An initial assessment of Suomi NPP VIIRS vegetation index EDR

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1 JOURNAL OF GEOPHYSICAL RESEARCH: ATMOSPHERES, VOL. 118, 1 16, doi: /2013jd020439, 2013 An initial assessment of Suomi NPP VIIRS vegetation index EDR M. Vargas, 1 T. Miura, 2 N. Shabanov, 3 and A. Kato 2 Received 26 June 2013; revised 26 September 2013; accepted 11 October [1] The Suomi National Polar-orbiting Partnership (S-NPP) satellite with Visible/Infrared Imager/Radiometer Suite (VIIRS) onboard was launched in October VIIRS is the primary instrument for a suite of Environmental Data Records (EDR), including Vegetation Index (VI) EDR, for weather forecasting and climate research. The VIIRS VI EDR operational product consists of the Top of the Atmosphere (TOA) Normalized Difference Vegetation Index (NDVI), the Top of the Canopy (TOC) Enhanced Vegetation Index (EVI), and per-pixel product quality information. In this paper, we report results of our assessment of the early VIIRS VI EDR (beta quality) using Aqua MODIS and NOAA-18 AVHRR/3 as a reference for May 2012 to March We conducted two types of analyses focused on an assessment of physical (global scale) and radiometric (regional scale) performances of VIIRS VI EDR. Both TOA NDVI and TOC EVI of VIIRS showed spatial and temporal trends consistent with the MODIS counterparts, whereas VIIRS TOA NDVI was systematically higher than that of AVHRR. Performance of the early VIIRS VI EDR was limited by a lack of adequate per-pixel quality information, commission/omission errors of the cloud mask, and uncertainties associated with the surface reflectance retrievals. A number of enhancements to the VI EDR are planned, including: (1) implementation of a TOC EVI back-up algorithm, (2) addition of more detailed quality flags on aerosols, clouds, and snow cover, and (3) implementation of gridding and temporal compositing. A web-based, product quality monitoring tool has been developed and automated product validation protocols are being prototyped. Citation: Vargas, M., T. Miura, N. Shabanov, and A. Kato (2013), An initial assessment of Suomi NPP VIIRS vegetation index EDR, J. Geophys. Res. Atmos., 118, doi: /2013jd Introduction [2] The first Visible/Infrared Imager/Radiometer Suite (VIIRS) sensor onboard the Suomi National Polar-orbiting Partnership (S-NPP) satellite platform, a precursor to the Joint Polar Satellite System (JPSS), was successfully launched in October VIIRS is slated to replace the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) sensor series and to continue the highly calibrated data stream initiated with Earth Observing System (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS) of the National Aeronautics and Space Administration (NASA) [Yu et al., 2005; Lee et al., 2006]. VIIRS incorporates many of the technological advancements developed for EOS-MODIS and a number of geophysical products, termed Environmental Data Records (EDRs), are 1 Center for Satellite Applications and Research, National Oceanic and Atmospheric Administration, College Park, Maryland, USA. 2 Department of Natural Resources and Environmental Management, University of Hawaii at Manoa, Honolulu, Hawaii, USA. 3 I.M. Systems Group, Rockville, Maryland, USA. Corresponding author: M. Vargas, NOAA, NCWCP E/RA2, 5830 University Research Court, Suite 2834, College Park, MD 20740, USA. (marco.vargas@noaa.gov) American Geophysical Union. All Rights Reserved X/13/ /2013JD produced from VIIRS data [Vogel et al., 2008], including VIIRS Vegetation Index EDR. [3] Spectral vegetation indices (VIs) have been used in operational monitoring of terrestrial vegetation. The Normalized Difference Vegetation Index (NDVI) from the AVHRR sensor series has been the most widely used index [Tucker, 1979]. The NDVI has operationally been used in drought monitoring [Brown et al., 2008], and numerical weather forecasting [Kurkowski et al., 2003; Miller et al., 2006] and global climate modeling [Zeng et al., 2002] as specifying surface boundary conditions. The NDVI is considered most directly related to absorption of photosynthetically active radiation, but also is often correlated with biomass or primary productivity [Myneni et al., 1995]. Developed for EOS-MODIS, the enhanced vegetation index (EVI) has been applied to various vegetation-climate science studies including land surface phenology [Zhang et al., 2003; Ganguly et al., 2010], ecosystem resilience [Ponce Campos et al., 2013], and gross primary productivity [Sims et al., 2008]. The EVI was designed to optimize the vegetation signal with improved sensitivity in high biomass regions and improved vegetation monitoring through a decoupling of the canopy background signal and a reduction in atmospheric aerosol influences [Huete et al., 2002]. [4] VIIRS Vegetation Index EDR includes two VIs, the Top of the Atmosphere (TOA) NDVI (AVHRR heritage) and the Top of Canopy (TOC) EVI (MODIS heritage). The 1

2 Table 1. Characteristics of VIIRS, MODIS, and AVHRR Bands Relevant to NDVI and EVI S-NPP VIIRS Aqua MODIS NOAA-18 AVHRR/3 Altitude 833 km 705 km 870 km Orbit Near-polar, sun-synchronous Near-polar, sun-synchronous Near-polar, sun-synchronous Equator crossing time 1:30 pm (ascending) 1:30 pm (ascending) 2:30 pm in 2012 (ascending) Repeat cycle 16 days 16 days 11 days Swath width 112 (±56 ), 3000 km (whiskbroom) 110 (±55 ), 2330 km(whiskbroom) 110 (±55 ), 2900 km (whiskbroom) Spectral bands (nm) Red (I1): 640 ( ) Red: 646 ( ) Red: ( ) NIR (I2): 865 ( ) NIR: 857 ( ) NIR: ( ) Blue (M3): 488 ( ) Blue: 466 ( ) Spatial resolution Red (I1) and NIR (I2) Red and NIR 1.09 km at nadir 375 m at nadir 250 m at nadir 1.7-by-3 km at θ v = by-0.62 km at θ a v = by-0.7 km at θ v = 55 2-by-6 km at edge (θ v = 68 ) 0.8-by-0.8 km at edge (θ v = 69.5 ) 0.5-by-1.2 km at edge (θ v = 65.4 ) Blue (M3) Blue 750 m at nadir 500 m at nadir 1.1-by-1.26 km at θ v = by-1.4 km at θ v = by-1.6 km at edge (θ v = 69.5 ) 1-by-2.4 km at edge (θ v = 65.4 ) a θ v - Satellite view zenith angle. VARGAS ET AL.: SUOMI NPP VIIRS VEGETATION INDEX EDR VI EDR has been produced on a daily, global basis since February 2012 when the initial checkout period of the instrument including calibration had been verified. In this paper, we present