Towards better quality of AVHRR composite images over land: Reduction of cloud contamination

REMOTE SENS. ENVIRON. 50:134-148 (1994)

Towards Better Quality of AVHRR Composite Images over Land: Reduction of Cloud Contamination G. Garik Gutman,* Aleksandr M. Ignatov,* and Steve Olson t A procedurefor reducing cloud contamination in NOAA / A VHRR composite imagery is proposed and applied to the NOAA global vegetation index data set. The suggested approach is based on thresholding the A VHRR / Channel 4 (11 /~m) brightness temperature. The global spacetime-angle dependent thresholds (monthly, 2°× 2 °, for 30°-viewing angle bins) are estimated in two steps. First, a clear-sky background for Channel 4, in terms of means and standard deviations, is developed from GVI weekly composite data using their "greenest" subsamples. Second, this background is used to estimate the space-time-angle dependent thresholds. Both the size of the "greenest" subsample for deriving the climatology and its use for constructing the thresholds are discussed in detail using targets in central United States and Thailand. The approach is generalized further and applied globally. Special attention is devoted to the development of quantitative criteria to estimate the eJficiency of both steps and to the improvement resulting from implementation of the proposed procedure of cloud screening.

INTRODUCTION Global weekly composite images from the Advanced Very High Resolution Radiometer (AVHRR) have been archived at NOAA/NESDIS since 1982 as the global vegetation index (GVI) data set, which was designed primarily for monitoring vegetation cover on a global scale (Tarpley, 1991). It represents a compressed, both in space and time, subsample of global area coverage

(GAC) data collected from the afternoon overpasses of the NOAA-7, -9, and -11 satellites. Spatial compression is achieved by mapping samples of the original 4-kin GAC data into a Plate Carree projection with a 0.15 ° x 0.15 ° latitude/longitude resolution (one out of several GAC pixels within the GVI map cell is selected randomly). Temporal compression is done by applying a special compositing procedure to the daily mapped data, which both compresses the data volume and reduces atmospheric effects (Holben, 1986). The maximum-value compositing (MVC) used for NOAA GVI is based on retaining the data at each map cell for the day of maximum difference between the raw counts in the AVHRR Channels 2 and 1 during a 7-day period. The physical basis of this approach is that the above difference is smaller in cloudy and/or turbid atmospheric conditions and is larger over vegetated surfaces under clear-sky atmospheric conditions. Since April 1985, this GVI weekly composite data set consists of global maps of the AVHRR counts in the Channels 1 (0.57-0.70 #m), 2 (0.72-0.98 /~m), 4 (10.3-11.3/~m), and 5 (11.4-12.4/tm), as well as the associated scan and solar zenith angles. Although a GVI weekly composite is often treated as an image, it has a mosaic structure, in which case adjacent map cells may correspond to different days of the week with different sun-target-sensor geometries and weather conditions. Its advantage is that the data are presented in a compressed form and on a regular space-time grid, which is convenient for analysis. Additionally, weekly composite images contain many fewer cloudy pixels than the original daily images. However, cloud contamination still persists in composite imagery, and this article deals with reduction of its influence on vegetation cover investigations.

* NOAA/NESDIS, Satellite Research Laboratory, Washington, DC t Research and Data Systems Corporation, Greenbelt Address correspondence to G. Garik Gutman, NOAA / NESDIS / ERA 12, 712 World Weather Bldg., Washington, DC 20233. Received 6 December 1993; revised 21 May 1994.

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BACKGROUND

Figure 1 is an example of a channel 4 GVI weekly composite image, the colder pixels associated with clouds 0034-4257 / 94 / $7.00 ©Elsevier Science Inc., 1994 655 Ave~me of the Americas, New York, NY 10010

Reduction of A VHRR Cloud Contamination 135

shown whiter than the surrounding clear pixels. It suggests that weekly compositing does not provide reasonably clear global images since some geographic regions are cloudy every day of the compositing period during NOAA satellite overpasses. This contamination must be removed before any geophysical analysis of the surface characteristics is possible. The mosaic structure of the composite significantly complicates application of the existing cloud screening techniques to these data. Other alternatives to current MVC procedures could serve more effectively to "clean" the daily NDVI (Viovy et al., 1992) or original multispectral GAC data (e.g., Saunders and Kriebel, 1988; Gallaudet and Simpson, 1991). Further compression of the data (spatial/temporal sampling) should be performed using more robust procedures, for example, by averaging those pixels within a certain space-time-angular grid box that pass cloud filters applied to the original GAC data (Goward et al., 1991; Gutman, 1991). The use of nonrobust statistics, based on extremal estimations as in the MVC, may result in selecting individual measurements that are subject to random errors, such as outliers (Viovy et al., 1992). Many lessons have been learned while working with GVI data set that was designed using the state-of-the-art of scientific understanding of the early '80s. In spite of the fact that the "damage has been done" by inadequate preprocessing (Goward et al., 1993), the GVI composite global 12-year data set contains much information, is readily available, and is continuing to be widely used by researchers. Thus "post-composite" screening procedures should be of interest to the GVI data users community. Our efforts to reduce cloud contamination are primarily aimed at studying vegetation in terms of the normalized difference vegetation index (NDVI) NDVI = (Ch2 - C h l ) / ( C h l + Ch2),

(1)

where Chl and Ch2 are measurements in AVHRR Channels 1 (visible) and 2 (near-IR), respectively. Further temporal compositing (2 weeks or longer) results in a gradual "clearing" of composite images with an increase of the compositing period, but there is no assurance that the resulting NDVI value indeed corresponds to cloud free conditions (Eck and Kalb, 1991; Goward et al., 1991). Although cloud contamination is reduced by such longer-term compositing (but not removed completely), a substantial amount of clearsky data is wasted in regions with cloud-free conditions. Most importantly is that the temporal resolution of the data is reduced, such that frequencies higher than the

Figure 1. Channel 4 GVI weekly composite image for 10-16 July 1989. Black: warm. White: cold.

