Administration Advanced Very High Resolution Radiometer Research Articles

Inferring vegetation response to drought at multiscale from long-term satellite imagery and meteorological data in Afghanistan

Drought caused by climate change has significantly increased vegetation vulnerability in Afghanistan during the last decades. This paper investigates vegetation response to drought at multiple scales across the country based on historical data from 1980 to 2020. It explores the multiscale relationships between drought as indicated by the grid-based standardised precipitation evapotranspiration index (SPEI) and vegetation condition as represented by the satellite-derived vegetation anomaly index (VAI). It also examines the links of dominant land cover with drought and their implications for agriculture. We assess the spatiotemporal correlations by integrating TerraClimate grids with timeseries data sourced from NOAA AVHRR (National Oceanic and Atmospheric Administration Advanced Very High-Resolution Radiometer), MODIS (Moderate Resolution Imaging Spectroradiometer) images and ESA CCI (European Space Agency Climate Change Initiative) land cover maps. We evaluated the effect of cumulative drought on predominant land covers. Our results show years 2000–2001 and 2017–2018 as the driest in the last four decades, and vegetation substantially correlated to drought with spatial and temporal variations across Afghanistan. The most sensitive vegetation response months are June to July and the most significant cumulative drought impacts on vegetation are 6 to 8 months. The predominant land covers more prone to negative effects under severe drought are (in order) shrubland, rainfed cropland, grassland, trees, and irrigated cropland. These findings suggest the sensitivity of vegetation to drought that future national scale drought management should be focused on. This study also demonstrates the usefulness of open-access data to researchers and planners in data-poor countries.

Monitoring Wetlands Ecosystems Using ALOS PALSAR (L-Band, HV) Supplemented by Optical Data: A Case Study of Biebrza Wetlands in Northeast Poland

The aim of the study was to elaborate the remote sensing methods for monitoring wetlands ecosystems. The investigation was carried out during the years 2002–2010 in the Biebrza Wetlands. The meteorological conditions at the test site varied from extremely dry to very wet. The authors propose applying satellite remote sensing data acquired in the optical and microwave spectrums to classify wetlands vegetation habitats for the assessment of vegetation changes and estimation of wetlands’ biophysical properties to improve monitoring of these unique, very often physically impenetrable, areas. The backscattering coefficients (σ°) calculated from ALOS PALSAR FBD (Advanced Land Observing Satellite, Phased Array type L-band Synthetic Aperture Radar, Fine Beam Dual Mode) images registered at cross polarization HV on 12 May 2008 were used to classify the main wetland communities using ground truth observations and the visual interpretation method. As a result, the σ° values were distributed among the six wetlands’ vegetation classes: scrubs, sedges-scrubs, sedges, reeds, sedges-reeds, rushes, and the areas of each community and changes were assessed. Also, the change in the biophysical variable as Leaf Area Index (LAI) is described using the information from PALSAR data. Strong linear relationships have been found between LAI and σ° derived for particular wetland classes, which then were applied to elaborate the maps of LAI distribution. The other variables used to characterize the changing environmental conditions are: surface temperature (Ts) calculated from NOAA AVHRR (National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer) and Normalized Difference Vegetation Index (NDVI) from ENVISAT MERIS (ENVIronmental SATellite MEdium Resolution Imaging Spectrometer). Differences of almost double Ts between “dry” and “wet” years were noticed that reflect observed weather conditions. The highest values of NDVI occurred in years with a sufficient amount of precipitation with the lowest in “dry” years. NDVI values variances within the same wetlands class resulted mainly from the differences in soil moisture. The results of this study show that the satellite data from microwave and optical spectrum gave the repetitive spatial information about vegetation growth conditions and could be used for monitoring wetland ecosystems.

