- Email: [email protected]
Effect of NOAA satellite orbital drift on AVHRR-derived phenological metrics
Int J Appl Earth Obs Geoinformation 62 (2017) 215–223
Contents lists available at ScienceDirect
Int J Appl Earth Obs Geoinformation journal homepage: www.elsevier.com/locate/jag
Effect of NOAA satellite orbital drift on AVHRR-derived phenological metrics
MARK
⁎
Lei Jia, , Jesslyn F. Brownb a b
ASRC Federal InuTeq LLC, Contractor to the U.S. Geological Survey (USGS), Earth Resources Observation and Science (EROS) Center, Sioux Falls, SD 57198, USA U.S. Geological Survey (USGS), Earth Resources Observation and Science (EROS) Center, Sioux Falls, SD 57198, USA
A R T I C L E I N F O
A B S T R A C T
Keywords: AVHRR NOAA satellite Orbital drift Solar zenith angle Phenology NDVI
The U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center routinely produces and distributes a remote sensing phenology (RSP) dataset derived from the Advanced Very High Resolution Radiometer (AVHRR) 1-km data compiled from a series of National Oceanic and Atmospheric Administration (NOAA) satellites (NOAA-11, −14, −16, −17, −18, and −19). Each NOAA satellite experienced orbital drift during its duty period, which influenced the AVHRR reflectance measurements. To understand the effect of the orbital drift on the AVHRR-derived RSP dataset, we analyzed the impact of solar zenith angle (SZA) on the RSP metrics in the conterminous United States (CONUS). The AVHRR weekly composites were used to calculate the growing-season median SZA at the pixel level for each year from 1989 to 2014. The results showed that the SZA increased towards the end of each NOAA satellite mission with the highest increasing rate occurring during NOAA-11 (1989–1994) and NOAA-14 (1995–2000) missions. The growing-season median SZA values (44°–60°) in 1992, 1993, 1994, 1999, and 2000 were substantially higher than those in other years (28°–40°). The high SZA in those years caused negative trends in the SZA time series, that were statistically significant (at α = 0.05 level) in 76.9% of the CONUS area. A pixel-based temporal correlation analysis showed that the phenological metrics and SZA were significantly correlated (at α = 0.05 level) in 4.1–20.4% of the CONUS area. After excluding the 5 years with high SZA (> 40°) from the analysis, the temporal SZA trend was largely reduced, significantly affecting less than 2% of the study area. Additionally, significant correlation between the phenological metrics and SZA was observed in less than 7% of the study area. Our study concluded that the NOAA satellite orbital drift increased SZA, and in turn, influenced the phenological metrics. Elimination of the years with high median SZA reduced the influence of orbital drift on the RSP time series.
1. Introduction The longest continuous satellite-derived land surface data record to date has been acquired from the Advanced Very High Resolution Radiometer (AVHRR), a sensor operating onboard the series of the National Oceanic and Atmospheric Administration (NOAA) – Polar-orbiting Operational Environmental Satellites (POES). Although the AVHRR was initially designed for meteorological purposes, the second generation of the instrument AVHRR/2 designed with five improved channels onboard the NOAA-7 satellite, which launched in 1981, allowed routine observation of terrestrial vegetation conditions (Tucker, 1996). AVHRR/3, the third generation of the instrument designed with six channels has been carried on the NOAA-15 satellite launched in 1998 and all subsequent NOAA satellites (NOAA-16, −17, −18, and −19). Since 2006, the AVHRR has also been operational aboard the European Organization for the Exploitation of Meteorological Satellites
⁎
Corresponding author. E-mail addresses: [email protected], [email protected] (L. Ji).
http://dx.doi.org/10.1016/j.jag.2017.06.013 Received 1 December 2016; Received in revised form 12 April 2017; Accepted 29 June 2017 0303-2434/ © 2017 Elsevier B.V. All rights reserved.
(EUMETSAT) Meteorological Operational satellite programme (MetOp) series (MetOp-A and MetOp-B). Several coarse resolution AVHRR normalized difference vegetation index (NDVI) datasets have been developed and used for global vegetation monitoring since the early 1980s, including: (1) The Global Vegetation Index (GVI): 0.15° resolution, daily data and weekly/ monthly composites, 1982–present (Tarpley, 1991); (2) The Pathfinder AVHRR Land (PAL): 0.083° resolution, 10-day composites, 1981–1999 (James and Kalluri, 1994); (3) The Global Inventory Modeling and Mapping Studies (GIMMS): 0.083° resolution, 15-day NDVI composites, 1981–2006 (Tucker et al., 2005); (4) The Land Long Term Data Record (LTDR) (Versions 1–4): 0.05° resolution, daily values, 1981–present (Pedelty et al., 2007); (5) The third generation GIMMS NDVI (NDVI3 g): 0.083° resolution, 15-day composites, 1981–2012 (Pinzon and Tucker 2014); and (6) The AVHRR-derived Vegetation Health Product (VHP): 4 km and 16 km resolutions, 7-day composites, 1981–present (Guo,
Int J Appl Earth Obs Geoinformation 62 (2017) 215–223
L. Ji, J.F. Brown
data for a given area (Privette et al., 1994). The changes of the overpass time results in the solar incidence angle to increase or decrease over time. Fig. 1 shows the equator crossing times of the six NOAA satellites (1989–2014) from which the data acquired are used to create the 1-km EROS AVHRR dataset. All of the six satellites show the gradual change in the equator crossing time, which is later progressively in the five afternoon satellites (NOAA-11, −14, −16, −18, and −19) and earlier progressively in the morning satellite (NOAA-17). The equator crossing time changes over time, which is more obvious for the platforms that are operational more than three years, such as NOAA-11, −14, and −17. The biggest change in the equator crossing time occurred for NOAA-11 (3.7 h) and NOAA-14 (2.9 h) missions within the six years of the satellite mission. The effect of the NOAA satellite orbital drift on the AVHRR data has been perceived and investigated by the remote sensing community since the early 1990s. Halpert (1991) examined the NDVI time series (1985–1990) derived from the GAC data for the latitudes 55°S–75°N, and they noticed that the variations in NDVI were associated with the changing solar zenith angle (SZA) due to the orbital drift in NOAA-9 and −11. Privette et al. (1995) used a simulation model to assess the NOAA-11 orbital drift on NDVI, and they found that the top-of atmosphere (TOA) NDVI increased substantially when the SZA was greater than 30°, but the top-of-canopy (TOC) NDVI increased only slightly with increasing SZA. Cihlar et al. (1998) examined a 1-km 10-day composite AVHRR dataset corrected for the influence of remaining clouds, atmospheric attenuation, and bidirectional reflectance from NOAA-11 and −14 (1993–1996) for Canada. They found that high SZA (> 55°) occurred in 1994 causing higher uncertainties in channels 1 and 2 reflectance and NDVI data. Kaufmann et al. (2000) assessed the effect of SZA on the reflectance and NDVI from AVHRR PAL assembled from NOAA-7, −9, and −11 (1981–1994) based on radiative transfer theory and empirical analysis. They concluded that NDVI was not significantly related to SZA except for over low vegetated areas, therefore the PAL NDVI data were not contaminated by the orbital drift and the changes in NOAA satellites. Kogan and Zhu (2001) investigated the GVI NDVI variations caused by the satellite orbital degradation and satellite shifts over time for various ecosystems across the world, and found poor stability of NDVI time series in desert and tropical forest. Slayback et al. (2003) found strong negative correlation between GIMMS NDVI and SZA (r = −0.89) in low latitudes (5°–25°N) of the northern hemisphere. They considered that the significant NDVI trend (1982–1999) was contaminated by the substantial SZA effect, although the NDVI trend in the areas from 35° N and higher was not apparently affected by SZA contamination. Pinzón et al. (2005) reported an evident trend in GIMMS NDVI (1981–2000) connected to the orbital drift or SZA variation in vegetated tropical areas, but desert areas and the northern latitudes appeared less affected by SZA changes. Sobrino et al. (2008) assessed the influence of the SZA anomaly on the GIMMS AVHRR data (2000–2006) and applied an iterative regression procedure to correct
2013). All of these coarse-resolution AVHRR datasets are reprocessed based on the Global Area Coverage (GAC) level-1b data. The GAC is a reduced resolution dataset processed by sampling one line out of every three and averaging four out of five samples along the scan line, yielding a nominally 1 × 4 km resolution with a 3 km gap between pixels across the scan line (https://www.nsof.class.noaa.gov/data_ available/avhrr/index.htm). Since 1989, the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center has produced a 1 km AVHRR dataset (referred to as “EROS AVHRR” thereafter) for the conterminous United States (CONUS) and Alaska (Eidenshink, 1992; Eidenshink 2006). The dataset is processed through radiometric calibration, atmospheric correction (including the corrections for effects of water vapor, ozone absorption, and Rayleigh scattering), geometric registration, cloud screening, and compositing that includes weekly and biweekly composites (Eidenshink 2006). The major advantage of the EROS AVHRR dataset is that it is produced from the higher resolution 1.1 km Local Area Coverage (LAC) data and processed to 1 km, while most other AVHRR compositing products are based on the Global Area Coverage (GAC) at approximately 1.1 × 4 km resolution. The LAC data are recorded onboard at original resolution of 1.1 km at the satellite nadir (Hastings and Emery, 1992). Compared to the GAC-based coarse resolution AVHRR products, the 1-km EROS AVHRR product has higher spatial details making it suitable for investigation and monitoring of vegetation conditions and dynamics at subnational and regional scales. Using the EROS AVHRR product, the USGS EROS Center has been routinely producing and distributing a historical remote sensing phenology (RSP) dataset at 1-km resolution for CONUS that dates back to 1989 (U.S. Geological Survey, 2015). The AVHRR RSP product consists of nine data layers corresponding to nine phenological metrics: Start of Season-Time (SOST), Start of Season-NDVI (SOSN), End of Season-Time (EOST), End of Season-NDVI (EOSN), Time of Maximum (MAXT), Maximum NDVI (MAXN), Duration (DUR), Amplitude (AMP), and Time Integrated NDVI (TIN). These phenological metrics measure critical phenological events and features extracted from the AVHRR NDVI time series using the statistical autoregressive moving average model (Reed et al., 1994). The definition and interpretation of the nine phenological metrics are listed in Table 1. The RSP data have been used successfully in studies on ecosystems analysis, environmental disasters, land use change, climate change, drought monitoring, and phenological forecasting (Brown et al., 2008; Middleton et al., 2013; Reed et al., 1996, 2003; Reed and Yang, 1997; Tieszen et al., 1997). The NOAA satellites have an extensively documented problem with orbital drift, especially in the later stages of their missions. Because the NOAA platforms do not include any system to stabilize their orbit over time, they deviate slowly from their nominal orbits (McGregor and Gorman, 1994). The orbital drift causes the satellite overpass time to occur progressively later (earlier) each day for afternoon (or morning) orbits, resulting in progressively later (or earlier) acquisition times for Table 1 Phenological metrics derived from the AVHRR NDVI time series.a RSP Dataset
Acronym
Definition
Phenological Interpretation
Start of Season−Time
SOST
Start of Season−NDVI End of Season−Time
SOSN EOST
Beginning of measurable photosynthesis in the vegetation canopy Level of photosynthetic activity at SOST End of measurable photosynthesis in the vegetation canopy
End of Season−NDVI Time of Maximum Maximum NDVI Duration Amplitude Time Integrated NDVI
EOSN MAXT MAXN DUR AMP TIN
Day of year identified as having a consistent upward trend in NDVI time series NDVI value (or baseline) identified at SOST Day of year identified at the end of a consistent downward trend in time series NDVI NDVI value corresponding with the day of year identified at EOST Day of year corresponding to the maximum NDVI in the annual time series Maximum NDVI in the annual time series Number of days from SOST and EOST Difference between MAXN and SOSN Daily integration of NDVI above the baseline between SOST and EOST
a
Level of photosynthetic activity at EOST Time of maximum photosynthesis in the canopy Maximum level of photosynthetic activity in the canopy Length of photosynthetic activity Maximum increase in canopy photosynthetic activity Canopy photosynthetic activity across the entire growing season
Adopted from USGS, “Conterminous U.S. 1 km AVHRR Remote Sensing Phenology Data” (U.S. Geological Survey, 2015).
