Effects of spectral response function on surface reflectance and NDVI measured with moderate resolution satellite sensors
Introduction
Satellite observation is a convenient and feasible tool for global monitoring of atmospheric and terrestrial environment due to frequent and global coverage. Among various satellite sensors, the Advanced Very High Resolution Radiometer (AVHRR) onboard the National Oceanic and Atmospheric Administration's (NOAA) polar orbiting satellites has the longest record for research and application (Cracknell, 1997). There are three series of the AVHRR instruments. The four-channel radiometers AVHRR/1 were launched onboard the Tiros-N and NOAA-6, -8, and -10. The five-channel radiometers AVHRR/2 were deployed on the platforms NOAA-7, -9, -11, -12, and -14 followed by a six-channel radiometer AVHRR/3 onboard the NOAA-15 and -16.
The range of AVHRR data applications is very broad. To name a few, e.g., the visible and near-infrared (NIR) channels of AVHRR are used for retrieving cloud parameters (Rossow, 1989), solar radiation budget (Hucek & Jacobowitz, 1995), determination of absorbed photosynthetically active radiation (APAR; Li, Moreau, & Cihlar, 1997), and retrievals of aerosol optical depth (AOD; Stowe, Ignatov, & Singh, 1997) and other parameters Gutman et al., 2000, Nakajima et al., 2001. One of the most important applications of the AVHRR thermal channels lies in estimation of global sea surface temperature (SST; Reynolds & Smith, 1993). The thermal channels are also used for determining land surface temperature and emissivity (Qin & Karnieli, 1999). Thermal AVHRR channels in combination with channels 1 and 2 are employed for forest fire detection and monitoring (Li, Nadon, & Cihlar, 2000).
An important application of AVHRR solar channels is the retrieval of surface reflectance to determine different land surface parameters such as surface cover type (Cihlar et al., in press), normalized difference vegetation index (NDVI; Kidwell, 1994), leaf area index (LAI; Chen, Rich, Gower, Norman, & Plummer, 1997), and other surface characteristics. New opportunities for global monitoring of terrestrial ecosystem are unfolding with the availability of Moderate Resolution Imaging Spectroradiometer (MODIS) data.
The processing of satellite data involves many steps. The final purpose of satellite data processing in land surface studies is to obtain the systematic maps of various quantitative physical parameters corrected for the intervening effect of atmosphere, effect of varying observational geometry, and specific sensor properties. Some of these corrections can be done quite accurately, like correction for Raleigh molecular scattering. Nonetheless, most of them may be implemented with some uncertainty due to limited knowledge of input information.
The important processing step is the data calibration. Despite numerous efforts, the results often vary among different investigators Brest et al., 1997, Gutman, 1999, Masonis & Warren, 2001, Rao & Chen, 1999, Tahnk & Coakley, 2001. Accurate calibration requires continuos monitoring of the gain and offset due to degradation of sensor sensitivity with time. The degradation may not necessarily be a linear function of time (Tahnk & Coakley, 2001). It is commonly agreed that for satellite sensors lacking onboard calibration in solar spectrum, the total relative uncertainties of calibration are within 5% (Rossow & Schiffer, 1999). An essential part of this uncertainty is related to the effect of spectral response function (SRF), when it is not accounted for properly during vicarious calibration or sensor intercalibration (Teillet et al., 2001).
Variable sun and observational geometry induces another source of systematic noise Gutman et al., 1989, Li et al., 1996. This angular effect is a combination of anisotropic reflective properties of the atmosphere and land surface. The effect must be accounted for in long-term studies of satellite data to obtain unbiased results Cihlar et al., 1998, Gutman, 1999. This is achieved by normalizing satellite image to common geometry using empirical anisotropic factors. They are derived either from sequence of satellite scenes collected over long period of time Cihlar et al., 1998, Trishchenko et al., 2001 or from special directional observations, like those ones from POLDER or MISR instruments (Csiszar, Gutman, Romanov, Leroy, & Hautecoeur, 2001). Neglecting angular correction in the AVHRR data processing, for example, may introduce biases in composite coarse resolution long-term reflectance datasets of the order of 1–2% depending on spectral band and surface type (Gutman, 1999). The effect becomes more significant (5–10%) for solar zenith angles (SZA) greater than 55°.
Numerous vegetation indices have been developed to monitor the state of vegetation from spaceborne instruments (Bannari, Morin, & Bonn, 1995). They were constructed to diminish atmospheric contamination, mitigate the influence of soil spectral reflectance signatures, or emphasize certain features of vegetation conditions. The set of advanced vegetation indices optimised for upcoming sensors is discussed by Gobron, Pinty, Verstraete, and Widlowski (2000). Nevertheless, NDVI remains the basic vegetation index most widely employed for global monitoring of vegetation. It is defined as the following ratio:where ρNIR and ρred are reflectances for visible (red) and NIR spectral bands.
