基于Landsat OLI的绿洲灌区土壤盐度最优预测尺度分析

doi:10.3864/j.issn.0578-1752.2017.15.011

Abstract

Abstract: 【Objective】 Based on the multi-scale remote sensing indicators and measured soil conductivity data, the objective of this study is to exploit the appropriate scales and indicators for inferring soil salinity in irrigation area of arid oasis, and to provide alternative schemes for rapid assessment of soil salinization in the study area. 【Method】Weigan-kuqa oasis located in southern Xinjiang was selected as the study area. Soil electrical conductivity of multiple soil layers (0-10 cm, 10-20 cm, 20-40 cm, 40-60 cm), as inference object, were analyzed. Raster resampling (30-1 000 m) and neighborhood extent (3×3 to 31×31) were employed as the method of scale transformation to generate multiple scales of environmental factors (with help of principal component analysis, tasseled cap, vegetation index, soil wet index) based on Landsat OLI image data, then, a total of 1 078 environmental variables (the former produced 352 variables, the latter produced 726 variables). On this basis, linear and nonlinear curve models were used to fit the correlation between soil salinity and environmental variables under the above mentioned method of scale transformation, and then the optimal prediction scale and environmental factor were explored. 【Result】Nonlinear curve models showed a significance between soil salinity and environmental factors compared to linear model under multiple scale (raster resampling model). Secondly, the fitting accuracy decreased when the spatial resolution become coarse. In this model, the best inference scale was 30 m, and the sensitive variable was three-band maximal gradient difference (TGDVI) at depth of 40-60 cm, and the remaining depths were implied by Extended Normalized Difference Vegetation index (ENDVI). The optimal inference scale in model of neighborhood extent was 180 m (filtering scale 3×3), and the best fit variables for each layer was EEVI. Compared with the grid resampling mode, the fitting accuracy of neighborhood extent was improved by 14.60%, 34.40%, 32.10% and 21.70% (from top layer to bottom one) compared to fomer model, respectively. 【Conclusion】Based on the model of neighborhood extent, the nonlinear model constructed by Extented Enhanced Vegetation Index(EEVI) (180 m, 3×3) is more suitable for the prediction of soil salinity variation in the study area.

Key words: soil salinization, Landsat OLI, scale analysis, oasis irrigation area, nonlinear model, Xinjiang

WEI Yang, DING JianLi, WANG Fei. Optimal Scale Analysis of Soil Salinity Prediction in Oasis Irrigated Area of Arid Land Based on Landsat OLI[J].Scientia Agricultura Sinica, 2017, 50(15): 2969-2982.

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