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compositing period cannot be monitored. In some areas of the world, especially during the growing and senescent seasons, this shorter-term variability is clearly pronounced. In this study, we are concerned mainly with procedures that do not result in a decrease in the temporal resolution of the GVI weekly composite data.

Cloud Detection in Composite Imagery Identification of cloud-contaminated cells in the composite maps is not a trivial task, especially over land. There have been many schemes developed for cloud detection in the instantaneous multispectral AVHRR imagery which use three types of procedures-absolute thresholding, spectral variability, and spatial uniformity (e.g., Saunders and Kriebel, 1988)-and a few that additionally involve temporal analysis (e.g., Gutman et al., 1987; Rossow and Garder, 1993). None of those algorithms, however, can be applied to composite images in a straightforward fashion. In particular, the spatial/ temporal uniformity tests cannot be used in their original formulation because the composite data are spread over a longer time interval, and adjacent pixels in the composite map could have been taken from different days of the week having quite different sun-targetsensor geometries and weather conditions. Additionally, some of the existing algorithms use the AVHRR Channel 3 and have been developed for application to data with specific resolution. They cannot be easily modified to be applicable to 16-km GVI map cells with missing Channel 3 information. Thus, only certain procedures from previous schemes can be used for cloud screening in GVI composite images, and then only after substantial modification. The procedures described below can be potentially utilized for reduction of cloud contamination in the composite images. Using the NDVI Itself The idea of using the NDVI itself for cloud screening is attractive for the reasons of simplicity, computer economy, and self-consistency. One can easily imagine a practical situation when the investigator has only NDVI without any additional information. This combination of channels partially cancels out the anisotropy effects (see, e.g., Curran, 1980), which makes NDVI data more uniform in space and time than the single-channel data. One of the possible ways to screen clouds in composite images using the NDVI itself involves statistical analysis of multiyear GVI data to derive a cloud-free climatology of NDVI, from which NDVI thresholds can then be estimated. The physical basis of this approach is the same as that underlying the MVC procedure: a decrease of the NDVI in cloudy and hazy conditions. However, when using NDVI thresholds, based on a multiyear climatology, one risks erroneously filtering out the clear-sky pixels over stressed/

senescent vegetation or bare soil, misclassifying them as cloudy. A "green" bias, produced this way in the retrieved NDVI, is highly undesirable because one of the most important challenges in vegetation monitori n g - detecting droughts-is likely to fail if dry anomalies are misclassified as cloud contamination. Such biased NDVI would be a poor candidate for use in surface parameterizations of global numerical climate models. Statistical filters have been used by some investigators to smooth the weekly composite NDVI time series (e.g., Malingreau, 1986; van Dijk et al., 1987; Kogan, 1990). The advantages of such procedures are that: 1) the temporal resolution of the original data is retained, 2) most of the cloudy pixels are removed as outliers in areas with frequent cloud-free conditions, and 3) variability due to sun-target-sensor geometry, weather, and spatial sampling is also smoothed out. Its disadvantage is that persistent cloud contamination over several weeks may result in suppressed NDVI values even in the filtered time series. Indeed, a robust smoothing procedure generates its result from the majority of the data points depicting a typical spatial / temporal pattern, outliers being rejected. In the case of persistent cloud occurrence, the risk of having the majority of pixels affected by clouds is high. The few existing clear pixels in this case may be rejected as outliers, and the smoothed time series is generated based on cloud contaminated pixels. If the yellow color identifies low NDVI, then, in contrast to thresholding that produces a "green bias," NDVI from such a filtering procedure may tend to have a "yellow bias." An example of the application of a five-point median filter to a weekly composite NDVI time series over Thailand is shown in Figure 2. During the monsoon season, the smoothed NDVI curve indicates an obvious example of "yellow bias." In order to demonstrate its cause, the corresponding Channel 4 brightness temperature data (not smoothed) are also shown in Figure 2. One can see that the temperatures during the rainy season are much lower than would be expected from clear sky observations over this area (many are below 270 K), which can only be explained by cloud contamination. Thus, persistent cloud contamination may result in misinterpretation of vegetation phenology during certain extended periods of the year if statistical filters (temporal, spatial, or a combination thereof) for NDVI alone are used. Additionally, any further NDVI correction for angular and atmospheric variability becomes impossible after the NDVI data have been statistically filtered. For these reasons, application of a statistical filter, either spatial or temporal, can be recommended only at the final stage of analysis, after cloud screening and possible corrections for all physical effects have been made, to remove residual noise, for example, from

Reduction of A VHRR Cloud Contamination 137

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sampling. That is, the statistical filtering should complete the processing of the NDVI data rather than precede it. Using the AVHRR measurements in Channels i and 2 provides an additional potential for cloud screening. However, the basic reason for their inability to discriminate between cloud contaminated pixels and the clear ones over bare soil or senescent/stressed vegetation is that in both situations the visible and near-IR reflectances are elevated and close to each o t h e r - t h e effect of the "uncertainty zone" (Gutman, 1992).

Using Thermal IR Data The physics of this approach is based on the fact that clouds over snow-free land are usually colder than the underlying surface. Cloud-contaminated regions can thus be detected as those of low brightness temperatures on composite thermal IR images. The question is how should the thresholds be selected. A simple "thermal mask" having a constant threshold can reduce cloud contamination only over limited areas and for limited time intervals. Eck and Kalb (1991) screened cloud contamination in composite imagery over Africa using information on the spatial distribution of air temperature. Air temperature data, however, is not readily available globally, and its conversion to brightness temperature for a particular spectral band and viewing direction, for a certain geographical region and season, is not a simple task. Additionally, the method of Eck and Kalb is not applicable for areas with large diurnal and seasonal temperature variability. For adequate realization of an approach based on thresholding of the AVHRR-brightness temperature, one should have a set of thresholds dependent upon space,

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time, and viewing geometryJ Such thresholds can be developed from a clear-sky climatology of brightness temperature in terms of its mean values and variations for any particular region, season, and scan-angle bin. Neither such a climatology nor methods for its use to estimate thresholds are presently available for the AVHRR thermal IR channels.

