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Sensors 2016, 16(2), 207; doi:10.3390/s16020207

An Improved STARFM with Help of an Unmixing-Based Method to Generate High Spatial and Temporal Resolution Remote Sensing Data in Complex Heterogeneous Regions

1
State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China
2
College of Resources Science and Technology, Beijing Normal University, Beijing 100875, China
*
Author to whom correspondence should be addressed.
Academic Editor: Assefa M. Melesse
Received: 27 October 2015 / Revised: 31 January 2016 / Accepted: 2 February 2016 / Published: 5 February 2016
(This article belongs to the Section Remote Sensors)
View Full-Text   |   Download PDF [4245 KB, uploaded 5 February 2016]   |  

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. View Full-Text
Keywords: unmixing-based method; spatial and temporal adaptive reflectance fusion model (STARFM); heterogeneity; data fusion; Landsat 8; MODIS unmixing-based method; spatial and temporal adaptive reflectance fusion model (STARFM); heterogeneity; data fusion; Landsat 8; MODIS
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Xie, D.; Zhang, J.; Zhu, X.; Pan, Y.; Liu, H.; Yuan, Z.; Yun, Y. An Improved STARFM with Help of an Unmixing-Based Method to Generate High Spatial and Temporal Resolution Remote Sensing Data in Complex Heterogeneous Regions. Sensors 2016, 16, 207.

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