Publications

Bai, XJ; Wang, PX; Wang, HS; Xie, Y (2017). An Up-Scaled Vegetation Temperature Condition Index Retrieved From Landsat Data with Trend Surface Analysis. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 10(8), 3537-3546.

Abstract
Drought causes great losses in regional agricultural production and decreases socioeconomic growth. The vegetation temperature condition index (VTCI) has a distinct advantage in monitoring the onset, duration, and intensity of droughts. With the development of modern remote sensing technologies, remotely sensed data with variable spatial and temporal resolution are used to generate multiscale maps of droughts. Therefore, understanding the scale effect and developing appropriate up-scaling methods to retrieve spatiotemporal drought variables across different scales is valuable. As an alternative to the commonly used window averaging (WA) method, we develop the trend surface analysis (TSA) method based on multiple regression analysis to up-scale Landsat-derived VTCI (Landsat-VTCI) images from a finer to a coarser resolution. The two methods are systematically evaluated in a case study according to various statistical indicators, including the spatial and frequency distributions of features, and the correlation coefficients and root mean square errors between up-scaled Landsat-VTCI images and moderate-resolution Imaging Spectroradiometer (MODIS)-derived VTCI (MODIS-VTCI) images. The results show that TSA is reliable and more suitable than WA for non-normally distributed Landsat-derived VTCIs, whereas the WA results are similar to the TSA results for normal distributions. The TSA method is flexible for any type of distribution of Landsat-VTCIs within a study area and can be programmed to up-scale spatial drought variables from a finer to a coarser spatial resolution because of its efficiency and flexibility compared to the WA method.

DOI:
10.1109/JSTARS.2017.2698444

ISSN:
1939-1404