Multispectral Potential A one-channel thermal IR threshold is effective for removing thick cold clouds, but the pixels with partial / thin cloudiness in the radiometer field-of-view may pass this filter. The brightness temperature difference in AVHRR Channels 4 and 5 is often used to detect this kind of cloud contamination over the ocean since it is small both for a cloudless surface and uniform thick clouds, the latter being detected and excluded with a one-channel thermal IR threshold, and is larger in the intermediate cases (Inoue, 1985). Although this principle is employed in most existing AVHRR cloud detection schemes (e.g., Saunders and Kriebel, 1988), it should be emphasized that the behavior of this parameter over vegetation / bare soil is still under investigation (Eck and Holben, 1994). Its global application for cloud screening in composite imagery over land needs further research effort.

I In principle, the thresholds should also depend upon sun elevation in order to account for two effects in regions with pronounced diurnal temperature variability: 1) difference in the local time between the left and right edges of the orbital swath; and 2) satellite orbit drift (later equator crossing) from year to year, which leads to a shift in the local time of the satellite overpasses. These effects are secondary compared with that resulting from geographical-seasonal-angular variation of the brightness temperature, and thus they are disregarded in the present article.

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Combination of thermal IR and shortwave data presents another potential in identifying satellite image pixels. In particular, Goward et al. (1985) and Price (1990) showed that a close relationship exists between brightness temperatures and NDVI in cloud-free images over nonuniform land areas. The physical basis is that the more heavily vegetated surfaces are associated with a larger latent heat flux and hence are cooler than the less vegetated ones. Cloudy pixels generally have an opposite dependence: smaller NDVI correspond to lower temperatures. In regions with nonuniform underlying surfaces, this principle could provide an important supplement to the thresholding of brightness temperature. Summarizing this section, we emphasize that 1) the use of the NDVI composite data without additional cloud screening may result in misinterpretation of vegetation phenology; 2) the use of NDVI itself for cloud screening is seriously hampered; 3) using Channels 1 and 2 may not discriminate between cloud contamination and bare soil/stressed vegetation; and 4) the thermal IR is most appropriate for reducing cloud contamination in the GVI data set. A cloud screening procedure may rely on either brightness temperature, T4, thresholding, or using (T4- T~), or T4/NDVI relationship, or a combination thereof. As a starting point, the present study uses the first simplest and globally applicable approach, based on thresholding the brightness temperature in the Channel 4 alone. GVI DATA CALIBRATION AND QUALITY CONTROL In this section, we describe the calibration of visible/ near IR and thermal IR GVI data and their quality control. This topic is of much importance for the present work, which uses long-term satellite time series: 6 years of GVI data from April 1985 to March 1991. This corresponds to the observational periods of two satellites: NOAA-9 and -11. Data from June 1991 onward have been excluded because of the Mt. Pinatubo eruption, which resulted in substantial aerosol contamination of the atmosphere.

GVI data set can perhaps be explained by a misperception that temperature information is of lower priority for vegetation studies. Note that the difference (T4 - T~) may accumulate errors up to 1 K, which may preclude its potential use as a cloud filter in this data set. The linear calibration procedure was shown to result in errors of up to 2 K because of nonlinearity of the sensor response (Weinreb et al., 1990). The nonlinearity correction depends upon the temperature of the internal target, which varies typically between 9°C and 19°C, but is not available in the GVI data set. We used the tabulated data of Weinreb et al. (1990) for mean target temperature (15.0°C and 13.9°C for NOAA-9 and -11, respectively). The error that may result from disregarding target temperature variability rarely exceeds 0.5 K, which is consistent with the absolute calibration accuracy, estimated by Weinreb et al. (1990) to be approximately 0.55 K.

Visible and Near-IR Channels The Channel 1 and 2 values in the GVI data set are original 10-bit GAC counts truncated to 8-bit accuracy. They were converted to reflectance factors using prelaunch calibration coefficients (Kidwell, 1990) and transformed to bidirectional reflectances R1 and R2 by dividing by the cosine of the solar zenith angle. These two channels of the AVHRR are not calibrated in orbit, and their sensitivity is known to degrade with time. In the present work, the time-dependent corrections for NOAA-9 and -11 suggested by Kaufman and Holben (1993) are used.

Quality Control In the GVI data set, quality control is needed because of misregistered measurements and dropout lines. The most obvious constraints on the data are: 1) solar zenith angle <90°; 2) scan angle <55°; 3) (R1 and R2)>0; 4) (T4 and Ts)< 325 K, the latter being a consequence of sensor saturation between 325 and 330 K (Davis, 1993). Also, poor quality data may be identified when they appear as outliers in R~, R2, T4, Ts, and/or their differences. In the present study, data with (R2- R1)> 30% (equivalent NDVI >0.7) or (T4- T~)< - 2 K or > + 10 K were excluded.