216
Int J Appl Earth Obs Geoinformation 62 (2017) 215–223
L. Ji, J.F. Brown
Fig. 1. Equatorial overpass time from NOAA-11, −14, −16, −17, −18, and −19 satellites. The data acquired from the six satellites are used to produce the EROS AVHRR dataset. Data Source: NOAA Center for Satellite Applications and Research (STAR), http://www.ospo.noaa.gov/Products/ppp/ navpage.html.
data layers: NDVI, reflectance of bands 1–5, atmospherically corrected reflectance of bands 1 and 2, satellite zenith angle, SZA, relative azimuth angle, acquisition date, quality control, and cloud mask. The weekly composites are produced using the maximum value compositing (MVC) method (Holben, 1986) with a data constraint that SZA not be higher than 80°. The data are gridded in the Lambert Azimuthal Equalarea projection. The entire time series of this study consists of the AVHRR data acquired from six NOAA satellites: NOAA-11 (1989–1994), NOAA-14 (1995–2000), NOAA-16 (2001–2003), NOAA17 (2004–2009), NOAA-18 (2010–2011), and NOAA-19 (2012–2014) (Fig. 1).
the orbital drift effect on the NDVI time series. Nagol et al. (2014) compared the LTDR (version 3) data (2003–2006) with the Moderate Resolution Imaging Spectroradiometer (MODIS) data, and they perceived a spurious NDVI trend of 0.0016 per year (or −0.0017 per year after the bidirectional reflectance distribution function correction) in the LTDR data caused by orbital drift. Sobrino and Julien (2016) evaluated the influence of orbital drift on the NDVI trend derived from the LTDR (version 4) data (1981–2013) and they noticed that NDVI was significantly correlated to the SZA changes only in small areas of the globe, mainly in the Southern Hemisphere. All previous studies agreed that the AVHRR data were affected by the SZA variations induced by satellite orbital drift, but the extent of the SZA influence varied by satellite, dataset, vegetation density and type, and geographic location. However, the effects of NOAA satellite orbital drift on land surface phenological measures derived from the AVHRR time series have not yet been well documented or understood. Sobrino and Julien (2016) reported a low impact of the orbital drift on phenology parameters (SOST and EOST) derived from the LTDR data, because SZA did not modify the shape of the annual phenological signals, but instead mainly influenced the absolute NDVI values. The objective of this study was to investigate the influence of the NOAA satellite orbital drift on the AVHRR RSP data. Specific goals of this study included examination and exploration of (1) impact of the NOAA satellite orbital drift on AVHRR SZA, (2) temporal trend in SZA time series, (3) relationships between the AVHRR SZA and the AVHRR phenological metrics, and (4) techniques to reduce the influence of the AVHRR SZA on the phenological metrics. Understanding how orbital drift and SZA affect AVHRR RSP metrics and making appropriate corrections are an immediate and important need for improving data quality and consistency of the RSP data in phenological studies.
2.2. AVHRR-derived phenological metrics In this study, we retrieved the phenological metrics from the 1-km AVHRR RSP dataset created by the USGS EROS Center (U.S. Geological Survey, 2015). The dataset includes nine phenological metrics (Table 1) mapped in the Lambert Azimuthal Equal-area projection. The metrics were calculated using the temporally smoothed biweekly NDVI data (Reed et al., 1994, 2003). The weighted least-squares regression method was used to smooth the NDVI time series in order to reduce cloud contamination and atmospheric effects (Brown et al., 2015; Swets et al., 1999). 3. Methods 3.1. Calculation of the growing-season SZA The 1-km AVHRR weekly composite dataset contains the SZA layer that shows the SZA value observed from the date when the maximum NDVI value appears within the weekly period. Normally, there are 52 weekly composites in a year. Because of the AVHRR sensor failure onboard NOAA-11, data records are missing from the middle of September through December 1994. Thus, there are only 37 weekly composites in 1994. We used the SZA weekly composites to calculate the growing season SZA at the pixel level for each year from 1989 to 2014. The growing-season SZA was defined as the median SZA value of all available weekly composites within the growing season (between the spring equinox and autumn equinox) of each year. The spring and autumn equinoxes occur around March 20 and September 23, respectively, although the two dates can vary slightly in different years. In this study, the annual SZA was also calculated using the median SZA value of all weekly composites over the year. But the growing-season SZA was preferred over the annual SZA in the analysis, because missing data in the winter time in a few years (1990, 1994, and 1995) caused biased
2. Datasets 2.1. AVHRR solar zenith angle SZA is the angle between the zenith (or the vertical) and the center of the sun. SZA is a function of time, day number, and latitude (Jacobson, 2005): cosθz = sinφ sinδ + cosφ cosδ cosω
(1)
where θz is SZA, φ is the latitude, δ is the declination angle (a function of the day of the year), ω is the hour angle (a measure of the local time). We retrieved the SZA data (1989–2014) from the EROS AVHRR weekly composite dataset for CONUS (Eidenshink, 1992; Eidenshink 2006; U.S. Geological Survey, 2016a). The AVHRR dataset includes 14 217
Int J Appl Earth Obs Geoinformation 62 (2017) 215–223
L. Ji, J.F. Brown
from 1989 to 2014, acquired from six NOAA satellites. The SZA value for each year indicated as the solid circle in Fig. 3 was calculated from the spatial median value of the growing-season SZA image (temporal median value between the spring and autumn equinoxes). The lower and upper error bars indicated the 10% and 90% quantiles, respectively, of the SZA image. For each NOAA satellite, there was an increasing trend in SZA. The SZA increased from 31° to 60° from 1989 to 1994, with an average rate of 5.8°/yr for the data from NOAA-11. Data from NOAA-14 during 1995 and 2000 increased from 29° to 53°, with an average rate of 4.8°/yr. From NOAA-16 to NOAA-19, the SZA values had very low increase rates within each satellite. SZA from NOAA-17 increased from 30° to 38° from 2004 to 2009, with an average rate of 1.6°/yr. The time series analysis of the SZA data highlighted several years with exceptionally high growing-season median SZA values, especially 1992 (44°), 1993 (54°), 1994 (60°), 1999 (44°), and 2000 (53°). These years in the dataset with high SZA existed mainly in the later years of the NOAA-11 and NOAA-14 life cycles. From the later missions of NOAA-16 to NOAA-19, SZA values ranged from 28° to 38°, which were relatively low and stable.
results in the annual median value calculation. 3.2. Temporal trend of SZA time series We created 26 annual SZA images from 1989 to 2014 using the growing-season median SZA values. A pixel-based trend analysis was performed by applying a linear regression to the SZA time series images. In the regression model, the dependent variable SZA was regressed on the independent variable year (yr): SZA = a + b(yr) + e
(2)
where, a is the intercept, b is the slope, and e is the random error. The slope b indicates the rate of the SZA change through time (year). The significance of the slope was tested using the F-statistic with H0: b = 0. Based on the F-values and p-values, we determined the significance level (α = 0.05) of the regression and created the maps of the significance levels. 3.3. Correlation of SZA and phenological metrics To quantify the influence of SZA on phenological metrics, we ran the Pearson correlation analysis for the two variables. At each pixel in the image, we calculated the Pearson correlation coefficient (r) for SZA (annual median and growing-season median) versus each of the nine phenological metrics through the time series from 1989 to 2014. We also tested the significance of the correlation coefficient using the t-test with H0: r = 0, which resulted in the t-values and the associated pvalues. Based on the p-value, we determined the significance of the correlation at α = 0.05 level. As the outputs of the correlation analysis, a suite of correlation maps were generated: r, t-value, p-value, significance level, and number of observations (or years).
4.2. Temporal trend of SZA time series We performed a per-pixel trend analysis for the growing-season median SZA images (26 years from 1989 to 2014), that demonstrated a negative SZA trend over the entire CONUS. Fig. 4 shows the maps of the SZA slopes (Fig. 4a) and the significance of the slopes (Fig. 4b). The SZA slope ranged from −0.79 to −0.10°/yr, with a mean value of −0.47°/ yr and a standard deviation of 0.09°/yr. Spatially, the SZA slope showed a general spatial pattern where trends were negatively steeper in the southern half of CONUS than the north and the west. Over 76.9% of the study area showed a significant negative SZA trend (at α = 0.05 level) (Fig. 4b). The negative SZA trend was caused by the high SZA values in NOAA-11 and NOAA-14 (Fig. 3). To examine the effect of high SZA values on the temporal SZA trend, we ran the trend analysis for the SZA time series excluding the five years with high SZA, i.e., 1992, 1993, 1994, 1999, and 2000. Fig. 4c and d shows the maps of the SZA slopes and the significance of the slopes after eliminating the five years of highest SZA. The trend without the five years had slopes ranges from −0.45to 0.18°/yr, with a mean value of −0.10°/yr and a standard deviation of 0.06°/yr. The SZA slope was greatly reduced after the highSZA years were excluded (Fig. 4c). The test of the SZA slope showed that 1.9% of the CONUS area had a significant SZA trend (at α = 0.05 level) (Fig. 4d). The AVHRR SZA trend (1989–2014) data are accessible from U.S. Geological Survey (2016b).
