flat area were distinguished by using the normalized differential vegetation index at the band length of 705 nm (NDVI705). The soil salt content of each area was predicted by various algorithms. A Normal Soil Salt Content Response Index (NSSRI) was constructed from continuum-removed reflectance (CR-reflectance) at wavelengths of 908.95 nm and 687.41 nm to predict the soil salt content in the coastal bare flat area (NDVI705 < 0.2). The soil adjusted salinity index (SAVI) was applied to predict the soil salt content in the vegetation-covered area (NDVI705 ≥ 0.2). The results demonstrate that 1) the new method significantly improves the accuracy of soil salt content mapping (R2= 0.6396, RMSE = 0.3591), and 2) HJ-HSI data can be used to map soil salt content precisely and are suitable for monitoring soil salt content on a large scale.
  • [1] Aldakheel Y Y, 2011. Assessing NDVI spatial pattern as related to irrigation and soil salinity management in Al-Hassa Oasis, Saudi Arabia. Journal of the Indian Society of Remote Sensing, 39(2): 171-180. doi:  10.1007/s12524-010-0057-z
    [2] Anderson-Cook C M, Alley M, Roygard J et al., 2002. Differentiating soil types using electromagnetic conductivity and crop yield maps. Soil Science Society of America Journal, 66(5): 1562-1570. doi:  10.2136/sssaj2002.1562
    [3] Bao Shidan, 2000. Soil and Agricultural Chemistry Analysis. Beijing: Agriculture Publication, 355-356. (in Chinese)
    [4] Bilgili A V, Cullu M A, van Es H et al., 2011. The use of hyperspectral visible and near infrared reflectance spectros­copy for the characterization of salt-affected soils in the Harran Plain, Turkey. Arid Land Research and Management, 25(1): 19-37. doi:  10.1080/15324982.2010.528153
    [5] Blackburn G A, 1998. Quantifying chlorophylls and caroteniods at leaf and canopy scales: An evaluation of some hyperspectral approaches. Remote Sensing of Environment, 66(3): 273-285.
    [6] Ding J L, Wu M C, Tiyip T, 2011. Study on soil salinization information in arid region using remote sensing technique. Agricultural Sciences in China, 10(3): 404-411. doi: 10.1016/ S1671-2927(11)60019-9
    [7] Ding Jianli, Yao Yuan, 2013. Evaluation of soil moisture contents under sparse vegetation coverage conditions using microwave remote sensing technology in arid region. Scientia Geogra­phica Sinica, 33(7): 837-843. (in Chinese)
    [8] Dutkiewicz A, Lewis M, Ostendorf B, 2009. Evaluation and comparison of hyperspectral imagery for mapping surface symptoms of dryland salinity. International Journal of Remote Sensing, 30(3): 693-719. doi:  10.1080/01431160802392612
    [9] Farifteh J, Farshad A, George R, 2006. Assessing salt-affected soils using remote sensing, solute modelling, and geophysics. Geoderma, 130(3): 191-206. doi: 10.1016/j.geoderma.2005.02. 003
    [10] Farifteh J, van der Meer F, van der Meijde M et al., 2008. Spectral characteristics of salt-affected soils: A laboratory experiment. Geoderma, 145(3-4): 196-206. doi: 10.1016/ j.geoderma.2008.03.011
    [11] Fang Ming, Chen Bangben, Hu Rongqin et al., 1990. Ecological salinization characters of sea beach soil in Jiangsu. Acta Pedologica Sinica, 27(3): 335-342. (in Chinese)
    [12] Fernandez-Buces N, Siebe C, Cram S et al., 2006. Mapping soil salinity using a combined spectral response index for bare soil and vegetation: A case study in the former lake Texcoco, Mexico. Journal of Arid Environments, 65(4): 644-667. doi:  10.1016/j.jaridenv.2005.08.005
    [13] Gallagher F J, Pechmann I, Bogden J D et al., 2008. Soil metal concentrations and productivity of Betula populifolia (gray birch) as measured by field spectrometry and incremental annual growth in an abandoned urban Brownfield in New Jersey. Environmental Pollution, 156(3): 699-706. doi:  10.1016/j.envpol.2008.06.013