Thermal IR Channels In generating the GVI data set, the raw GAC 10-bit counts in Channels 4 and 5 are converted to radiances using a linear calibration procedure with two coefficients calculated from periodic viewing of an onboard black body and deep space. The radiances are converted to the brightness temperatures T4 and T5 (Kidwell, 1990), which are truncated to an 8-bit format. This results in a loss of accuracy: T4 and T5 are digitized with a step of 0.5 K for values greater than 242 K and 1.0 K otherwise. The crudeness of the thermal IR data in the

METHODOLOGY

The Proposed Approach: Two-Step Analysis A method proposed here potentially intends to use all spectral information available in the GVI data set, and hence is referred hereafter as the multispectral algorithm for screening composite imagery (MASCI). Its first version presented in this article is based on thresholding applied to the Channel 4 brightness temperature, T4, alone. The thresholds are assumed to depend on space

Reduction of A VHRR Cloud Contamination 139

(latitude ~ and longitude 2), time (t month), and viewing angle (®v). We postulate that the global maps of time and angular dependent T4-thresholds T(~,2,t,®,) (the data with T4 < T are ascribed to cloud) can be estimated as a linear combination of climatological clear-sky means T4((o,,~,,t,Ot,) and standard deviations a~4(~o,A,t,®v)

T(V,X,t,O~) = T4(¢,X,t,e~) + yaT4(v,2,t,®~),

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with some empirical parameter y. The problem of T4 thresholds is thus reduced to development of a spacetime-angle (STA)-dependent clear-sky climatology and the choice of 7.

Development of Clear-Sky Climatology from GVI Data In order to develop the AVHRR T4 clear-sky climatology, one needs a multiannual global time series of mapped AVHRR data. In principle, clear-sky background maps of multiannual means and standard deviations of brightness temperature in the spectral band compatible with AVHRR Channel 4 could be derived by processing ISCCP 2.5 ° products obtained mostly from geostationary satellite data. However, this task is tedious and not straightforward. Additionally, that product would allow neither refining the above clear-sky climatology using IGBP (1 km 2) or Pathfinder (8 km 2) data sets (IGBP, 1992; Ohring and Dodge, 1992) nor further development of MASCI to include procedures based on using (T4 - Ts) and T4 / NDVI regressions. The NOAA GVI data set is presently the most appropriate candidate for this task. 2 Figure 1, however, indicates that the GVI data are very cloudy. We thus face the problem of using the cloud contaminated GVI data for constructing a tool that in turn can be applied to GVI itself (or other AVHRR data) for cloud screening purposes. The approach proposed here to tackle this "vicious circle" relies on two basic premises. The first is an assumption that during a particular time interval in a given geographic region, and within a specific

2 The ISCCP AVHRR time series, which are shorter-term and with coarser (30-km) sampling than that of GVI, could be utilized in a similar fashion. In perspective, long-term mapped global AVHRR time series will be availablefrom the NASA/ NOAAPathfinderproject (Ohring and Dodge, 1992). The Pathfinder data set is anticipated to be superior in some aspects (resolution,availabilityof Channel 3 data, longer temporal coverage,improvedpreprocessing)to GVI. Although the NOAA GVI has been criticized for many deficiencies(Gowardet al., 1993), it maintains its importance for two major reasons. The first is that the developed clear-skyclimatologycan be directly used as a first step in any AVHRR cloud identification scheme or as a "postcomposite" cloud mask as in the present study. The second reason is that the GVI data set is being used as a "battleground"for developing and testing techniques to make use of/improve the AVHRR time series. The experience gained can be utilized, with necessary adjustments, for tackling similar problems in the Pathfinder data. Particularly, the current methodology for deriving a clear-sky climatology could be used and refined.

viewing angular range (STA-grid box), one can attain a certain number of clear view observations. The second is an assumption that these clear pixels correspond to those with the highest values of NDVI. The suggested method for developing the clear-sky climatology is thus based on calculating T4 means and standard deviations corresponding to that subsample of the total STA box population hereafter referred to as "greenest." Note that the assumption of higher NDVI corresponding to clear pixels may not hold over unvegetated areas such as deserts or snow; hence some other physical principles should be used here instead. The presently derived climatology over such regions is unreliable, but it is of little concern to vegetation studies. Similar to the MVC procedure, the present method assumes that within a certain STA box one can find a few clear pixels, which are specifically those with the highest NDVI values. But contrary to the MVC, which uses the NDVI in such detected pixels and disregards the associated brightness temperature, the present approach does not employ the NDVI in the greenest subsample; rather, the associated thermal IR data are used to develop a clear-sky background, which is subsequently used for clear/cloud identification independently of the NDVI. The greenest subsample is selected separately for each year that is appropriate for the climatology development, as different years should be represented on an equal basis. This also accounts for the effects of NDVI interannual variability attributable to changes in land/atmosphere conditions and illumination geometry.

Sensitivity Analysis At both stages of the analysis, empirical parameters are to be used (size of the greenest percentile g for estimating climatological T4 and crT4; and the parameter y for estimating thresholds T). An essential part of the MASCI approach is development of quantitative criteria for choosing these parameters. We attempt to define quantitatively the degree to which the climatology developed is indeed "clear sky," and verify that the data that pass the thresholding based on this climatology are cloud free. The choice of both empirical parameters is based on a sensitivity analysis of climatological T4 and aT4 to the greenest percentile g and of the resulting NDVI to y. More details on the sensitivity analysis procedure are given in later sections.

Regional Analysis and Its Global Generalization Both empirical parameters g and y are site specific and seasonally dependent. In this work, however, we assume that the values of g and y may be estimated from a few case studies and then used globally. In order to do this, one should select a few representative STA boxes. For detailed analysis, we used the GVI time series for two

140 Gutman et al.

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Kansas and central Thailand in January and July. Also shown are the mean T4 and T4- aT4 derived from the 25% greenest subsample. 2 ° x 2 ° target areas that are quite contrasting in their bioclimatic conditions: eastern Kansas in the central United States (~ = 39°N; ). = 97°W), which is mostly prairie grassland, and central Thailand (tp = 15°N; 2 = 100°E), which is mostly maize/rice fields and forest. The scattergrams T4 / NDVI for January and July over these two areas in Figure 3 demonstrate that they are representative of four basic situations that can result from a combination of a clear/cloudy atmosphere with a uniform/ nonuniform underlying surface. Kansas provides, to a good approximation, an example of a uniform land surface (more so in winter). In summer, it is frequently cloud free. In winter, this region is often cloudy a n d / o r snow covered. Thailand is nonuniform as compared with Kansas• During the monsoon season between May and September, this region is characterized by turbulent weather with much cloudiness and rain. Between November and April, it resembles arid climates with frequent clear skies and no rain. These features are clearly traced in Figure 3.

