The NOAA Global Vegetation Index product — A review

Palaeogeography, Palaeoclimatology, Palaeoecology (Global and Planetary Change Section), 90 (1991) 189-194

189

Elsevier Science Publishers B.V., Amsterdam

Condensed Paper

The N O A A Global Vegetation Index product - A review J.D.

Tarpley

Satellite Research Lab, NOAA /NESDIS, 5627 Allentown Road, Camp Springs, MD 20746, USA

(Received December 14, 1990, accepted January 8, 1991)

ABSTRACT Tarpley, J.D., 1991. The NOAA Global Vegetation Index product - A review. Palaeogeogr., Palaeoclimatol., Palaeoecol. (Global Planet. Change Sect.), 90: 189-194. NOAA has produced a Global Vegetation Index (GVI) product on a weekly basis since April, 1982. The GVI product provides a low-resolution real-time global data base of vegetation index Advanced Very High Resolution Radiometer (AVHRR) channel values, and observation geometry. Changes and improvements in the GVI since its beginning are described and a detailed description of current processing is provided. The GVI product will be changed with the launch of NOAA K, and additional satellite data from the Advanced Microwave Sounding Unit that provides information on surface conditions will be added to the mapped data base. A "second generation" GVI product that will provide a more stable, better calibrated product for climate monitoring will be added in the future.

Introduction

In April, 1982, N O A A b e g a n the p r o d u c t i o n of g l o b a l vegetation index m a p s on a weekly basis. T h e p r o d u c t , t e r m e d the N O A A G l o b a l Vegetation I n d e x (GVI), p r o v i d e s the user c o m m u n i t y a l o w - r e s o l u t i o n real-time global d a t a b a s e of veget a t i o n - i n d e x A V H R R clear radiances, clear brightness temperatures, a n d some viewing g e o m e t r y d a t a (Kidwell, 1990). The initial intent was to p r o v i d e a d a t a source for timely assessment of g l o b a l agriculture, b u t since then o t h e r uses for the G V I have been investigated, including c l i m a t e m o n i t o r i n g ( M a l i n g r e a u , 1986; G a l l o , 1990), d r o u g h t d e t e c t i o n ( G a l l o a n d H e d d i n g h a u s , 1989; G u t m a n , 1990; K o g a n , 1989), studies of large scale l a n d - c o v e r classification ( T u c k e r et al., 1985), a n d the use o f a vegetation index to specify surface

b o u n d a r y c o n d i t i o n s in w e a t h e r a n d c l i m a t e prediction m o d e l s ( M i n t z a n d W a l k e r , 1990). Production of the GVI data set G l o b a l full r e s o l u t i o n satellite d a t a sets, even at A V H R R resolution, are t o o large for convenience. T h e G V I d a t a are averaged, s a m p l e d , a n d c o m p o s i t e d to reduce the size of a g l o b a l d a t a set to a b o u t 0.1% of its original volume. T h e A V H R R processing into d a i l y m a p s is i l l u s t r a t e d in Fig. 1. O n b o a r d the s p a c e c r a f t the original 1-km resolution d a t a (local a r e a coverage, L A C ) are a v e r a g e d a n d s a m p l e d into 4-kin pixels (global a r e a coverage, G A C ) b y m e a n s of the following p r o c e d u r e : F o u r consecutive pixels a l o n g a scan line are averaged, a single pixel is s k i p p e d , the next four averaged, a n d so on to the e n d o f the scan line. T h e

190

Fig. 1. The processing of AVHRR data from 1 km resolution LAC data into the low resolution GVI data set shown. The LAC data are averaged and sampled to 4 km with the relationship between the LAC and GAC pixels shown in the top two arrays. The GAC data mapping to a given GVI map cell is illustrated by the square shown superimposed on the GAC scans.

next two LAC scan fines are skipped, then the process is repeated for the third scan line. The resulting G A C pixels and their relationship to the original data are illustrated in Fig. 1. The G A C pixels are 4 km by 1 km in size, representing a 3 by 5 km area. The G A C data are mapped pixel by pixel according to the following procedure: A single scan line is processed at a time. The map location is calculated for each pixel along the scan and the pixel values are placed in the map. The illustration shows pixels from several scan lines that fall within a single map cell. As each pixel is mapped to the cell, it replaces any previous pixel value in the cell. This produces a map that contains the last pixel m a p p e d to each cell. If another orbit contains data that map to a cell containing data from the previous orbit, the new data replaces the old.

