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Effects of spectral response function on surface reflectance and NDVI measured with moderate resolution satellite sensors: Extension to AVHRR NOAA-17, 18 and METOP-A
Remote Sensing of Environment 113 (2009) 335–341
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Remote Sensing of Environment j o u r n a l h o m e p a g e : w w w. e l s e v i e r. c o m / l o c a t e / r s e
Effects of spectral response function on surface reflectance and NDVI measured with moderate resolution satellite sensors: Extension to AVHRR NOAA-17, 18 and METOP-A Alexander P. Trishchenko ⁎ Canada Centre for Remote Sensing, Natural Resources Canada, Ottawa, Ontario, Canada K1A 0Y7
a r t i c l e
i n f o
Article history: Received 22 May 2008 Received in revised form 29 September 2008 Accepted 4 October 2008 Keywords: Satellite Surface reflectance AVHRR NOAA NDVI Spectral response function Spectral correction Global change detection
a b s t r a c t This work extends the previous study of Trishchenko et al. [Trishchenko, A. P., Cihlar, J., & Li, Z. (2002). Effects of spectral response function on surface reflectance and NDVI measured with moderate resolution satellite sensors. Remote Sensing of Environment 81 (1), 1–18] that analyzed the spectral response function (SRF) effect for the Advanced Very High Resolution Radiometer (AVHRR) onboard the NOAA satellites NOAA-6 to NOAA16 as well as the Moderate Resolution Imaging Spectroradiometer (MODIS), the VEGETATION sensor (VGT) and the Global Imager (GLI). The developed approach is now applied to cover three new AVHRR sensors launched in recent years on NOAA-17, 18, and METOP-A platforms. As in the previous study, the results are provided relative to the reference sensor AVHRR NOAA-9. The differences in reflectance among these three radiometers relative to the AVHRR NOAA-9 are similar to each other and range from −0.015 to 0.015 (−20% to +2% relative) for visible (red) channel, and from −0.03 to 0.02 (−5% to 5%) for the near infrared (NIR) channel. The absolute change in the Normalized Difference Vegetation Index (NDVI) ranged from −0.03 to +0.06. Due to systematic biases of the visible channels toward smaller values and the NIR channels toward slightly larger values, the overall systematic biases for NDVI are positive. The polynomial approximations are provided for the bulk spectral correction with respect to the AVHRR NOAA-9 for consistency with previous study. Analysis was also conducted for the SRF effect only among the AVHRR-3 type of radiometer on NOAA-15, 16, 17, 18 and METOP-A using AVHRR NOAA-18 as a reference. The results show more consistency between sensors with typical correction being under 5% (or 0.01 in absolute values). The AVHRR METOP-A reveals the most different behavior among the AVHRR-3 group with generally positive bias for visible channel (up to + 5%, relative), slightly negative bias for the NIR channel (1%–2% relative), and negative NDVI bias (−0.02 to +0.005). Polynomial corrections are also suggested for normalization of AVHRR on NOAA-15, 16, 17 and METOP-A to AVHRR NOAA-18. Crown Copyright © 2008 Published by Elsevier Inc. All rights reserved.
1. Introduction Trishchenko et al. (2002) provided an analysis and recommendations regarding the effect of spectral response function (SRF) on reflectances and normalized difference vegetation index (NDVI) for various moderate resolution sensors. The SRF effect was analyzed for the Advanced Very High Resolution Radiometer (AVHRR) onboard the NOAA satellites NOAA-6 to NOAA-16 as well as the Moderate Resolution Imaging Spectroradiometer (MODIS), the VEGETATION sensor (VGT) and the Global Imager (GLI). The results were derived relative to the reference sensor AVHRR NOAA-9 for consistency with the approach of the International Satellite Cloud Climatology Project (ISCCP) (Rossow & Schiffer, 1999). The study of Trishchenko et al. (2002) demonstrated that the differences in SRF are significant and must be taken into account, in ⁎ 588 Booth Street, Ottawa, Ontario, Canada K1A 0Y7. Tel.: +1 613 995 57 87; fax: +1 613 947 14 06. E-mail address: [email protected].
particular for studies concerning the inter-annual variations in satellite time series. The effect is comparable in magnitude to the uncertainties caused by sensor calibration, atmospheric and angular correction and can lead to systematic biases if neglected. Even among “the same type” instruments such as AVHRR, the effect of the varying spectral response function on surface and top of the atmosphere (TOA) spectral reflectances and the Normalized Difference Vegetation Index (NDVI) is sufficiently large to require correction. Relative to the AVHRR/NOAA-9, differences between various AVHRR sensors were found to vary from −25% to +12% for visible (red) reflectance, and from −2% to +4% for the near infrared (NIR) reflectance. The absolute differences in NDVI among various AVHRRs ranged from −0.02 to + 0.06. The most consistent with AVHRR/NOAA-9 results were obtained for AVHRR/NOAA-11 and −12. The corrections must be implemented for other AVHRRs and especially for the AVHRR/3 on NOAA-15 and −16. Reflectances and NDVI from MODIS differ from AVHRR/NOAA-9 by as much as 30–40%. Likewise, VGT and GLI also exhibit considerable differences relative to AVHRR observations and should be always corrected in comparing long-term tome series.
0034-4257/$ – see front matter. Crown Copyright © 2008 Published by Elsevier Inc. All rights reserved. doi:10.1016/j.rse.2008.10.002
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relative to AVHRR NOAA-18. The results for the AVHRR-3 group may be of interest if only the last decade is analyzed. As in the previous study of Trishchenko et al. (2002) the recommendations are provided for reflectances in spectral band 1 (red) and band 2 (near infrared — NIR) ρNIR at the surface and the TOA levels, as well as for the vegetation index NDVI defined as
NDVI =
ρNIR −ρred ; ρNIR + ρred
The 6S radiative transfer code (Vermote et al., 1997) was employed for model simulation of the signal at the TOA level under various atmospheric conditions and observational geometries similar to Trishchenko et al. (2002). The set of surface spectral reflectances was employed in the same manner as in Trishchenko et al. (2002). Namely, 17 representative spectra were selected for 12 surface classes: 1) coniferous forest, 2) deciduous broadleaf forest, 3) closed shrubland, 4) open shrubland, 5) drygrass/savanna, 6) grassland, 7) cropland, 8) crop/natural vegetation mosaic, 9) barren/desert, 10) water bodies, 11) fresh snow, 12) coarse granular snow. The sources of surface spectral information are described in Trishchenko et al. (2002). The solar zenith angle (SZA) varied from 0° to 85°. The viewing zenith angle (VZA) varied from 0° to 65°, and the relative azimuth angle (RAA) varied from 0° to 180°. The total water vapor amount in the atmosphere varied from 0.05 cm to 5 cm, ozone columnar amount varied from 150 DU to 450 DU. Aerosol optical depth varied from 0.01 to 0.6. Fig. 1. The spectral response functions of visible (red) and NIR channels for the AVHRR NOAA-17 (a), NOAA-18 (b) and METOP-A (c) shown as solid lines relative to the AVHRR NOAA-9 (dashed line). Typical spectral reflectance curve for green vegetation is shown on each panel as dash–dot line.