Thailand provides a touchstone for checking applicability of the proposed approach based on T4 climatology in terms of means and standard deviations. Such an approach is appropriate only in the case of a normal distribution of T4 and is obviously handicapped over inhomogeneous underlying surface. Because T4 decreases with NDVI here, the estimate of T4 for the greenest subsample is obviously biased to lower temperatures, although it is still referred to as "climatology" in the present article for consistency• The four described situations provide a basis for choosing parameters g and ?, which are then used globally. DEVELOPMENT OF A CLIMATOLOGY The STA-Grid Box In developing a global T4 climatology, we used 2 ° x 2 ° latitude/longitude boxes and a monthly time interval. For the GVI composite, this yields between 760 and 960 measurements per month for 1 year.

Reduction of AVHRR Cloud Contamination 141

The composite images comprise observations made at different viewing angles ®v. We stratified the scan angles into three bins: 1) between 45 ° and 15 ° in the backscatter direction (viewing away from the sun); 2) within 15 ° near nadir; and 3) between 15 ° and 45 ° in the forward scatter direction. Using an equidistant step in the cosine of the scattering angle in the visible/ near-IR region, or in the secant of the viewing angle in the thermal IR, might appear more consistant with the physics of radiative transfer in the land-atmosphere system. However, the requirement of a compromise between two regions of the spectrum resulted in the choice of an equidistant step in the viewing angle in the first version of MASCI. Viewing angles I®ol >45 ° were not considered because of strong atmospheric and surface bidirectional effects. This restriction results in discarding almost one third of the data because of a strong bias in the number of backscatter observations in GVI composite imagery over vegetated areas (Gutman, 1991). Thus, the STA box used in this study is (2 ° × 2 °) / 1 month /30 °, with the number of observations for each box varying irregularly depending on cloud occurrence during the compositing period and the atmospheresurface anisotropy. The clear-sky means T4 and standard deviations a~4 were estimated using its greenest subsample.

manifests itself in a pronounced T4 / NDVI negative correlation in January (see Figs. 3 and the last subsection of the second section). This situation is a typical example of a cold-biased climatology. The large values of standard deviation for g = 100 % are mostly determined by clouds during the rainy season. Once again, the difference between -T4 and crT4estimates for 10th and 25th percentiles is statistically insignificant. Figure 3 suggests that in relatively cloud-free cases, such as Kansas in July and Thailand in January, the estimate of a~4 for g= 100% seems to better describe the variability of clear-sky T4, whereas temperatures for 25th and 10th percentiles are underestimated. But the requirement of a universal percentile g, applicable globally, results in a compromise value of g= 25%. These deficiencies of the present clear-sky climatology can be compensated for by appropriately choosing the 7 parameter in Eq. (2). The analysis carried out in the present subsection provided a basis for using the 25%-greenest subsample to develop the global clear-sky statistics of T4 and aT4. Using the 100%-sample may lead to substantial biases in both means and standard deviations over cloudy regions; using the smaller 10%-subsample reduces the number of observations 2.5 times without any essential improvement of the result.

Climatological Global Maps of T4 and o't4 Choosing the Size of the Greenest Subsample g From the four basic situations in Figure 3, one may see that in some geographical regions and during certain seasons, much of the composite GVI data are cloud-free (g--" 100%) and may be used for climatology development (Kansas in July; Thailand in January). In others, persistent clouds may decrease this percentile significantly (Kansas in January; Thailand in July). To choose a compromise value of g for global application, we estimated the 6-year monthly means T4 and standard deviations cr~4 over Kansas and Thailand for three different percentiles: g= 100% (total sample), 25%, and 10%. The results are presented in Figure 4. For Kansas (Fig. 4a), the results of T4 estimation depend only slightly upon the subsample used._Within the uncertainty interval of ~ + 3E (E = a~4 / ~/N being the standard error of T4 estimate) that corresponds to the 99% confidence level, the T4-estimates appear almost identical for either g = 10%, 25%, or even 100% over the whole year. The standard deviations a~4 depend more significantly, up to a factor of 2, whether the total sample or only its greenest part is used. For g _ 25%, both T4 and a~4 are stabilized and remain invariant within their uncertainty intervals. For Thailand (Fig. 4b), using the greenest subsample during the dry winter with relatively small cloudiness results in a decrease in T4. This is a consequence of the nonuniformity of the underlying surface, which

The T4 and aT4 global maps for July (nadir) are given in Figure 5. These maps, and those for other months and viewing directions, should be validated before further use because they have been developed based on just a few case studies and may be in error elsewhere. Validation cannot be done using some reference AVHRR Channel 4 brightness climatology because it was the latter's absence in the literature that forced us to develop it in the present study. One can evaluate the quality of the presented global T4 climatology by checking its consistency with the known typical magnitudes, worldwide distribution, and the spatial coherence of temperature and its variability. In this regard, both mean and standard deviation maps exhibit reasonable features.

Deficiences in the Developed Climatology A few peculiarities should be noted that result from inadequacies of the procedure for climatology development or from specifics of the data used. In the regions with nonuniform vegetation, where the assumption of normal distribution of T4 fails, the developed climatology is cold-biased and underestimates variability. In the regions with persistent cloudiness, the climatology may still be cloud contaminated. The only solution for this problem is to develop a climatology using data from other sources or other times of the day when there are clear skies.