4. Results and discussion 4.1. Growing-season SZA images We created AVHRR growing-season median SZA images for each year from 1989 to 2014. In statistical summary of the SZA images, all observations including all pixels within the CONUS boundary for all 26 years had a median value of 34°, minimum and maximum values of 17° and 68°, respectively, and the 10% and 90% quintiles of 27° and 52°, respectively. Fig. 2 displays the time series images of the growingseason SZA from 1989 to 2014 [Data are available at U.S. Geological Survey (2016b)]. From the SZA images, the SZA values showed a general spatial pattern where values increased from south to north. The spatial SZA variation for each year was indicated by the range of SZA values within the image. On average, the difference between the maximum and minimum values was 20.2°, and the difference between the 10% and 90% quantiles was 6.7° over the 26 years. Comparing the SZA images between different years, it was obvious that certain years (e.g., 1991–1994, 1998–2000, and 2009) had higher SZA than others years. The year 1994 had the highest SZA, followed by 1993 and 2000. When the SZA images were compared within each individual NOAA satellite platform (e.g., NOAA-11), the SZA images showed an increasing trend through the years. For NOAA-11 satellite, the median SZA images were gradually higher from 1989 to 1994, and the SZA was the highest in 1994. The time series data from the NOAA-14 satellite showed a similar trend as NOAA-11, but the SZA change rate and magnitude in NOAA-14 were slightly lower than those in NOAA-11. Data from the NOAA-17 satellite covered six years, which also showed the increasing trend in SZA, although the change rate and magnitude were much lower than those in NOAA-11 and NOAA-14. As NOAA-16, −18, and −19 provided data for only two or three years, they did not show apparent temporal changes in SZA. The time series plot in Fig. 3 shows the temporal SZA variations
4.3. Correlation between SZA and phenological metrics A Pearson correlation was run for the entire time series (26 years from 1989 to 2014) of the growing-season SZA against each of the nine annual phenological metrics for all pixels in the CONUS [Data are available at U.S. Geological Survey (2016b)]. Although some metrics are associated with a specific season of the year, for example SOST, MAXT, and EOST correspond to the early, middle, and late growing season, the metrics are calculated using the sequential NDVI observations throughout a year and even adjacent years (Reed et al., 1994). Therefore, we used the growing-season median SZA rather than the SZA from a specific season to correlate the metrics that represented the annual phenological characteristics. Fig. 5 shows the maps of the correlation coefficients between SZA and phenological metrics and the significance of the correlation coefficients. The significance level (α = 0.05) is the result of the t-test for the correlation coefficient, consisting three categories: Significant Negative, Not Significant, and Significant Positive. In general, high negative and positive correlations (r < −0.5 and r > 0.5) occurred over parts of the CONUS for all 218
Int J Appl Earth Obs Geoinformation 62 (2017) 215–223
L. Ji, J.F. Brown
Fig. 2. Annual growing-season median SZA images from 1989 to 2014 observed from various NOAA satellites. Each SZA image shows the median SZA value during the growing season, which is defined as the time period between the spring equinox (around 20 March) and the autumn equinox (around 23 September).
end of season (EOST and EOSN) both showed relatively high areas of significant correlations (15.6% and 20.4%, respectively) with the SZA. The high correlations between SZA and the phenological metrics in the study area indicated that the change in the SZA induced by the orbital shift affected the phenological measurements, especially the ones specifying the NDVI value (i.e. SOSN, EOSN, MAXN, AMP, and TIN). The relationships between SZA and phenological metrics were complicated because the change in SZA might influence the measurements of the surface reflectance and hence NDVI, the source data for calculating the phenological metrics. Previous studies documented the influence of SZA on NDVI, but the magnitude and extent of the
phenological metrics (Fig. 5). Compared with the time metrics (SOST, EOST, MAXT, and DUR), the NDVI metrics (SOSN, EOSN, MAXN, AMP, and TIN) showed higher positive and negative correlations with SZA. The maps of the significance show the locations of the pixels with significant correlations between the phenological metrics and SZA (Fig. 5). Table 2 presents the percent area with significant and nonsignificant correlations between SZA and phenological metrics over the CONUS. The significant correlations between SZA and phenological metrics occurred in 4.1–20.4% of the study area. The time metrics (SOST, MAXT, and DUR) had less area with significant correlation (4.1–5.6%) than the NDVI metrics (11.0–20.4%). The measures for the
Fig. 3. Time series plot of the annual growing-season median SZA from 1989 to 2014. The solid circle is the spatial median SZA value in a SZA image. The lower error bar is the 10% quantile and the upper error bar is the 90% quantile.
219
Int J Appl Earth Obs Geoinformation 62 (2017) 215–223
L. Ji, J.F. Brown
Fig. 4. Maps of the temporal trend in SZA time series. (a) SZA time series trend (1989–2014). (b) Test of the SZA time series trend (1989–2014). (c) SZA time series trend (1989–2014, excluding 1992, 1993, 1994, 1999, and 2000). (d) Test of the SZA time series trend (1989–2014, excluding 1992, 1993, 1994, 1999, and 2000).
dramatically (from 15.6% to 6.2% and from 20.4% to 3.3%, relatively). As a result, in most of the study area (> 93.8%), the phenological metrics were not significantly correlated to the change of SZA.
influence varied depending on geographic regions, land cover types, and vegetation conditions (Kaufmann et al., 2000; Nagol et al., 2014; Pinzón et al., 2005; Privette et al., 1995; Slayback et al., 2003; Sobrino et al., 2008; Sobrina and Julien, 2016). From our study, the time metrics indicating the timings of the phenological events were less influenced by SZA than the NDVI metrics. This was probably because the annual phenological transitions extracted from the time series were not dependent on change in the actual NDVI, with the exception of the end of the season (EOST). In other words, the shape of the NDVI time-series vector stayed more consistent than the magnitude of NDVI regardless of the SZA. To understand the impact of the high SZA on the phenological measurements, we ran correlation analysis for the SZA and phenological metrics for the 1989–2014 time series, excluding the years with high SZA values. In this way, we could better understand whether relatively stable and lower SZA values had any relationship to the phenological metrics. The analysis was done for the time series of 21 years, after removing five years (1992, 1993, 1994, 1999, and 2000), which had growing-season median SZA greater than 40°. Fig. 6 shows the maps of the correlation coefficients and the significance levels (α = 0.05) of the correlation coefficients [Data are available at U.S. Geological Survey (2016b)]. It was clear that the magnitude of correlation coefficients was reduced substantially for the phenology data calculated after removing the high SZA years. The areas with the significant correlations between SZA and metrics, either negative or positive, were much lower for the reduced time series than the complete time series. Table 2 indicates that the areas with significant correlation between the SZA and the phenological metrics decreased for all metrics, which were observed only in 1.0–6.2% of the study area. The area of significant correlations of the SZA with EOST and EOSN declined
4.4. Methods for correcting the SZA effect on phenological metrics From the analysis of the SZA time series, we found a significant impact of orbital shift on the AVHRR-derived RSP data. Correcting for the effect of SZA changes on the phenological metrics was considered critical for appropriate use of the EROS AVHRR RSP dataset in phenological studies. In this study, we examined the SZA variations and the influence on phenological metrics using the time series that excludes the years with high SZA (> 40°). The analysis of the data after removing 1992, 1993, 1994, 1999, and 2000 showed that the temporal trend of SZA was not significant in most of the CONUS area (98.1%). Moreover, the SZA was not significantly correlated to the phenological metrics in most of the study area (93.8–99.0%). To study long-term trends and variations in RSP data, elimination of poor quality observations from the time series over many years may not deter scientific interpretation. However, if the interest of a study is the phenological characteristics for all individual years in the entire time series, a complete dataset without missing observations would be necessary. For this purpose, the phenological metrics of all years needs to be retained, but an adjustment for the SZA effect should be applied to the metrics. An ideal method for solving the orbital drift problem is the correction of the effect on the NDVI time series data that are used to derive RSP. Cihlar et al. (1997) applied a procedure of the Atmospheric, Bidirectional, and Contamination Corrections (ABC3) model to the 1km AVHRR 10-day composite dataset for the Canadian landmass, resulting in the NDVI data corrected for the SZA effect. A modified 220
Int J Appl Earth Obs Geoinformation 62 (2017) 215–223
L. Ji, J.F. Brown
Fig. 5. Correlation coefficients and the test of the significance between phenological metrics and SZA for time series 1989–2014. Table 2 Percent of area (%) with significant or non-significant correlation between phenological metrics and SZA (1989–2014). Phenological Metrics
SOST SOSN EOST EOSN MAXT MAXN DUR AMP TIN
1989–2014 (complete)
1989–2014 (excluding 5 high-SZA years)
Sig. Negative
Sig. positive
Not Sig.
Sig. Negative
Sig. positive
Not Sig.
2.7 2.6 14.3 18.8 3.5 13.7 3.5 12.1 8.2
2.0 8.4 1.3 1.6 2.1 2.5 0.6 2.0 5.0
95.3 89.0 84.4 79.6 94.4 83.8 95.9 85.9 86.8
1.3 0.2 5.2 0.7 3.1 2.2 3.1 2.1 3.0
2.0 0.8 1.0 2.6 1.5 1.5 0.9 1.1 1.2
96.7 99.0 93.8 96.7 95.4 96.3 96.0 96.8 95.8
the adjusted cumulative distribution function (ACDF) method was able to effectively eliminate most of the artifact interannual trend in the NDVI time series. To correct for the orbital drift effect on the GIMMS AVHRR data (2000–2006) in Africa, Sobrino et al. (2008) developed an iterative regression method to remove the channel data anomalies affected by the SZA anomaly. All the techniques mentioned above are useful for removing spurious NDVI variation induced by the SZA effect in AVHRR data. Phenology measures created using AVHRR data
version of the ABC3 model (ABC3V2) was proposed by Cihlar et al. (2004) to improve the accuracy of the AVHRR reflectance estimates. Pinzón et al. (2005) used the adaptive empirical mode decomposition (EMD) model to remove spurious SZA trend induced by satellite drift from the GIMMS NDVI data and to reconstruct the NDVI data without SZA variation. Jiang et al. (2008) investigated the adjustment to the AVHRR NDVI data (1982–2004) to reduce the data bias caused by the orbital drift, sensor degradation, and instrumental changes. They found 221
Int J Appl Earth Obs Geoinformation 62 (2017) 215–223
L. Ji, J.F. Brown
Fig. 6. Correlation coefficients and the test of the significance between phenological metrics and SZA for time series 1989–2014, excluding 1992, 1993, 1994, 1999, and 2000.
5. Conclusions
corrected for orbital drift will better reflect the natural phenological characteristics while minimizing the artifacts’ influences. Although the investigation of the methods for correcting orbital drift effect on AVHRR NDVI data is important, this topic is not the focus of this study. Our study showed that the AVHRR-derived phenological metrics were affected by the change of SZA induced by the NOAA satellite orbital drift, while correction for the SZA effect was not performed in the 1-km EROS AVHRR dataset. The AVHRR RSP product (U.S. Geological Survey, 2015) provides a valuable data source for long-term phenological research and applications for CONUS at regional and subcontinental scales, but straight use of these RSP data without further corrections for the SZA effect may cause spurious results. Specifically, the variation in the time series of phenological metrics may be influenced by the variation in SZA. Although our study shows that the time metrics (especially SOST, MAXT, and DUR) are much less influenced by SZA than the NDVI metrics, caution should be exercised if the dataset is used directly. At a minimum, users should use caution related to analysis results of AVHRR phenological metrics for 1992, 1993, 1994, 1999, and 2000. A comprehensive understanding of the effect of SZA on AVHRR NDVI and RSP products needs further analyses and studies. Correction for the SZA effect on AVHRR data and RSP datasets is strongly advised for users who employ the products in ecological and environmental investigation and management.