The method was found quite efficient and was applied in many studies. Swinnen and Veroustraete (2008) used it for extending the SPOT-VEGETATION NDVI time series back in time with AVHRR data for Southern Africa and found that accounting for the SRF effect considerably improved the consistency between sensors. Stow et al. (2004) employed it for remote sensing studies of vegetation and landcover change in Arctic tundra ecosystems. Li et al. (2007) considered these results in assessing the cloud discrimination capabilities of current and future sensors. Chuvieco et al. (2008) used proposed SRF corrections in generation of long-time series of burn areas in the boreal forest using data for different AVHRR sensors processed at the Canada Centre for Remote Sensing (CCRS) (Latifovic et al., 2005). Venturini et al. (2004) employed these results for analysis of evaporative fractions estimated from AVHRR and MODIS over South Florida. The study of Teillet et al. (2007) emphasized the importance of SRF effects on sensor radiometric cross-calibration. An alternative approach for correction of SRF effect was proposed in Trishchenko et al. (2008) based on empirical regression between observations for overlapping periods. This method, however, requires significant preprocessing efforts to derive first the parameters of bi-directional reflectance distribution function. The AVHRR continues to be widely used operational sensor for large number of environmental and climate applications. After the study of Trishchenko et al. (2002) three new AVHRR radiometers were launched on NOAA-17, 18 and Meteorological Operational satellite (METOP-A) platforms. This paper provides recommendations regarding the SRF effect for these new sensors to achieve consistency in time series of clear-sky products derived from historical AVHRR observations. As in the previous study, the results are provided relative to the reference sensor AVHRR NOAA-9. In addition, similar analysis was conducted only among all five AVHRR-3 type of sensors flown on NOAA-15, 16, 17, 18 and METOP-A. The differences were assessed
Fig. 2. The absolute (solid triangles) and relative (open circles) differences in surface and TOA reflectances for channels 1, 2 and NDVI between the AVHRR NOAA-17 and the AVHRR NOAA-9. All data points are plotted versus NDVI of particular sensor. Quadratic best fits for absolute (solid) and relative (dashed) differences are also shown. Parameters of fitting curves are given in Table 1.
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3. Corrections for AVHRR NOAA-17, 18 and METOP relative to NOAA-9 The spectral response function effect in this section is determined relative to AVHRR NOAA-9 selected as a reference sensor in Trishchenko et al. (2002). This radiometer is often considered as a reference instrument follow to Rossow and Schiffer (1999). Previous study of Trishchenko et al. (2002) analyzed the SRF effect for the AVHRR radiometer onboard the NOAA satellites NOAA-6 to NOAA-16 as well as MODIS, VGT and GLI sensors. With addition of three new sensors, this collection of AVHRR sensors includes now 13 instruments. Three new radiometers continue the series of AVHRR-3 type instruments that include also AVHRR NOAA-15 and 16. Unlike all previous AVHRR instruments that were launched on the NOAA platforms, the most recent AVHRR was launched in 2006 onboard METOP-A satellite in the framework of the joint program established by the European Space Agency (ESA) and the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT). The AVHRR contributed by NOAA is also scheduled to be launched on follow-up satellites METOP-B, and METOP-C. Figs. 2–4 show the absolute and relative differences in reflectance or NDVI between specific sensor and the AVHRR NOAA-9. The reflectance is computed according to Eq. (1).
λmax
~ðλÞ = ρ
∫ Sz ðλÞF ðλÞdλ
λmin
λmax
;
ð1Þ
∫ SA ðλÞF ðλÞdλ
λmin
Fig. 3. The same as Fig. 2, but for the AVHRR NOAA-18. Parameters of fitting curves are given in Table 2.
where S↑ (λ), S↓ (λ) are the upward or downward solar spectral radiances and F (λ) is the instrument spectral response function. The
The paper is organized as follows. Section 2 describes the special features of instrument spectral response functions for three AVHRR sensors on NOAA-17, 18 and METOP-A. Section 3 presents results and derived corrections relative to AVHRR NOAA-9 sensor. Section 4 provides results of analysis and recommendations for corrections of AVHRR-3 radiometers onboard NOAA-15, 16, 17, and METOP-A relative to AVHRR NOAA-18. Section 5 summarizes the study. 2. Spectral response functions for AVHRR NOAA-17,18 and METOP-A The spectral response functions for AVHRR bands 1 and 2 of NOAA17, 18 and METOP-A are shown in Fig. 1a–c together with curves for AVHRR NOAA-9 plotted for the reference. The data for spectral response functions were taken from the NOAA Center for Application and Research (STAR) web-site http://www.orbit.nesdis.noaa.gov/ smcd/spb/fwu/solar_cal/spec_resp_func/index.html. The typical spectral dependence of green vegetation is also shown in Fig. 1. The major observed differences are seen as more narrow shape of band 1 for the AVHRR NOAA-17, 18 and METOP-A relative to AVHRR NOAA-9, and as a shift of SRF for band 2 of the AVHRR NOAA-17, 18 and METOP-A away from the red edge region around 0.7 µm toward longer wavelengths. The above differences lead to the following systematic differences in general: a) slightly darker band 1 reflectances, b) slightly brighter band 2 reflectances, c) slightly larger NDVI. All these features were observed in a previous study Trishchenko et al. (2002) for AVHRR-3 type of sensors. They are manifested in a similar way for new sensors although there are some minor differences related to a specific SRF shape for each spectral band. An interesting feature of Fig. 1c is a small spectral leak for band 1 of the AVHHR METOP-A in the blue region which leads to some noticeable differences for the results at the TOA level due to intensive Rayleigh and aerosol scattering for short wavelengths.
Fig. 4. The same as Fig. 2, but for the AVHRR METOP-A. Parameters of fitting curves are given in Table 3.
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the surface level because they can be determined relatively easy from Figs. 2–4 for each sensor. The results in Figs. 2–4 are plotted against NDVI computed at the surface or TOA level depending on the analysis. Although the magnitude of SRF effect is influenced by many factors, such as surface reflectance spectrum, atmospheric state, geometry of observations that may alter the spectral composition of the radiance spectra within the instrument spectral response, we found that NDVI serves as a good indicator of the effect (Trishchenko et al., 2002). This occurs because the effect of spectral overlap or spectral separation between visible and NIR channels over the “red” edge (0.7 µm) in the pixel surface reflectance spectrum is related to NDVI. In addition, NDVI generally characterizes the shape of surface spectra, and as such it also influences the magnitude of SRF effect. The quadratic fits are also shown in Figs. 2–4 for all panels except panel f) that shows the relative NDVI differences. This is because relative difference may be very large due to division by small NDVI value when it is close to zero. Except the limited number of points for the extreme geometry conditions (large viewing and solar zenith angles) the dependence of differences on NDVI is captured reasonably well by quadratic fit. The parameters of quadratic fit for each sensor, spectral band and TOA or surface level are given in Table 1 for the AVHRR NOAA-17, Table 2 for the AVHRR NOAA-18, Table 3 for the AVHRR METOP-A. The tables also contain r2 and σ (coefficient of determination and standard deviation of the fit) for each fitting curve. The r2-values vary between 0.75 and 0.98 except for the NDVI relative difference and the NIR TOA reflectance for the AVHRR METOP-A where they can be as low as 0.3–0.5. The σ-values of quadratic fit for absolute differences are typically better than 0.005. The relative σ-values are close to 1% or smaller for most cases, but they can be slightly higher than 2% for the relative NDVI fit. They are close to 1% for relative differences for visible band. The overall distribution of differences describing the SRF effect for new AVHRR sensors relative to AVHRR NOAA-9 is shown in Fig. 5. It presents the histograms of relative and absolute differences for visible, NIR reflectance and NDVI at the TOA level for each sensor. The differences in reflectance among three radiometers relative to AVHRR NOAA-9 are similar and range from −0.015 to 0.015 (−20% to +2% relative) for visible channel, and from −0.03 to 0.02 (−5% to +5%) for NIR channel. Absolute change in NDVI ranges from −0.03 to +0.06. Due to systematic biases of visible channels toward smaller values and NIR
Fig. 5. Absolute (left panels) and relative (right panels) differences in TOA reflectances and NDVI for the AVHRRs on NOAA-17 (top), NOAA-18 (center) and METOP-A (bottom) relative to the AVHRR NOAA-9.
