Inversion of Soil Organic Matter Content in Wetland Using Multispectral Data Based on Soil Spectral Reconstruction
CHEN Si-ming1, 3, 4, ZOU Shuang-quan1, 4*, MAO Yan-ling2, 4, LIANG Wen-xian1, 4, DING Hui1, 4
1. College of Forestry, Fujian Agriculture and Forestry University, Fuzhou 350002, China
2. College of Resources and Environment, Fujian Agriculture and Forestry University, Fuzhou 350002, China
3. Minjiang University, Fuzhou 350108, China
4. Fujian Provincial Ornamental Germplasm Resources Innovation & Engineering Application Research Center, Fujian Agriculture and Forestry University Fuzhou 350002, China
Abstract：Soil organic matter (SOM) is an important element of wetland ecosystem. Quick and wide monitor of SOM content with multispectral remote sensing technique has the vital significance to protect wetland ecosystem. More previous studies on the estimation of SOM content used hyperspectral analysis, while using multispectral was less. The main reason is that the spectral anomaly of multispectral data caused by spectral mixing of different objects affects the inversion accuracy of SOM content in wetland. Therefore, to avoid the spectral anomaly, this paper took the Shanyutan wetlands of Minjiang River Estuary as a survey region, trying to use Linear Spectral Unmixing Model(LSUM) to separate the pixel of original image and reconstruct the soil spectrum. Then, the correlation analyses between 2 different spectra (the raw spectrum and the reconstructed spectrum) and SOM content were done. Finally, according to correlation results, an inversion model for SOM content was established. The result showed that LSUM can effectively eliminate vegetation endmembers of the original image, reducing the reflection interference of most roads and buildings. The reconstructed spectral characteristic curve was closer to the spectral curve of soil under natural condition. It indicated that the effect of spectral reconstruction was remarkable; Compared to the correlation coefficients between 2 different spectra and SOM content, the reconstruct spectrum was more appropriate for reflecting the correlation between the soil spectrum and soil organic matter in the study area; using the reconstructed spectrum to build the predicting model could obtain more robust prediction accuracies than using the raw spectrum. Its values of R2 and F were increased by 0.124 and 2.223 respectively. And RMSE was reduced by 0.106. Moreover, through the 1∶1 line test, model of the reconstructed spectrum had a better fitting between the predicted and the measured. These results suggested that using LSUM has been proven to be effective in removing the spectral anomaly, ensuring a transferrable model for SOM content under natural condition. The study will provide some practical technology to monitor the SOM content in wetland by multispectral data.
陈思明，邹双全，毛艳玲，梁文贤，丁 卉. 土壤光谱重建的湿地土壤有机质含量多光谱反演[J]. 光谱学与光谱分析, 2018, 38(03): 912-917.
CHEN Si-ming, ZOU Shuang-quan, MAO Yan-ling, LIANG Wen-xian, DING Hui. Inversion of Soil Organic Matter Content in Wetland Using Multispectral Data Based on Soil Spectral Reconstruction. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(03): 912-917.
[1] St Luce M, Ziadi N, Zebarth B J, et al. Geoderma, 2014, (232-234): 449.
[2] CHEN Yi-yun，QI Kun，LIU Yao-lin，et al(陈奕云，漆 锟，刘耀林, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析)，2015, 35(6): 1705.
[3] Anne N J P，Abd-Elrahman A H，Lewis D B，et al. International Journal of Applied Earth Observation and Geoinformation, 2014, 33: 47.
[4] Conforti M，Buttafuoco G，Leone A P，et al. Catena, 2013, 110: 44.
[5] LIU Zheng-chun, ZENG Yong-nian, HE Li-li, et al(刘正春，曾永年，何丽丽，等). Remote Sensing Technology and Application(遥感技术与应用)，2012，27(2)：159.
[6] Abd-EI Monsef H，Khalifa I H，Faisal M. Arabian Journal of Geosciences，2015, 8(11): 9285.
[7] WANG Lian-xi, XU Sheng-nan, LI Qi, et al(王连喜，徐胜男，李 琪，等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报)，2016，32(5)：182.
[8] Bilgili A V，van Es H M，Akbas F，et al. Journal of Arid Environments, 2010, 74(2): 229.
[9] GAO Zhi-hai，BAI Li-na，WANG Beng-yu，et al(高志海, 白黎娜, 王琫瑜, 等). Scientia Silvae Sinicae(林业科学), 2011, 47(6): 9.
[10] ZHANG Fa-sheng，QU wei，YIN Guang-hua，et al(张法升, 曲 威, 尹光华, 等). Chinese Journal of Applied Ecology(应用生态学报), 2010, 21(4): 883.
[11] ZHAO Jie-peng, ZHANG Xian-feng, BAO Hui-yi, et al(赵杰鹏，张显峰，包慧漪，等). Journal of Infrared Millimeter Waves(红外与毫米波学报)，2012，31(2)：137.