We conducted this study on the relationships between SZA and phenological metrics for CONUS based on 1-km EROS AVHRR and RSP datasets (1989–2014) to understand the NOAA satellite orbital drift effect on the NDVI-derived phenological measurements. This study highlights the following findings and conclusions:
• Within the time frame of the EROS AVHRR dataset (1989–2014), •
•
222
SZA increased for each NOAA satellite mission. Of all of the six satellites, NOAA-11(1989–1994) and NOAA-14 (1995–2000) had the highest rates of increase. In the entire time series including the data from all six satellites, 1992, 1993, 1994, 1999, and 2000 had much higher median SZA values (44°–60°) than other years (28°–40°). The high SZA in those years caused negative temporal trends in the SZA time series, that were statistically significant (at α = 0.05 level) in 76.9% of the CONUS area. The correlation analysis performed at the pixel level on the entire time series showed that the phenological metrics and SZA were significantly correlated (at α = 0.05 level) in 4.1–20.4% of the CONUS area. In general, the time metrics (SOST, MAXT, and DUR) were less influenced by the change in SZA than the NDVI-value
Int J Appl Earth Obs Geoinformation 62 (2017) 215–223
L. Ji, J.F. Brown
• •
long-term anomalous trends in NOAA’s global vegetation index data sets. IEEE Trans. Geosci. Remote Sens. 46, 409–422. Kaufmann, R.K., Zhou, L., Knyazikhin, Y., Shabanov, N.V., Myneni, R.B., Tucker, C.J., 2000. Effect of orbital drift and sensor changes on the time series of AVHRR vegetation index data. IEEE Trans. Geosci. Remote Sens. 38, 2584–2597. Kogan, F.N., Zhu, X., 2001. Evolution of long-term errors in NDVI time series: 1985–1999. Adv. Space Res. 28 (149-115). McGregor, J., Gorman, A.J., 1994. Some considerations for using AVHRR data in climatological studies: I, Orbital characteristics of NOAA satellites. Int. J. Remote Sens. 15, 537–548. Middleton, A.D., Kauffman, M.J., McWhirter, D.E., Cook, J.G., Cook, R.C., Nelson, A.A., Jimenez, M.D., Klaver, R., 2013. Animal migration amid shifting patterns of phenology and predation: lessons from a Yellowstone elk herd. Ecology 94, 1245–1256. Nagol, J.R., Vermote, E.F., Prince, S.D., 2014. Quantification of impact of orbital drift on inter-annual trends in AVHRR NDVI data. Remote Sens. 6, 6680–6687. http://dx.doi. org/10.3390/rs6076680. Pedelty, J., Devadiga, S., Masuoka, E., Brown, M., Pinzon, J., Tucker, C., Vermote, E., Prince, S., Nagol, J., Justice, C., Roy, D., Ju, J., Schaaf, C., Liu, J., Privette, J., Pinheiro, A., 2007. Generating a long-term land data record from the AVHRR and MODIS instruments. IEEE Geosci. Remote Sens. Symp. 2007, 1021–1025. http://dx. doi.org/10.1109/IGARSS.2007.4422974. Pinzón, J.E., Brown, M.E., Tucker, C.J., 2005. EMD correlation of orbital drift artifacts in satellite data stream. In: Huang, N.E., Shen, S.S. (Eds.), Hilbert-Huang Trasform and Its Applications. World Scientific Publishing Co. Pte. Ltd., Singapore. Pinzon, J.E., Tucker, C.J., 2014. A non-stationary 1981–2012 AVHRR NDVI3 g time series. Remote Sens. 6, 6929–6960. http://dx.doi.org/10.3390/rs6086929. Privette, J.L., Fowler, C., Wick, G.A., Baldwin, D., Emery, W.J., 1995. Effects of orbital drift on advanced very high resolution radiometer products: normalized difference vegetation index and sea surface temperature. Remote Sens. Environ. 53, 164–171. Reed, B.C., Yang, L., 1997. Seasonal vegetation characteristics of the United States. Geocarto Int. 12, 65–71. Reed, B.C., Brown, J.F., VanderZee, D., Loveland, T.R., Merchant, J.W., Ohlen, D.O., 1994. Measuring phenological variability from satellite imagery. J. Veg. Sci. 5, 703–714. Reed, B.C., Loveland, T.R., Tieszen, L.L., 1996. An approach for using AVHRR data to monitor U.S. Great Plains grasslands. Geocarto Int. 11, 13–22. Reed, B.C., White, M.A., Brown, J.F., 2003. Remote sensing phenology. In: Schwartz, M.D. (Ed.), Phenology: an Integrative Science. Kluwer Academic Publishing, Dordrecht, The Netherlands, pp. 365–381. Slayback, D.A., Pinzon, J.E., Los, S.O., Tucker, C.J., 2003. Northern hemisphere photosynthetic trends 1982–99. Glob. Change Biol. 9, 1–15. Sobrino, J., Julien, Y., 2016. Exploring the validity of the long-term data record V4 database for land surface monitoring. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 9, 3607–3614. Sobrino, J.A., Julien, Y., Atitar, M., Nerry, F., 2008. NOAA-AVHRR orbital drift correction from solar zenithal angle data. IEEE Trans. Geosci. Remote Sens. 46, 4014–4019. Swets, D.L., Reed, B.C., Rowland, J.R., Marko, S.E., 1999. A weighted least-squares approach to temporal smoothing of NDVI. In: Proceedings of the 1999 ASPRS Annual Conference, From Image to Information. Portland, Oregon, May 17–21, 1999, Bethesda, Maryland. (American Society for Photogrammetry and Remote Sensing, CD-ROM). Tarpley, J.D., 1991. The NOAA global vegetation index product − a review. Palaeogeogr. Palaeoclimatol. Palaeoecol. (Glob. Planet. Change Sect.) 90, 189–194. Tieszen, L.L., Reed, B.C., Bliss, N.B., Wylie, B.K., DeJong, D.D., 1997. NDVI, C3 and C4 production, and distributions in Great Plains grassland land cover classes. Ecol. Appl. 7 (59-58). Tucker, C.J., Pinzón, J.E., Brown, M.E., Slayback, D.A., Pak, E.W., Mahoney, R., Vermote, E.F., El Saleous, N., 2005. An extended AVHRR 8-km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data. Int. J. Remote Sens. 26, 4485–4498. Tucker, C.J., 1996. History of the use of AVHRR data for land applications. In: D’Souza, G., Belward, A.S., Malingreau, J.-P. (Eds.), Advances in the Use of NOAA AVHRR Data for Land Applications. Kluwer Academic, Dordrecht, The Nethlands, pp. 1–19. U.S. Geological Survey, 2015. Conterminous U.S. 1. http://dx.doi.org/10.5066/ F7PC30G1. (Accessed 16 November 2015). U.S. Geological Survey, 2016a. AVHRR normalized difference vegetation index (NDVI) composites. Earth Resources Observation and Science Center Database. U. S. Geological Surveyhttp://dx.doi.org/10.5066/F7707ZKN. (Accessed 8 February 2016). U.S. Geological Survey, 2016b. Effect of NOAA satellite orbital drift on AVHRR-derived phenological metrics. Earth Resources Observation and Science Center Database. U.S. Geological Surveyhttp://dx.doi.org/10.5066/F7G73BVV. (Accessed 7 December 2016).
metrics (SOSN, EOSN, MAXN, AMP, and TIN). The EOST was an exception and showed a relatively high area of influence from the SZA. Using the data that eliminated the 5 years with high SZA (> 40°), the temporal SZA trend was largely reduced, significantly affecting less than 2% of the study area. With the reduced time series, the significant correlation between the phenological metrics and SZA occurred in only 1.0–6.2% of the area. We advise users to employ a correction of the SZA effect before the EROS AVHRR RSP data are used in phenological studies. For the phenological trend analysis, elimination of the high-SZA years might be the simplest approach. A more comprehensive method for correcting the SZA effect on the RSP data should be applied to the NDVI data used in derivation of the RSP metrics.
Acknowledgments This work was funded by the U.S. Geological Survey (USGS) Land Remote Sensing Program under the Climate and Land Use Change mission area. The work by Lei Ji was performed under USGS Contract G13PC00028. We thank K. Gallo for reviewing the manuscript and providing valuable comments. We also thank N. M. Velpuri for reviewing the datasets and the metadata produced from this study. The authors are grateful to the anonymous reviewers who provided useful suggestions improving this article. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. References Brown, J.F., Wardlow, B.D., Tadesse, T., Hayes, M.J., Reed, B.C., 2008. The Vegetation Drought Response Index (VegDRI): a new integrated approach for monitoring drought stress in vegetation. GISci. Remote Sens. 45, 16–46. Brown, J.F., Howard, D., Wylie, B., Frieze, A., Ji, L., Gacke, C., 2015. Application-ready expedited MODIS data for operational land surface monitoring of vegetation condition. Remote Sens. 7, 16226–16240. http://dx.doi.org/10.3390/rs71215825. Cihlar, J., Ly, H., Li, Z., Chen, J., Pokrant, H., Huang, F.T., 1997. Multitemporal, multichannel AVHRR data sets for land biosphere studies − artifacts and corrections. Remote Sens. Environ. 60, 35–57. Cihlar, J., Chen, J.M., Li, Z., Huang, F., Latifovic, R., Dixon, R., 1998. Can interannual land surface signal be discerned in composite AVHRR data. J. Geophys. Res. 103, 23163–23172. http://dx.doi.org/10.1029/98jd00050. Cihlar, J., Latifovic, R., Chen, J., Trishchenko, A., Du, Y., Fedosejevs, G., Guindon, B., 2004. Systematic corrections of AVHRR image composites for temporal studies. Remote Sens. Environ. 89, 217–233. Eidenshink, J.C., 1992. The 1990 conterminous US AVHRR data set. Photogramm. Eng. Remote Sens. 58, 809–813. Eidenshink, J., 2006. A 16-year time series of 1 km AVHRR satellite data of the conterminous United States and Alaska. Photogramm. Eng. Remote Sens. 72, 1027–1035. Guo, W., 2013. AVHRR Vegetation Health Product (AVHRR-VHP) User Guide. I.M. Systems Group, Inc., Rockville, Maryland. http://www.star.nesdis.noaa.gov/smcd/ emb/vci/VH_doc/VHP_uguide_v1.4_2013_1221.pdf. Halpert, 1991. Climate monitoring using an AVHRR-based vegetation index. Palaeogeogr. Palaeoclimatol. Palaeoecol. (Glob. Planet. Change Sect.) 90, 201–205. Hastings, D.A., Emery, W.J., 1992. The advanced very high resolution radiometer (AVHRR): a brief reference guide. Photogramm. Eng. Remote Sens. 58, 1183–1188. Holben, B.N., 1986. Characteristics of maximum-value composite images from temporal AVHRR data. Int. J. Remote Sens. 7, 1417–1434. Jacobson, M.Z., 2005. Fundamentals of Atmospheric Modeling, second ed. Cambridge University Press, Cambridge, UK. James, M.E., Kalluri, S.N.V., 1994. The Pathfinder AVHRR land data set: an improved coarse resolution data set for terrestrial monitoring. Int. J. Remote Sens. 17, 3347–3363. Jiang, L., Tarpley, J.D., Mitchell, K.E., Zhou, S., Kogan, F.N., Guo, W., 2008. Adjusting for
223
Contents lists available at ScienceDirect
Int J Appl Earth Obs Geoinformation journal homepage: www.elsevier.com/locate/jag
Effect of NOAA satellite orbital drift on AVHRR-derived phenological metrics
MARK
⁎
Lei Jia, , Jesslyn F. Brownb a b
ASRC Federal InuTeq LLC, Contractor to the U.S. Geological Survey (USGS), Earth Resources Observation and Science (EROS) Center, Sioux Falls, SD 57198, USA U.S. Geological Survey (USGS), Earth Resources Observation and Science (EROS) Center, Sioux Falls, SD 57198, USA
A R T I C L E I N F O
A B S T R A C T
Keywords: AVHRR NOAA satellite Orbital drift Solar zenith angle Phenology NDVI
The U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center routinely produces and distributes a remote sensing phenology (RSP) dataset derived from the Advanced Very High Resolution Radiometer (AVHRR) 1-km data compiled from a series of National Oceanic and Atmospheric Administration (NOAA) satellites (NOAA-11, −14, −16, −17, −18, and −19). Each NOAA satellite experienced orbital drift during its duty period, which influenced the AVHRR reflectance measurements. To understand the effect of the orbital drift on the AVHRR-derived RSP dataset, we analyzed the impact of solar zenith angle (SZA) on the RSP metrics in the conterminous United States (CONUS). The AVHRR weekly composites were used to calculate the growing-season median SZA at the pixel level for each year from 1989 to 2014. The results showed that the SZA increased towards the end of each NOAA satellite mission with the highest increasing rate occurring during NOAA-11 (1989–1994) and NOAA-14 (1995–2000) missions. The growing-season median SZA values (44°–60°) in 1992, 1993, 1994, 1999, and 2000 were substantially higher than those in other years (28°–40°). The high SZA in those years caused negative trends in the SZA time series, that were statistically significant (at α = 0.05 level) in 76.9% of the CONUS area. A pixel-based temporal correlation analysis showed that the phenological metrics and SZA were significantly correlated (at α = 0.05 level) in 4.1–20.4% of the CONUS area. After excluding the 5 years with high SZA (> 40°) from the analysis, the temporal SZA trend was largely reduced, significantly affecting less than 2% of the study area. Additionally, significant correlation between the phenological metrics and SZA was observed in less than 7% of the study area. Our study concluded that the NOAA satellite orbital drift increased SZA, and in turn, influenced the phenological metrics. Elimination of the years with high median SZA reduced the influence of orbital drift on the RSP time series.
