title: An Improved STARFM with Help of an Unmixing-Based Method to Generate High Spatial and Temporal Resolution Remote Sensing Data in Complex Heterogeneous Regions.
abstract: Remote sensing technology plays an important role in monitoring rapid changes of the Earth's surface. However, sensors that can simultaneously provide satellite images with both high temporal and spatial resolution haven't been designed yet. This paper proposes an improved spatial and temporal adaptive reflectance fusion model (STARFM) with the help of an Unmixing-based method (USTARFM) to generate the high spatial and temporal data needed for the study of heterogeneous areas. The results showed that the USTARFM had higher accuracy than STARFM methods in two aspects of analysis: individual bands and of heterogeneity analysis. Taking the predicted NIR band as an example, the correlation coefficients (r) for the USTARFM, STARFM and unmixing methods were 0.96, 0.95, 0.90, respectively (p-value < 0.001); Root Mean Square Error (RMSE) values were 0.0245, 0.0300, 0.0401, respectively; and ERGAS values were 0.5416, 0.6507, 0.8737, respectively. The USTARM showed consistently higher performance than STARM when the degree of heterogeneity ranged from 2 to 10, highlighting that the use of this method provides the capacity to solve the data fusion problems faced when using STARFM. Additionally, the USTARFM method could help researchers achieve better performance than STARFM at a smaller window size from its heterogeneous land surface quantitative representation.
authors: State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China. xiedfeng@mail.bnu.edu.cn.|College of Resources Science and Technology, Beijing Normal University, Beijing 100875, China. xiedfeng@mail.bnu.edu.cn.
authors: Dengfeng
authors: D
authors: Xie
authors: State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China. zhangjs@bnu.edu.cn.|College of Resources Science and Technology, Beijing Normal University, Beijing 100875, China. zhangjs@bnu.edu.cn.
authors: Jinshui
authors: J
authors: Zhang
authors: State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China. zhuxiufang@bnu.edu.cn.|College of Resources Science and Technology, Beijing Normal University, Beijing 100875, China. zhuxiufang@bnu.edu.cn.
authors: Xiufang
authors: X
authors: Zhu
authors: State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China. pyz@bnu.edu.cn.|College of Resources Science and Technology, Beijing Normal University, Beijing 100875, China. pyz@bnu.edu.cn.
authors: Yaozhong
authors: Y
authors: Pan
authors: State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China. 201521190003@mail.bnu.edu.cn.|College of Resources Science and Technology, Beijing Normal University, Beijing 100875, China. 201521190003@mail.bnu.edu.cn.
authors: Hongli
authors: H
authors: Liu
authors: State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China. miki@mail.bnu.edu.cn.|College of Resources Science and Technology, Beijing Normal University, Beijing 100875, China. miki@mail.bnu.edu.cn.
authors: Zhoumiqi
authors: Z
authors: Yuan
authors: State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China. 201421190041@mail.bnu.edu.cn.|College of Resources Science and Technology, Beijing Normal University, Beijing 100875, China. 201421190041@mail.bnu.edu.cn.
authors: Ya
authors: Y
authors: Yun
keywords: Landsat 8; MODIS; data fusion; heterogeneity; spatial and temporal adaptive reflectance fusion model (STARFM); unmixing-based method