|其他摘要||Saline-alkali soil is a world wide threat for ecological environment and agriculture. This thesis designed one experiment of various soil water content with saline desert soil, and another experiment of various soil salt types (NaCl, Na2SO4, and Na2CO3) and contents with aeolian soil. The information of soil water and salt conent and soil spectra including visible-near infrared and thermal infrared spectra were obtained synchronously. Analyzing spectral response to the changes of soil water and salt content, models were built for soil water and salt estimation. The methods of partial least squares regression (PLSR) and spectral index were used for modeling, and the correction of Akaike Information Criterion (AICc) and Relative Percent Deviation (RPD) were used for model evalution. Based on the above studies, the results were concluded below:
1. Duing to salt type of Na2SO4 occupied a main place in the saline soil Northwest China, indoor simulation experiment need considering the height of soil column, and we proved that soil column higher than 7cm was good enough for field situation simulation. The second experiment showed that the main influence on spectra was not salt content but salt type.
2. Modeling soil water content with the method of PLSR based on hyperspectra, we found that the most sensitive bands were 1370nm and 1955nm, and the optimal model contained 6 bands for soil water estimation with RPD of 1.51 and R2 of 0.69. When modeling soil salt content, only the model for salt type of Na2CO3 contained the visible band, and with a RPD of 3.49 and R2 of 0.92. Bands involved in models for other salt types all located at near infrared range, and all models were high accurate. The method of spectral index was not good enough for soil water estimation, but good enough for soil salt estimation, nonetheless the accuracy was inferior to PLSR.
3. Modeling soil water content with the method of PLSR based on thermal infrared spectra, we found that the most sensitive bands were 8.596μm and 8.769μm, and the optimal model contained 7 bands for soil water estimation with a RPD of 1.58 and R2 of 0.71. When modeling soil salt content, only the model for salt type of Na2SO4 chose raw spectra to estimate soil water content, and the sensitive bands spread over 9-14μm. Models for other salt types chose derivative spectra and the sensitive bands centralized in 8-9μm, and all models could predict soil salt content well. The method of spectral index did not work for soil water and salt content estimation, indicating that PLSR worked better than spectral index. This might result from the method of PLSR had the ability to choose more bands than spectral index.
4. The result of model validation with field data was not good enough. To tell the difference between models built with experiment data and field data, we conducted modeling with field data and found that visible spectra contributed much more than infrared spectra, and the model achieved the accuracy with a RPD of 2.06 and R2 of 0.81. The model identified with method of spectral index could also have the ability to perdict soil salt content, but the accuracy was not as good as PLSR. The dominant bands focused on the visible range, indicating that soil color was the main factor influencing soil spectra.
5. Soil photoes reflecting soil color were used to estimate soil salt content. Four color components (RGB and Gray) were drawn from the photoes, and the percentage of each brightness in each color component was calculated. Brightness value partition could take full advantage of the percentages, and the final partition number was identified as nine. Based on these processes, soil salt content was modeled with a R2 of 0.9.|