Inter-Sensor Comparison between THEOS and Landsat 5 TM Data in a Study of Two Crops Related to Biofuel in Thailand

Remote Sensing

Naruemon Phongaksorn²  Nitin K. Tripathi²  Sivanappan Kumar¹ 

[1] Energy, School of Environment, Resources and Development, Asian Institute of Technology, Thailand; E-Mail:;Remote Sensing and Geographic Information Systems, School of Engineering and Technology, Asian Institute of Technology, Thailand; E-Mail:

关键词: biofuel crops;    THEOS;    Landsat 5 TM;    radiometric calibration;    6S model;    surface reflectance;    NDVI;    spatial accuracy assessment;   

DOI  :  10.3390/rs4020354

来源: mdpi

【 摘 要 】

Knowledge of the spatial distribution of biofuel crops is an important criterion to determine the sustainability of biofuel energy production. Remotely sensed image analysis is a proven and effective tool for describing the spatial distribution of crops using vegetation characteristics. Increases in the number of options and availability of satellite sensors have expanded the horizon of choices of imagery sources for appropriate image acquisitions. The Thailand Earth Observation System (THEOS) satellite is one of the newest satellite sensors. The growing number of satellite sensors warrants their comparative evaluation and the standardization of data obtained from various sensors. This study conducted an inter-sensor comparison of the visible/near-infrared surface reflectance and Normalized Difference Vegetation Index (NDVI) data collected from the Landsat 5 Thematic Mapper (TM) and THEOS. The surface reflectance and the derived NDVI of the sensors were randomly obtained for two biofuel crops, namely, cassava and sugarcane. These crops had low values of visible surface reflectance, which were not significantly (p < 0.05) different. In contrast, the crops had high values of near-infrared surface reflectance that differed significantly (p > 0.05) between the crops. Strong linear relationships between the remote sensing products for the examined sensors were obtained for both cassava and sugarcane. The regression models that were developed can be used to compute the NDVI for THEOS using those determined from Landsat 5 TM and vice versa for the given biofuel crops.

【 授权许可】

CC BY   
© 2012 by the authors; licensee MDPI, Basel, Switzerland.