1. Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographical Sciences and Natural Resources Research (IGSNRR), Chinese Academy of Sciences, Beijing 100101, China 2. University of Chinese Academy of Sciences, Beijing 100049, China 3. Key Laboratory of Agriculture Engineering in Structure and Environment of Ministry of Agriculture, China Agricultural University, Beijing 100083, China
Abstract：In this paper, based on the measurements of soil elements content and infrared spectra of 26 soil samples collected in more than 10 places, the relationship between soil emissivity in mid-infrared bands and the content of 11 soil elements including organic matters such as NO3-N, P, K, Ca, Mg, Cu, Fe, Mn, Zn and pH are analyzed. The bands where the soil elements content are significantly correlated with emissivity are given. And soil elements content estimation method is established based on the soil emissivity spectra with the partial least squares regression model and multiple stepwise regression model. The results show that: (1) In 8~10 μm, the correlation coefficient (R2) between Ca and soil emissivity is the highest, followed by Mg, Mn and Fe, with the highest correlation coefficient of 0.85 and the lowest, 0.52. In the range of 6~8 μm, the correlations between the contents of K, Fe, NO3-N, Zn and emissivity decrease gradually, with the highest correlation coefficient of 0.75 and the lowest 0.48. In 10~14 μm, the correlation between soil elements contents and emissivity is the highest for Mn, followed successively by P and K. (2) The scatter plot of soil emissivity and pH value has a parabola relation basically. The emissivity is the highest when pH value is 7, while the emissivity decreases gradually with the gradual decrease of pH value. (3) The accuracy of the estimated soil elements content from the partial least squares regression method is higher than that from the multiple stepwise regression method. It is noted that R2 between the measurements and the estimates for the elements of Cu, Fe and Ca from the partial least squares regression method are very high (larger than 0.9). Additionally, using the simulated emissivity spectrum in the ASTER thermal infrared bands, modeling R2 and validation R2 between the measurements and the estimates for the elements of Ca from the multiple stepwise regression method are high (0.774 and 0.892, respectively). Using the simulated emissivity spectrum in the MODIS infrared bands, modeling R2 and validation R2 for Ca and Fe are higher than 0.85, and modeling R2 and validation R2 for Mg, K are higher than 0.5. As a whole, the emissivity spectrum in ASTER band 10 and band 11 and MODIS bands 28, 29, 30 are more sensitive to soil elements content, and thus they are more suitable for the estimation of soil elements content.
Key words：Soil emissivity;Mid-infrared spectra;Soil elements;Partial least squares regression;Multiple stepwise regression;ASTER;MODIS
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