spectral radiances are computed at the TOA and surface levels. The surface reflectance is assumed to be Lambertian. The results for each sensor: AVHRR NOAA-17, 18 and METOP-A are plotted in Figs. 2–4. The statistical distributions of differences at the TOA are displayed in Fig. 5. The distributions are not shown at
Table 1 NOAA−9 Parameters of quadratic best fit to absolute spectral correction Δρ = ρ − ρNOAA-9 and relative spectral correction Δρ = ρ−ρ ρNOAA−9 ðkÞ for visible (red) channel (Ch.1) reflectance, NIR channel (Ch.2) reflectance and NDVI for the AVHRR NOAA-17 relative to the AVHRR NOAA-9. X denotes NDVI observed by the AVHRR NOAA-17 Parameter Ch.1 Surf Ch.1 TOA Ch.2 Surf Ch.2 TOA NDVI Surf NDVI TOA
r2
Absolute correction 2
0.00026 − 0.0224 X + 0.0121 X 0.00089 − 0.0221 X + 0.0131 X2 −0.00191 + 0.0174 X − 0.0029 X2 −0.00218 + 0.0147 X − 0.0007 X2 −0.00077 + 0.0897 X − 0.0340 X2 −0.00141 + 0.0752 X − 0.0144 X2
0.81 0.76 0.84 0.80 0.97 0.98
σ 0.0021 0.0024 0.0022 0.0020 0.0035 0.0027
Relative correction (%) 2
−0.110 + 2.774 X − 35.725 X 0.111 − 6.455 X − 16.418 X2 −0.160 + 4.650 X − 1.027 X2 −0.537 + 5.452 X − 1.629 X2 8.302 − 1.687 X − 0.507 X2 –
r2
σ (%)
0.98 0.96 0.95 0.94 0.30 –
1.30 0.95 0.28 0.32 1.10 -
r2
σ (%)
0.98 0.96 0.93 0.88 0.41 –
1.32 0.99 0.44 0.62 2.07 -
ρ denotes a channel reflectance or NDVI at the surface (Surf) or TOA level.
Table 2 The same as Table 1 but for the AVHRR NOAA–18 Parameter Ch.1 Surf Ch.1 TOA Ch.2 Surf Ch.2 TOA NDVI Surf NDVI TOA
r2
Absolute correction 2
0.00011 − 0.0191 X + 0.0075 X 0.00178 − 0.0254 X + 0.0141 X2 −0.00308 + 0.0265 X − 0.0085 X2 −0.00520 + 0.0233 X − 0.0070 X2 −0.00162 + 0.0947 X − 0.0338 X2 −0.00661 + 0.0875 X − 0.0132 X2
0.80 0.75 0.81 0.68 0.97 0.97
σ 0.0022 0.0030 0.0032 0.0038 0.0039 0.0037
Relative correction (%) 2
− 0.059 + 4.801 X − 39.601 X 0.305 − 6.849 X − 18.754 X2 − 0.292 + 6.724 X − 2.257 X2 − 1.443 + 7.278 X − 2.486 X2 11.030 − 12.849 X + 9.878 X2 –
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Table 3 The same as Table 1 but for the AVHRR METOP-A Parameter Ch.1 Surf Ch.1 TOA Ch.2 Surf Ch.2 TOA NDVI Surf NDVI TOA
r2
Absolute correction 2
0.00016 − 0.0238 X + 0.0152 X 0.00189 − 0.0208 X + 0.0122 X2 −0.00406 + 0.0308 X − 0.0150 X2 −0.00752 + 0.0266 X − 0.0131 X2 −0.00150 + 0.1055 X − 0.0571 X2 −0.01216 + 0.0784 X − 0.0173 X2
0.79 0.76 0.75 0.53 0.98 0.94
σ
Relative correction (%)
0.0020 0.0023 0.0036 0.0054 0.0031 0.0049
2
−0.146 + 0.307 X − 30.282 X 0.617 − 5.942 X − 13.625 X2 −0.393 + 6.881 X − 3.069 X2 −2.170 + 7.187 X − 2.858 X2 15.195 − 25.667 X + 18.001 X2 –
r2
σ (%)
0.98 0.93 0.89 0.74 0.76 –
1.32 1.08 0.53 0.95 2.17 -
The previous study by Trishchenko et al. (2002) showed that the most significant SRF effect is observed between AVHRR instruments from different groups. This is especially true for the differences between AVHRR-3 group that includes AVHRR NOAA-15, 16, 17, 18 and METOP-A and the rest. To understand better how significant are these differences among the most recent AVHRRs, we conducted a similar analysis within this group using the AVHRR NOAA-18 as a reference sensor. It is expected that these results will be useful for analysis of consistency in the AVHRR time series for the last decade after the launch of NOAA-15 in 1998. Because the spectral response functions for these AVHRR radiometers are quite similar, one can expect that the magnitude of the SRF effect will be smaller than the one determined relative to the AVHRR NOAA-9. Calculations confirmed that this is indeed the case. The differences for the AVHRR NOAA-15, 16 and 17
were quite close. The SRF effect for the AVHRR METOP-A is somewhat larger due to more distinct shape of the spectral response curves of visible and NIR bands. The results of analysis are shown in Figs. 6–8. Figs. 6 and 7 show similar results as in Figs. 2–4, but computed for the AVHRR NOAA-17 and the AVHRR METOP-A relative to the AVHRR NOAA-18. We do not show figures for the AVHRR NOAA-15 and 16 because results look similar. The parameters of quadratic regression for each sensor relative to the AVHRR NOAA-18 are provided in Tables 4–7 for the AVHRR NOAA-15, 16, 17 and METOP-A correspondingly. Fig. 8 shows the distribution of differences for all sensors including the AVHRR NOAA15 and 16. Results in Fig. 8 are shown at the TOA level, similar to Fig. 5. The absolute and relative errors are shown on the panels on the left and right side correspondingly. The magnitude of the SRF effect within the AVHRR-3 group computed relative to the AVHRR NOAA-18 is noticeably smaller than the magnitude of the effect computed relative to the AVHRR NOAA-9. The differences are smaller by about factor of 3. They normally vary within ±0.01 for absolute values (except the METOP-A TOA NDVI
Fig. 6. The absolute (solid triangles) and relative (open circles) differences in surface and TOA reflectances for channels 1 and 2 and NDVI between the AVHRR NOAA-17 and the AVHRR NOAA-18. All data points are plotted versus NDVI of particular sensor. Parameters of fitting curves are given in Table 6.
Fig. 7. The same as Fig. 6, but for the AVHRR METOP-A. Parameters of fitting curves are given in Table 7.
channels toward slightly larger values, overall systematic biases for NDVI are mostly positive. 4. The SRF effect among AVHRR-3 sensors relative to AVHRR NOAA-18
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where the difference can be as low as −0.02). The relative differences are usually within ±5%. Although the coefficients of determination r2 presented in Tables 4–7 for quadratic fit are smaller, indicating the lesser quality of regression, the overall standard deviations σ are typically less than 0.5% and only in two instances (relative NDVI differences for the AVHRR NOAA-15 and 16) are close or exceed 1%. The standard deviations for absolute differences are also several times smaller than previously described in Section 3. This is explained by smaller magnitude of the SRF effect for sensors with similar SRF. The smaller magnitude of the effect reduces the correlation while the quality of regression fit on the absolute scale may still be very good. 5. Conclusions Long-term monitoring of the Earth's environment by satellite sensors requires consistent and comparable measurements. One of the longest satellite time series used for terrestrial monitoring and climate change studies come from the NOAA AVHRR sensors spanning the period of almost three decades. During this period thirteen AVHRR sensors have been operated by the NOAA. Despite similar design and comparable spectral channels, each instrument has unique properties in terms of spectral response functions. This new study provides an extension of previous work by Trishchenko et al. (2002) to recently launched AVHRR sensors onboard NOAA-17, 18 and METOP-A. The results could be used for improving consistency between AVHRR sensors by apply corrections for the spectral response function effect in the way as it was proposed in the previous study. The corrections are derived to normalize all sensors to AVHRR NOAA-9. They are obtained as the second order polynomials of sensorobserved NDVI. The NDVI was used as a predictor because it is related to the effect of spectral overlap or spectral separation between visible and NIR channels over the “red” edge (0.7 µm) in the pixel reflectance spectrum (Trishchenko et al., 2002). In addition, NDVI generally characterizes the shape of surface spectra, and as such it also influences the magnitude of SRF effect. Although the magnitude of SRF effect depends on many factors, such as surface reflectance spectrum, atmospheric state, geometry of observations, NDVI was found to be the most essential factor and was employed for parameterization. The magnitude of the SRF effect for new AVHRR sensors onboard NOAA-17, 18 and METOP-A was found similar to the values reported in previous study for AVHRR NOAA-15 and 16. The differences in
Fig. 8. The absolute (left panels) and relative (right panels) differences in TOA reflectances and NDVI for AVHRRs among AVHRR-3 radiometers on NOAA-15 (a–b), NOAA-16 (c–d), NOAA-17 (e–f) and METOP-A (g–h) relative to the AVHRR NOAA-18.