    [14] Gamon J, Serrano L, Surfus J, 1997. The photochemical reflectance index: An optical indicator of photosynthetic radia­tion use efficiency across species, functional types, and nutrient levels. Oecologia, 112(4): 492-501. doi:  10.1007/s004420050337
    [15] Ghosh G, Kumar S, Saha S, 2012. Hyperspectral satellite data in mapping salt-affected soils using linear spectral unmixing analysis. Journal of the Indian Society of Remote Sensing, 40(1): 129-136. doi:  10.1007/s12524-011-0143-x
    [16] Gitelson A, Merzlyak M N, 1994. Spectral reflectance changes associated with autumn senescence of aesculus hippocastanum L. and Acer platanoides L. leaves. spectral features and relation to chlorophyll estimation. Journal of Plant Physiology, 143(3): 286-292. doi: 10.1016/S0176-1617(11)81633-0
    [17] Gu Fengxue, Zhang Yuandong, Chu Yu et al., 2002. Primary analysis on groundwater, soil moisture and salinity in Fukang Oasis of Southern Junggar Basin. Chinese Geographical Science, 12(4): 333-338. doi: 10.1007/s11769-002-0038-4
    [18] Horler D, Dockray M, Barber J, 1983. The red edge of plant leaf reflectance. International Journal of Remote Sensing, 4(2): 273-288. doi:  10.1080/01431168308948546
    [19] Huete A R, 1988. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3): 295-309. doi:  10.1016/0034-4257(88)90106-X
    [20] Kinal J, Stoneman G, Williams M, 2006. Calibrating and using an EM31 electromagnetic induction meter to estimate and map soil salinity in the jarrah and karri forests of south-western Australia. Forest Ecology And Management, 233(1): 78-84. doi:  10.1016/j.foreco.2006.06.003
    [21] Lobell D, Lesch S, Corwin D et al., 2010. Regional-scale assessment of soil salinity in the Red River Valley using multi-year MODIS EVI and NDVI. Journal of Environmental Quality, 39(1): 35-41. doi:  10.2134/jeq2009.0140
    [22] Mashimbye Z E, Cho M A, Nell J P et al., 2012. Model-based integrated methods for quantitative estimation of soil salinity from hyperspectral remote sensing data: A case study of selected south African soils. Pedosphere, 22(5): 640-649. doi:  10.1016/S1002-0160(12)60049-6
    [23] Merzlyak M N, Gitelson A A, Chivkunova O B et al., 1999. Non-destructive optical detection of pigment changes during leaf senescence and fruit ripening. Physiologia Plantarum, 106(1): 135-141. doi:  10.1034/j.1399-3054.1999.106119.x
    [24] Metternicht G, Zinck J, 2003. Remote sensing of soil salinity: Potentials and constraints. Remote Sensing of Environment, 85(1): 1-20. doi:  10.1016/S0034-4257(02)00188-8
    [25] Mulder V L, de Bruin S, Schaepman M E et al., 2011. The use of remote sensing in soil and terrain mapping—A review. Geoderma, 162(1-2): 1-19. doi: 10.1016/j.geoderma.2010.12. 018
    [26] Penuelas J, Filella I, 1998. Visible and near-infrared reflectance techniques for diagnosing plant physiological status. Trends In Plant Science, 3(4): 151-156. doi: 10.1016/S1360-1385(98) 01213-8
    [27] Rao B R M, Sankar T R, Dwivedi R S et al., 1995. Spectral behavior of salt-affected soils. International Journal of Remote Sensing, 16(12): 2125-2136. doi:  10.1080/01431169508954546
    [28] Shamsi S R F, Zare SAbtahi S A, 2013. Soil salinity charac­teristics using moderate resolution imaging spectroradiometer (MODIS) images and statistical analysis. Archives of Agro­nomy and Soil Science, 59(4): 471-489. doi: 10.1080/03650340. 2011.646996
    [29] Slavich P, Petterson G, 1990. Estimating average rootzone salinity from electromagnetic induction (EM-38) measurements. Soil Research, 28(3): 453-463. doi:  10.1071/SR9900453
    [30] Su Zilong, Zhang Guanghui, Yu Yan, 2013. Soil moisture characteristic of different land use types in the typical black soil region of northeast China. Scientia Geographica Sinica, 33(9): 1104-1110. (in Chinese)
    [31] Verrelst J, Schaepman M E, Koetz B et al., 2008. Angular sensitivity analysis of vegetation indices derived from CHRIS/PROBA data. Remote Sensing of Environment, 112(5): 2341-2353. doi:  10.1016/j.rse.2007.11.001