Extending the SPOT-VEGETATION NDVI Time Series (1998–2006) Back in Time With NOAA-AVHRR Data (1985–1998) for Southern Africa

A new consistent long-term normalized difference vegetation index (NDVI) time series at a 1-km2 resolution for Southern Africa that is based on the data from Satellite Pour l'Observation de la Terre VEGETATION (VGT) (1998-2006) and the National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (AVHRR) (1985-1998) has been produced for vegetation-dynamics monitoring purposes. This paper presents the evaluation of the newly processed AVHRR data set, as well as the integration of this data set with the VGT archive. First, the AVHRR processing chain and the resulting AVHRR data set have been investigated with respect to calibration accuracy, cloud masking, and atmospheric and geometric correction. Second, different calibration approaches, spectral response (SR) functions, spatial resolutions, overpass times, and geometries of observation for the VGT and AVHRR data sets have been compared for a common observation period. The application of published correction functions accounting for the SR differences for both sensors considerably improved the consistency between both data sets. An r2 of 0.85 is obtained between paired samples of the NDVI from the VGT and the newly processed AVHRR archive. After the application of the correction functions, the slope of the regression line between the two NDVI data sets was much closer to the 1:1 line. The performance of the correction functions differed among vegetation types. The largest reduction in the root-mean-square error between the NDVI of both sensors is obtained from areas with higher biomass. Large parts of the remaining variability are suggested to be attributed to the bidirectional reflectance distribution function effects, as demonstrated by the intersensor NDVI time-series variability versus the intrasensor NDVI time-series variability.

Evaluation of Split-Window Land Surface Temperature Algorithms for Generating Climate Data Records

Land surface temperature (LST) is a key indicator of the Earth's surface energy and is used in a range of hydrological, meteorological, and climatological applications. As needed for most modeling and climate analysis applications, LST products that are generated from polar-orbiting meteorological satellite sensors have spatial resolutions from several hundred meters to several kilometers and have (quasi) daily temporal resolution. These sensors include the National Oceanic and Atmospheric Administration advanced very high resolution radiometer (AVHRR), the earth observing system moderate resolution imaging spectroradiometer (MODIS), and the forthcoming visible/infrared imager radiometer suite (VIIRS) series, to be flown onboard the National Polar-Orbiting Operational Environmental Satellite System (VIIRS flights begin approximately 2009). Generally, split-window algorithms are used with these sensors to produce LST products. In this paper, we evaluated nine published LST algorithms (or, in some cases, their slight variants) to determine those that are most suitable for generating a consistent LST climate data record across these satellite sensors and platforms. A consistent set of moderate-resolution atmospheric transmission simulations were used in determining the appropriate coefficients for each algorithm and sensor (AVHRR, MODIS, and VIIRS) combination. Algorithm accuracy was evaluated over different view zenith angles, surface-atmosphere temperature combinations, and emissivity errors. Both simulated and actual remote sensing data were used in the evaluation. We found that the nine heritage algorithms can effectively be collapsed into three groups of highly similar performance. We also demonstrated the efficacy of an atmospheric path-length correction term that is added to the heritage algorithms. We conclude that the algorithms depending on both the mean and difference of band emissivities (Group 1 in our nomenclature) are most accurate and stable over a wide range of conditions, provided that the emissivity can be well estimated a priori . Where the emissivity cannot be well estimated, the Group 3 algorithms (which do not depend on the emissivity difference) modified with the path-length correction term perform better.

A comparison of BRDF models for the normalization of satellite optical data to a standard sun-target-sensor geometry

Climate change studies require consistent, long time series, surface reflectance data. The characterization of the bidirectional reflectance distribution function (BRDF) is important for normalizing the solar radiation reflected from the earth's surface. We evaluated four BRDF models to identify the preferred approach to the normalization of multiyear National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (AVHRR) and SPOT4-VEGETATION (VGT) composite images to a common illumination and viewing geometry. Four models by the following authors were included: Walthall, Roujean, Ross-Li, and a new nonlinear temporal angular model (NTAM). NTAM accounts for hotspot effects and also responds to seasonal changes in land cover properties (using vegetation indexes as surrogate temporal measures). We compared the performance of the models under different scenarios of coefficient derivation and model application including model ability to reproduce theoretical BRDF curves, model consistency in single, multiyear, and incomplete sampling schemes, and comparison of AVHRR and LANDSAT Thematic Mapper surface reflectance prior and after BRDF normalization. We found that in all the tests, NTAM yielded the best fits between the observed and estimated values. NTAM requires eight coefficients and a lengthier iterative procedure to derive the coefficients, but the resulting coefficients are applied to the entire growing season rather than one temporal window. NTAM also performed well for different sensors (AVHRR, VGT) and geographic areas (Canada, east Asia, southern United States). Our results contradict the often-encountered perception that semiempirical BRDF models for angular normalization are all similarly effective, and the research on this topic is mature. We also describe a procedure for routine normalization of satellite optical data. For northern ecosystems, the NTAM coefficients derived from AVHRR and VGT data for Canada are available via ftp://ccrs.nrcan.gc.ca.