J.D. T A R P L E Y

The data that are mapped are the Channels 1 (visible) and 2 (near-IR) counts truncated to 8 bits, two sets of Normalized Difference Vegetation Index (NDVI) calculated from counts and from albedo, and Channels 4 and 5 thermal radiances converted to 8 bit " G O E S " counts that can be converted to brightness temperature (Kidwell, 1990). The albedo-based N D V I uses prelaunch calibration coefficients to convert Channels 1 and 2 counts to albedo. Ancillary mapped data includes scan angle and solar zenith angle for the mapped channel values. Vegetation index maps are produced daily by mapping all daylight passes of the afternoon polar orbiter. The map projection has been either polar stereographic or latitude-longitude (see Fig. 2). On any single day much of the Earth is obscured by clouds. Many of the clouds particularly at the afternoon overpass time of the satellite, are small subpixel clouds that are not obvious in the data but have a significant effect on the radiances and the vegetation index. Clouds have larger reflectances in the visible than in the near infrared, so they act to decrease the vegetation index values. This property of clouds is used to reduce cloud contamination in the weekly G V I maps by daily compositing (Holben, 1986). For the compositing period (7 days), only channel data and vegetation index for days with the largest C h 2 - Chl difference (called Difference Vegetation Index, DVI) are retained at each map array location. This eliminates clouds from the composite except for areas that were cloudy for all seven days. The vegetation index calculated weekly for the G V I is a normalized difference, N D V I = ( C h 2 - C h l ) / (Ch2 + Chl), where uncalibrated count values are used in the calculation. A second N D V I calculated from albedo derived using prelaunch calibration coefficients has been added. The basic map projection of the GVI and the types of data mapped has changed and is expected to change again within a few years. Figure 2 illustrates these changes. F r o m 1982 through 1984, the GVI was produced in a polar stereographic projection with a resolution of 13 km at the equator and 26 km at the poles (Tarpley et al., 1984). During this period only Channels 1 and 2 and the N D V I were archived. In 1985 the basic map pro-

191

THE NOAA GLOBAL VEGETATION INDEX PRODUCT

jection was changed to latitude-longitude, and temperature observations from Channels 4 and 5, solar zenith angle, and scan angle were added to the mapped data set. The additional mapped data were added to allow a user of the GVI to better screen clouds and correct for scan angle geometry. The polar stereographic projection was dropped because insufficient computer time was available to do both the mapping transformations (latitude-longitude information is already in the l b data) and calibration of the thermal data. By 1993 or before, the GVI map projection will be changed back to po!ar stereographic and the G M T time of

observation and relative azimuth angle between the sun and satellite will be added to the mapped data. With the launch of N O A A K, scheduled for 1994, several channels of Advanced Microwave Sounding Unit ( A M S U ) data will be mapped to the same projection. The mapped data base will then contain time and space coincident observations for atmospheric window regions spanning the visible through the microwave. The projection is reverting to polar stereographic because several important products to be derived from this data base are for high latitudes, specifically snowcover, snowpack parameters, and sea ice. The microwave

1982-1984 1024 MAPPED VALUES: CH1, CH2, NDVI SAMPLING : LAST GAC MAPPED TO CELL 1024

COMPOSITE : 7 DAY MAX DVI PROJECTION : POLAR STERO - 13 KM AT EQ, 26 KM AT POLE

1985 - PRESENT 2500

75ON

MAPPED VALUES: CH1, CI--12, CH4, CH5, NDVI, SCAN ANGLE, SOLAR ZENITH ANGLE SAMPLING : LAST GAC MAPPED TO CELL

904 COMPOSITE : 7 DAY MAX DVI PROJECTION : LAT - LONG. - 16 KM AT EQ, 16X8 KMAT60 °

55Os

19931024 MAPPED VALUES : CH1, CH2, CH3A, CH4, CI-IS, AMSU WINDOW CHANNELS, SCAN ANGLE, SOLAR ZENITH ANGLE, RE/.. AZIMUTH ANGLE, TIME 1024 SAMPLING : MIN SCAN ANGLE COMPOSITE : SELECTABLE PROJECTION : POLAR STERO - 13 KM AT EQ, 26 KM AT POLE OTHER : v I S CALIBRATION COEFFICIENTS - PRELAUNCH AND CURRENT F i g . 2. Summary of past, present, and future projections and mapped quantities in the GVI data set.