results of our initial assessment of the S-NPP VIIRS Vegetation Index EDR operational product conducted using the first year data set and demonstrate its performance with respect to Aqua MODIS and NOAA-18 AVHRR VI data sets. 2. VIIRS Vegetation Index EDR [5] As mentioned above, VIIRS Vegetation Index EDR currently consists of the two vegetation indices, TOA NDVI and TOC EVI, generated daily at the Imagery resolution (0.375 km at nadir) over land in swath/granule format [JPSS VVI ATBD, 2011] EV I TOC ¼ ð1þl NDVI TOA ¼ ρ TOA I2 Þ ρ TOC I2 ρ TOC I1 ρ TOA I1 = ρ TOC I2 = ρ TOA I2 þc I1 ρ TOC I1 þ ρ TOA I1 C M3 ρ TOC M3 (1) þl where the spectral bands ρ I1 and ρ I2 are the red and NIR reflectances, respectively; L, C I1, and C M3 are constants; ρ M3 is the blue band. Currently, L =1,C I1 = 6, and C M3 = 7.5 were adopted for VIIRS TOC EVI. The M3 band (Moderate resolution, km at nadir) has twice the cell dimension of the I1 and I2 bands (Imagery resolution, km at nadir), so its value is applied to four equivalent-area array cells. The VI EDR is bidirectional, representing measurements for actual sensor view and sun angle geometry. [6] It should be noted that the VIIRS VI algorithm adopted the earlier form of EVI equation [Huete et al., 1999]. Thus, the VIIRS EVI equation slightly differs from that of MODIS, that is, the gain factor of the latter is not a function of L, but independent of L (defined as G)[Huete et al., 2002]. In the current VIIRS algorithm, L is set to 1 (the same as MODIS), hence its gain factor is 2 (equation 2). In the MODIS EVI equation, the gain factor is set to 2.5 (G = 2.5). The EVI gain factor does not have any physical meaning, but merely changes the dynamic range of index values. [7] S-NPP VIIRS sensor and platform characteristics relevant to VI EDR are summarized and compared to those (2) of Aqua MODIS and NOAA-18 AVHRR in Table 1. The S-NPP platform altitude is higher than that of the Aqua platform, but both S-NPP VIIRS and Aqua MODIS have a 16 day repeat cycle with 1:30 pm equator crossing. The S-NPP and NOAA-18 platforms are at similar altitudes, but the latter has a different orbital repeat cycle (11 days) from those of VIIRS and MODIS. While the NOAA-18 platform had an equator crossing time of 1:30 pm at launch, it shifted to 2:30 pm in 2012 due to gradual orbital drift of the platform [Price, 1991]. [8] The VIIRS red band (I1) is closer to the AVHRR counterpart than that of MODIS, whereas the NIR band of VIIRS (I2) is more similar to the MODIS counterpart than that of AVHRR (Table 1). The largest difference is seen in the blue band. The VIIRS and MODIS blue bands cover different wavelengths regions where the former encompasses longer wavelengths than the latter. The AVHRR sensor does not have a band in the blue wavelength region and, therefore, TOC EVI cannot be produced. [9] VIIRS provides higher spatial resolution data than AVHRR, but lower than MODIS. VIIRS swath width is comparable with that of AVHRR, but wider than that of MODIS. It should be noted that VIIRS uses a unique approach of bow-tie removal through pixel aggregation which controls the pixel growth toward the end of the scan edge [Cao et al., 2013]. As a result, the VIIRS pixel size only doubles at the edge of scan Product Maturity [10] The VI EDR product is going through the following validation maturity stages: Beta, Provisional, Validation Stage 1, Validation Stage 2, and Validation Stage 3 or fully validated. This period is referred to as the extensive calibration/validation phase. The VI EDR has achieved a Beta maturity status in February 2013 by which VI EDR data produced on and after 2 May 2012 are considered at the beta quality level. A betaquality product is an early released product that has been minimally validated, may still contain significant errors (rapid changes can be expected), is available to allow users to gain familiarity with data formats and parameters, and is not appropriate as the basis for quantitative scientific publications and applications [NPOESS Cal/Val plan, 2009]. 2

3 2.2. Product Format [11] The VIIRS VI EDR operational product is generated as s granules at Imagery resolution in HDF5 format. The granule file contains TOA NDVI and TOC EVI; each data set contains 1536 rows and 6400 columns. Also included in the products are three quality flag (QF) layers on land/water mask, cloud confidence, aerosol loadings, and exclusion conditions. [12] The data product ID for the VI EDR is VIVIO (VIIRS Vegetation Index Operational product). An example of the file naming convention for the VI EDR operational product is: VIVIO_npp_d _t _e _b07490_ c _noaa_ops.h5. [13] The file naming convention includes the following fields delimited by underscores: Data Product ID, Spacecraft ID, Data Start Date, Data Start Time, Data Stop Time, Orbit Number, Creation Date, Origin, Domain Description, and Extension. A full description of each of the file name fields is available in the JPSS Common Data Format Control Book [JPSS CDFCB, 2011]. [14] The inputs to the VI EDR algorithm are the calibrated TOA reflectance, termed the sensor data record (SDR), atmospherically corrected surface reflectance (SR) intermediate product (IP), and geo-angle data set that contains solar zenith and azimuth, and view zenith and azimuth angles on a perpixel basis. [15] The VIIRS Cloud Mask (VCM) IP is used in the generation of the SR IP and selected fields are passed onto the VI EDR. The VCM algorithm uses a number of cloud detection tests to classify each pixel into four categories: Confidently Cloudy, Probably Cloudy, Probably Clear, and Confidently Clear [JPSS VCM ATBD, 2011]. The approach currently implemented for VI EDR is to not execute the algorithm over Confidently Cloudy pixels and to assign the fill value of 65,355 to those pixels. [16] The primary data portal for S-NPP products is NOAA s Comprehensive Large Array-Data Stewardship System (CLASS) web site ( saa/products/welcome). Data delivered to CLASS from the Interface Data Processing Segment (IDPS), the primary production system for VIIRS data products, have a latency of 6 hours. 