The Retrieval of Effective Particle Radius and Liquid Water Path of Low-Level Marine Clouds from NOAA AVHRR Data

Abstract An algorithm was developed to retrieve both the optical thickness and the effective particle radius of low-level marine clouds simultaneously from National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (AVHRR) data. The algorithm uses the combination of the visible (channel 1) and the middle-infrared (channel 3) reflected radiation. The thermal component in the middle infrared was corrected with the thermal-infrared (channel 4) radiance by a statistical technique using a regressive formula. The liquid water path (i.e., vertically integrated liquid water content) was also estimated as a by-product. The algorithm was applied to AVHRR datasets for which almost-synchronized airborne observations were conducted around the First International Satellite Cloud Climatology Project (ISCCP) Regional Experiment (FIRE) and the Western North Pacific Experiment (WENPEX) regions. The two regions are different in the characteristics of cloud fields:summer stratus and stratiform c...

The Global Fire Product: Daily fire occurrence from April 1992 to December 1993 derived from NOAA AVHRR data

Global active fire maps have been produced over a 21-month period from April 1992 to December 1993. A contextual active fire detection algorithm has been applied to the NOAA AVHRR (National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer) 1.1 km images provided by the IGBP-DIS (International Geosphere-Biosphere Programme Data and Information System) 1 km AVHRR Global Land Project data set. The Global Fire Product (GFP) is composed of daily fire position tables, 10-day synthesis raster format maps containing fire density and cloud/no-data information; it is now available as the first global scale description of the spatial and temporal distribution of active vegetation fire. In answer to science community requirements the GFP supplies information which can be used to estimate fire impacts on atmospheric chemistry, climate, land use and land cover changes.

Simultaneous Land Surface Temperature–Emissivity Retrieval in the Infrared Split Window

Abstract A combined land surface temperature–emissivity retrieval algorithm is developed and tested for Geostationary Operational Environmental Satellite (GOES)-Imager and National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (AVHRR) split-window channels. By assuming that the spectral emissivities are constant over a short time period (12–24 h), two sets of split-window radiance measurements taken at two different times are used to retrieve two spectral emissivities and two land surface temperatures (LSTs) simultaneously. The algorithm employs an optimization scheme rather than a direct solver for a system of equations because of constraint requirements. The retrieved variables minimize the rms differences between measured satellite radiances and those predicted by a spectrally detailed radiative transfer model. A GOES-8 version of the algorithm is validated with in situ radiometer measurements from the Department of Energy’s Atmospheric Radiation Measurement Program Cl...

Satellite Microphysical Retrievals for Land-Based Fog with Validation by Balloon Profiling

Abstract Digital data from the National Oceanic and Atmospheric Administration Advanced very High Resolution Radiometer (AVHRR) satellite instrument provides multispectral images in visible near-infrared and thermal infrared wave bands, which have been utilized to develop retrieval techniques for estimating the droplet effective radius and optical depth of land-based fog. The retrieval methods are based on multiple scattering calculations that simulate the increased near-infrared absorption by fog layers with increasing droplet size and liquid water path. The AVHRR thermal window channels are utilized to remove the effects of thermal emission in the near-infrared band. New instrumentation and field sampling methods have been developed for obtaining detailed vertical profiles of fog droplet size distributions and thermodynamic conditions in fog decks. The in situ measurements derived from the field observations were employed to test the satellite retrieval techniques. Intercomparison shows a close correspo...