142 Gutman et al.

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Since satellite passes are in the midafternoon, the measured brightness temperatures correspond, typically, to maximum temperatures during the day rather than to diurnal average conditions. Thus, in fact, we have developed a clear-sky midafternoon climatology. We mentioned earlier that the concept for developing the climatology used in the present w o r k - " t h e greenest is the clearest"-fails on both low and high ends of the T4 scale, that is, over snow and deserts. For this reason, both mean values and standard deviations are unreliable in such regions. Additionally, over the hottest areas in Figure 4b (Sahara, Saudi Arabia, and Iraq) both T4 and cr~4 are most probably underestimated because of sensor saturation. Fortunately, both situations are of little practical interest for vegetation studies. Recall that development of clear-sky climatology is not the goal of the present study, but is merely a tool to construct thresholds for cloud screening. As such, its deficiences will be compensated at least partially, by appropriate choosing of the empirical parameter y in the next section. DEVELOPMENT OF THRESHOLDS FROM CLIMATOLOGY In the fourth section the problem of thresholds estimation from clear-sky T4 climatology was reduced to choosing the ? parameter in Eq. (2). The cloud-free data are identified as those with T4 > T4 + ?a~4. If y is negative and large in absolute value (y--" - 0o), then no thresholding is d o n e - a l l data pass. If y is positive and large (y ~ + 0o), no data pass this filter. In the case of a normal distribution of cloud-free T4, which corresponds to a uniform underlying surface, the choice of y is reduced to that of what percentile p of clear points one keeps after applying a filter (p > 80%

for y = - 1 ; p > 9 5 % for y = - 2 ; etc.). The problem, however, is that the lower threshold allows more cloud to remain in the data. For example, using y = - 1 will result in losing = 20% of the clear data, but certainly excluding most of cloud contamination. Using y = - 2 will result in retaining up to 98 % of the cloudless data, bu~ more cloudiness may remain after such a crude filter. As in the case of the size of the greenest subsample, it is again necessary to have a quantitative criterium for choosing ?. The situation is further complicated over nonuniform underlying surfaces, where the T4 distribution for cloud-free conditions is not statistically normal. Bearing in mind these features of the T4 climatology, one should take care in choosing an appropriate y. The choice of the y-parameter in our study is based on the fact that the mean NDVI within any STA grid box is depressed in cloudy conditions and gradually increases as the cloud screening threshold is tightened (y increases). We assume that after some critical value of y = y0, further increase of y > y0 does not result in an improvement of the NDVI and leads only to a lesser accuracy because of fewer observations. We have calculated monthly mean NDVI as a function of parameter y, after cloud screening, over the Kansas and Thailand targets for January and July (Fig. 6). Error bars indicate the 99% confidence level (_+ 3e) in the mean clear-sky NDVI. Kansas in July is frequently cloud-free. For this reason, an increase of y up to 0 does not result in an increase of NDVI: within the uncertainty intervals, mean NDVI does not depend upon y. For y >0, the mean NDVI even starts to decrease, but by only a few hundredths of an NDVI unit. Still this decrease is statistically significant, which is a consequence of residual nonuniformity in this target. Kansas in January is often cloudy and snow-covered. For this reason, an

composite images for each 2 ° x 2 ° land surface areas, aT4 accounts for spatiotemporal (intramonthly and interannual) variability within each 2 ° x 2 ° area.

Figure 5. Global July maps of clear-sky background of T4 (top) and crT4 (bottom) for nadir viewing bin based on 6-year (1985-1990) time series of weekly

144 Gutman et al.

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increase in y results in a pronounced increase of NDVI. For ), > = - 1 NDVI stabilizes, remaining constant for higher y values. Enlarged error bars for positive ), values result from poorer statistics due to few observations. A compromise value for y between - 1 and 0 may be used for a Kansas region over a whole year. Thailand in January is predominantly cloud-free similar to Kansas in July. For this reason, these two targets show a similar behavior of NDVI versus ),. The Thailand area is less uniform than Kansas; therefore NDVI appears constant in a narrower range of), < - 0.5, and it decreases further very sharply for larger ), (by a few tenths as compared to few hundredths over Kansas), the latter being the consequence of a pronounced inhomogeneity of this target. Thailand in July provides an example of a cloudy atmosphere over a nonuniform surface. The values of the ),-parameter producing a maximum NDVI are between - 1 . 5 and + 1.5. The compromise value of ), for Thailand is within the range of ), between ~ - 1.0 and - 0.5. Combining results of the analysis for both targets, one can compromise on a generic value for 3' between - 1.0 and - 0.5: NDVI estimation is insensitive to variation of the empirical parameter in this range. Note that a possible reason for ), being slightly displaced above - 1, which is the value that seems to be natural in the case of a T4-normal distribution, may follow from the need to compensate, at least partially, for the previously described "cold" bias in the T4-climatology and underestimated aT4 that may result from using the "greenest-isthe-clearest" approach over nonuniform areas. In what follows, we use ), = - 1 rather than - 0.5, which allows more data for vegetation analysis.

RESULTS O F APPLYING MASCI FOR NDVI ANALYSIS The values of T4 and T = T 4 - aT4 are shown in Figure 3. One can see that the threshold T = T4-aT4 does a reasonable job of eliminating most cold pixels with depressed NDVI over Kansas in January and over Thailand in July. We do not lose too many "green" pixels over Thailand that are cold because of nonuniformity of the underlying surface, but cloud contamination persists in the left part of the scattergram just above the T threshold. One may assume that a better job of cloud screening could be done here by other methods, for example, using (T4- T~), or searching for a T4/NDVI regression, Over Kansas in July and, to a lesser extent, over Thailand in January, we still lose many "green" points when using y = - 1. A smaller value of y (e.g., - 2 ) would be more appropriate here. However, their loss does not result in distortion of the monthly mean area-averaged NDVI or in any serious deterioration of the statistics. Seasonality of NDVI over Kansas and Thailand Results of applying MASCI to a GVI time series are demonstrated in Figure 7, in which monthly averages of NDVI over the Thailand and Kansas targets are given for two very different consecutive years: 1988 and 1989. Both regions experienced droughts during July and August of 1988, whereas the same period in 1989 was characterized by optimal moisture conditions (U.S. Department of Agriculture, 1988 and 1989). These general features are easily noticed in Figure 7 by comparing the NDV! peaks for these two years. In Kansas, application of MASCI does not influence the monthly averaged NDV] for most of the year. The