3. Methods [17] Two types of analyses were conducted in this study. First, we performed a global-scale analysis and intercomparison of VIIRS VI EDR and heritage VI data records from Aqua MODIS and NOAA-18 AVHRR. The objective was to quantify physical performance of global VIIRS VI retrievals. This analysis used minimal data screening and relied on statistical properties of a large number of observations to reach the objective. Second, analysis of VIIRS VI was performed at regional and site scales using Aqua MODIS data as a reference. This analysis was aimed to assess radiometric accuracy of VIIRS VIs. These two analyses covered opposite sides of the trade-off between stringent data screening and global coverage and are designed to complement each other Global Physical Analysis [18] Specific objectives of the global physical analysis were to: (1) evaluate physical data validity, including detection of anomalies and seasonal trends, inspecting relationship between various VIs and sensor channel data, monitoring of accuracy of VI screening with QFs and (2) quantify consistency of the global VIIRS VIs with respect to heritage VI data records from MODIS and AVHRR. [19] VIIRS, Aqua MODIS, and NOAA-18 AVHRR VI products over the period 2 May 2012 to 31 March 2013 were used in this analysis. VIIRS Vegetation Index (VIVIO), Surface Reflectance (IVISR), and Geolocation (GIMGO) products in granule format were obtained and used to generate daily global gridded TOA NDVI and TOC EVI, and corresponding QF and geometry data sets. The MODIS data sources were Aqua MODIS 16-day TOC EVI (MYD13A2 Collection 5) in the gridded MODIS tile format (set of 1 km MODIS Land tiles, sinusoidal projection), Aqua MODIS daily TOA reflectance (MYD02HKM Collection 5) in the swath format, and daily TOC reflectances (MYD09CMG Collection 5) in the Climate Modeling Grid (CMG) format (geographic projection, 0.05 degree). The daily MODIS data were processed into daily TOA NDVI and TOC EVI. We also used the Combined Aqua-Terra MODIS land cover (MCD12C1 Collection 5.1) in the CMG format. This product provided eight-biome LAI/FPAR classification for the year This land cover classification was aggregated to six vegetation classes (grasses, shrubs, broadleaf crops, savannah, broadleaf forests, and needleleaf forests) and one nonvegetation class (barren) (Figure 1). The land cover was utilized to evaluate performance of VIs as a function of biome type. Additionally, we used a sample of MODIS snow product (MYD10C2 Collection 5) in the CMG format for March The snow cover product was used to evaluate TOC EVI retrievals over snow-covered regions. Finally, we used TOA NDVI weekly composites derived from NOAA-18 AVHRR. [20] VIIRS data were obtained from GRAVITE (Government Resource for Algorithm Verification, Independent Testing, and Evaluation). GRAVITE was developed to support the S-NPP Community Collaborative Calibration/Validation Program. GRAVITE has four main components: technical library, central processing and data distribution capability, software repository, and a whole-system triage tool. AVHRR data were also obtained from NOAA/NESDIS/STAR. MODIS data were downloaded from LAADS (NASA s Level 1 and Atmosphere Archive and Distribution System). All products were projected to common geographic projection at 0.18 degree resolution using nearest-neighbor resampling to allow for direct comparison. Finally, VI products in daily format (VIVIO, derived from MYD02HKM, derived from MYD09CMG) were composite over 16 days for comparison to MODIS composite products and 7 days for comparison to AVHRR composite products. As the implementation of the same MODIS compositing scheme (utilized to generate 16-day MYD13A2) was not possible with VIIRS VI data due to lack of the required QC information, we implemented a Constrained Maximum Value (CMV) scheme [Huete et al., 2002]. [21] In implementing global analysis, we performed minimal data screening, that is, we screened out pixels with exclusion conditions (mostly Confidently Cloudy, cf. section 3.1.3) and pixels with TOC EVI anomalies (cf. section 4.3). Our preliminary sensitivity studies (cf. section 4.3) indicated that the current VIIRS Cloud Mask allowed significant 3

4 Figure 1. Global land cover map derived from Combined Terra-Aqua MODIS LAI/FPAR land cover product (MCD12C1, ver. 5.1) for year This ancillary product was utilized in this study to stratify statistical analysis of VI products by land cover class. commission/omission errors and using additional screening with Probably Cloudy + Probably Clear did not improve VI statistics substantially, but reduced the amount of observations. We further recognized that other factors (aerosols contamination, bidirectional reflectance distribution function (BRDF) effect, geolocation errors, differences in gridding, and compositing algorithms) were relatively minor compared to cloud contamination and had random nature on the global scale. In this analysis, we report statistical properties of observations and relied on the fact that opposite random effects canceled out each other. Also, to achieve higher level of confidence, we repeated the analysis for both daily and composite pairs of VIIRS, MODIS, and AVHRR data (subject to availability). An alternative approach of using MODIS quality assessment (QA) flags was explored at the regional/local scale and these results are reported in the Radiometric Accuracy Assessment section (section 3.2.2). [22] The global analysis has been automated with the webbased JPSS VI quality monitoring tool at NOAA/NESDIS/ STAR ( htm). This tool automatically downloads data, generates global gridded VI products, and also generates the required composite products. Given required VI products are in the common grid format, the tool executes a standard analysis (creates global VI maps, VI anomalies and statistics) VIIRS vs. MODIS Comparison [23] Our analysis of various features of global VIIRS VI product was based on cross-comparison to reference heritage MODIS and AVHRR vegetation indices. To implement the approach, we constructed (on per-pixel basis) pairs (VIIRS VI (cmp1), Reference VI (cmp2)), where Reference is MODIS (or AVHRR) and cmp1 and cmp2 are temporal compositing intervals. Two types of analyses were performed based on relationship between cmp1 and cmp2. Type I analysis corresponds to the case when cmp1 = cmp2, and can be daily, 7-day (AVHRR