Garik Gutman

Vegetation indices from AVHRR: An update and future prospects

Abstract Procedures that are currently applied to data from Advanced Very High Resolution Radiometer (AVHRR) in deriving vegetation indices are analyzed. It is shown that the type of compositing used has a great impact on the final product. Some misconceptions concerning the global vegetation index (GVI) data set are discussed. Cloud-screened daily data over Kansas prairie during 1 month were used to develop a viewing angle correction for the observed range of sun angles and for that particular surface type. This example indicates that the variability in the calculated vegetation indices could be reduced, thus raising the confidence level in statistical comparisons of observations for different years. The correction developed is applied to a different crop area and improvement in the results is demonstrated. Errors in the calculations are analyzed. Prospects for future improvements in processing vegetation index data are discussed.

Use of NDVI and Land Surface Temperature for Drought Assessment: Merits and Limitations

Abstract A large number of water- and climate-related applications, such as drought monitoring, are based on spaceborne-derived relationships between land surface temperature (LST) and the normalized difference vegetation index (NDVI). The majority of these applications rely on the existence of a negative slope between the two variables, as identified in site- and time-specific studies. The current paper investigates the generality of the LST–NDVI relationship over a wide range of moisture and climatic/radiation regimes encountered over the North American continent (up to 60°N) during the summer growing season (April–September). Information on LST and NDVI was obtained from long-term (21 years) datasets acquired with the Advanced Very High Resolution Radiometer (AVHRR). It was found that when water is the limiting factor for vegetation growth (the typical situation for low latitudes of the study area and during the midseason), the LST–NDVI correlation is negative. However, when energy is the limiting fact...

Global distribution of cloud cover derived from NOAA/AVHRR operational satellite data

Abstract NOAA/NESDIS is developing an algorithm for the remote sensing of global cloud cover using multi-spectral radiance measurements from the Advanced Very High Resolution Radiometer (AVHRR) on-board NOAA polar orbiting satellites. The current (Phase 1) algorithm uses a sequence of “universal” threshold tests to classify all 2×2 pixel arrays of GAC (4 km) observations into clear, mixed and cloudy categories. A subsequent version of the algorithm (Phase II) will analyze the previous 9-day series of mapped ( 1 2 degree) “clear” array data to replace the “universal” thresholds with space and time specific values. This will provide more accurate estimates of cloud amount for each pixel. The current algorithm is being implemented into the operational data processing stream for testing and evaluation of experimental products. Eventually, it is intended for use operationally to support weather and climate diagnosis and forecasting programs, as well as to provide clear sky radiance data sets for other remote sensing parameters, e.g., vegetation index, aerosol optical thickness, and sea surface temperature.

Retrieving microphysical properties near the tops of potential rain clouds by multispectral analysis of AVHRR data

Abstract Properties of potentially precipitating cloud tops are retrieved from the radiances emitted and reflected from them, as measured by the Advanced Very High Resolution Radiometer (AVHRR) onboard the NOAA-11 satellite. Only clouds that are optically thick in the visible wave band and filling the field of view are considered as candidates for precipitation. Therefore, effects of emitted and reflected radiation from below the clouds, which is important in semi-transparent or broken clouds, are avoided altogether. The retrieval is done by comparing the measured radiance to the theoretically calculated radiance from clouds having various microphysical properties. The likelihood for precipitation formation processes is then estimated and verified against actual observations of precipitation, using a weather radar. It was shown that optically thick clouds with retrieved particle effective radius greater than about 14 βm correspond well to areas with radar echoes, indicating the existence of precipitation size particles. This results is consistent with the fact that existence of drops having a radius of at least 12 μm is required for efficient precipitation formation in clouds with relatively warm tops, by the mechanisms of warm rain processes as well as ice multiplication processes.

On the use of long-term global data of land reflectances and vegetation indices derived from the advanced very high resolution radiometer

The availability of advanced very high resolution radiometer (AVHRR) time series of global shortwave data for the past two decades motivated many scientists to investigate interannual variability and trends in land surface conditions. For these studies the observed change in radiances due to two varying factors, namely, sensor responsivity and illumination conditions, must be known a priori because of the degradation of AVHRR shortwave channels and the orbit drift of afternoon spacecraft. The current work analyzes the behavior of global land AVHRR shortwave time series data for the last 12 years, processed using postlaunch calibration, and investigates their usefulness for the monitoring of global land surface processes. Its focus is on verifying the postlaunch calibrations for the AVHRR sensors on board NOAA 11 and 14. It is assumed that the NOAA 9 AVHRR calibration is correct so that the changing illumination effects can be parameterized based on its data. After accounting for the illumination effects, the residual trends in data, averaged over global deserts and rain forests, are attributed to calibration discrepancies. In particular, NOAA 11 calibration was found to yield only small residuals, whereas NOAA 14 calibration produced significant unrealistic global increase in both reflectances and vegetation indices. The artificial trends caused by the combination of calibration residuals and satellite-orbit drift should be removed to alleviate their misidentification as real trends in Earths climate system and to make statistical studies of anomalies more reliable. This study draws attention to the above aspects of time series analysis with the available global AVHRR data and suggests ways to improve these data for interannual variability studies.

