Abstract：Soil organic matter (SOM) is one of the most important measuring indexes of soil fertility. How to predict SOM spatial distribution precisely has great significance to soil carbon storage estimation and precision agriculture development. Traditional measurement of SOM, although with higher accuracy, consumes a lot of labor resources and costs long-term monitoring period, therefore, it is hard to achieve dynamic monitor of SOM. Spectroscopy technique has been used in SOM and other soil physicochemical parameters quick measurement. However spatial inversion model accuracy of SOM based on remote sensing images is relatively lower than laboratory model accuracy due to the influence of soil moisture, roughness and so on. In recent years, most studies have not eliminated the effect of moisture. Since moisture has great influence on SOM spectra reflectance, this study introduced the temporal information combined with the spectral information in order to solve this problem. Soil moisture has differences in multi period remote sensing images, and the spectra reflectance is also different. Based on the combination of reflectance from of two periods remote sensing images, the spectral index was constructed to predict SOM in this study. MODIS images of study area acquired in this study area (Blacksoil zone) because of the advantage of high temporal resolution. Spectra reflectance of MODIS images were used to analyze the effect of moisture on soil spectral reflectance, and then the spectral prediction models of SOM were built based on the comprehensive impacts of SOM and soil moisture. The results shows that: (1) the accuracy of SOM prediction model based on single image was lower without consideration of moisture effect, The Root mean square error (RMSE) of SOM prediction model were 0.591, 0.522, 0.545, 0.553, and the determination coefficient (R2) were 0.505, 0.614, 0.562, 0.568, 0.645 respectively based on the day of year (DOY) 117, 119, 130, 140, 143 single image. (2) Model with multi temporal images (DOY119 and 143) which considered the effect of moisture and SOM showed better predictive ability. RMSE was 0.442 while R2 was 0.723. Therefore the accuracy and stability of the model were significantly improved, and it can be used to predict the spatial distribution of SOM in regional scale. This study provides important information for regional soil fertility evaluation, soil carbon storage estimation, and precision agriculture development.
刘焕军，宁东浩，康 苒，金慧凝，张新乐*，盛 磊 . 考虑含水量变化信息的土壤有机质光谱预测模型 [J]. 光谱学与光谱分析, 2017, 37(02): 566-570.
LIU Huan-jun, NING Dong-hao, KANG Ran, JIN Hui-ning, ZHANG Xin-le*, SHENG Lei . A Study on Predicting Model of Organic Matter Contend Incorporating Soil Moisture Variation . SPECTROSCOPY AND SPECTRAL ANALYSIS, 2017, 37(02): 566-570.
[1] NI Jin-zhi，XU Jian-min，XIE Zheng-miao，et al(倪进治, 徐建民, 谢正苗, 等). Plant Nutrition and Fertilizer Science(植物营养与肥料学报), 2001, 7(4): 374. [2] SONG Xiao-yu，WANG Ji-hua，LIU Liang-yun，et al(宋晓宇，王纪华，刘良云，等). Scientia Agricultura Sinica(中国农业科学)，2007，40(9): 1996. [3] Marchetti A, Piccini C, Francaviglia R, et al. Pedosphere, 2012, 22(2): 230. [4] LIU Huan-jun，ZHANG Bai，ZHAO Jun，et al(刘焕军，张 柏，赵 军，等). Acta Pedologica Sinica(土壤学报)，2007，44(1): 27. [5] Stoner E R, Baumgardner M F. Soil Science Society of America Journal, 1982, 45(6): 1161. [6] Chen F，Kissell D E，West L T，et al. Soil Sci. Soc. Am. J.，2000，64(2): 746. [7] Fox G A，Sabbagh G J. Soil Sci. Soc. Am. J.，2002，66(6): 1922. [8] 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. [9] LIU Huan-jun，ZHAO Chun-jiang，WANG Ji-hua，et al(刘焕军, 赵春江, 王纪华, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2011, 27(8): 211. [10] Condit H R. Photogramm. Eng.，1970，36: 955. [11] Frazier B E, Cheng Y, Frazier B E, et al. Remote Sensing of Environment, 1989, 28(89): 317.