Table 4 The same as Table 1 but for the AVHRR NOAA-15 relative to the AVHRR NOAA-18 Parameter Ch.1 Surf Ch.1 TOA Ch.2 Surf Ch.2 TOA NDVI Surf NDVI TOA
r2
Absolute correction 2
0.00012 − 0.0029 X + 0.0040 X − 0.00093 + 0.0027 X − 0.0003 X2 0.00233 − 0.0151 X + 0.0136 X2 0.00527 − 0.0120 X + 0.0122 X2 0.00159 − 0.0078 X + 0.0040 X2 0.00892 − 0.0119 X − 0.0031 X2
0.48 0.29 0.47 0.21 0.68 0.59
σ
Relative correction (%) 2
r2
σ (%)
0.0003 0.0011 0.0017 0.0032 0.0010 0.0032
−0.030 − 2.186 X + 4.314 X −0.240 − 0.010 X + 2.920 X2 0.291 − 2.722 X + 2.519 X2 1.610 − 2.063 X + 1.553 X2 −5.242 + 21.885 X − 19.512 X2 –
0.80 0.65 0.49 0.25 0.73 –
0.29 0.37 0.29 0.61 1.45 -
σ
Relative correction (%)
r2
σ (%)
0.97 0.82 0.54 0.31 0.55 –
0.31 0.46 0.18 0.34 1.00 -
Table 5 The same as Table 1 but for the AVHRR NOAA-16 relative to the AVHRR NOAA-18 Parameter Ch.1 Surf Ch.1 TOA Ch.2 Surf Ch.2 TOA NDVI Surf NDVI TOA
r2
Absolute correction 2
0.00012 − 0.0026 X + 0.0049 X −0.00110 + 0.0044 X − 0.0008 X2 0.00149 − 0.0099 X + 0.0089 X2 0.00310 − 0.0073 X + 0.0072 X2 0.00121 − 0.0042 X − 0.0052 X2 0.00572 − 0.0113 X − 0.0071 X2
0.62 0.39 0.50 0.23 0.88 0.73
0.0004 0.0014 0.0011 0.0019 0.0011 0.0026
2
−0.032 − 4.249 X + 10.341 X −0.288 + 0.265 X + 5.364 X2 0.190 − 1.829 X + 1.686 X2 0.944 − 1.329 X + 1.011 X2 −2.576 + 11.419 X − 11.334 X2 –
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Table 6 The same as Table 1 but for the AVHRR NOAA-17 relative to the AVHRR NOAA-18 Parameter Ch.1 Surf Ch.1 TOA Ch.2 Surf Ch.2 TOA NDVI Surf NDVI TOA
r2
Absolute correction 2
0.00013 − 0.0032 X + 0.0045 X −0.00103 + 0.0036 X − 0.0009 X2 0.00119 − 0.0092 X + 0.0055 X2 0.00314 − 0.0087 X + 0.0060 X2 0.00094 − 0.0057 X + 0.0003 X2 0.00567 − 0.0132 X − 0.0015 X2
0.49 0.31 0.71 0.38 0.81 0.70
σ
Relative correction (%) 2
r2
σ (%)
0.0004 0.0013 0.0010 0.0020 0.0009 0.0026
− 0.046 − 2.556 X + 5.311 X − 0.241 + 0.172 X + 3.427 X2 0.141 − 2.110 X + 1.286 X2 0.962 − 2.032 X + 1.019 X2 − 2.406 + 9.862 X − 9.230 X2 –
0.86 0.68 0.83 0.55 0.58 –
0.31 0.43 0.16 0.38 0.88 -
σ
Relative correction (%)
r2
σ (%)
0.95 0.74 0.65 0.04 0.84 –
0.43 0.85 0.14 0.40 0.85 -
Table 7 The same as Table 1 but for the AVHRR METOP-A relative to the AVHRR NOAA-18 Parameter Ch.1 Surf Ch.1 TOA Ch.2 Surf Ch.2 TOA NDVI Surf NDVI TOA
r2
Absolute correction 2
0.00006 − 0.0050 X + 0.0081 X 0.00027 + 0.0042 X − 0.0011 X2 − 0.00096 + 0.0044 X − 0.0068 X2 − 0.00245 + 0.0037 X − 0.0069 X2 0.00008 + 0.0117 X − 0.0248 X2 − 0.00609 − 0.0093 X − 0.0048 X2
0.64 0.29 0.44 0.07 0.71 0.34
reflectance among three radiometers relative to AVHRR NOAA-9 range from −0.015 to 0.015 (−20% to +2% relative) for visible channel (red), and from −0.03 to 0.02 (−5% to 5%) for NIR channel. The absolute change in NDVI ranged from −0.03 to +0.06. Due to systematic biases of visible channels toward smaller values and NIR channels toward slightly larger values, the overall systematic biases for NDVI are positive. The coefficient of determination (r2) for quadratic regression vary mostly between 0.75 and 0.98 except for NDVI relative difference and NIR TOA reflectance for METOP-A. The standard deviation (σ) of quadratic fit for absolute differences are typically better than 0.005. The relative σ-values are close to 1% or smaller for most cases, except relative NDVI fit. To characterize the magnitude of the SRF effect among AVHRR-3 type of radiometers onboard NOAA-15, 16, 17, 18, and METOP-A a similar analysis was completed by selecting AVHRR NOAA-18 as a reference instrument. The magnitude of observed SRF effect among this group was found smaller by about factor of 3 relative to numbers reported above with a reference to AVHRR NOAA-9. The quadratic polynomial corrections as function of NDVI are also provided for these sensors to normalize the reflectances and NDVI to AVHRR NOAA-18 values. Acknowledgements This work was conducted at the Canada Centre for Remote Sensing (CCRS), Earth Sciences Sector of the Department of Natural Resources Canada as part of the Project J35 of the “Enhancing Resilience in a Changing Climate” Program. The study was also supported by the Canadian Space Agency under the Government Related Initiative Program (GRIP). References Chuvieco, E., Englefield, P., Trishchenko, A. P., & Luo, Y. (2008). Generation of long time series of burn area maps of the boreal forest from NOAA-AVHRR composite data. Remote Sensing of Environment, 112(5), 2381−2396.
0.0006 0.0016 0.0007 0.0017 0.0023 0.0047
2
−0.108 − 6.069 X + 13.160 X 0.332 + 1.120 X + 6.930 X2 −0.099 + 0.182 X − 0.853 X2 −0.780 − 0.086 X − 0.360 X2 3.721 − 11.136 X + 6.780 X2 –
Latifovic, R., Trishchenko, A. P., Chen, J., Park, W. M., Khlopenkov, K. V., Fernandes, R., Pouliot, D., Ungurenu, C., Wang, S., & Cihlar, J. (2005). Generating historical AVHRR 1-km baseline satellite data records over Canada suitable for climate change studies.Canadian Journal of Remote Sensing, 31, 324−346 No. 5. Li, Z., Li, J., Menzel, W. P., Schmit, T. J., & Ackerman, S. A. (2007). Comparison between current and future environmental satellite imagers on cloud classification using MODIS. Remote Sensing of Environment, 108(3), 311−326. Rossow, W. B., & Schiffer, R. A. (1999). Advances in understanding clouds from ISCCP. Bulletin of the American Meteorological Society, 80, 2261−2287. Stow, D. A., Hope, A., McGuire, D., Verbyla, D., Gamon, J., Huemmrich, F., Houston, S., Racinef, C., Sturmg, M., Tapeh, K., Hinzman, L., Yoshikawai, K., Tweediej, C., Noylek, B., Silapaswanl, C., Douglasm, D., Griffithn, B., Jiao, G., Epsteino, H., Walkerp, D., Daeschnera, S., Petersena, A., Zhouq, L., & Myneni, R. (2004). Remote sensing of vegetation and land-cover change in Arctic Tundra Ecosystems. Remote Sensing of Environment, 89(3), 281−308. Swinnen, E., & Veroustraete, F. (2008). Extending the SPOT-VEGETATION NDVI time series (1998–2006) back in time with NOAA-AVHRR data (1985–1998) for Southern Africa. IEEE Transactions on Geoscience and Remote Sensing, 46(2), 558−572. Teillet, P. M., Fedosejevs, G., Thome, K. J., & Barker, J. L. (2007). Impacts of spectral band difference effects on radiometric cross-calibration between satellite sensors in the solar-reflective spectral domain. Remote Sensing of Environment, 110(3), 393−409. Trishchenko, A. P., Cihlar, J., & Li, Z. (2002). Effects of spectral response function on surface reflectance and NDVI measured with moderate resolution satellite sensors. Remote Sensing of Environment, 81(1), 1−18. Trishchenko, A. P., Luo, Y., Khlopenkov, K. V., & Wang, S. (2008). A method to derive the multi-spectral surface albedo consistent with MODIS from historical AVHRR and VGT satellite data. Journal of Applied Meteorology and Climatology, 47(4), 1199−1221. Venturini, V., Bisht, G., Islam, S., & Jiang, L. (2004). Comparison of evaporative fractions estimated from AVHRR and MODIS sensors over South Florida. Remote Sensing of Environment, 93(1–2), 77−86. Vermote, E., Tanré, D., Deuzé, J. L., Herman, M., & Moricette, J. J. (1997). Second simulation of the satellite signal in the solar spectrum: An overview. IEEE Transactions on Geoscience and Remote Sensing, 35, 675−686.