Automated monitoring of snow cover over North America with multispectral Satellite data

Current National Oceanic and Atmospheric Administration (NOAA) operational global- and continental-scale snow cover maps are produced interactively by visual analysis of satellite imagery. This snow product is subjective, and its preparation requires a substantial daily human effort. The primary objective of the current study was to develop an automated system that could provide NOAA analysts with a first-guess snow cover map and thus to reduce the human labor in the daily snow cover analysis. The proposed system uses a combination of observations in the visible, midinfrared, and infrared made by the Imager instrument aboard Geostationary Operational Environmental Satellites (GOES) and microwave observations of the Special Sensor Microwave Imager (SSM/I) aboard the polar-orbiting Defense Meteorological Satellite Program platform. The devised technique was applied to satellite data for mapping snow cover for the North American continent during the winter season of 1998/99. To assess the system performance, the automatically produced snow maps were compared with the NOAA interactive operational product and were validated against in situ land surface observations. Validation tests revealed that in 85% of cases the automated snow maps fit exactly the ground snow cover reports. Snow identification with the combination of GOES and SSM/I observations was found to be more efficient than the one based solely on satellite microwave data. Comparisons between the automated maps and the NOAA operational product have shown their good agreement in the distribution of snow cover and its area coverage. The accuracy of the automated product was found to be similar to and sometimes higher than the accuracy of the operational snow cover maps manually produced at NOAA.

Mapping global land surface albedo from NOAA AVHRR

A set of algorithms is combined for a simple derivation of land surface albedo from measurements of reflected visible and near-infrared radiation made by the advanced very high resolution radiometer (AVHRR) onboard the National Oceanic and Atmospheric Administration (NOAA) polar orbiting satellites. The system consists of a narrowband-to-broadband conversion and bidirectional correction at the top of the atmosphere and an atmospheric correction. We demonstrate the results with 1 month worth of data from the NOAA National Environmental Satellite, Data, and Information Service (NESDIS) global vegetation index (GVI) weekly data set and the NOAA/NASA Pathfinder Atmosphere (PATMOS) project daily data. Error analysis of the methodology indicates that the surface albedo can be retrieved with 10–15% relative accuracy. Monthly albedo maps derived from September 1989 GVI and PATMOS data agree well except for small discrepancies attributed mainly to different preprocessing and residual atmospheric effects. A 5-year mean September map derived from the GVI multiannual time series is consistent with that derived from low-resolution Earth Radiation Budget Experiment data as well as with a September map compiled from ground observations and used in many numerical weather and climate models. Instantaneous GVI-derived albedos were found to be consistent with surface albedo measurements over various surface types. The discrepancies found can be attributed to differences in areal coverage and representativeness of the satellite and ground data. The present pilot study is a prototype for a routine real-time production of high-resolution global surface albedo maps from NOAA AVHRR Global Area Coverage (GAC) data.

The Enhanced NOAA Global Land Dataset from the Advanced Very High Resolution Radiometer

Abstract Global mapped data of reflected radiation in the visible (0.63 μm) and near-infrared (0.85 μm) wavebands of the Advanced Very High Resolution Radiometer (AVHRR) onboard National Oceanic and Atmospheric Administration satellites have been collected as the global vegetation index (GVI) dataset since 1982. Its primary objective has been vegetation studies (hence its title) using the normalized difference vegetation index (NDVI) calculated from the visible and near-IR data. The second-generation GVI, which started in April 1985, has also included brightness temperatures in the thermal IR (11 and 12,um) and the associated observation-illumination geometry. This multiyear, multispectral, multisatellite dataset is a unique tool for global land studies. At the same time, it raises challenging remote sensing and data management problems with respect to uniformity in time, enhancement of signal-to-noise ratio, retrieval of geophysical parameters from satellite radiances, and large data volumes. The authors...

Global land monitoring from AVHRR: potential and limitations

Abstract Global Vegetation Index ( GVI) time series of visible, near-IR and thermal IR Advanced Very High Resolution Radiometer (AVHRR)weekly composite data with a 015° spatial resolution collected from NOAA-9 and -11 satellites have been used to develop a prototype global land monitoring system. The system is based on standardized anomalies of the Normalized Difference Vegetation Index (NDVI) and channel 4 brightness temperature ( T4 )for the period April 1985-September 1994. Processing included: post-launch updated calibration; cloud screening; filling in the cloud induced data gaps by monthly averaging and spatial interpolation; suppressing residual noise by smoothing; calculating 5-year monthly means and standard deviations of NDVI and T4and their standardized anomalies. The derived anomalies show potential for detecting and interpreting the seasonal cycle and statistically significant interannual variability. Yet, discontinuities and residua! trends can be traced in time series of NDVI and T4. Discon...

Towards Monitoring Droughts from Space

Abstract The utility of the midafternoon satellite derived surface temperatures for detecting drought events is examined using the NOAA-9 AVHRR data over the U.S. Great Plains during 1986–88. The interannual differences in monthly mean clear-sky temperature and in monthly mean normalized difference vegetation index are compared to the corresponding differences in the Palmer Drought Index. Results indicate that the thermal data from polar orbiters may be very useful in detecting the interannual changes in surface moisture when the change in vegetation index fails to produce a significant signal.