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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.

Inconsistency among Landsat Sensors in Land Surface Mapping: A Comprehensive Investigation Based on Simulation