145

Reduction of A VHRR Cloud Contamination

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error bars of the estimates (99% confidence level for the results after screening) indicate that only during three months in 1988 (January, February, and October) the NDVI differs significantly from the original (nonscreened) values. One can see from Figure 7 that these three months are characterized by spikes in the visible reflectance and corresponding drops in the brightness temperature. These results provide direct confirmation of the importance of cloud screening over Kansas in winter, something which already has been indirectly demonstrated in the previous sections. On the other hand, a good agreement of the results before and after screening for Kansas in summer could be expected because of frequent clear sky conditions. Thailand has a seasonality of cloud occurrences opposite to that of Kansas. The rainy season starts in May and continues through October. The daytime images taken during this time of the year are very cloudy.

Figure 7 shows an obvious improvement in GVI data after MASCI application, especially for the monsoon season. The superiority over the original GVI data is manifested not only by the NDVI and temperatures being greater, and the reflectances lower during the rainy seasons of 1988 and 1989, but also by smoother annual curves in contrast to the sharp drops in NDVI in August 1988 and July 1989 that are observed in the original GVI data. The decreases in the unscreened NDVI during the rainy season are statistically significant and may be misinterpreted as surface changes, such as drought conditions. This is important for numerical modelers who would like to use NDVI quantitatively.

Global Application of MASCI Figure 8 shows an NDVI weekly composite (the same week as in Fig. 1) before (top) and after (bottom) MASCI application. The regions identified as cloudy are shown

Figure 8. Weekly composite NDVI for 10-16 July 1989 with and without cloud mask (MASCI-generated overlay in white). The corresponding T4 image is shown in Figure 1.

Reduction of A VHRR Cloud Contamination

in white. In general, the visually obvious cloudy regions in Figure 1 have been successfully identified with the MASCI algorithm. These are the areas in the Intratropical Convergence Zone (Amazon, Central Africa, India, and Southeastern Asia) and other areas, such as Northern and Central Europe, Northeastern Canada, Eastern Australia). Comparison with Figure 8 (top) shows that usually the regions identified as cloudy are associated with depressed NDVI. A visual comparison of the results using 7 = - 1 and ?----0.5 (not shown) reveals only small changes in the cloud-identified areas, which suggests that the regionally adjusted parameter y is acceptable for the globe. A most important consideration is that the application of MASCI leads to a loss of much data: up to one third of the globe is flagged as cloudy. Recall that our goal was to develop a procedure that would not result in a loss of temporal resolution. But, in fact, in some regions during certain seasons persistent cloudiness during several weeks over large areas results in a loss of up to 100% of the data, and the only way to generate an NDVI product in this case would be to sacrifice temporal and/or spatial resolution. Although it may be impossible to reach our goal completely, we believe that an absence of information is preferable to using cloud-contaminated data and misinterpretating vegetation phenology. Additionally, a space/time sacrifice, inevitable in certain seasons over some geographical regions, is not necessary worldwide. A few ways to fill in the blank areas resulting from MASCI application are under investigation, but they are beyond the scope of the present paper. Summing up, note that the improvement in GVI composite data after MASCI application manifests itself in 1) a smoother and more reasonable behavior of NDVI, visible reflectances, and brightness temperatures and 2) statistically significant increases in NDVI, decreases of visible reflectances, and increases of brightness temperatures in GVI data for cloudy / rainy seasons. The second conclusion is important when NDVI is used as a quantitative parameters, for example, in global numerical models. CONCLUSIONS NOAA GVI weekly composite imagery, formed using the maximum value compositing procedure, is strongly cloud-contaminated. This dictates development of special cloud screening procedures. The use of NDVI thresholding results in a "green" bias, whereas smoothing of the NDVI without cloud screening is shown to produce a "yellow" bias. We propose a procedure to reduce cloud contamination that provides a first step towards improving the AVHRR composite imagery for vegetation studies. The described version of the multispectral algorithm for screening composite imagery (MASCI) is based

14 7

on thresholding Channel 4 brightness temperature T4. The derivation of thresholds is done in two steps. First, a special statistical procedure is applied to the GVI time series to produce a spatially, temporally, and angularly dependent global clear-sky climatology in terms of monthly means T4 and standard deviations aT4 on 2 ° × 2 ° latitude/longitude grids in three view angle bins. Second, the thresholds are estimated as a linear combination of the above climatological parameters. A detailed sensitivity analysis has been carried out to estimate quantitatively the efficiency of both the climatology development and its use for forming thresholds for targets in eastern Kansas and central Thailand representing a combination of clear / cloudy atmospheres with uniform / nonuniform surfaces. The thresholds have been applied to identifying cloud contaminated pixels in the GVI weekly composite imagery. The qualitative improvement after MASCI application manifests itself in smoother and more reasonable seasonal behavior of NDVI, visible reflectances, and brightness temperatures. The quantitative improvement was demonstrated with statistically significant increases in NDVI and brightness temperatures and decreases in visible reflectances over regions with long persistently cloudy periods. The areas where the proposed global T4 climatology, and therefore the MASCI approach, may be in error are in snow covered regions and in deserts; here the basic premises of the methodology fail. The MASCI scheme should be modified for such areas, although this is of little importance for vegetation studies on a large spatiotemporal scale. The present article describes the first step towards improvement of the quality of composite images. Although the climatology developed may be biased in nonuniform regions, it effectively serves to screen out most of cloud contamination. Future effort should be directed at developing a multispectral methodology for screening residual cloud contamination, preferably using daily GAC resolution data, that will allow development of an improved NDVI climatology. This is being done in the framework of the NOAA/NASA Pathfinder project. The efficiency of cloud screening can be improved by refining the threshold maps to 1 ° × 1 ° using 8-km Pathfinder Land data set. The clear-sky background should be checked against high resolution AVHRR clear scenes and the ISCCP data products. Procedures for interpolating the data lost as a result of cloud contamination are being developed. This study has been supported by the NOAA Climate and Global Change Program. The authors thank M. Halpert of Climate Analysis Center/NOAA, and P. Schultz and R. Hucek of Research and Data Systems Corporation (RDC) for their assistance in the initial stage of this study. Assistance in producing images of global maps by D. Sullivan of RDC is appreciated.