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Remote Sensing of Environment j o u r n a l h o m e p a g e : w w w. e l s e v i e r. c o m / l o c a t e / r s e
Effects of spectral response function on surface reflectance and NDVI measured with moderate resolution satellite sensors: Extension to AVHRR NOAA-17, 18 and METOP-A Alexander P. Trishchenko ⁎ Canada Centre for Remote Sensing, Natural Resources Canada, Ottawa, Ontario, Canada K1A 0Y7
a r t i c l e
i n f o
Article history: Received 22 May 2008 Received in revised form 29 September 2008 Accepted 4 October 2008 Keywords: Satellite Surface reflectance AVHRR NOAA NDVI Spectral response function Spectral correction Global change detection
a b s t r a c t This work extends the previous study of Trishchenko et al. [Trishchenko, A. P., Cihlar, J., & Li, Z. (2002). Effects of spectral response function on surface reflectance and NDVI measured with moderate resolution satellite sensors. Remote Sensing of Environment 81 (1), 1–18] that analyzed the spectral response function (SRF) effect for the Advanced Very High Resolution Radiometer (AVHRR) onboard the NOAA satellites NOAA-6 to NOAA16 as well as the Moderate Resolution Imaging Spectroradiometer (MODIS), the VEGETATION sensor (VGT) and the Global Imager (GLI). The developed approach is now applied to cover three new AVHRR sensors launched in recent years on NOAA-17, 18, and METOP-A platforms. As in the previous study, the results are provided relative to the reference sensor AVHRR NOAA-9. The differences in reflectance among these three radiometers relative to the AVHRR NOAA-9 are similar to each other and range from −0.015 to 0.015 (−20% to +2% relative) for visible (red) channel, and from −0.03 to 0.02 (−5% to 5%) for the near infrared (NIR) channel. The absolute change in the Normalized Difference Vegetation Index (NDVI) ranged from −0.03 to +0.06. Due to systematic biases of the visible channels toward smaller values and the NIR channels toward slightly larger values, the overall systematic biases for NDVI are positive. The polynomial approximations are provided for the bulk spectral correction with respect to the AVHRR NOAA-9 for consistency with previous study. Analysis was also conducted for the SRF effect only among the AVHRR-3 type of radiometer on NOAA-15, 16, 17, 18 and METOP-A using AVHRR NOAA-18 as a reference. The results show more consistency between sensors with typical correction being under 5% (or 0.01 in absolute values). The AVHRR METOP-A reveals the most different behavior among the AVHRR-3 group with generally positive bias for visible channel (up to + 5%, relative), slightly negative bias for the NIR channel (1%–2% relative), and negative NDVI bias (−0.02 to +0.005). Polynomial corrections are also suggested for normalization of AVHRR on NOAA-15, 16, 17 and METOP-A to AVHRR NOAA-18. Crown Copyright © 2008 Published by Elsevier Inc. All rights reserved.
1. Introduction Trishchenko et al. (2002) provided an analysis and recommendations regarding the effect of spectral response function (SRF) on reflectances and normalized difference vegetation index (NDVI) for various moderate resolution sensors. The SRF effect was analyzed for the Advanced Very High Resolution Radiometer (AVHRR) onboard the NOAA satellites NOAA-6 to NOAA-16 as well as the Moderate Resolution Imaging Spectroradiometer (MODIS), the VEGETATION sensor (VGT) and the Global Imager (GLI). The results were derived relative to the reference sensor AVHRR NOAA-9 for consistency with the approach of the International Satellite Cloud Climatology Project (ISCCP) (Rossow & Schiffer, 1999). The study of Trishchenko et al. (2002) demonstrated that the differences in SRF are significant and must be taken into account, in ⁎ 588 Booth Street, Ottawa, Ontario, Canada K1A 0Y7. Tel.: +1 613 995 57 87; fax: +1 613 947 14 06. E-mail address: [email protected].
particular for studies concerning the inter-annual variations in satellite time series. The effect is comparable in magnitude to the uncertainties caused by sensor calibration, atmospheric and angular correction and can lead to systematic biases if neglected. Even among “the same type” instruments such as AVHRR, the effect of the varying spectral response function on surface and top of the atmosphere (TOA) spectral reflectances and the Normalized Difference Vegetation Index (NDVI) is sufficiently large to require correction. Relative to the AVHRR/NOAA-9, differences between various AVHRR sensors were found to vary from −25% to +12% for visible (red) reflectance, and from −2% to +4% for the near infrared (NIR) reflectance. The absolute differences in NDVI among various AVHRRs ranged from −0.02 to + 0.06. The most consistent with AVHRR/NOAA-9 results were obtained for AVHRR/NOAA-11 and −12. The corrections must be implemented for other AVHRRs and especially for the AVHRR/3 on NOAA-15 and −16. Reflectances and NDVI from MODIS differ from AVHRR/NOAA-9 by as much as 30–40%. Likewise, VGT and GLI also exhibit considerable differences relative to AVHRR observations and should be always corrected in comparing long-term tome series.
0034-4257/$ – see front matter. Crown Copyright © 2008 Published by Elsevier Inc. All rights reserved. doi:10.1016/j.rse.2008.10.002
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relative to AVHRR NOAA-18. The results for the AVHRR-3 group may be of interest if only the last decade is analyzed. As in the previous study of Trishchenko et al. (2002) the recommendations are provided for reflectances in spectral band 1 (red) and band 2 (near infrared — NIR) ρNIR at the surface and the TOA levels, as well as for the vegetation index NDVI defined as
NDVI =
ρNIR −ρred ; ρNIR + ρred
The 6S radiative transfer code (Vermote et al., 1997) was employed for model simulation of the signal at the TOA level under various atmospheric conditions and observational geometries similar to Trishchenko et al. (2002). The set of surface spectral reflectances was employed in the same manner as in Trishchenko et al. (2002). Namely, 17 representative spectra were selected for 12 surface classes: 1) coniferous forest, 2) deciduous broadleaf forest, 3) closed shrubland, 4) open shrubland, 5) drygrass/savanna, 6) grassland, 7) cropland, 8) crop/natural vegetation mosaic, 9) barren/desert, 10) water bodies, 11) fresh snow, 12) coarse granular snow. The sources of surface spectral information are described in Trishchenko et al. (2002). The solar zenith angle (SZA) varied from 0° to 85°. The viewing zenith angle (VZA) varied from 0° to 65°, and the relative azimuth angle (RAA) varied from 0° to 180°. The total water vapor amount in the atmosphere varied from 0.05 cm to 5 cm, ozone columnar amount varied from 150 DU to 450 DU. Aerosol optical depth varied from 0.01 to 0.6. Fig. 1. The spectral response functions of visible (red) and NIR channels for the AVHRR NOAA-17 (a), NOAA-18 (b) and METOP-A (c) shown as solid lines relative to the AVHRR NOAA-9 (dashed line). Typical spectral reflectance curve for green vegetation is shown on each panel as dash–dot line.