compositing interval), or 16-day (MODIS compositing period). This type of analysis is suitable for a direct VIs intercomparison from two sensors as both are retrieved over the same time interval. Type II analysis corresponds to the case when cmp1 << cmp2, namely, cmp1 is daily and cmp2 is 16 days (MODIS) or 7 days (AVHRR). This type of analysis is suitable for monitoring of atmospheric (mostly cloud) contamination of VIIRS daily VI data when compared to a clean multiday composite Reference VI, where residual atmospheric effect is minimized. [24] VI anomalies were used to characterize cross-sensor VI consistency, defined (on per-pixel basis) as VIIRS VI (cmp1) minus Reference VI (cmp2). Again, based on the compositing interval (cmp1 or cmp2), anomalies are suitable for Type I or Type II analysis. [25] Type I analysis was implemented for intercomparison between VIIRS and MODIS. Methods of comparison included direct cross comparison of VI maps, maps of VI anomalies, time series of statistics including Mean (VI anomaly), STD (VI anomaly), R 2 (VIIRS, MODIS), and scatterplots (VIIRS vs. MODIS) of VI and Surface Reflectances. Availability of various MODIS data sets allowed us to evaluate all VIs of interest (TOA NDVI and TOC EVI) and Surface Reflectances (TOC Red, TOC Blue, and TOC NIR). Type II analysis was implemented to evaluate screening capabilities of VIIRS Cloud Mask for VI applications (cf. section 3.1.3) VIIRS vs. AVHRR Comparison [26] The methodology used for cross comparison between VIIRS and MODIS (reference) VI has been also implemented for the comparison between VIIRS and AVHRR (reference) VI. However, only 7-day TOA NDVI composite data were available from AVHRR. No surface reflectance bands from AVHRR were available. We performed Type I analysis between VIIRS and AVHRR data. 4

5 Figure 2. Global maps of VIIRS VI EDR (TOA NDVI and TOC EVI) and corresponding Aqua MODIS VI products. Data sets are presented as 16-day composites for 3 18 July Cloud Mask and VI [27] VIIRS Cloud Mask (VCM) plays a major role in VIIRS VI Quality Control (QC), since cloud contamination compromises image utilization for land surface studies. We used the current version (beta) of VCM in this study. The objective was to evaluate commission (cloud overscreening) and omission (cloud leakage) errors of VCM. The approach was to use VI reference data clean from the impact of the atmosphere (i.e., MODIS 16-day composite) and construct the anomalies for Type II analysis, VIIRS (daily) minus MODIS (16-day composite). In our analysis, we assumed that Clouds could be identified by negative anomalies (daily VI data contaminated by clouds have lower values than VI data from clean MODIS reference). Using the above assumption, we evaluated Commission (cloud over estimation, or false alarms) and Omission (cloud underestimation, or leakage) VCM errors Radiometric Accuracy Assessment Temporal Profiles [28] One important aspect of VIIRS VI EDR is to capture and describe seasonal evolution of vegetation. Subsets of a set of VIIRS products, including VI EDR and two upstream products of Cloud Mask and SR IPs, were obtained over select AmeriFlux sites for May 2012 to March 2013 from NASA s Land Project Evaluation and Test Element (Land PEATE). TOA NDVI and TOC EVI pixels at AmeriFlux tower sites were extracted and screened for cloud, cloud shadow, and heavy aerosol contaminations using quality flags (QFs) obtained from Cloud Mask IP. [29] MODIS TOA reflectance (MYD02HKM Collection 5) granules and MODIS daily surface reflectance (MYD09GA Collection 5) tiles that contained the select AmeriFlux sites were obtained from LAADS Web for the same time period. MODIS TOA reflectance layers were reprojected to the same sinusoidal projection as the surface reflectance tiles. Both MODIS TOA and surface reflectances were screened for cloud, cloud shadow, and heavy aerosol contaminations using quality assurance (QA) flags contained in the surface reflectance tiles and pixel reflectances corresponding to the tower sites were extracted. TOA NDVI and TOC EVI were computed from the screened, extracted pixel reflectances. [30] The extracted VIIRS TOA NDVI and TOC EVI time series were plotted along with the corresponding MODIS VI time series. The temporal profiles were compared to examine whether the temporal trajectories of VIIRS VI EDR were comparable to those of MODIS Assessment Using Near-Nadir Observation Pairs [31] Radiometric accuracy and stability of VIIRS VI EDR were assessed with respect to Aqua MODIS using nearsimultaneous, near-nadir (view zenith angle < 7.5 ) observation pairs obtained from overlapped orbital tracks. By focusing our analysis on near-identical geometric conditions between VIIRS and MODIS, the impact of BRDF on our accuracy assessment was minimized or negligible. [32] A variety of other factors can cause differences in VIs from two different platforms or sensors. A good comprehensive list is given by Swinnen and Veroustraete [2008]. For VIIRS and MODIS, as well as VIIRS and AVHRR, the following factors can be considered affecting their VI differences, including geolocation accuracy, spatial resolution, gridding/resampling scheme, and cloud mask algorithm, to name a few. This radiometric accuracy assessment was designed to minimize the effects of these factors and to focus on the effects of sensor calibration, spectral band-pass differences, and atmospheric correction algorithm differences. [33] For nonpolar regions, overlapping tracks were located evenly throughout the globe during a 16-day repeat cycle. Many of the tracks on the western side of the Earth were, however, over the Pacific or Atlantic Oceans, whereas there was reasonable coverage of the overlapping tracks on land over the eastern side (the Eurasian and Australian continents) every 8 days. Solar zenith angle differences between VIIRS and MODIS observations changed from ~1 near the equator to 2 3 at the midlatitude zones. [34] VIIRS VI EDR and SR IP granules were obtained from Land PEATE and reprojected onto 0.01 geographic projection with nearest neighbor resampling, and mosaicked into global maps using VIIRS reprojection tool developed 5