J.D. TARPLEY

192 PSG ( 13- 26 KM)

PSG (26- 52 KM)

102

4 j'K

~

1024

AMSU 24 31 50 52 89

GHz GHz GHz GHz GHz

QTHER SOLAR ZENITH ANGLE TIME SCAN ANGLE REL AZIMUTH ANGLE

AVHRR ALL BANDS AMSU 89 GHz 157 GHz OTHER SOLAR ZENITH ANGLE TIME SCAN ANGLE REL AZIMUTH ANGLE

Fig. 3. Global mapped data bases (NOAA K, L, M) (twice daily snapshots of the world). With the launch of NOAA K (1994) and later spacecraft NOAA plans to produce daily global mapped data bases of surface data. All atmospheric window channels on AVHRR and AMSU will be mapped in time and spatially coincident data sets.

data will be mapped at two resolutions, depending on the resolution of the AMSU. Figure 3 illustrates the mapped data base from NOAA K, L, M. If sufficient computer resources are installed in NESDIS operations before the launch of NOAA K then the polar stereo GVI will be implemented before 1994. Improvements to the GVI

A goal for the NOAA GVI product is a random, nonbiased sample of global vegetation. The current product falls short of this goal because of residual cloud contamination, calibration problems, and side effects of the processing. Persistent cloudiness causes the vegetation index for some parts of the world to retain cloud contamination in the 7 day composits, resulting in a bias toward lower clear sky values. Regions with frequent cloudy periods, sometimes lasting longer than the seven day compositing period, cannot be monitored accurately on a weekly interval. Meth-

ods for cloud detection in the weekly maps have been developed for some regions, but these methods only allow avoidance of cloud-contaminated GVI data, they do not provide an improved GVI product. Instability in the A V H R R visible sensors also introduces a bias into the vegetation index. Channels 1 and 2 are calibrated before launch, but are not monitored for stability in orbit. In general, the visible and near-infrared channels change at different rates, causing a vegetation index bias that varies with time. Various in-orbit calibration procedures making use of natural surfaces that are assumed stable with time are under investigation (cf. Abel, 1990). When several cloud-free observations of an area are obtained during the compositing period, the clear observation with a scan angle that results in the highest Difference Vegetation Index (DVI) will be saved in the composite. Over vegetated surfaces, DVI is observed to be highest in the backscatter direction; for many deserts it is highest for forward scatter. Figure 4 shows the strong scan angle biases for two different surface types. These scan angle biases will not in general result in the highest value of NDVI, so a second bias toward a lower vegetation index is introduced. Other biases may be introduced into the GVI by orbital drift of the spacecraft. The effects of orbital drift on solar zenith angle are illustrated in Fig. 4. Note the discontinuity in solar zenith angle caused by the replacement of NOAA-9 by NOAA-11 in November, 1988. Unless a precise bidirectional reflectance model is available for each surface, it is difficult to distinguish between vegetation index drift caused by calibration, orbit drift, and real changes at the surface. Improvements to the GVI will come in two ways. Research to provide better cloud screening and scan angle corrections is being done under the NOAA Climate and Global Change program. These methods, if successful, will be applicable to the weekly GVI and will increase the signal-tonoise ratio of the product. Pilot techniques have already been investigated. A second generation GVI will eventually be produced from data that is "target processed", that is, arrays of pixels will be used to do accurate

193

THE NOAA GLOBAL VEGETATION INDEX PRODUCT

LIBYAN DESERT

© Z <

-40

60

SCAN ANGLE

i[41i UH~lllillll~li~illlililllL~HJ~jlldlIH IIIIHIIIIIIm RIIII Jlllllllll~lIHfflU IIHIHH IlIHIIIIItIHtlllll~ll~HIHIllllHI~III~IIIIIHH JUH Irllm m I~HIIIII H #1 ~IIIIIIIIIIIII~ilEUIIIHIHIIIIIIIHIIIIII~HII

0

20

40

60

80

60-[ %

r.,,O

II

120

140

160

180

200

220

HU H ~IHIHllLI

240

F O R E S T - E A S T E R N U.S.

80E

40!

1O0

f _

E

© Z < 1

-20-

q -40-[

~

~

'

/ ....