The method was found quite efficient and was applied in many studies. Swinnen and Veroustraete (2008) used it for extending the SPOT-VEGETATION NDVI time series back in time with AVHRR data for Southern Africa and found that accounting for the SRF effect considerably improved the consistency between sensors. Stow et al. (2004) employed it for remote sensing studies of vegetation and landcover change in Arctic tundra ecosystems. Li et al. (2007) considered these results in assessing the cloud discrimination capabilities of current and future sensors. Chuvieco et al. (2008) used proposed SRF corrections in generation of long-time series of burn areas in the boreal forest using data for different AVHRR sensors processed at the Canada Centre for Remote Sensing (CCRS) (Latifovic et al., 2005). Venturini et al. (2004) employed these results for analysis of evaporative fractions estimated from AVHRR and MODIS over South Florida. The study of Teillet et al. (2007) emphasized the importance of SRF effects on sensor radiometric cross-calibration. An alternative approach for correction of SRF effect was proposed in Trishchenko et al. (2008) based on empirical regression between observations for overlapping periods. This method, however, requires significant preprocessing efforts to derive first the parameters of bi-directional reflectance distribution function. The AVHRR continues to be widely used operational sensor for large number of environmental and climate applications. After the study of Trishchenko et al. (2002) three new AVHRR radiometers were launched on NOAA-17, 18 and Meteorological Operational satellite (METOP-A) platforms. This paper provides recommendations regarding the SRF effect for these new sensors to achieve consistency in time series of clear-sky products derived from historical AVHRR observations. As in the previous study, the results are provided relative to the reference sensor AVHRR NOAA-9. In addition, similar analysis was conducted only among all five AVHRR-3 type of sensors flown on NOAA-15, 16, 17, 18 and METOP-A. The differences were assessed
Fig. 2. The absolute (solid triangles) and relative (open circles) differences in surface and TOA reflectances for channels 1, 2 and NDVI between the AVHRR NOAA-17 and the AVHRR NOAA-9. All data points are plotted versus NDVI of particular sensor. Quadratic best fits for absolute (solid) and relative (dashed) differences are also shown. Parameters of fitting curves are given in Table 1.
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3. Corrections for AVHRR NOAA-17, 18 and METOP relative to NOAA-9 The spectral response function effect in this section is determined relative to AVHRR NOAA-9 selected as a reference sensor in Trishchenko et al. (2002). This radiometer is often considered as a reference instrument follow to Rossow and Schiffer (1999). Previous study of Trishchenko et al. (2002) analyzed the SRF effect for the AVHRR radiometer onboard the NOAA satellites NOAA-6 to NOAA-16 as well as MODIS, VGT and GLI sensors. With addition of three new sensors, this collection of AVHRR sensors includes now 13 instruments. Three new radiometers continue the series of AVHRR-3 type instruments that include also AVHRR NOAA-15 and 16. Unlike all previous AVHRR instruments that were launched on the NOAA platforms, the most recent AVHRR was launched in 2006 onboard METOP-A satellite in the framework of the joint program established by the European Space Agency (ESA) and the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT). The AVHRR contributed by NOAA is also scheduled to be launched on follow-up satellites METOP-B, and METOP-C. Figs. 2–4 show the absolute and relative differences in reflectance or NDVI between specific sensor and the AVHRR NOAA-9. The reflectance is computed according to Eq. (1).
λmax
~ðλÞ = ρ
∫ Sz ðλÞF ðλÞdλ
λmin
λmax
;
ð1Þ
∫ SA ðλÞF ðλÞdλ
λmin
Fig. 3. The same as Fig. 2, but for the AVHRR NOAA-18. Parameters of fitting curves are given in Table 2.
where S↑ (λ), S↓ (λ) are the upward or downward solar spectral radiances and F (λ) is the instrument spectral response function. The
The paper is organized as follows. Section 2 describes the special features of instrument spectral response functions for three AVHRR sensors on NOAA-17, 18 and METOP-A. Section 3 presents results and derived corrections relative to AVHRR NOAA-9 sensor. Section 4 provides results of analysis and recommendations for corrections of AVHRR-3 radiometers onboard NOAA-15, 16, 17, and METOP-A relative to AVHRR NOAA-18. Section 5 summarizes the study. 2. Spectral response functions for AVHRR NOAA-17,18 and METOP-A The spectral response functions for AVHRR bands 1 and 2 of NOAA17, 18 and METOP-A are shown in Fig. 1a–c together with curves for AVHRR NOAA-9 plotted for the reference. The data for spectral response functions were taken from the NOAA Center for Application and Research (STAR) web-site http://www.orbit.nesdis.noaa.gov/ smcd/spb/fwu/solar_cal/spec_resp_func/index.html. The typical spectral dependence of green vegetation is also shown in Fig. 1. The major observed differences are seen as more narrow shape of band 1 for the AVHRR NOAA-17, 18 and METOP-A relative to AVHRR NOAA-9, and as a shift of SRF for band 2 of the AVHRR NOAA-17, 18 and METOP-A away from the red edge region around 0.7 µm toward longer wavelengths. The above differences lead to the following systematic differences in general: a) slightly darker band 1 reflectances, b) slightly brighter band 2 reflectances, c) slightly larger NDVI. All these features were observed in a previous study Trishchenko et al. (2002) for AVHRR-3 type of sensors. They are manifested in a similar way for new sensors although there are some minor differences related to a specific SRF shape for each spectral band. An interesting feature of Fig. 1c is a small spectral leak for band 1 of the AVHHR METOP-A in the blue region which leads to some noticeable differences for the results at the TOA level due to intensive Rayleigh and aerosol scattering for short wavelengths.
Fig. 4. The same as Fig. 2, but for the AVHRR METOP-A. Parameters of fitting curves are given in Table 3.
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the surface level because they can be determined relatively easy from Figs. 2–4 for each sensor. The results in Figs. 2–4 are plotted against NDVI computed at the surface or TOA level depending on the analysis. Although the magnitude of SRF effect is influenced by many factors, such as surface reflectance spectrum, atmospheric state, geometry of observations that may alter the spectral composition of the radiance spectra within the instrument spectral response, we found that NDVI serves as a good indicator of the effect (Trishchenko et al., 2002). This occurs because the effect of spectral overlap or spectral separation between visible and NIR channels over the “red” edge (0.7 µm) in the pixel surface reflectance spectrum is related to NDVI. In addition, NDVI generally characterizes the shape of surface spectra, and as such it also influences the magnitude of SRF effect. The quadratic fits are also shown in Figs. 2–4 for all panels except panel f) that shows the relative NDVI differences. This is because relative difference may be very large due to division by small NDVI value when it is close to zero. Except the limited number of points for the extreme geometry conditions (large viewing and solar zenith angles) the dependence of differences on NDVI is captured reasonably well by quadratic fit. The parameters of quadratic fit for each sensor, spectral band and TOA or surface level are given in Table 1 for the AVHRR NOAA-17, Table 2 for the AVHRR NOAA-18, Table 3 for the AVHRR METOP-A. The tables also contain r2 and σ (coefficient of determination and standard deviation of the fit) for each fitting curve. The r2-values vary between 0.75 and 0.98 except for the NDVI relative difference and the NIR TOA reflectance for the AVHRR METOP-A where they can be as low as 0.3–0.5. The σ-values of quadratic fit for absolute differences are typically better than 0.005. The relative σ-values are close to 1% or smaller for most cases, but they can be slightly higher than 2% for the relative NDVI fit. They are close to 1% for relative differences for visible band. The overall distribution of differences describing the SRF effect for new AVHRR sensors relative to AVHRR NOAA-9 is shown in Fig. 5. It presents the histograms of relative and absolute differences for visible, NIR reflectance and NDVI at the TOA level for each sensor. The differences in reflectance among three radiometers relative to AVHRR NOAA-9 are similar and range from −0.015 to 0.015 (−20% to +2% relative) for visible channel, and from −0.03 to 0.02 (−5% to +5%) for NIR channel. Absolute change in NDVI ranges from −0.03 to +0.06. Due to systematic biases of visible channels toward smaller values and NIR
Fig. 5. Absolute (left panels) and relative (right panels) differences in TOA reflectances and NDVI for the AVHRRs on NOAA-17 (top), NOAA-18 (center) and METOP-A (bottom) relative to the AVHRR NOAA-9.