6 Figure 3. (a) Global maps of VI anomalies for VIIRS-MODIS Type I analysis. Anomalies are defined as VIIRS VI (cmp1) minus MODIS VI (cmp2), where VI is TOA NDVI or TOC EVI, and cmp1 = cmp2 is daily or 16-day composites. Analysis for the case when cmp1 = cmp2, is called Type I analysis and is suitable for cross-sensor VI comparison over the same time interval (this figure); case when cmp1 = daily and cmp2 = multiday composite, refers to Type II analysis, utilized for cloud leakage monitoring (Figures 6a b). Daily data are for 6 July 2012, while 16-day composites are for 3 18 July (b) Global time series of statistics for VIIRS-MODIS Type I analysis. Statistics includes Mean (VI anomaly), STD (VI anomaly), and R 2 (VIIRS VI (cmp1), MODIS VI (cmp2)), where VI anomaly = VIIRS VI (cmp1) minus MODIS VI (cmp2), VI = TOA NDVI, or TOC EVI and cmp1 = cmp2 (Type I analysis) is daily or 16-day composites. Global all land pixels statistics are complimented by those over individual land cover classes (Figure 1). Time series cover period 2 May 2012 to 31 March and available from Land PEATE every 8 or 16 days from May 2012 to March For the same data days, MODIS daily TOA reflectance (MYD02HKM Collection 5) and daily surface reflectance (MYD09 Collection 5) were obtained from NASA s LAADS Web, reprojected onto 0.01 geographic projection with nearest neighbor resampling using MODIS reprojection tool Swath (USGS), and mosaicked into TOA-NDVI and TOC-EVI global maps. [35] These VIIRS and MODIS VI global mosaics were screened for cloud, cloud shadow, high aerosol loading, and snow/ice using QA flags included in MYD09. VIIRS QFs were not used here because VIIRS QFs available in the VI EDR and SR IP only provided two cloud mask flags which were still subject to commission and omission errors as described later in section 4.3. These global maps were further masked with the constraint of view zenith angle < 7.5. This view angle roughly equaled the range of view zenith angle over the Landsat swath. [36] The QA-screened, view zenith angle-masked strips of the VI maps were spatially averaged over a 7 pixel-by-7 pixel 6

7 Table 2. Summary Statistics on Consistency Between VIIRS and Reference (MODIS, AVHRR) VI Products VIIRS Data Set TOA NDVI TOC EVI Reference NOAA-18 AVHRR Aqua MODIS Aqua MODIS Mean (VIIRS-Reference) STD (VIIRS-Reference) R 2 (VIIRS, Reference) window to reduce the impacts of resolution difference and misregistration, from which accuracy (bias or mean difference), precision (standard deviation), and uncertainty (root mean square error) (APU) metric values were computed for TOA- NDVI and TOC-EVI for every 8-16 days using MODIS as a reference (i.e., VIIRS minus MODIS) [JPSS VVI ATBD, 2011]. A time series of the derived APU metric values were averaged to obtain mean APU values over the year. In evaluating the APU metric values, we referenced results of hyperspectral simulation analyses conducted as part of VIIRS prelaunch validation exercises [Kim et al., 2010; Miura et al., 2013]. 4. Results 4.1. Intercomparison of VIIRS and MODIS VIs [37] Sample 16-day compositing global maps of VIIRS VI product (TOA NDVI and TOC EVI data) and corresponding Aqua MODIS VI composites for 3 18 July 2012 are shown in Figure 2. The spatial distribution of VIs from VIIRS matches that from MODIS and follows the expected patterns for given season and vegetation type (see Figure 1) and matched those reported in the literature [Huete et al., 2002]. As reported elsewhere [e.g., Myneni et al., 1995; Huete et al., 2002], the two vegetation indices have different sensitivity to vegetation abundance: TOA NDVI approaches saturation in dense forests, while TOC EVI has a substantially lower range of variations, but exhibits more uniform sensitivity over whole dynamic range. Overall, VIIRS VI product shows reasonably good consistency with its Aqua MODIS counterpart. However, small differences are noticeable, i.e., MODIS TOA NDVI exhibits slightly higher values and MODIS TOC EVI has a slightly higher contrast between lower and higher values. [38] Figure 3a shows samples of VIIRS VI anomalies with respect to MODIS reference (VIIRS VI minus MODIS VI) for TOA NDVI and TOC EVI. First, consider the maps of TOA NDVI anomalies (top row). The majority of the Globe is covered with anomalies close to zero (but slightly negative). A few patches over dense forests (Amazon, Central Africa and Oceania) are covered with positive anomalies in daily data, but those are negligible in the composite version. Next, consider the maps of TOC EVI anomalies (bottom row). On the global scale, there seemed to exist a balanced mix of patches with slightly negative anomalies (Siberia, Amazon) and positive ones (North Africa). Thus, considering daily and composite data we conclude that VIIRS TOA NDVI was slightly lower than MODIS TOA NDVI, while VIIRS and MODIS TOC EVI were roughly the same over the Globe as a whole. [39] Figure 3b quantifies the consistency of TOA NDVI and TOC EVI from VIIRS and MODIS using time series of anomalies of Mean, STD and R 2 over the period 2 May 2012 through 31 March Statistics were calculated using Type I analysis for daily and 16-day composite data. For daily anomalies of TOA NDVI, the mean is very close to zero throughout the year, STD ~0.15 (highest values were obtained for the broadleaf forests, seasonality is noticeable with maximum value in August and minimum in March), and R 2 ~0.55 (lowest values were found for broadleaf and needleleaf forest). Results for the compositing version are similar, except a small negative bias ( 0.07) observed and R 2 improved to 0.65 over most of biomes except for needleleaf forests. Finally, for daily TOC EVI, the mean anomaly oscillates around zero throughout the annual cycle, STD ~0.2 (highest value found for needleleaf forests), R 2 ~ (lowest values found for broadleaf and needleleaf forests). Results for the composite version are similar. Table 2 summarizes calculated consistency metrics between VIIRS and MODIS VI and AVHRR VI composite products. [40] Figures 4a and 4b provide further insight into the consistency between VIIRS and MODIS VI using histograms and scatterplots. Type I analysis was implemented only for daily data (6 July 2013). Results for composite data were similar and are not presented here. First, closely inspect histograms of VIs and SRs. Shapes