Attempts have been made to use the AVHRR data for long-term monitoring of land reflectances and vegetation indices Cihlar et al., 2001, Gutman, 1999, Kaufman et al., 2000. These and other studies on long-term monitoring are motivated by the availability of quality AVHRR time series for the period of nearly 20 years. Although the construction and characteristics of all AVHRR instruments are quite similar, they are not identical among all missions. Consequently, the effect of varying spectral response may create an artificial noise imposed upon a subtle natural variability. This artifact should be examined thoroughly before comparing data between different missions to determine possible changes in satellite climatic records. So far, the effects of SRFs have not been considered carefully in such studies. Some influence of the spectral characteristic of the satellite sensors on remote sensing of vegetation indices has been studied for forested regions (Teillet, Staenz, & Williams, 1997) and during vicarious calibrations procedures (Teillet et al., 2001). Nevertheless, systematic characterisation of these effects for various representative surface spectral signatures on a global scale and for all AVHRR sensors has not been addressed properly. Analysis of long-term satellite products from various missions may require corrections to account for differences in SRF that have not been investigated.
Our study is aimed to fill this gap and to provide quantitative estimates for the effect of SRF among all AVHRR missions. Differences between AVHRR and MODIS, Global Imager (GLI), and Vegetation sensors (VGT) are also considered. To achieve this goal, some representative surface spectral reflectance curves were selected from two observation sources. The first is a database of spectral observation made by the PROBE-1 instrument (Secker, Staenz, Budkewitsch, & Neville, 1999) at the Canada Centre for Remote Sensing (CCRS). The second one is the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) spectral library (available from http://speclib.jpl.nasa.gov). The details of the databases are given below. The 6S radiative transfer code (Vermote, Tanré, Deuzé, Herman, & Morcette, 1997) was employed for model simulation of the signal at the top of the atmosphere (TOA) level under various atmospheric conditions and observational geometries.
The paper is organized as follows. Section 2 describes the special features of instrument SRFs. Section 3 discusses the surface spectral library and modeling of satellite signal at the TOA level. Section 4 presents results and an analysis of comparisons between various sensors. Validation results of the model simulation using real satellite observations are shown in Section 5. Section 6 summarizes the research.
Section snippets
Sensor SRFs
The SRFs for AVHRR NOAA-6–16, MODIS, GLI, and VGT compatible channels in visible and NIR are shown in Fig. 1a–c. The three panels of Fig. 1 present SRFs for different types of AVHRR: AVHRR/1 (a), installed on morning satellites, AVHRR/2 (b), which was operational on afternoon satellites, and a morning satellite NOAA-12. The bottom panel (c) shows SRFs for AVHRR/3 (NOAA-15 and -16), MODIS, GLI, and VGT. A typical spectrum of green vegetation is also plotted for reference. Though similar, these
Surface spectral data and modeling
To encompass a potential range of variability in surface reflectance and NDVI, a set of representative spectra for various surface targets were compiled, following the classification scheme used in the NASA Surface and Atmospheric Radiation Budget (SARB) Project (Rutan & Charlock, 1997). The complete scheme for the SARB Project included 20 different surface classes. Since we had no measurements for some of the surface types and yet the particular focus of this study is on the boreal ecosystem,
Radiative transfer modeling
The 6S radiative transfer model was employed to simulate the TOA signal. The wavelength increment of the model is 2.5 nm, which allows us to accurately resolve all spectral features of the targets and instrument SRF. Baseline simulations were conducted for US62 atmospheric profile with total water vapor (TWV) columnar amount scaled to 1.5 cm, representative for the boreal region in summer time (Cihlar, Tcherednichenko, Latifovic, Li, & Chen, 2000). The ozone content was set to 350 Dobson units
Application to real satellite data
To test the modeling results, we compared two pairs of images over identical areas. One pair is for AVHRR images acquired by NOAA-14 and -15 and another pair is for AVHRR/NOAA-14 and MODIS. The images in each pair were taken very close in time so that temporal changes do not affect the comparisons. Two AVHRR images were taken over an area of Northern Ontario (Canada) observed on July 15, 2000 in the morning (NOAA-15) and afternoon (NOAA-14). The area is approximately 250×250 km centered around
Conclusions
Long-term monitoring of the Earth's environment by satellite sensors require consistent and comparable measurements. In this paper, we evaluated the effect of a major sensor parameter, namely, the SRF, on the consistency of observations made by moderate resolution sensors commonly used for surface and atmospheric studies. Starting with TIROS-N in 1978, these sensors have provided a long time series of satellite data, which contain rich information pertaining to the state and changes of many
Acknowledgements
Authors are grateful to J.-C. Deguise and R. Hitchcock of CCRS for making PROBE-1 data available for this study. We acknowledge the use of spectral data from JPL ASTER spectral library (http://speclib.jpl.nasa.gov). The authors also thank Gunar Fedosejevs for his valuable comments and discussion. This research was partially supported by the Biological and Environmental Research Program (BER), U.S. Department of Energy, Grant No. DE-FG02-02ER63351.
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