spectral radiances are computed at the TOA and surface levels. The surface reflectance is assumed to be Lambertian. The results for each sensor: AVHRR NOAA-17, 18 and METOP-A are plotted in Figs. 2–4. The statistical distributions of differences at the TOA are displayed in Fig. 5. The distributions are not shown at
Table 1 NOAA−9 Parameters of quadratic best fit to absolute spectral correction Δρ = ρ − ρNOAA-9 and relative spectral correction Δρ = ρ−ρ ρNOAA−9 ðkÞ for visible (red) channel (Ch.1) reflectance, NIR channel (Ch.2) reflectance and NDVI for the AVHRR NOAA-17 relative to the AVHRR NOAA-9. X denotes NDVI observed by the AVHRR NOAA-17 Parameter Ch.1 Surf Ch.1 TOA Ch.2 Surf Ch.2 TOA NDVI Surf NDVI TOA
r2
Absolute correction 2
0.00026 − 0.0224 X + 0.0121 X 0.00089 − 0.0221 X + 0.0131 X2 −0.00191 + 0.0174 X − 0.0029 X2 −0.00218 + 0.0147 X − 0.0007 X2 −0.00077 + 0.0897 X − 0.0340 X2 −0.00141 + 0.0752 X − 0.0144 X2
0.81 0.76 0.84 0.80 0.97 0.98
σ 0.0021 0.0024 0.0022 0.0020 0.0035 0.0027
Relative correction (%) 2
−0.110 + 2.774 X − 35.725 X 0.111 − 6.455 X − 16.418 X2 −0.160 + 4.650 X − 1.027 X2 −0.537 + 5.452 X − 1.629 X2 8.302 − 1.687 X − 0.507 X2 –
r2
σ (%)
0.98 0.96 0.95 0.94 0.30 –
1.30 0.95 0.28 0.32 1.10 -
r2
σ (%)
0.98 0.96 0.93 0.88 0.41 –
1.32 0.99 0.44 0.62 2.07 -
ρ denotes a channel reflectance or NDVI at the surface (Surf) or TOA level.
Table 2 The same as Table 1 but for the AVHRR NOAA–18 Parameter Ch.1 Surf Ch.1 TOA Ch.2 Surf Ch.2 TOA NDVI Surf NDVI TOA
r2
Absolute correction 2
0.00011 − 0.0191 X + 0.0075 X 0.00178 − 0.0254 X + 0.0141 X2 −0.00308 + 0.0265 X − 0.0085 X2 −0.00520 + 0.0233 X − 0.0070 X2 −0.00162 + 0.0947 X − 0.0338 X2 −0.00661 + 0.0875 X − 0.0132 X2
0.80 0.75 0.81 0.68 0.97 0.97
σ 0.0022 0.0030 0.0032 0.0038 0.0039 0.0037
Relative correction (%) 2
− 0.059 + 4.801 X − 39.601 X 0.305 − 6.849 X − 18.754 X2 − 0.292 + 6.724 X − 2.257 X2 − 1.443 + 7.278 X − 2.486 X2 11.030 − 12.849 X + 9.878 X2 –
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Table 3 The same as Table 1 but for the AVHRR METOP-A Parameter Ch.1 Surf Ch.1 TOA Ch.2 Surf Ch.2 TOA NDVI Surf NDVI TOA
r2
Absolute correction 2
0.00016 − 0.0238 X + 0.0152 X 0.00189 − 0.0208 X + 0.0122 X2 −0.00406 + 0.0308 X − 0.0150 X2 −0.00752 + 0.0266 X − 0.0131 X2 −0.00150 + 0.1055 X − 0.0571 X2 −0.01216 + 0.0784 X − 0.0173 X2
0.79 0.76 0.75 0.53 0.98 0.94
σ
Relative correction (%)
0.0020 0.0023 0.0036 0.0054 0.0031 0.0049
2
−0.146 + 0.307 X − 30.282 X 0.617 − 5.942 X − 13.625 X2 −0.393 + 6.881 X − 3.069 X2 −2.170 + 7.187 X − 2.858 X2 15.195 − 25.667 X + 18.001 X2 –
r2
σ (%)
0.98 0.93 0.89 0.74 0.76 –
1.32 1.08 0.53 0.95 2.17 -
The previous study by Trishchenko et al. (2002) showed that the most significant SRF effect is observed between AVHRR instruments from different groups. This is especially true for the differences between AVHRR-3 group that includes AVHRR NOAA-15, 16, 17, 18 and METOP-A and the rest. To understand better how significant are these differences among the most recent AVHRRs, we conducted a similar analysis within this group using the AVHRR NOAA-18 as a reference sensor. It is expected that these results will be useful for analysis of consistency in the AVHRR time series for the last decade after the launch of NOAA-15 in 1998. Because the spectral response functions for these AVHRR radiometers are quite similar, one can expect that the magnitude of the SRF effect will be smaller than the one determined relative to the AVHRR NOAA-9. Calculations confirmed that this is indeed the case. The differences for the AVHRR NOAA-15, 16 and 17
were quite close. The SRF effect for the AVHRR METOP-A is somewhat larger due to more distinct shape of the spectral response curves of visible and NIR bands. The results of analysis are shown in Figs. 6–8. Figs. 6 and 7 show similar results as in Figs. 2–4, but computed for the AVHRR NOAA-17 and the AVHRR METOP-A relative to the AVHRR NOAA-18. We do not show figures for the AVHRR NOAA-15 and 16 because results look similar. The parameters of quadratic regression for each sensor relative to the AVHRR NOAA-18 are provided in Tables 4–7 for the AVHRR NOAA-15, 16, 17 and METOP-A correspondingly. Fig. 8 shows the distribution of differences for all sensors including the AVHRR NOAA15 and 16. Results in Fig. 8 are shown at the TOA level, similar to Fig. 5. The absolute and relative errors are shown on the panels on the left and right side correspondingly. The magnitude of the SRF effect within the AVHRR-3 group computed relative to the AVHRR NOAA-18 is noticeably smaller than the magnitude of the effect computed relative to the AVHRR NOAA-9. The differences are smaller by about factor of 3. They normally vary within ±0.01 for absolute values (except the METOP-A TOA NDVI
Fig. 6. The absolute (solid triangles) and relative (open circles) differences in surface and TOA reflectances for channels 1 and 2 and NDVI between the AVHRR NOAA-17 and the AVHRR NOAA-18. All data points are plotted versus NDVI of particular sensor. Parameters of fitting curves are given in Table 6.
