Effectiveness of upscaling the vegetation temperature condition index retrieved from Landsat data using an algorithm combining the trend surface analysis and the spatial variation weight method

Abstract

Taking the Guanzhong Plain of China as the research area, a method combining the trend surface analysis (TSA) method and the spatial variation weight method (SVWM) was used to upscale the vegetation temperature condition index (VTCI) retrieved from Landsat 8 from a finer spatial resolution to a coarser. The upscaled results were compared with VTCIs retrieved from Aqua MODIS to provide technical support for the comprehensive application of drought monitoring results on two spatial scales. Meanwhile, the upscaled methods were systematically evaluated in a case study according to various indicators, including the spatial distribution characteristics for drought, the statistical characteristics, the semivariogram function (SVF), the structural similarity (SSIM), the correlation coefficient (r), and the root mean square error (RMSE). The results show that the dominant class variability weighted (DCVW) method performed better than did the arithmetic average variability weighted (AAVW) and median pixel variability weighted (MPVW) methods in terms of the statistical characteristics, SSIM, correlation and RMSE, and the upscaling model based on the union of TSA and DCVW had the highest accuracy. The DCVW is a promising tool for upscaling Landsat-VTCI images of the Guanzhong Plain from finer to coarser spatial resolutions because of its efficiency and flexibility.

Keywords

• upscaling method
• vegetation temperature condition index
• the trend surface analysis method
• the spatial variation weight method
• effectiveness evaluation
• the guanzhong plain of china