Normalization of NDVI from Different Sensor System using MODIS Products as Reference

35th International Symposium on Remote Sensing of Environment

地球科学;生态环境科学

Wenxia, Gan^1 ; Huanfeng, Shen^2 ; Liangpei, Zhang^1 ; Wei, Gong^1

State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, 430079, Hubei, China^1

School of Resource and Environmental Sciences, Wuhan University, Wuhan, 430079, Hubei, China^2

关键词: Biophysical parameter retrievals;    Cloud contamination;    Multiple sensor systems;    Normalized difference vegetation index;    Physical approaches;    Satellite signals;    Spatial informations;    Surface reflectance;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/17/1/012225/pdf
DOI  :  10.1088/1755-1315/17/1/012225

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

Medium Resolution NDVI(Normalized Difference Vegetation Index) from different sensor systems such as Landsat, SPOT, ASTER, CBERS and HJ-1A/1B satellites provide detailed spatial information for studies of ecosystems, vegetation biophysics, and land cover. Limitation of sensor designs, cloud contamination, and sensor failure highlighted the need to normalize and integrate NDVI from multiple sensor system in order to create a consistent, long-term NDVI data set. In this paper, we used a reference-based method for NDVI normalization. And present an application of this approach which covert Landsat ETM+ NDVI calculated by digital number (NDVIDN) to NDVI calculated by surface reflectance (NDVISR) using MODIS products as reference, and different cluster was treated differently. Result shows that this approach can produce NDVI with highly agreement to NDVI calculated by surface reflectance from physical approaches based on 6S (Second Simulation of the satellite Signal in the Solar Spectrum). Although some variability exists, the cluster specified reference based approach shows considerable potential for NDVI normalization. Therefore, NDVI products in MODIS era from different sources can be combined for time-series analysis, biophysical parameter retrievals, and other downstream analysis.