1. Introduction The longest continuous satellite-derived land surface data record to date has been acquired from the Advanced Very High Resolution Radiometer (AVHRR), a sensor operating onboard the series of the National Oceanic and Atmospheric Administration (NOAA) – Polar-orbiting Operational Environmental Satellites (POES). Although the AVHRR was initially designed for meteorological purposes, the second generation of the instrument AVHRR/2 designed with five improved channels onboard the NOAA-7 satellite, which launched in 1981, allowed routine observation of terrestrial vegetation conditions (Tucker, 1996). AVHRR/3, the third generation of the instrument designed with six channels has been carried on the NOAA-15 satellite launched in 1998 and all subsequent NOAA satellites (NOAA-16, −17, −18, and −19). Since 2006, the AVHRR has also been operational aboard the European Organization for the Exploitation of Meteorological Satellites
⁎
Corresponding author. E-mail addresses: [email protected], [email protected] (L. Ji).
http://dx.doi.org/10.1016/j.jag.2017.06.013 Received 1 December 2016; Received in revised form 12 April 2017; Accepted 29 June 2017 0303-2434/ © 2017 Elsevier B.V. All rights reserved.
(EUMETSAT) Meteorological Operational satellite programme (MetOp) series (MetOp-A and MetOp-B). Several coarse resolution AVHRR normalized difference vegetation index (NDVI) datasets have been developed and used for global vegetation monitoring since the early 1980s, including: (1) The Global Vegetation Index (GVI): 0.15° resolution, daily data and weekly/ monthly composites, 1982–present (Tarpley, 1991); (2) The Pathfinder AVHRR Land (PAL): 0.083° resolution, 10-day composites, 1981–1999 (James and Kalluri, 1994); (3) The Global Inventory Modeling and Mapping Studies (GIMMS): 0.083° resolution, 15-day NDVI composites, 1981–2006 (Tucker et al., 2005); (4) The Land Long Term Data Record (LTDR) (Versions 1–4): 0.05° resolution, daily values, 1981–present (Pedelty et al., 2007); (5) The third generation GIMMS NDVI (NDVI3 g): 0.083° resolution, 15-day composites, 1981–2012 (Pinzon and Tucker 2014); and (6) The AVHRR-derived Vegetation Health Product (VHP): 4 km and 16 km resolutions, 7-day composites, 1981–present (Guo,
Int J Appl Earth Obs Geoinformation 62 (2017) 215–223
L. Ji, J.F. Brown
data for a given area (Privette et al., 1994). The changes of the overpass time results in the solar incidence angle to increase or decrease over time. Fig. 1 shows the equator crossing times of the six NOAA satellites (1989–2014) from which the data acquired are used to create the 1-km EROS AVHRR dataset. All of the six satellites show the gradual change in the equator crossing time, which is later progressively in the five afternoon satellites (NOAA-11, −14, −16, −18, and −19) and earlier progressively in the morning satellite (NOAA-17). The equator crossing time changes over time, which is more obvious for the platforms that are operational more than three years, such as NOAA-11, −14, and −17. The biggest change in the equator crossing time occurred for NOAA-11 (3.7 h) and NOAA-14 (2.9 h) missions within the six years of the satellite mission. The effect of the NOAA satellite orbital drift on the AVHRR data has been perceived and investigated by the remote sensing community since the early 1990s. Halpert (1991) examined the NDVI time series (1985–1990) derived from the GAC data for the latitudes 55°S–75°N, and they noticed that the variations in NDVI were associated with the changing solar zenith angle (SZA) due to the orbital drift in NOAA-9 and −11. Privette et al. (1995) used a simulation model to assess the NOAA-11 orbital drift on NDVI, and they found that the top-of atmosphere (TOA) NDVI increased substantially when the SZA was greater than 30°, but the top-of-canopy (TOC) NDVI increased only slightly with increasing SZA. Cihlar et al. (1998) examined a 1-km 10-day composite AVHRR dataset corrected for the influence of remaining clouds, atmospheric attenuation, and bidirectional reflectance from NOAA-11 and −14 (1993–1996) for Canada. They found that high SZA (> 55°) occurred in 1994 causing higher uncertainties in channels 1 and 2 reflectance and NDVI data. Kaufmann et al. (2000) assessed the effect of SZA on the reflectance and NDVI from AVHRR PAL assembled from NOAA-7, −9, and −11 (1981–1994) based on radiative transfer theory and empirical analysis. They concluded that NDVI was not significantly related to SZA except for over low vegetated areas, therefore the PAL NDVI data were not contaminated by the orbital drift and the changes in NOAA satellites. Kogan and Zhu (2001) investigated the GVI NDVI variations caused by the satellite orbital degradation and satellite shifts over time for various ecosystems across the world, and found poor stability of NDVI time series in desert and tropical forest. Slayback et al. (2003) found strong negative correlation between GIMMS NDVI and SZA (r = −0.89) in low latitudes (5°–25°N) of the northern hemisphere. They considered that the significant NDVI trend (1982–1999) was contaminated by the substantial SZA effect, although the NDVI trend in the areas from 35° N and higher was not apparently affected by SZA contamination. Pinzón et al. (2005) reported an evident trend in GIMMS NDVI (1981–2000) connected to the orbital drift or SZA variation in vegetated tropical areas, but desert areas and the northern latitudes appeared less affected by SZA changes. Sobrino et al. (2008) assessed the influence of the SZA anomaly on the GIMMS AVHRR data (2000–2006) and applied an iterative regression procedure to correct
2013). All of these coarse-resolution AVHRR datasets are reprocessed based on the Global Area Coverage (GAC) level-1b data. The GAC is a reduced resolution dataset processed by sampling one line out of every three and averaging four out of five samples along the scan line, yielding a nominally 1 × 4 km resolution with a 3 km gap between pixels across the scan line (https://www.nsof.class.noaa.gov/data_ available/avhrr/index.htm). Since 1989, the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center has produced a 1 km AVHRR dataset (referred to as “EROS AVHRR” thereafter) for the conterminous United States (CONUS) and Alaska (Eidenshink, 1992; Eidenshink 2006). The dataset is processed through radiometric calibration, atmospheric correction (including the corrections for effects of water vapor, ozone absorption, and Rayleigh scattering), geometric registration, cloud screening, and compositing that includes weekly and biweekly composites (Eidenshink 2006). The major advantage of the EROS AVHRR dataset is that it is produced from the higher resolution 1.1 km Local Area Coverage (LAC) data and processed to 1 km, while most other AVHRR compositing products are based on the Global Area Coverage (GAC) at approximately 1.1 × 4 km resolution. The LAC data are recorded onboard at original resolution of 1.1 km at the satellite nadir (Hastings and Emery, 1992). Compared to the GAC-based coarse resolution AVHRR products, the 1-km EROS AVHRR product has higher spatial details making it suitable for investigation and monitoring of vegetation conditions and dynamics at subnational and regional scales. Using the EROS AVHRR product, the USGS EROS Center has been routinely producing and distributing a historical remote sensing phenology (RSP) dataset at 1-km resolution for CONUS that dates back to 1989 (U.S. Geological Survey, 2015). The AVHRR RSP product consists of nine data layers corresponding to nine phenological metrics: Start of Season-Time (SOST), Start of Season-NDVI (SOSN), End of Season-Time (EOST), End of Season-NDVI (EOSN), Time of Maximum (MAXT), Maximum NDVI (MAXN), Duration (DUR), Amplitude (AMP), and Time Integrated NDVI (TIN). These phenological metrics measure critical phenological events and features extracted from the AVHRR NDVI time series using the statistical autoregressive moving average model (Reed et al., 1994). The definition and interpretation of the nine phenological metrics are listed in Table 1. The RSP data have been used successfully in studies on ecosystems analysis, environmental disasters, land use change, climate change, drought monitoring, and phenological forecasting (Brown et al., 2008; Middleton et al., 2013; Reed et al., 1996, 2003; Reed and Yang, 1997; Tieszen et al., 1997). The NOAA satellites have an extensively documented problem with orbital drift, especially in the later stages of their missions. Because the NOAA platforms do not include any system to stabilize their orbit over time, they deviate slowly from their nominal orbits (McGregor and Gorman, 1994). The orbital drift causes the satellite overpass time to occur progressively later (earlier) each day for afternoon (or morning) orbits, resulting in progressively later (or earlier) acquisition times for Table 1 Phenological metrics derived from the AVHRR NDVI time series.a RSP Dataset
Acronym
Definition
Phenological Interpretation
Start of Season−Time
SOST
Start of Season−NDVI End of Season−Time
SOSN EOST
Beginning of measurable photosynthesis in the vegetation canopy Level of photosynthetic activity at SOST End of measurable photosynthesis in the vegetation canopy
End of Season−NDVI Time of Maximum Maximum NDVI Duration Amplitude Time Integrated NDVI
EOSN MAXT MAXN DUR AMP TIN
Day of year identified as having a consistent upward trend in NDVI time series NDVI value (or baseline) identified at SOST Day of year identified at the end of a consistent downward trend in time series NDVI NDVI value corresponding with the day of year identified at EOST Day of year corresponding to the maximum NDVI in the annual time series Maximum NDVI in the annual time series Number of days from SOST and EOST Difference between MAXN and SOSN Daily integration of NDVI above the baseline between SOST and EOST
a
Level of photosynthetic activity at EOST Time of maximum photosynthesis in the canopy Maximum level of photosynthetic activity in the canopy Length of photosynthetic activity Maximum increase in canopy photosynthetic activity Canopy photosynthetic activity across the entire growing season
Adopted from USGS, “Conterminous U.S. 1 km AVHRR Remote Sensing Phenology Data” (U.S. Geological Survey, 2015).
