Paper Info
Title
Inconsistency Among Landsat Sensors In Land Surface Mapping: A Comprehensive Investigation Based On Simulation
Abstract
Comprehensive investigations on the between-sensor comparability among Landsat sensors have been relatively limited compared with the increasing use of multi-temporal Landsat records in time series analyses. More seriously, the sensor-related difference has not always been considered in applications. Accordingly, comparisons were conducted among all Landsat sensors available currently, including Multispectral Scanner (MSS), Thematic Mappers (TM), Enhanced Thematic Mappers (ETM+), and Operational Land Imager (OLI)) in land cover mapping, based on a collection of synthesized, multispectral data. Compared to TM, OLI showed obvious between-sensor differences in channel reflectance, especially over the near infrared (NIR) and shortwave infrared (SWIR) channels, and presented positive bias in vegetation spectral indices. OLI did not always outperform TM and ETM+ in classification, which related to the methods used. Furthermore, the channels over SWIR of TM and its successors contributed largely to enhancement of inter-class separability and to improvement of classification. Currently, the inclusion of MSS data is confronted with significant challenges regarding the consistency of surface mapping. Considering the inconsistency among the Landsat sensors, it is applicable to generate a consistent time series of spectral indices through proper transformation models. Meanwhile, it suggests the generation of specific class(es) based on interest instead of including all classes simultaneously.
Year
DOI
Venue
2021
10.3390/rs13071383
REMOTE SENSING
Keywords
DocType
Volume
Landsat, NDVI, classification, time series, random forest, OLI, change detection, JM distance
Journal
13
Issue
Citations 
PageRank 
7
0
0.34
References 
Authors
0
7
Name
Order
Citations
PageRank
Feng Chen100.68
Chenxing Wang223.73
Yuansheng Zhang300.34
Zhenshi Yi400.34
Qiancong Fan501.69
Lin Liu600.34
Yuejun Song700.34