    [32] Wang Qiao, Wu Chuanqing, Li Qing et al., 2010. Chinese HJ-1A/B satellites and data characteristics. Science China (Earth Sciences), 53(1): 51-57. doi:  10.1007/s11430-010-4139-0
    [33] Wei D, Yan H, Song X S et al., 2001. Hydrochemical charac­teristics of salt marsh wetlands in western Songnen Plain. Journal of Geographical Sciences, 11(2): 217-223. doi:  10.1007/BF02888693
    [34] Weng Y, Gong P, Zhu Z, 2010. A spectral index for estimating soil salinity in the Yellow River Delta region of China using EO-1 Hyperion data. Pedosphere, 20(3): 378-388. doi: 10.1016/ S1002-0160(10)60027-6
    [35] Weng Yongling, Gong Peng, 2006. A review on remote sensing technique for salt affected soils. Scientia Geographica Sinica, 26(3): 369-375. (in Chinese)
    [36] Xu Yongming, Zhao Qiaohua, Ba Yaer et al., 2012. Spatio- temporal variations of land surface evapotranspiration of Bosten Lake Basin based on MODIS data. Scientia Geogra­phica Sinica, 32(11): 1353-1357. (in Chinese)
    [37] Zhang M M, SuY P, Wu P, 2011. Using HJ-I satellite remote sensing data to surveying the Saline soil distribution in Yinchuan Plain of China. African Journal of Agricultural Research, 6(32): 6592-6597. doi:  10.5897/AJAR11.130
    [38] Zhang T, Zeng S, Gao Y et al., 2011. Using hyperspectral vegetation indices as a proxy to monitor soil salinity. Eco­logical Indicators, 11(6): 1552-1562. doi: 10.1016/j.ecolind. 2011.03.025
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Monitoring Soil Salt Content Using HJ-1A Hyperspectral Data: A Case Study of Coastal Areas in Rudong County, Eastern China

doi: 10.1007/s11769-014-0693-2
Funds:  Under the auspices of National Natural Science Foundation of China (No. 41230751, 41101547), Scientific Research Foundation of Graduate School of Nanjing University (No. 2012CL14)
    Corresponding author: PU Lijie

Abstract: Hyperspectral data are an important source for monitoring soil salt content on a large scale. However, in previous studies, barriers such as interference due to the presence of vegetation restricted the precision of mapping soil salt content. This study tested a new method for predicting soil salt content with improved precision by using Chinese hyperspectral data, HuanJing-Hyper Spectral Imager (HJ-HSI), in the coastal area of Rudong County, Eastern China. The vegetation-covered area and coastal bare flat area were distinguished by using the normalized differential vegetation index at the band length of 705 nm (NDVI705). The soil salt content of each area was predicted by various algorithms. A Normal Soil Salt Content Response Index (NSSRI) was constructed from continuum-removed reflectance (CR-reflectance) at wavelengths of 908.95 nm and 687.41 nm to predict the soil salt content in the coastal bare flat area (NDVI705 < 0.2). The soil adjusted salinity index (SAVI) was applied to predict the soil salt content in the vegetation-covered area (NDVI705 ≥ 0.2). The results demonstrate that 1) the new method significantly improves the accuracy of soil salt content mapping (R2= 0.6396, RMSE = 0.3591), and 2) HJ-HSI data can be used to map soil salt content precisely and are suitable for monitoring soil salt content on a large scale.

LI Jianguo, PU Lijie, ZHU Ming, DAI Xiaoqing, XU Yan, CHEN Xinjian, ZHANG Lifang, ZHANG Runsen. Monitoring Soil Salt Content Using HJ-1A Hyperspectral Data: A Case Study of Coastal Areas in Rudong County, Eastern China[J]. Chinese Geographical Science, 2015, 25(2): 213-223. doi: 10.1007/s11769-014-0693-2
Citation: LI Jianguo, PU Lijie, ZHU Ming, DAI Xiaoqing, XU Yan, CHEN Xinjian, ZHANG Lifang, ZHANG Runsen. Monitoring Soil Salt Content Using HJ-1A Hyperspectral Data: A Case Study of Coastal Areas in Rudong County, Eastern China[J]. Chinese Geographical Science, 2015, 25(2): 213-223. doi: 10.1007/s11769-014-0693-2
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