216
Int J Appl Earth Obs Geoinformation 62 (2017) 215–223
L. Ji, J.F. Brown
Fig. 1. Equatorial overpass time from NOAA-11, −14, −16, −17, −18, and −19 satellites. The data acquired from the six satellites are used to produce the EROS AVHRR dataset. Data Source: NOAA Center for Satellite Applications and Research (STAR), http://www.ospo.noaa.gov/Products/ppp/ navpage.html.
data layers: NDVI, reflectance of bands 1–5, atmospherically corrected reflectance of bands 1 and 2, satellite zenith angle, SZA, relative azimuth angle, acquisition date, quality control, and cloud mask. The weekly composites are produced using the maximum value compositing (MVC) method (Holben, 1986) with a data constraint that SZA not be higher than 80°. The data are gridded in the Lambert Azimuthal Equalarea projection. The entire time series of this study consists of the AVHRR data acquired from six NOAA satellites: NOAA-11 (1989–1994), NOAA-14 (1995–2000), NOAA-16 (2001–2003), NOAA17 (2004–2009), NOAA-18 (2010–2011), and NOAA-19 (2012–2014) (Fig. 1).
the orbital drift effect on the NDVI time series. Nagol et al. (2014) compared the LTDR (version 3) data (2003–2006) with the Moderate Resolution Imaging Spectroradiometer (MODIS) data, and they perceived a spurious NDVI trend of 0.0016 per year (or −0.0017 per year after the bidirectional reflectance distribution function correction) in the LTDR data caused by orbital drift. Sobrino and Julien (2016) evaluated the influence of orbital drift on the NDVI trend derived from the LTDR (version 4) data (1981–2013) and they noticed that NDVI was significantly correlated to the SZA changes only in small areas of the globe, mainly in the Southern Hemisphere. All previous studies agreed that the AVHRR data were affected by the SZA variations induced by satellite orbital drift, but the extent of the SZA influence varied by satellite, dataset, vegetation density and type, and geographic location. However, the effects of NOAA satellite orbital drift on land surface phenological measures derived from the AVHRR time series have not yet been well documented or understood. Sobrino and Julien (2016) reported a low impact of the orbital drift on phenology parameters (SOST and EOST) derived from the LTDR data, because SZA did not modify the shape of the annual phenological signals, but instead mainly influenced the absolute NDVI values. The objective of this study was to investigate the influence of the NOAA satellite orbital drift on the AVHRR RSP data. Specific goals of this study included examination and exploration of (1) impact of the NOAA satellite orbital drift on AVHRR SZA, (2) temporal trend in SZA time series, (3) relationships between the AVHRR SZA and the AVHRR phenological metrics, and (4) techniques to reduce the influence of the AVHRR SZA on the phenological metrics. Understanding how orbital drift and SZA affect AVHRR RSP metrics and making appropriate corrections are an immediate and important need for improving data quality and consistency of the RSP data in phenological studies.
2.2. AVHRR-derived phenological metrics In this study, we retrieved the phenological metrics from the 1-km AVHRR RSP dataset created by the USGS EROS Center (U.S. Geological Survey, 2015). The dataset includes nine phenological metrics (Table 1) mapped in the Lambert Azimuthal Equal-area projection. The metrics were calculated using the temporally smoothed biweekly NDVI data (Reed et al., 1994, 2003). The weighted least-squares regression method was used to smooth the NDVI time series in order to reduce cloud contamination and atmospheric effects (Brown et al., 2015; Swets et al., 1999). 3. Methods 3.1. Calculation of the growing-season SZA The 1-km AVHRR weekly composite dataset contains the SZA layer that shows the SZA value observed from the date when the maximum NDVI value appears within the weekly period. Normally, there are 52 weekly composites in a year. Because of the AVHRR sensor failure onboard NOAA-11, data records are missing from the middle of September through December 1994. Thus, there are only 37 weekly composites in 1994. We used the SZA weekly composites to calculate the growing season SZA at the pixel level for each year from 1989 to 2014. The growing-season SZA was defined as the median SZA value of all available weekly composites within the growing season (between the spring equinox and autumn equinox) of each year. The spring and autumn equinoxes occur around March 20 and September 23, respectively, although the two dates can vary slightly in different years. In this study, the annual SZA was also calculated using the median SZA value of all weekly composites over the year. But the growing-season SZA was preferred over the annual SZA in the analysis, because missing data in the winter time in a few years (1990, 1994, and 1995) caused biased
2. Datasets 2.1. AVHRR solar zenith angle SZA is the angle between the zenith (or the vertical) and the center of the sun. SZA is a function of time, day number, and latitude (Jacobson, 2005): cosθz = sinφ sinδ + cosφ cosδ cosω
(1)
where θz is SZA, φ is the latitude, δ is the declination angle (a function of the day of the year), ω is the hour angle (a measure of the local time). We retrieved the SZA data (1989–2014) from the EROS AVHRR weekly composite dataset for CONUS (Eidenshink, 1992; Eidenshink 2006; U.S. Geological Survey, 2016a). The AVHRR dataset includes 14 217
Int J Appl Earth Obs Geoinformation 62 (2017) 215–223
L. Ji, J.F. Brown
from 1989 to 2014, acquired from six NOAA satellites. The SZA value for each year indicated as the solid circle in Fig. 3 was calculated from the spatial median value of the growing-season SZA image (temporal median value between the spring and autumn equinoxes). The lower and upper error bars indicated the 10% and 90% quantiles, respectively, of the SZA image. For each NOAA satellite, there was an increasing trend in SZA. The SZA increased from 31° to 60° from 1989 to 1994, with an average rate of 5.8°/yr for the data from NOAA-11. Data from NOAA-14 during 1995 and 2000 increased from 29° to 53°, with an average rate of 4.8°/yr. From NOAA-16 to NOAA-19, the SZA values had very low increase rates within each satellite. SZA from NOAA-17 increased from 30° to 38° from 2004 to 2009, with an average rate of 1.6°/yr. The time series analysis of the SZA data highlighted several years with exceptionally high growing-season median SZA values, especially 1992 (44°), 1993 (54°), 1994 (60°), 1999 (44°), and 2000 (53°). These years in the dataset with high SZA existed mainly in the later years of the NOAA-11 and NOAA-14 life cycles. From the later missions of NOAA-16 to NOAA-19, SZA values ranged from 28° to 38°, which were relatively low and stable.
results in the annual median value calculation. 3.2. Temporal trend of SZA time series We created 26 annual SZA images from 1989 to 2014 using the growing-season median SZA values. A pixel-based trend analysis was performed by applying a linear regression to the SZA time series images. In the regression model, the dependent variable SZA was regressed on the independent variable year (yr): SZA = a + b(yr) + e
(2)
where, a is the intercept, b is the slope, and e is the random error. The slope b indicates the rate of the SZA change through time (year). The significance of the slope was tested using the F-statistic with H0: b = 0. Based on the F-values and p-values, we determined the significance level (α = 0.05) of the regression and created the maps of the significance levels. 3.3. Correlation of SZA and phenological metrics To quantify the influence of SZA on phenological metrics, we ran the Pearson correlation analysis for the two variables. At each pixel in the image, we calculated the Pearson correlation coefficient (r) for SZA (annual median and growing-season median) versus each of the nine phenological metrics through the time series from 1989 to 2014. We also tested the significance of the correlation coefficient using the t-test with H0: r = 0, which resulted in the t-values and the associated pvalues. Based on the p-value, we determined the significance of the correlation at α = 0.05 level. As the outputs of the correlation analysis, a suite of correlation maps were generated: r, t-value, p-value, significance level, and number of observations (or years).
4.2. Temporal trend of SZA time series We performed a per-pixel trend analysis for the growing-season median SZA images (26 years from 1989 to 2014), that demonstrated a negative SZA trend over the entire CONUS. Fig. 4 shows the maps of the SZA slopes (Fig. 4a) and the significance of the slopes (Fig. 4b). The SZA slope ranged from −0.79 to −0.10°/yr, with a mean value of −0.47°/ yr and a standard deviation of 0.09°/yr. Spatially, the SZA slope showed a general spatial pattern where trends were negatively steeper in the southern half of CONUS than the north and the west. Over 76.9% of the study area showed a significant negative SZA trend (at α = 0.05 level) (Fig. 4b). The negative SZA trend was caused by the high SZA values in NOAA-11 and NOAA-14 (Fig. 3). To examine the effect of high SZA values on the temporal SZA trend, we ran the trend analysis for the SZA time series excluding the five years with high SZA, i.e., 1992, 1993, 1994, 1999, and 2000. Fig. 4c and d shows the maps of the SZA slopes and the significance of the slopes after eliminating the five years of highest SZA. The trend without the five years had slopes ranges from −0.45to 0.18°/yr, with a mean value of −0.10°/yr and a standard deviation of 0.06°/yr. The SZA slope was greatly reduced after the highSZA years were excluded (Fig. 4c). The test of the SZA slope showed that 1.9% of the CONUS area had a significant SZA trend (at α = 0.05 level) (Fig. 4d). The AVHRR SZA trend (1989–2014) data are accessible from U.S. Geological Survey (2016b).
