Abstract
Long-term monitoring of regional and global environment changes often depends on the combined use of multi-source sensor data. The most widely used vegetation index is the normalized difference vegetation index (NDVI), which is a function of the red and near-infrared (NIR) spectral bands. The reflectance and NDVI data sets derived from different satellite sensor systems will not be directly comparable due to different spectral response functions (SRF), which has been recognized as one of the most important sources of uncertainty in the multi-sensor data analysis. This study quantified the influence of SRFs on the red and NIR reflectances and NDVI derived from 31 Earth observation satellite sensors. For this purpose, spectroradiometric measurements were performed for paddy rice grown under varied nitrogen levels and at different growth stages. The rice canopy reflectances were convoluted with the spectral response functions of various satellite instruments to simulate sensor-specific reflectances in the red and NIR channels. NDVI values were then calculated using the simulated red and NIR reflectances. The results showed that as compared to the Terra MODIS, the mean relative percentage difference (RPD) ranged from −12.67% to 36.30% for the red reflectance, −8.52% to −0.23% for the NIR reflectance, and −9.32% to 3.10% for the NDVI. The mean absolute percentage difference (APD) compared to the Terra MODIS ranged from 1.28% to 36.30% for the red reflectance, 0.84% to 8.71% for the NIR reflectance, and 0.59% to 9.32% for the NDVI. The lowest APD between MODIS and the other 30 satellite sensors was observed for Landsat5 TM for the red reflectance, CBERS02B CCD for the NIR reflectance and Landsat4 TM for the NDVI. In addition, the largest APD between MODIS and the other 30 satellite sensors was observed for IKONOS for the red reflectance, AVHRR1 onboard NOAA8 for the NIR reflectance and IKONOS for the NDVI. The results also indicated that AVHRRs onboard NOAA7-17 showed higher differences than did the other sensors with respect to MODIS. A series of optimum models were presented for remote sensing data assimilation between MODIS and other sensors.
Highlights
In the past several decades, satellite remote sensing has played a vital role in providing up-to-date and detailed information for monitoring atmospheric and terrestrial environments at the regional, continental, and global scales
Among the various spectral vegetation indices derived from remotely sensed imagery, one of the most widely used vegetation indices is the normalized difference vegetation index (NDVI), which is defined as the difference between the red and near-infrared (NIR) reflectance divided by their sum [3,4]
The simulated reflectances for the 31 different satellite sensors were calculated to quantify the influence of the varying spectral response functions on the target reflectance in the red band, NIR band and NDVI
Summary
In the past several decades, satellite remote sensing has played a vital role in providing up-to-date and detailed information for monitoring atmospheric and terrestrial environments at the regional, continental, and global scales. Such information is typically generated based on remotely sensed images processed into spectral vegetation indices [1,2]. NDVI has been used in a range of applications including the study of vegetation–climate interactions [12,13,14], detection of long-term vegetation changes [15,16], assessment of vegetation functional characteristics [17,18,19] and modeling of the global carbon balance [10,20]. NDVI time series data has been successfully used in a variety of applications, including global change investigations, phenological studies, crop growth monitoring and yield prediction, drought and desertification monitoring, wildfire assessment, and climatic and biogeochemical modeling
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