of histograms for MODIS and VIIRS data generally replicate each other. However, looking at the histogram of TOA NDVI anomalies, one can notice a slight shift of the peak of distribution toward negative values: compared to MODIS, VIIRS TOA NDVI are lower. In contrast, histogram of TOC EVI anomalies is centered at zero, indicating close match. However, when inspecting histograms of anomalies of input channel data to construct TOC EVI (TOC Red, TOC Blue, TOC NIR), one can notice that TOC Red and Blue channels are lower, while TOC NIR is slightly higher. One-to-one scatterplots (Figure 4b) for VIs and SRs further visualize collected observations. One may be interested in the mechanism of how TOC EVI balances inconsistencies in the input SR to generate values, similar to MODIS. Consider definition of TOC EVI (equation 2). Compared to MODIS, the numerator without gain, TOC NIR TOC Red, is higher for VIIRS compared to MODIS, given that TOC NIR is higher and TOC Red is lower. Denominator, if not lower, definitely cannot increase in the same proportion due to counter-balancing effects of TOC Red and TOC Blue channels and additional weight of constant term. Therefore, ratio of the numerator (without gain) to numerator is higher in case of VIIRS compared to MODIS. However, VIIRS uses lower gain factor (2.0) compared to that of MODIS (2.5). This counter-balancing effect of different gain factors and differences in SRs results in observed consistency of TOC EVI between VIIRS and MODIS Intercomparison of VIIRS and AVHRR VIs [41] The results of the comparison between VIIRS and AVHRR VI (TOA NDVI) are reported in Figures 5a and 5b. A Type I analysis was implemented for 7-day composites 7

8 Figure 4. (a) Global histograms of VI and Surface Reflectances (SR) as well as their anomalies for VIIRS-MODIS Type I analysis. VI anomaly is defined as VIIRS VI(cmp1) MODIS VI(cmp2). SR anomalies are defined similarly; VI = TOA NDVI and TOC EVI, SR = TOC Red, TOC Blue, and TOC NIR, and cmp1 = cmp2 (Type I analysis) is daily. Also shown are curves of Mean and STD for VI (and SR) anomalies as function of VI (and SR) values. Data are for 6 July (b) Global scatterplots of VI and Surface Reflectances (SR) for VIIRS-MODIS Type I analysis. VIIRS VI(cmp1), VIIRS SR(cmp1), MODIS SR (cmp2), and MODIS VI(cmp2) were used, where VI = TOA NDVI, and TOC EVI, SR = TOC Red, TOC Blue, and TOC NIR, and cmp1 = cmp2 (Type I analysis) is daily. Data are for 6 July only. The map of VI anomalies (VIIRS TOA NDVI minus AVHRR TOA NDVI) for composite 1 7 July 2012 (Figure 5a) shows a systematic positive bias over most of land pixels, especially broadleaf forest and savannah (Amazon, Central Africa, India, China, and Oceania). Europe and Russia exhibit a mix of patches with positive and negative anomalies. The time series of statistics (mean, STD, and R 2 ) quantify the anomalies: a mean global bias of 8

9 Figure 5. (a) Global map of VI anomalies and time series of statistics for VIIRS-AVHRR Type I analysis. Statistics include Mean (VI anomalies), STD (VI anomalies), and R 2 (VIIRS VI (cmp1), AVHRR VI (cmp2)), where VI anomaly = VIIRS VI(cmp1) minus AVHRR VI(cmp2), and cmp1 = cmp2 (Type I anomaly) is 7-day composite and VI = TOA NDVI. Global all land pixels statistics are complimented by statistics over individual seven land cover classes (Figure 1). Global map is for 1 7 July Time series cover period 1 March 2012 to 31 March (b) Global histograms and scatterplot of VI (and anomalies) for VIIRS-AVHRR Type I analysis. VI anomaly is defined as VIIRS VI (cmp1) AVHRR VI (cmp2), VI = TOA NDVI, and cmp1 = cmp2 (Type I analysis) is 7-day composite. Also shown are curves of Mean and STD for VI anomalies as function of VI values. Data for 1 7 July about is persistent throughout the annual cycle (highest bias is observed for broadleaf forests and lowest is for needleleaf forest due to mix of patches with positive and negative anomalies). STD is about (slight oscillation exist with a maximum in August and a minimum in March; highest values are observed for broad leaf forest and lowest are for needleleaf forest). R 2 is about and very unstable with large variations. Next, consider the histograms of VI and its anomalies (Figure 5b). Noticeable is the fact that the shape of the TOA NDVI histograms for both sensors VIIRS and AVHRR are similar; however, the VIIRS histogram is stretched toward higher values, resulting in the observed bias. The scatterplot of VIIRS vs. AVHRR TOA NDVI is helpful to characterize the inconsistency: while this is not a one-to-one line, the relationship for most of the pixels is nearly linear with intercept of 0.1 and slope > 1. Note that intercomparisons between MODIS and AVHRR VIs have been performed in the past and it has been reported that MODIS exhibits higher values than AVHRR VI data and that the bias increases with increasing VI values [Huete et al., 2002]. A prelaunch validation study conducted by Miura et al. [2013] indicated that the band-pass differences between VIIRS and NOAA-18 AVHRR red and NIR channels can result in their TOA NDVI differences (VIIRS minus AVHRR) of Cloud Mask and VI [42] Results of the evaluation of commission/omission errors of VCM for VI applications are presented in Figure 6a. Type II analysis was implemented for VIIRS TOC EVI (daily) minus MODIS TOC EVI (16-day composites) anomalies. The top row shows a map of VI anomalies and histograms for pixels falling into Confidently Clear + Probably Clear + Probably Cloudy mask. White areas on the map correspond to pixels marked by VCM as confidently Cloudy (this is a standard screening used in the Global Analysis). Large negative anomalies (cloud leakage) are observed mostly in the northern high latitudes and over dense forests of Amazonia and Central Africa with persistent cloud coverage. Cloud leakage is not detected over other regions with persistent cloud coverage (India and Oceania), as those regions are masked out correctly as Confidently Cloudy. Looking at the histograms of VIIRS TOC EVI (daily) and MODIS TOC EVI (16-day composite), one can notice that the MODIS distribution has two peaks, one over low values and another 9

10 Figure 6 10