14.8 Gutman et al.

Constructive comments while reviewing the manuscript by NOAA / NESDIS scientists Drs. D. Tarpley, P. McClain, G. Ohring, A. Gruber, and L. Stowe are greatly appreciated. Critical reviews by Dr. J. Simpson and the anonymous reviewers contributed to a substantial improvement of this article. This work was done while A. M. I. held a National Research Council Associateship to NOAA /NESDIS, on leave from the Marine Hydrophysics Institute, Sevastopol, Crimea, Ukraine.

REFERENCES Curran, P. (1980), Multispectral remote sensing of vegetation amount, Prog. Phys. Geogr. 4:315-341. Davis, P. A. (1993), Spectral radiance-temperature conversions for measurements by AVHRR thermal Channels 3, 4, 5, NOAA Tech. Report, NESDIS 71. Eck, T. F., and Holben, B. N. (1994), AVHRR split window temperature differences and total precipitable water over land surfaces, Int. J. Remote Sens. 15:567-582. Eck, T. F., and Kalb, V. L. (1991), Cloud-screening for Africa using a geographically and seasonally variable infrared threshold, Int. J. Remote Sens. 12:1205-1221. Gallaudet, T. C., and Simpson, J. J. (1991), Automated cloud screening of AVHRR imagery using split-and-merge clustering, Remote Sens. Environ. 38:77-121. Goward, S. N., Cruickshanks, G. D., and Hope, A. S. (1985), Observed relation between thermal emission and reflected spectral radiance of a complex vegetated landscape, Remote Sens. Environ. 18:137-146. Goward, S. N., Markham, B., Dye, D. G., Dulaney, W., and Yang, J. (1991), Normalized difference vegetation index measurements from the Advanced Very, High Resolution Radiometer, Remote Sens. Environ. 35:257-277. Goward, S. N., Dye, D. G., Turner, S., and Yang, J. (1993), Objective assessment of the NOAA global vegetation index data product, Int. J. Remote Sens. 14:3365-3394. Gutman, G. (1991), Vegetation indices from AVHRR: an update and future prospects, Remote Sens. Environ. 35:121138. Gutman, G. (1992), Satellite daytime image classification for global studies of Earth's surface parameters from polar orbiters, Int. J. Remote Sens. 13:209-234. Gutman, G., Tarpley, D., and Ohring, G. (1987), Cloud-screening for determination of land surface characteristics in a reduced resolution data set, Int. J. Remote Sens. 8:859-870. Holben, B. N. (1986), Characteristics of maximum-value composite images from temporal AVHRR data, Int. J. Remote Sens. 7:1417-1434.

IGBP (1992), Global change Report No. 20, Improved global data for land applications: a proposal for a new high resolution data set. J. R. G. Townshend, Ed., Stockholm, 87 pp. Inoue, T. (1985), On the temperature and effective emissivity determination of semitransparent cirrus clouds by bi-spectral measurements in the 10/~m window region, J. Meteorol. Soc. Jpn. 63:88-99. Kaufman, Y. J., and Holben, B. N. (1993), Calibration of the AVHRR visible and near-IR bands by atmospheric scattering, ocean glint and desert reflection, Int. J. Remote Sens. 14:21-52. Kidwell, K. (1990), Global Vegetation Index User's Guide, U.S. Department of Commerce, National Oceanic and Atmospheric Administration, 49 pp. Kogan, F. N. (1990), 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. Ohring, G., and Dodge, J. C. (1992), The NOAA/NASA Pathfinder Program, in IRS'92: Current Problems in Atmospheric Radiation, Deepak, Hampton, VA, pp. 405-408. Price, J. C. (1990), Using spatial context in satellite data to infer regional scale evapotranspiration, IEEE Trans. Geosci. Remote Sens. 28:940-948. Rossow, W. B., and Garder, L. C. (1993), Cloud detection using satellite measurements of infrared and visible radiances for ISCCP, J. Climate 2:419-458. Saunders, R. W., and Kriebel, K. T. (1988), An improved method for detecting clear sky and cloudy radiances from AVHRR data, Int. J. Remote Sens. 9:123-150. Tarpley, J. D. (1991), The NOAA global vegetation index p r o d u c t - a review, PaIaeogeogr. Palaeoclim. PalaeoecoI. 90: 189-194. U.S. Department of Agriculture (1988, 1989), Weekly Crop and Weather Bulletin, Vol. 75. Van Dijk, A., Callis, S. L., Sakamoto, C. M., and Decker, W. L. (1987), Smoothing vegetation index profiles: an alternative method for reducing radiometric disturbance in NOAA/ AVHRR data, Photogramm. Eng. Remote Sens. 53:10591067. Viovy, N., Arino, O., and Belward, A. S. (1992), The best index slope extraction (BISE): a method for reducing noise in NDVI time-series, Int. J. Remote Sens. 13:1585-1590. Weinreb, M. P., Hamilton, G., Brown, S., and Koszor, R. J. (1990), Nonlinearity corrections in calibration of Advanced Very High Resolution Radiometer infrared channels, J. Geophys. Res. 95:7381-7388.