Next Article in Journal
Energy Efficient Medium Access Control Protocol for Clustered Wireless Sensor Networks with Adaptive Cross-Layer Scheduling
Previous Article in Journal
How Angular Velocity Features and Different Gyroscope Noise Types Interact and Determine Orientation Estimation Accuracy
Article Menu

Export Article

Open AccessArticle
Sensors 2015, 15(9), 24002-24025; doi:10.3390/s150924002

Generating Daily Synthetic Landsat Imagery by Combining Landsat and MODIS Data

1
The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
2
Laboratory of Digital Earth Sciences, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
*
Author to whom correspondence should be addressed.
Academic Editor: Assefa M. Melesse
Received: 14 July 2015 / Revised: 10 September 2015 / Accepted: 14 September 2015 / Published: 18 September 2015
(This article belongs to the Section Remote Sensors)
View Full-Text   |   Download PDF [1579 KB, uploaded 18 September 2015]   |  

Abstract

Owing to low temporal resolution and cloud interference, there is a shortage of high spatial resolution remote sensing data. To address this problem, this study introduces a modified spatial and temporal data fusion approach (MSTDFA) to generate daily synthetic Landsat imagery. This algorithm was designed to avoid the limitations of the conditional spatial temporal data fusion approach (STDFA) including the constant window for disaggregation and the sensor difference. An adaptive window size selection method is proposed in this study to select the best window size and moving steps for the disaggregation of coarse pixels. The linear regression method is used to remove the influence of differences in sensor systems using disaggregated mean coarse reflectance by testing and validation in two study areas located in Xinjiang Province, China. The results show that the MSTDFA algorithm can generate daily synthetic Landsat imagery with a high correlation coefficient (R) ranged from 0.646 to 0.986 between synthetic images and the actual observations. We further show that MSTDFA can be applied to 250 m 16-day MODIS MOD13Q1 products and the Landsat Normalized Different Vegetation Index (NDVI) data by generating a synthetic NDVI image highly similar to actual Landsat NDVI observation with a high R of 0.97. View Full-Text
Keywords: spatial and temporal data fusion; remote sensing; MODIS; Landsat; FROM-GLC spatial and temporal data fusion; remote sensing; MODIS; Landsat; FROM-GLC
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).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Wu, M.; Huang, W.; Niu, Z.; Wang, C. Generating Daily Synthetic Landsat Imagery by Combining Landsat and MODIS Data. Sensors 2015, 15, 24002-24025.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top