11 Figure 7. VIIRS TOA NDVI and TOC EVI temporal profiles plotted along with MODIS TOA NDVI and TOC EVI, respectively. at high values. However, the VIIRS histogram shows only a single peak at low values. Therefore, the Confidently Clear + Probably Clear + Probably Cloudy mask includes cloudy pixels and needs to be reduced to eliminate cloud leakage. Next, we tested the screening capabilities of the Probably Clear + Probably Cloudy mask. Figure 6a shows the map (and corresponding histograms) of TOC EVI anomalies falling into Probably Clear + Probably Cloudy mask. Both, the spatial map and histograms indicate that the Probably mask does include a substantial amount of cloud-contaminated pixels, especially in the northern high latitudes, but also provides significant portion of false alarms (pixels with positive VI anomalies with valid VIIRS VI values). Such false alarms are scattered in the northern high latitudes and also concentrated over the west coast of Africa. The histogram of anomalies further highlights significant portion of Figure 6. (a) Evaluating performance (commission and omission errors) of VIIRS Cloud Mask (VCM) for VIIRS VI product using VIIRS-MODIS Type II analysis. Top panel shows map of VI anomalies and histograms for pixels falling into Confidently Clear + Probably Clear + Probably Cloudy mask. Middle panel shows the same but for Probably Clear + Probably Cloudy mask and Bottom panel for Confidently Clear mask. Anomalies are defined as VIIRS VI (cmp1) minus MODIS VI (cmp2), where VI is TOC EVI, cmp1 < cmp2 (Type II analysis), cmp1 = daily and cmp2 = 16-day composites. The assumption is that cloud contamination in daily VIIRS VI is identified with negative anomalies. Daily VIIRS data are for 6 July 2012; compositing MODIS data are for 3 18 July Global time series of statistics for VIIRS-AVHRR Type II anomaly analysis. Statistics includes Mean (VI anomaly), STD (VI anomaly), and R 2 (VIIRS VI(cmp1), AVHRR VI (cmp2)), where VI anomaly = VIIRS VI(cmp1) minus AVHRR VI(cmp2), VI = TOA NDVI, and cmp1 < cmp2 (Type II analysis) is cmp1 = daily and cmp2 = 7-day composites. Global all land pixels statistics are complimented by statistics over individual seven land cover classes (Figure 1). Time series cover period 1 March 2012 to 31 March

12 Figure 8. VIIRS VI APU metrics in reference to the Aqua MODIS counterparts: (a) TOA NDVI, (b) TOC EVI, and (c) TOC EVI with the VIIRS EVI gain factor set to 2.5. screened out pixels with close to zero or positive anomalies. In addition, total amount of screened pixels by Probably Clear + Probably Cloudy is large compared to that falling under Confidently Clear + Probably Clear + Probably Cloudy, i.e., 117,732/401,326 = 29%. This illustrates the Commission error (false alarms) of the VCM. [43] The Omission error (cloud leakage) is illustrated in the bottom panel of Figure 6a. The VI anomalies shown are those falling in the single remaining category Confidently Clear. Note that the cloud leakage over northern high latitudes was substantially reduced; however, in expense to significantly lower coverage of remaining pixels as compared to top panel ( Confidently Clear + Probably Clear + Probably Cloudy mask). Still, even the Confidently Clear mask allows some cloud leakage in the northern high latitudes. The histogram of anomalies is skewed toward negative anomalies. Also, comparing the statistics shown in the top and bottom panels, one notices that eliminating from the analysis pixels falling into Probably Clear + Probably Cloudy categories does not substantially improve the mean bias ( vs ) or reduces STD (0.174 vs ). [44] Figure 6b shows time series of statistics for anomalies discussed above. The mean anomaly is a good indicator of cloud leakage; the mean bias is negative and peaks during summer (in winter VI is low and clouds are barely distinguishable from snow on the ground). Overall, VCM does capture major cloud contamination of VIIRS VI, Probably Clear + Probably Cloudy categories do help to reduce cloud leakage. However, fine tuning of VCM is required to achieve the VI application needs (i.e., site-level validation work, high-precision remote-sensing and climate applications, including phenology studies) Temporal Profiles [45] In Figure 7, VIIRS VI temporal profiles are plotted along with the MODIS counterparts for four AmeriFlux sites: Bartlett Experimental Forest (deciduous broadleaf forest, DBF), Harvard Forest (DBF), Audubon Ranch (semi-arid grassland), and Sky Oaks - Young Stand (chaparral, closed shrubland). [46] VIIRS VIs showed seasonal changes comparable to those of MODIS for all the four sites. For Bartlett and Harvard Forests, VIIRS and MODIS VIs were high during the summer period (May August) and gradually decreased during the fall period (September November) (Figures 7a d). For the Audubon Ranch semi-arid grassland, VIIRS and MODIS VIs increased moderately in July, which likely corresponded to grass growth following a monsoon season, gradually decreased throughout the fall, and remained low in the winter and early spring periods (December March) (Figures 7e and 7f). All VIs from VIIRS and MODIS changed very little throughout the year for the Sky Oaks chaparral (Figures 7g and 7h). These time series contained several data gaps due to QA/QF screening. Our examination of the QA/QF screening indicated that (1) cloud shadows generally decreased both TOA NDVI and TOC EVI values and (2) the VCM cloud shadow flag was conservative, apparently flagging larger areas than actual cloud-shadowed areas (results not shown here). [47] VIIRS TOA NDVI values were nearly the same as MODIS TOA NDVI values for the two forest and chaparral sites (Figures 7a, 7c, and 7g), and slightly higher for the semi-arid grassland site (Figure 7e). Particularly noticeable in TOA NDVI were secondary variations in the two forest sites (Figures 7a and 7c). In comparison to MODIS TOA NDVI, VIIRS TOA NDVI varied largely from day to day, which should be attributed to residual cloud contaminations and/or highly variable atmosphere. [48] While VIIRS and MODIS TOC EVIs showed good compatibility in terms of their seasonality and magnitudes of day-to-day secondary variation, VIIRS TOC EVI was consistently lower than MODIS TOC EVI (Figures 7b, 7d, 7f, and 7h). The gain factor for VIIRS TOC EVI was

13 Figure 9 13