Temporal and spatial characteristics of Saharan dust outbreaks

Temporal and spatial characteristics of moderate to heavy northern African aerosol outbreaks over the North Atlantic Ocean for the period of January 1989 through December 1992 are presented using NOAA's advanced very high resolution radiometer (AVHRR) aerosol optical thickness (AOT) data. The episodic nature of the transport and deposition of northern African aeolian material as well as the intra‐annual variability of these aerosol outbreaks is detailed. Saharan dust outbreaks are generally observed within relatively well‐defined zonal corridors that are approximately 10° wide latitude. The latitude of these westward outbreaks progresses from south to north during the first 6–7 months of a year. After reaching its northernmost extent (∼20°−25°N) around August, the area of aerosol outbreaks then moves southward. The first six months of a year are the most active in terms of aerosol outbreak frequency and spatial extent. The annual peak in outbreak activity occurs most frequently during the months of February through April. The annual westward mass flux of northern African aeolian material over the North Atlantic during the years detailed in this study range from an annual minimum of 130 Mt for 1990 to an annual maximum of 460 Mt for 1991. These fluxes are not supplied in a steady fashion. Approximately 50% of the annual westward mass flux of northern African aerosols is transported during 20% of a given year. The annual mass fluxes presented in this study are found to be consistent with other independent estimates of the westward mass flux of northern African aerosols. The relationship of these aerosol loadings to sub‐Saharan rainfall is consistent as well with earlier observations. Aerosol loadings are also presented and compared to regional sedimentation rates. The impact of the transport and deposition of this aeolian material on marine biogeochemical cycles of the North Atlantic is addressed briefly with the calculation of the annual mass flux of various nutritive species. Lastly, the continued development of the National Oceanographic and Atmospheric Administration's AVHRR AOT observations and analyses may lead to a North African aeolian emission system analog useful in the geologic sedimentary record.

Scripps satellite archive browsing for localized environments

Scripps Satellite Archive Browsing for Localized Environments (SSable) is a new interactive tool for browsing the Scripps Satellite Oceanography Center's (SSOC) archive of satellite data in your laboratory or office. The archive contains data from the National Atmospheric and Oceanic Administration/Advanced Very High Resolution Radiometer(AVHRR) series of satellites dating from 1979 to present. SSOC maintains a capture station, and data are continually being added to the SSable system within 10 minutes of capture completion.SSable provides an easy‐to‐use, pointand‐click, mouse driven user interface developed around the OSF/Motif widget set and the X Window protocol. All options are selected by either directly clicking the mouse on the desired push button, or pulling down a sub‐menu from a push‐button. A “fill‐inthe‐blanks” window appears to guide the user in defining the operation. All SSable option menus have a cancel button to abort the operation. Users can search for data by time, day, geographic coordinate or, if the user knows the structure of the Scripps archive, by explicit archive number.

AN EXPERT SYSTEM FOR INTERPRETING MESOSCALE FEATURES IN OCEANOGRAPHIC SATELLITE IMAGES

Thermal infrared images of the ocean obtained from satellite sensors are widely used for the study of ocean dynamics. The derivation of mesoscale ocean information from satellite data depends to a large extent on the correct interpretation of infrared oceanographic images. The difficulty of the image analysis and understanding problem for oceanographic images is due in large part to the lack of precise mathematical descriptions of the ocean features, coupled with the time varying nature of these features and the complication that the view of the ocean surface is typically obscured by clouds, sometimes almost completely. Towards this objective, the present paper describes a hybrid technique that utilizes a nonlinear probabilistic relaxation method and an expert system for the oceanographic image interpretation problem. This paper highlights the advantages of using the contextual information in the feature labeling algorithm. The need for an expert system and its feedback in automatic interpretation of oceanic features is discussed. The paper presents some important results of the series of experiments conducted at the Remote Sensing Branch, of the Naval Oceanographic and Atmospheric Research Laboratory, on the National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (AVHRR) imagery data. The results clearly indicate the drastic improvement in labeling due to the oceanographic expert system.