Study Of The Thermal Infrared Emissivity Spectra In The Soil Salinization In Arid Land

In arid and semi-arid inland areas, precipitation, evaporation and strong ability tomigrate in the form of surface water evaporates, the minerals in the water accumulationin the soil surface, soil salinization in order to form a poly-type table based. Soilsalinization has been a serious threat to the local agricultural production, ecologicalstability and economic development. Xinjiang is rich in coal, oil and mineral resources,but the harsh environment, the oasis area of very few countries hinder the westerndevelopment strategy, soil salinization timely and effective monitoring, governance,whether it is agriculture or economic development this is a serious problem.Using remote sensing tools can in a timely manner, access to a wide range ofground information, dynamic monitoring, remote sensing technology in the currentdynamic monitoring of salinization mainly visible-near infrared (300-2500μm)sensing the reflected light mainly because the ground simple data acquisition, satellitedata by environmental factors is relatively small. Although thermal infrared remotesensing an early start, but the development is very slow, because its subject is a bigenvironmental impact, both ground-based sensors and space borne sensors, ground-based data in the process of obtaining, are affected by temperature, moisture,atmospheric downward radiation data quality is relatively decreased. In recent years,due to the unique advantages of thermal infrared remote sensing in mineral exploration,urban heat island effect, surface temperature, moisture retrieval aspects, making it anindispensable means of remote sensing. This article thermal infrared emissivity spectraunique characteristics applied to practical problems of soil salinization, the quantitativeanalysis of saline soil emissivity spectral information for space borne sensoridentification information foundation soil salinity.In this paper, spectrum smoothing iterative method to separate the soil emissivityand temperature instruments for the elimination of environmental and human-causederrors when collecting spectral emissivity data as possible to get the real soil emissivity spectral information, the use of adjacent averaging, average median filtering (MedianFilter), S-Golay average, Gaussian filter (Gaussian Filter) smoothing methodsmoothing four kinds of soil samples for filtering noise spectral data processing, andthe introduction of smoothness index to the four kinds of single correlation coefficientnoise method of comparative analysis showed that the Gaussian filtering noise betterthan others. The raw spectral data were de-noised reciprocal transformation,logarithmic transformation, the first derivative, second derivative, five kinds oftransformation in the form of normalized ratio, quantitative analysis of the relationshipbetween each data transformation in the form of spectral data and soil salinity.Compared the characteristics of thermal infrared emissivity spectra of differentobjects, analyzes the thermal infrared emissivity of quartz and soil characteristics,indicating that the soil because the main ingredient is SiO2, which inherited the spectralemissivity spectra characteristic of quartz, but with soil containing salt increased, thegradual disappearance of the spectral characteristics of quartz, and with the increase ofsalt content, emission rate also increased, proportional to the change, when the sampleof salt crystals and quartz spectral features disappear.Respectively, using stepwise multiple regression model and partial least squaresmodel, using six different forms of data modeling, by fitting the model results andevaluate the merits of the prediction accuracy of each model. By comparing the twomodels of analysis, stepwise multiple regression model results using the data in theform of the establishment of the logarithmic transformation and R2is the highestprediction accuracy up to0.99or more; using partial least squares model for each datain the form of modeling effects and prediction accuracy is almost the same, very stableR2were above0.96, so both models have performed very good.