4. Results and discussion 4.1. Growing-season SZA images We created AVHRR growing-season median SZA images for each year from 1989 to 2014. In statistical summary of the SZA images, all observations including all pixels within the CONUS boundary for all 26 years had a median value of 34°, minimum and maximum values of 17° and 68°, respectively, and the 10% and 90% quintiles of 27° and 52°, respectively. Fig. 2 displays the time series images of the growingseason SZA from 1989 to 2014 [Data are available at U.S. Geological Survey (2016b)]. From the SZA images, the SZA values showed a general spatial pattern where values increased from south to north. The spatial SZA variation for each year was indicated by the range of SZA values within the image. On average, the difference between the maximum and minimum values was 20.2°, and the difference between the 10% and 90% quantiles was 6.7° over the 26 years. Comparing the SZA images between different years, it was obvious that certain years (e.g., 1991–1994, 1998–2000, and 2009) had higher SZA than others years. The year 1994 had the highest SZA, followed by 1993 and 2000. When the SZA images were compared within each individual NOAA satellite platform (e.g., NOAA-11), the SZA images showed an increasing trend through the years. For NOAA-11 satellite, the median SZA images were gradually higher from 1989 to 1994, and the SZA was the highest in 1994. The time series data from the NOAA-14 satellite showed a similar trend as NOAA-11, but the SZA change rate and magnitude in NOAA-14 were slightly lower than those in NOAA-11. Data from the NOAA-17 satellite covered six years, which also showed the increasing trend in SZA, although the change rate and magnitude were much lower than those in NOAA-11 and NOAA-14. As NOAA-16, −18, and −19 provided data for only two or three years, they did not show apparent temporal changes in SZA. The time series plot in Fig. 3 shows the temporal SZA variations
4.3. Correlation between SZA and phenological metrics A Pearson correlation was run for the entire time series (26 years from 1989 to 2014) of the growing-season SZA against each of the nine annual phenological metrics for all pixels in the CONUS [Data are available at U.S. Geological Survey (2016b)]. Although some metrics are associated with a specific season of the year, for example SOST, MAXT, and EOST correspond to the early, middle, and late growing season, the metrics are calculated using the sequential NDVI observations throughout a year and even adjacent years (Reed et al., 1994). Therefore, we used the growing-season median SZA rather than the SZA from a specific season to correlate the metrics that represented the annual phenological characteristics. Fig. 5 shows the maps of the correlation coefficients between SZA and phenological metrics and the significance of the correlation coefficients. The significance level (α = 0.05) is the result of the t-test for the correlation coefficient, consisting three categories: Significant Negative, Not Significant, and Significant Positive. In general, high negative and positive correlations (r < −0.5 and r > 0.5) occurred over parts of the CONUS for all 218
Int J Appl Earth Obs Geoinformation 62 (2017) 215–223
L. Ji, J.F. Brown
Fig. 2. Annual growing-season median SZA images from 1989 to 2014 observed from various NOAA satellites. Each SZA image shows the median SZA value during the growing season, which is defined as the time period between the spring equinox (around 20 March) and the autumn equinox (around 23 September).
end of season (EOST and EOSN) both showed relatively high areas of significant correlations (15.6% and 20.4%, respectively) with the SZA. The high correlations between SZA and the phenological metrics in the study area indicated that the change in the SZA induced by the orbital shift affected the phenological measurements, especially the ones specifying the NDVI value (i.e. SOSN, EOSN, MAXN, AMP, and TIN). The relationships between SZA and phenological metrics were complicated because the change in SZA might influence the measurements of the surface reflectance and hence NDVI, the source data for calculating the phenological metrics. Previous studies documented the influence of SZA on NDVI, but the magnitude and extent of the
phenological metrics (Fig. 5). Compared with the time metrics (SOST, EOST, MAXT, and DUR), the NDVI metrics (SOSN, EOSN, MAXN, AMP, and TIN) showed higher positive and negative correlations with SZA. The maps of the significance show the locations of the pixels with significant correlations between the phenological metrics and SZA (Fig. 5). Table 2 presents the percent area with significant and nonsignificant correlations between SZA and phenological metrics over the CONUS. The significant correlations between SZA and phenological metrics occurred in 4.1–20.4% of the study area. The time metrics (SOST, MAXT, and DUR) had less area with significant correlation (4.1–5.6%) than the NDVI metrics (11.0–20.4%). The measures for the
Fig. 3. Time series plot of the annual growing-season median SZA from 1989 to 2014. The solid circle is the spatial median SZA value in a SZA image. The lower error bar is the 10% quantile and the upper error bar is the 90% quantile.
219
Int J Appl Earth Obs Geoinformation 62 (2017) 215–223
L. Ji, J.F. Brown
Fig. 4. Maps of the temporal trend in SZA time series. (a) SZA time series trend (1989–2014). (b) Test of the SZA time series trend (1989–2014). (c) SZA time series trend (1989–2014, excluding 1992, 1993, 1994, 1999, and 2000). (d) Test of the SZA time series trend (1989–2014, excluding 1992, 1993, 1994, 1999, and 2000).
dramatically (from 15.6% to 6.2% and from 20.4% to 3.3%, relatively). As a result, in most of the study area (> 93.8%), the phenological metrics were not significantly correlated to the change of SZA.
influence varied depending on geographic regions, land cover types, and vegetation conditions (Kaufmann et al., 2000; Nagol et al., 2014; Pinzón et al., 2005; Privette et al., 1995; Slayback et al., 2003; Sobrino et al., 2008; Sobrina and Julien, 2016). From our study, the time metrics indicating the timings of the phenological events were less influenced by SZA than the NDVI metrics. This was probably because the annual phenological transitions extracted from the time series were not dependent on change in the actual NDVI, with the exception of the end of the season (EOST). In other words, the shape of the NDVI time-series vector stayed more consistent than the magnitude of NDVI regardless of the SZA. To understand the impact of the high SZA on the phenological measurements, we ran correlation analysis for the SZA and phenological metrics for the 1989–2014 time series, excluding the years with high SZA values. In this way, we could better understand whether relatively stable and lower SZA values had any relationship to the phenological metrics. The analysis was done for the time series of 21 years, after removing five years (1992, 1993, 1994, 1999, and 2000), which had growing-season median SZA greater than 40°. Fig. 6 shows the maps of the correlation coefficients and the significance levels (α = 0.05) of the correlation coefficients [Data are available at U.S. Geological Survey (2016b)]. It was clear that the magnitude of correlation coefficients was reduced substantially for the phenology data calculated after removing the high SZA years. The areas with the significant correlations between SZA and metrics, either negative or positive, were much lower for the reduced time series than the complete time series. Table 2 indicates that the areas with significant correlation between the SZA and the phenological metrics decreased for all metrics, which were observed only in 1.0–6.2% of the study area. The area of significant correlations of the SZA with EOST and EOSN declined
4.4. Methods for correcting the SZA effect on phenological metrics From the analysis of the SZA time series, we found a significant impact of orbital shift on the AVHRR-derived RSP data. Correcting for the effect of SZA changes on the phenological metrics was considered critical for appropriate use of the EROS AVHRR RSP dataset in phenological studies. In this study, we examined the SZA variations and the influence on phenological metrics using the time series that excludes the years with high SZA (> 40°). The analysis of the data after removing 1992, 1993, 1994, 1999, and 2000 showed that the temporal trend of SZA was not significant in most of the CONUS area (98.1%). Moreover, the SZA was not significantly correlated to the phenological metrics in most of the study area (93.8–99.0%). To study long-term trends and variations in RSP data, elimination of poor quality observations from the time series over many years may not deter scientific interpretation. However, if the interest of a study is the phenological characteristics for all individual years in the entire time series, a complete dataset without missing observations would be necessary. For this purpose, the phenological metrics of all years needs to be retained, but an adjustment for the SZA effect should be applied to the metrics. An ideal method for solving the orbital drift problem is the correction of the effect on the NDVI time series data that are used to derive RSP. Cihlar et al. (1997) applied a procedure of the Atmospheric, Bidirectional, and Contamination Corrections (ABC3) model to the 1km AVHRR 10-day composite dataset for the Canadian landmass, resulting in the NDVI data corrected for the SZA effect. A modified 220
Int J Appl Earth Obs Geoinformation 62 (2017) 215–223
L. Ji, J.F. Brown
Fig. 5. Correlation coefficients and the test of the significance between phenological metrics and SZA for time series 1989–2014. Table 2 Percent of area (%) with significant or non-significant correlation between phenological metrics and SZA (1989–2014). Phenological Metrics
SOST SOSN EOST EOSN MAXT MAXN DUR AMP TIN
1989–2014 (complete)
1989–2014 (excluding 5 high-SZA years)
Sig. Negative
Sig. positive
Not Sig.
Sig. Negative
Sig. positive
Not Sig.
2.7 2.6 14.3 18.8 3.5 13.7 3.5 12.1 8.2
2.0 8.4 1.3 1.6 2.1 2.5 0.6 2.0 5.0
95.3 89.0 84.4 79.6 94.4 83.8 95.9 85.9 86.8
1.3 0.2 5.2 0.7 3.1 2.2 3.1 2.1 3.0
2.0 0.8 1.0 2.6 1.5 1.5 0.9 1.1 1.2
96.7 99.0 93.8 96.7 95.4 96.3 96.0 96.8 95.8
the adjusted cumulative distribution function (ACDF) method was able to effectively eliminate most of the artifact interannual trend in the NDVI time series. To correct for the orbital drift effect on the GIMMS AVHRR data (2000–2006) in Africa, Sobrino et al. (2008) developed an iterative regression method to remove the channel data anomalies affected by the SZA anomaly. All the techniques mentioned above are useful for removing spurious NDVI variation induced by the SZA effect in AVHRR data. Phenology measures created using AVHRR data
version of the ABC3 model (ABC3V2) was proposed by Cihlar et al. (2004) to improve the accuracy of the AVHRR reflectance estimates. Pinzón et al. (2005) used the adaptive empirical mode decomposition (EMD) model to remove spurious SZA trend induced by satellite drift from the GIMMS NDVI data and to reconstruct the NDVI data without SZA variation. Jiang et al. (2008) investigated the adjustment to the AVHRR NDVI data (1982–2004) to reduce the data bias caused by the orbital drift, sensor degradation, and instrumental changes. They found 221
Int J Appl Earth Obs Geoinformation 62 (2017) 215–223
L. Ji, J.F. Brown
Fig. 6. Correlation coefficients and the test of the significance between phenological metrics and SZA for time series 1989–2014, excluding 1992, 1993, 1994, 1999, and 2000.
5. Conclusions
corrected for orbital drift will better reflect the natural phenological characteristics while minimizing the artifacts’ influences. Although the investigation of the methods for correcting orbital drift effect on AVHRR NDVI data is important, this topic is not the focus of this study. Our study showed that the AVHRR-derived phenological metrics were affected by the change of SZA induced by the NOAA satellite orbital drift, while correction for the SZA effect was not performed in the 1-km EROS AVHRR dataset. The AVHRR RSP product (U.S. Geological Survey, 2015) provides a valuable data source for long-term phenological research and applications for CONUS at regional and subcontinental scales, but straight use of these RSP data without further corrections for the SZA effect may cause spurious results. Specifically, the variation in the time series of phenological metrics may be influenced by the variation in SZA. Although our study shows that the time metrics (especially SOST, MAXT, and DUR) are much less influenced by SZA than the NDVI metrics, caution should be exercised if the dataset is used directly. At a minimum, users should use caution related to analysis results of AVHRR phenological metrics for 1992, 1993, 1994, 1999, and 2000. A comprehensive understanding of the effect of SZA on AVHRR NDVI and RSP products needs further analyses and studies. Correction for the SZA effect on AVHRR data and RSP datasets is strongly advised for users who employ the products in ecological and environmental investigation and management.