Fig. 7. The same as Fig. 6, but for the AVHRR METOP-A. Parameters of fitting curves are given in Table 7.
channels toward slightly larger values, overall systematic biases for NDVI are mostly positive. 4. The SRF effect among AVHRR-3 sensors relative to AVHRR NOAA-18
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where the difference can be as low as −0.02). The relative differences are usually within ±5%. Although the coefficients of determination r2 presented in Tables 4–7 for quadratic fit are smaller, indicating the lesser quality of regression, the overall standard deviations σ are typically less than 0.5% and only in two instances (relative NDVI differences for the AVHRR NOAA-15 and 16) are close or exceed 1%. The standard deviations for absolute differences are also several times smaller than previously described in Section 3. This is explained by smaller magnitude of the SRF effect for sensors with similar SRF. The smaller magnitude of the effect reduces the correlation while the quality of regression fit on the absolute scale may still be very good. 5. Conclusions Long-term monitoring of the Earth's environment by satellite sensors requires consistent and comparable measurements. One of the longest satellite time series used for terrestrial monitoring and climate change studies come from the NOAA AVHRR sensors spanning the period of almost three decades. During this period thirteen AVHRR sensors have been operated by the NOAA. Despite similar design and comparable spectral channels, each instrument has unique properties in terms of spectral response functions. This new study provides an extension of previous work by Trishchenko et al. (2002) to recently launched AVHRR sensors onboard NOAA-17, 18 and METOP-A. The results could be used for improving consistency between AVHRR sensors by apply corrections for the spectral response function effect in the way as it was proposed in the previous study. The corrections are derived to normalize all sensors to AVHRR NOAA-9. They are obtained as the second order polynomials of sensorobserved NDVI. The NDVI was used as a predictor because it is related to the effect of spectral overlap or spectral separation between visible and NIR channels over the “red” edge (0.7 µm) in the pixel reflectance spectrum (Trishchenko et al., 2002). In addition, NDVI generally characterizes the shape of surface spectra, and as such it also influences the magnitude of SRF effect. Although the magnitude of SRF effect depends on many factors, such as surface reflectance spectrum, atmospheric state, geometry of observations, NDVI was found to be the most essential factor and was employed for parameterization. The magnitude of the SRF effect for new AVHRR sensors onboard NOAA-17, 18 and METOP-A was found similar to the values reported in previous study for AVHRR NOAA-15 and 16. The differences in
Fig. 8. The absolute (left panels) and relative (right panels) differences in TOA reflectances and NDVI for AVHRRs among AVHRR-3 radiometers on NOAA-15 (a–b), NOAA-16 (c–d), NOAA-17 (e–f) and METOP-A (g–h) relative to the AVHRR NOAA-18.
Table 4 The same as Table 1 but for the AVHRR NOAA-15 relative to the AVHRR NOAA-18 Parameter Ch.1 Surf Ch.1 TOA Ch.2 Surf Ch.2 TOA NDVI Surf NDVI TOA
r2
Absolute correction 2
0.00012 − 0.0029 X + 0.0040 X − 0.00093 + 0.0027 X − 0.0003 X2 0.00233 − 0.0151 X + 0.0136 X2 0.00527 − 0.0120 X + 0.0122 X2 0.00159 − 0.0078 X + 0.0040 X2 0.00892 − 0.0119 X − 0.0031 X2
0.48 0.29 0.47 0.21 0.68 0.59
σ
Relative correction (%) 2
r2
σ (%)
0.0003 0.0011 0.0017 0.0032 0.0010 0.0032
−0.030 − 2.186 X + 4.314 X −0.240 − 0.010 X + 2.920 X2 0.291 − 2.722 X + 2.519 X2 1.610 − 2.063 X + 1.553 X2 −5.242 + 21.885 X − 19.512 X2 –
0.80 0.65 0.49 0.25 0.73 –
0.29 0.37 0.29 0.61 1.45 -
σ
Relative correction (%)
r2
σ (%)
0.97 0.82 0.54 0.31 0.55 –
0.31 0.46 0.18 0.34 1.00 -
Table 5 The same as Table 1 but for the AVHRR NOAA-16 relative to the AVHRR NOAA-18 Parameter Ch.1 Surf Ch.1 TOA Ch.2 Surf Ch.2 TOA NDVI Surf NDVI TOA
r2
Absolute correction 2
0.00012 − 0.0026 X + 0.0049 X −0.00110 + 0.0044 X − 0.0008 X2 0.00149 − 0.0099 X + 0.0089 X2 0.00310 − 0.0073 X + 0.0072 X2 0.00121 − 0.0042 X − 0.0052 X2 0.00572 − 0.0113 X − 0.0071 X2
0.62 0.39 0.50 0.23 0.88 0.73
0.0004 0.0014 0.0011 0.0019 0.0011 0.0026
2
−0.032 − 4.249 X + 10.341 X −0.288 + 0.265 X + 5.364 X2 0.190 − 1.829 X + 1.686 X2 0.944 − 1.329 X + 1.011 X2 −2.576 + 11.419 X − 11.334 X2 –
A.P. Trishchenko / Remote Sensing of Environment 113 (2009) 335–341
341
Table 6 The same as Table 1 but for the AVHRR NOAA-17 relative to the AVHRR NOAA-18 Parameter Ch.1 Surf Ch.1 TOA Ch.2 Surf Ch.2 TOA NDVI Surf NDVI TOA
r2
Absolute correction 2
0.00013 − 0.0032 X + 0.0045 X −0.00103 + 0.0036 X − 0.0009 X2 0.00119 − 0.0092 X + 0.0055 X2 0.00314 − 0.0087 X + 0.0060 X2 0.00094 − 0.0057 X + 0.0003 X2 0.00567 − 0.0132 X − 0.0015 X2
0.49 0.31 0.71 0.38 0.81 0.70
σ
Relative correction (%) 2
r2
σ (%)
0.0004 0.0013 0.0010 0.0020 0.0009 0.0026
− 0.046 − 2.556 X + 5.311 X − 0.241 + 0.172 X + 3.427 X2 0.141 − 2.110 X + 1.286 X2 0.962 − 2.032 X + 1.019 X2 − 2.406 + 9.862 X − 9.230 X2 –
0.86 0.68 0.83 0.55 0.58 –
0.31 0.43 0.16 0.38 0.88 -
σ
Relative correction (%)
r2
σ (%)
0.95 0.74 0.65 0.04 0.84 –
0.43 0.85 0.14 0.40 0.85 -
Table 7 The same as Table 1 but for the AVHRR METOP-A relative to the AVHRR NOAA-18 Parameter Ch.1 Surf Ch.1 TOA Ch.2 Surf Ch.2 TOA NDVI Surf NDVI TOA
r2
Absolute correction 2
0.00006 − 0.0050 X + 0.0081 X 0.00027 + 0.0042 X − 0.0011 X2 − 0.00096 + 0.0044 X − 0.0068 X2 − 0.00245 + 0.0037 X − 0.0069 X2 0.00008 + 0.0117 X − 0.0248 X2 − 0.00609 − 0.0093 X − 0.0048 X2
0.64 0.29 0.44 0.07 0.71 0.34
reflectance among three radiometers relative to AVHRR NOAA-9 range from −0.015 to 0.015 (−20% to +2% relative) for visible channel (red), and from −0.03 to 0.02 (−5% to 5%) for NIR channel. The absolute change in NDVI ranged from −0.03 to +0.06. Due to systematic biases of visible channels toward smaller values and NIR channels toward slightly larger values, the overall systematic biases for NDVI are positive. The coefficient of determination (r2) for quadratic regression vary mostly between 0.75 and 0.98 except for NDVI relative difference and NIR TOA reflectance for METOP-A. The standard deviation (σ) of quadratic fit for absolute differences are typically better than 0.005. The relative σ-values are close to 1% or smaller for most cases, except relative NDVI fit. To characterize the magnitude of the SRF effect among AVHRR-3 type of radiometers onboard NOAA-15, 16, 17, 18, and METOP-A a similar analysis was completed by selecting AVHRR NOAA-18 as a reference instrument. The magnitude of observed SRF effect among this group was found smaller by about factor of 3 relative to numbers reported above with a reference to AVHRR NOAA-9. The quadratic polynomial corrections as function of NDVI are also provided for these sensors to normalize the reflectances and NDVI to AVHRR NOAA-18 values. Acknowledgements This work was conducted at the Canada Centre for Remote Sensing (CCRS), Earth Sciences Sector of the Department of Natural Resources Canada as part of the Project J35 of the “Enhancing Resilience in a Changing Climate” Program. The study was also supported by the Canadian Space Agency under the Government Related Initiative Program (GRIP). References Chuvieco, E., Englefield, P., Trishchenko, A. P., & Luo, Y. (2008). Generation of long time series of burn area maps of the boreal forest from NOAA-AVHRR composite data. Remote Sensing of Environment, 112(5), 2381−2396.
0.0006 0.0016 0.0007 0.0017 0.0023 0.0047
2
−0.108 − 6.069 X + 13.160 X 0.332 + 1.120 X + 6.930 X2 −0.099 + 0.182 X − 0.853 X2 −0.780 − 0.086 X − 0.360 X2 3.721 − 11.136 X + 6.780 X2 –
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