1 !I

SCAN ANG LF -@0

i i i H i l l i H i i i i i i l l l i l ill IIIIH i i i l l ~ i l l i l l i l l lTl l l l l l l l l l l l l l i l r l l ~ l l riiHrllll l ill Hllm Ill ~rlli llliIHIliH l l l l l l l l l l [ IH I l l l l l l l l lH H l ~

1985

1986

1987

1988

SOLAR ZENITH ANGLE ~

H IF ill IH ill ~ll I [iHIr IHil ill Illlll IH II [H IH Irl lIH![ HI I I I ~ N ~ T !

1989

.....

SCAN ANGLI ~.

Fig. 4. Zolar zenith and scan angle time series. The effects of weekly compositing and orbit drift on the GVI viewing geometry are shown for two surface types, desert (top) and deciduous forest (bottom). Average solar zenith angle and scan angle are shown for 300 km regions from the weekly GVI data sets Negative scan angles are in the backscaner direction.

cloud screening, before mapping the channel values and vegetation index. Target processing allows comparison of adjacent pixels; scenes with high pixel-to-pixel variability are very likely to be cloudy. A prior knowledge of target characteristics will also be used to screen clouds. This approach has potential for much more reliable cloud detection and removal, and would allow the derivation of vegetation index target statistics (mean, maxi-

mum, minimum, standard deviation, etc.) that would provide more information to the user of the product. Correction of the vegetation index and individual channel values for variable atmospheric effects caused by water vapor, aerosols, and ozone is difficult because daily information on these quantities is not easily available. Appending a m a p p e d and composited set of atmospheric precipitable

194

w a t e r o b t a i n e d f r o m N M C o p e r a t i o n a l a n a l y s e s is b e i n g c o n s i d e r e d . If t h e g l o b a l p r e c i p i t a b l e w a t e r fields are o f s u f f i c i e n t a c c u r a c y , t h e n t h e y c o u l d b e u s e d to c o r r e c t C h a n n e l 2 r a d i a n c e s . T h e G V I p r o d u c t is a v a i l a b l e , a l o n g w i t h a U s e r ' s G u i d e , f r o m the Satellite D a t a S e r v i c e s D i v i s i o n at the f o l l o w i n g a d d r e s s : N O A A / NESDIS/SDSD, Princeton Executive Square, Rm 100, 5627 A l l e n t o w n R o a d , C a m p Springs, M D 20746, U S A . Tel. (301) 763-8402.

References Abel, P., 1990. Report of the Workshop on Radiometric Calibration of Satellite Sensors of Reflected Solar Radiation, March 27-28, 1990, Camp Springs, Md. NOAA Tech. Rep. NESDIS 55. Gallo, H.P, 1990. Satellite-derived vegetation indices: a new climate variable? Proc. Symp. on Global Change Systems, Anaheim, Ca. Am. Meteorol. Soc.

J.D. T A R P L E Y

Gallo, K.P. and Heddinghaus, T.R., 1989. Proc. Sixth Conf. on Applied Climatology, Charleston, S.C. Am. Meteorol. Soc. Gutman, G.G., 1990. Towards monitoring droughts from space. J. Climate, 3: 282-295. Holben, B.N., 1986. Characteristics of maximum-value composite images from temporal AVHRR data. Int. J. Remote Sens. 7: 1417-1434. Kidwell, K.B., 1990. Global Vegetation Index Users Guide. NOAA/NESDIS, Satellite Data Services Division, Washington, D.C. Kogan, F.N., 1989. Remote sensing of weather impacts on vegetation in non-homogeneous areas. Int. J. Remote Sens., 11: 1405-1419. Malingreau, J.P., 1986. Global vegetation dynamics: satellite observations over Asia. Int. J. Remote Sens., 7: 1121-1146. Mintz, Y. and Walker, G., 1990. Energy and water budgets of the land surface Part II: Derived from the measured vegetation index, surface air temperature and precipitation. J. Climate. Tarpley, J.D., Schneider, S.R. and Money, R.L., 1984. Global vegetation indices from the NOAA-7 meteorological satellite. J. Clim. Appl. Meteorol., 23: 491-494. Tucker, C.J., Townshend, J.R.G. and Goff, T.E., 1985. African land-cover classification using satellite data. Science, 227: 369-375.