We conducted this study on the relationships between SZA and phenological metrics for CONUS based on 1-km EROS AVHRR and RSP datasets (1989–2014) to understand the NOAA satellite orbital drift effect on the NDVI-derived phenological measurements. This study highlights the following findings and conclusions:
• Within the time frame of the EROS AVHRR dataset (1989–2014), •
•
222
SZA increased for each NOAA satellite mission. Of all of the six satellites, NOAA-11(1989–1994) and NOAA-14 (1995–2000) had the highest rates of increase. In the entire time series including the data from all six satellites, 1992, 1993, 1994, 1999, and 2000 had much higher median SZA values (44°–60°) than other years (28°–40°). The high SZA in those years caused negative temporal trends in the SZA time series, that were statistically significant (at α = 0.05 level) in 76.9% of the CONUS area. The correlation analysis performed at the pixel level on the entire time series showed that the phenological metrics and SZA were significantly correlated (at α = 0.05 level) in 4.1–20.4% of the CONUS area. In general, the time metrics (SOST, MAXT, and DUR) were less influenced by the change in SZA than the NDVI-value
Int J Appl Earth Obs Geoinformation 62 (2017) 215–223
L. Ji, J.F. Brown
• •
long-term anomalous trends in NOAA’s global vegetation index data sets. IEEE Trans. Geosci. Remote Sens. 46, 409–422. Kaufmann, R.K., Zhou, L., Knyazikhin, Y., Shabanov, N.V., Myneni, R.B., Tucker, C.J., 2000. Effect of orbital drift and sensor changes on the time series of AVHRR vegetation index data. IEEE Trans. Geosci. Remote Sens. 38, 2584–2597. Kogan, F.N., Zhu, X., 2001. Evolution of long-term errors in NDVI time series: 1985–1999. Adv. Space Res. 28 (149-115). McGregor, J., Gorman, A.J., 1994. Some considerations for using AVHRR data in climatological studies: I, Orbital characteristics of NOAA satellites. Int. J. Remote Sens. 15, 537–548. Middleton, A.D., Kauffman, M.J., McWhirter, D.E., Cook, J.G., Cook, R.C., Nelson, A.A., Jimenez, M.D., Klaver, R., 2013. Animal migration amid shifting patterns of phenology and predation: lessons from a Yellowstone elk herd. Ecology 94, 1245–1256. Nagol, J.R., Vermote, E.F., Prince, S.D., 2014. Quantification of impact of orbital drift on inter-annual trends in AVHRR NDVI data. Remote Sens. 6, 6680–6687. http://dx.doi. org/10.3390/rs6076680. Pedelty, J., Devadiga, S., Masuoka, E., Brown, M., Pinzon, J., Tucker, C., Vermote, E., Prince, S., Nagol, J., Justice, C., Roy, D., Ju, J., Schaaf, C., Liu, J., Privette, J., Pinheiro, A., 2007. Generating a long-term land data record from the AVHRR and MODIS instruments. IEEE Geosci. Remote Sens. Symp. 2007, 1021–1025. http://dx. doi.org/10.1109/IGARSS.2007.4422974. Pinzón, J.E., Brown, M.E., Tucker, C.J., 2005. EMD correlation of orbital drift artifacts in satellite data stream. In: Huang, N.E., Shen, S.S. (Eds.), Hilbert-Huang Trasform and Its Applications. World Scientific Publishing Co. Pte. Ltd., Singapore. Pinzon, J.E., Tucker, C.J., 2014. A non-stationary 1981–2012 AVHRR NDVI3 g time series. Remote Sens. 6, 6929–6960. http://dx.doi.org/10.3390/rs6086929. Privette, J.L., Fowler, C., Wick, G.A., Baldwin, D., Emery, W.J., 1995. Effects of orbital drift on advanced very high resolution radiometer products: normalized difference vegetation index and sea surface temperature. Remote Sens. Environ. 53, 164–171. Reed, B.C., Yang, L., 1997. Seasonal vegetation characteristics of the United States. Geocarto Int. 12, 65–71. Reed, B.C., Brown, J.F., VanderZee, D., Loveland, T.R., Merchant, J.W., Ohlen, D.O., 1994. Measuring phenological variability from satellite imagery. J. Veg. Sci. 5, 703–714. Reed, B.C., Loveland, T.R., Tieszen, L.L., 1996. An approach for using AVHRR data to monitor U.S. Great Plains grasslands. Geocarto Int. 11, 13–22. Reed, B.C., White, M.A., Brown, J.F., 2003. Remote sensing phenology. In: Schwartz, M.D. (Ed.), Phenology: an Integrative Science. Kluwer Academic Publishing, Dordrecht, The Netherlands, pp. 365–381. Slayback, D.A., Pinzon, J.E., Los, S.O., Tucker, C.J., 2003. Northern hemisphere photosynthetic trends 1982–99. Glob. Change Biol. 9, 1–15. Sobrino, J., Julien, Y., 2016. Exploring the validity of the long-term data record V4 database for land surface monitoring. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 9, 3607–3614. Sobrino, J.A., Julien, Y., Atitar, M., Nerry, F., 2008. NOAA-AVHRR orbital drift correction from solar zenithal angle data. IEEE Trans. Geosci. Remote Sens. 46, 4014–4019. Swets, D.L., Reed, B.C., Rowland, J.R., Marko, S.E., 1999. A weighted least-squares approach to temporal smoothing of NDVI. In: Proceedings of the 1999 ASPRS Annual Conference, From Image to Information. Portland, Oregon, May 17–21, 1999, Bethesda, Maryland. (American Society for Photogrammetry and Remote Sensing, CD-ROM). Tarpley, J.D., 1991. The NOAA global vegetation index product − a review. Palaeogeogr. Palaeoclimatol. Palaeoecol. (Glob. Planet. Change Sect.) 90, 189–194. Tieszen, L.L., Reed, B.C., Bliss, N.B., Wylie, B.K., DeJong, D.D., 1997. NDVI, C3 and C4 production, and distributions in Great Plains grassland land cover classes. Ecol. Appl. 7 (59-58). Tucker, C.J., Pinzón, J.E., Brown, M.E., Slayback, D.A., Pak, E.W., Mahoney, R., Vermote, E.F., El Saleous, N., 2005. An extended AVHRR 8-km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data. Int. J. Remote Sens. 26, 4485–4498. Tucker, C.J., 1996. History of the use of AVHRR data for land applications. In: D’Souza, G., Belward, A.S., Malingreau, J.-P. (Eds.), Advances in the Use of NOAA AVHRR Data for Land Applications. Kluwer Academic, Dordrecht, The Nethlands, pp. 1–19. U.S. Geological Survey, 2015. Conterminous U.S. 1. http://dx.doi.org/10.5066/ F7PC30G1. (Accessed 16 November 2015). U.S. Geological Survey, 2016a. AVHRR normalized difference vegetation index (NDVI) composites. Earth Resources Observation and Science Center Database. U. S. Geological Surveyhttp://dx.doi.org/10.5066/F7707ZKN. (Accessed 8 February 2016). U.S. Geological Survey, 2016b. Effect of NOAA satellite orbital drift on AVHRR-derived phenological metrics. Earth Resources Observation and Science Center Database. U.S. Geological Surveyhttp://dx.doi.org/10.5066/F7G73BVV. (Accessed 7 December 2016).
metrics (SOSN, EOSN, MAXN, AMP, and TIN). The EOST was an exception and showed a relatively high area of influence from the SZA. Using the data that eliminated the 5 years with high SZA (> 40°), the temporal SZA trend was largely reduced, significantly affecting less than 2% of the study area. With the reduced time series, the significant correlation between the phenological metrics and SZA occurred in only 1.0–6.2% of the area. We advise users to employ a correction of the SZA effect before the EROS AVHRR RSP data are used in phenological studies. For the phenological trend analysis, elimination of the high-SZA years might be the simplest approach. A more comprehensive method for correcting the SZA effect on the RSP data should be applied to the NDVI data used in derivation of the RSP metrics.
Acknowledgments This work was funded by the U.S. Geological Survey (USGS) Land Remote Sensing Program under the Climate and Land Use Change mission area. The work by Lei Ji was performed under USGS Contract G13PC00028. We thank K. Gallo for reviewing the manuscript and providing valuable comments. We also thank N. M. Velpuri for reviewing the datasets and the metadata produced from this study. The authors are grateful to the anonymous reviewers who provided useful suggestions improving this article. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. References Brown, J.F., Wardlow, B.D., Tadesse, T., Hayes, M.J., Reed, B.C., 2008. The Vegetation Drought Response Index (VegDRI): a new integrated approach for monitoring drought stress in vegetation. GISci. Remote Sens. 45, 16–46. Brown, J.F., Howard, D., Wylie, B., Frieze, A., Ji, L., Gacke, C., 2015. Application-ready expedited MODIS data for operational land surface monitoring of vegetation condition. Remote Sens. 7, 16226–16240. http://dx.doi.org/10.3390/rs71215825. Cihlar, J., Ly, H., Li, Z., Chen, J., Pokrant, H., Huang, F.T., 1997. Multitemporal, multichannel AVHRR data sets for land biosphere studies − artifacts and corrections. Remote Sens. Environ. 60, 35–57. Cihlar, J., Chen, J.M., Li, Z., Huang, F., Latifovic, R., Dixon, R., 1998. Can interannual land surface signal be discerned in composite AVHRR data. J. Geophys. Res. 103, 23163–23172. http://dx.doi.org/10.1029/98jd00050. Cihlar, J., Latifovic, R., Chen, J., Trishchenko, A., Du, Y., Fedosejevs, G., Guindon, B., 2004. Systematic corrections of AVHRR image composites for temporal studies. Remote Sens. Environ. 89, 217–233. Eidenshink, J.C., 1992. The 1990 conterminous US AVHRR data set. Photogramm. Eng. Remote Sens. 58, 809–813. Eidenshink, J., 2006. A 16-year time series of 1 km AVHRR satellite data of the conterminous United States and Alaska. Photogramm. Eng. Remote Sens. 72, 1027–1035. Guo, W., 2013. AVHRR Vegetation Health Product (AVHRR-VHP) User Guide. I.M. Systems Group, Inc., Rockville, Maryland. http://www.star.nesdis.noaa.gov/smcd/ emb/vci/VH_doc/VHP_uguide_v1.4_2013_1221.pdf. Halpert, 1991. Climate monitoring using an AVHRR-based vegetation index. Palaeogeogr. Palaeoclimatol. Palaeoecol. (Glob. Planet. Change Sect.) 90, 201–205. Hastings, D.A., Emery, W.J., 1992. The advanced very high resolution radiometer (AVHRR): a brief reference guide. Photogramm. Eng. Remote Sens. 58, 1183–1188. Holben, B.N., 1986. Characteristics of maximum-value composite images from temporal AVHRR data. Int. J. Remote Sens. 7, 1417–1434. Jacobson, M.Z., 2005. Fundamentals of Atmospheric Modeling, second ed. Cambridge University Press, Cambridge, UK. James, M.E., Kalluri, S.N.V., 1994. The Pathfinder AVHRR land data set: an improved coarse resolution data set for terrestrial monitoring. Int. J. Remote Sens. 17, 3347–3363. Jiang, L., Tarpley, J.D., Mitchell, K.E., Zhou, S., Kogan, F.N., Guo, W., 2008. Adjusting for
223