14 (1 + L, where L = 1) (section 2), while the gain factor for MODIS TOC EVI was 2.5 [Huete et al., 2002]. This difference in the gain factor was mainly responsible for the observed consistent difference in the two EVIs Radiometric Accuracy Assessment [49] The derived mean APU metrics of VIIRS VI EDR are plotted in Figure 8. Accuracy (bias) of VIIRS TOA-NDVI had a similar trend to the one previously predicted via hyperspectral simulation analyses that evaluated and quantified VIIRS vs. MODIS NDVI bias due to spectral band-pass differences [e.g., Miura et al., 2013]. Accuracy (bias) was positive at lower NDVI values and close to zero for higher NDVI values, indicating that VIIRS TOA NDVI was on average higher than the MODIS counterpart (Figure 8a). The magnitude of the bias was, however, slightly higher than that predicted by the hyperspectral analysis. [50] The bias of VIIRS TOC-EVI drastically changed throughout its dynamics range (Figure 8b). The TOC EVI bias was zero (i.e., VIIRS TOC EVI = MODIS TOC EVI), but gradually increased in magnitude, exceeding 0.10 EVI units above 0.5 EVI values. This trend was due mainly to the different gain factor value adopted in VIIRS. This effect of the gain factor was eliminated from APU metrics by setting the VIIRS EVI gain to 2.5 (Figure 8c). The VIIRS TOC EVI bias was zero when EVI was zero, but always positive for the rest of EVI dynamic range, indicating that VIIRS TOC EVI was always higher than the MODIS counterpart. This trend was the same as that predicted via hyperspectral simulation studies, which was attributed mainly to the disparate blue bands between VIIRS and MODIS [e.g., Kim et al., 2010]. As observed in TOA-NDVI, however, the magnitude of the bias from this study was slightly larger than the one observed in the hyperspectral analysis. There were several potential sources of these subtle differences in the observed and predicted biases for TOA NDVI and TOC EVI, including solar zenith angle differences and residual cloud contaminations; however, we were unable to identify particular causes in this study EVI Anomalies [51] The problem of the TOC EVI anomalous retrievals is illustrated in Figures 9a and 9b. Figure 9a shows a set of four maps over northern high latitudes: VIIRS daily TOC EVI, TOC NDVI, SZA for 19 March 2013 and MODIS 8 day snow cover for March As the snow cover map indicates, northern high latitudes are covered by snow at the time interval of interest. Over those areas, TOC NDVI is close to zero or negative. One would expect that TOC EVI also would be low/negative. However, selected patches exhibit artifacts, that is, TOC EVI have large positive values (>1). In the presented map of VIIRS TOC EVI (top map), patches with anomalous retrievals are located on the southern border of Siberia and Kazakhstan, northern shore of Siberia and Greenland. Tracing time series of daily VIIRS TOC EVI maps on the JPSS VI web monitoring tool, we noticed that those anomalies are generally small by spatial coverage and not persistent in space and time. Also, they are not related to spatial features of the landscape: a sharp transition from areas contaminated by anomalies to regular values is observed when crossing swath boundary (patch of SZA > 80 degrees at the northern shore of Siberia). The cause of the problem is that certain combination of SR values in the relevant channels is out of domain of definition TOC EVI (equation 2). This situation is illustrated in Figure 9b. Consider three cases (organized column-wise in Figure 9): (a) TOC NDVI < 0 and TOC EVI > 0, (b) TOC NDVI < 0 and TOC EVI < 0, and (c) TOC NDVI > 0 and TOC EVI > 0. The first case corresponds to anomalies, the second corresponds to normal relationship between NDVI and EVI over nonvegetated (snow-covered) regions, and the third to normal relationship for vegetated surfaces. For each of the three cases, the first row shows scatterplots of TOC EVI vs. TOC NDVI, the second scatterplots of TOC Red vs. TOC NIR for the above three cases, and the third column shows scatterplot of TOC Blue vs. TOC Red for the same cases. According to spectral curve for vegetated surfaces [Jacquemoud and Baret, 1990], the following relationship holds between surface reflectances over vegetated surfaces: TOC NIR > TOC Red > TOC Blue. For retrievals over snow-covered regions, TOC NIR ~ TOC Red ~ TOC Blue. But in the case of anomalous retrievals, TOC Blue > TOC Red > TOC NIR. The last case is not handled correctly by the TOC EVI algorithm: the denominator becomes small and changes its sign from positive to negative; combined with a negative numerator, this situation results in large positive values of TOC EVI. These types of anomalous retrievals were also reported for MODIS TOC EVI [Huete et al., 2002] and this issue was resolved using a two-channel TOC EVI [Solano et al., 2010; Jiang et al., 2008] and also by compositing data (anomalous retrievals are not persistent). 5. Discussions and Conclusion [52] In this study, we assessed quality and algorithm performance of the early VIIRS VI EDR product by product intercomparison with Aqua MODIS and NOAA-18 AVHRR/3. In general, the early VIIRS VI EDR product was found radiometrically performing well. Both TOA NDVI and TOC EVI of VIIRS showed consistent spatial and temporal trends to the MODIS counterparts, whereas VIIRS TOA NDVI was systematically higher than that of Figure 9. (a) (Top three panels) VIIRS daily TOC EVI, TOC NDVI, and SZA for 19 March 2013 and (bottom panel) MODIS 8 day composite snow cover map (MYD10C2) for March Anomalous TOC EVI retrievals are small patches with high TOC EVI (dark red) over the areas with negative TOC NDVI. b. Global scatterplots of VIIRS VIs (TOC EVI vs. TOC NDVI) and Surface Reflectances (TOC Red vs. TOC Blue vs. TOC NIR) in support of analysis of TOC EVI anomalous retrievals. Those are defined by inconsistency of TOC NDVI and TOC EVI (TOC NDVI < 0 but TOC EVI > 0). Left column presents scatterplots of VI and SR for the case of anomalous retrievals, while remaining columns present plots for normal retrievals. Anomalies arise due to anomalous relationship between VIIRS channels. For vegetated surfaces, NIR > Red > Blue. However, in the anomalous areas, the relationship inverses, Blue > Red > NIR. Data are for 19 March