The absolute radiometric calibration of the advanced very high resolution radiometer

The measurement conditions are described for an intensive field campaign at White Sands Missile Range for the calibration of the AVHRRs on NOAA-9, NOAA-10 and NOAA-11, LANDSAT-4 TM and SPOT. Three different methods for calibration of AVHRRs by reference to a ground surface site are reported, and results from these methods are compared. Significant degradations in NOAA-9 and NOAA-10 AVHRR responsivities occurred since prelaunch calibrations were completed. As of February 1988, degradations in NOAA-9 AVHRR responsivities were on the order of 37 percent in channel and 41 percent in channel 2, and for the NOAA-10 AVHRR these degradations were 42 and 59 percent in channels 1 and 2, respectively

Senegalese land surface change analysis and biophysical parameter estimation using NOAA AVHRR spectral data

Surface biophysical estimates were derived from analysis of NOAA Advanced Very High Spectral Resolution (AVHRR) spectral data of the Senegalese area of west Africa. The parameters derived were of solar albedo, spectral visible and near-infrared band reflectance, spectral vegetative index, and ground temperature. Wet and dry linked AVHRR scenes from 1981 through 1985 in Senegal were analyzed for a semi-wet southerly site near Tambacounda and a predominantly dry northerly site near Podor. Related problems were studied to convert satellite derived radiance to biophysical estimates of the land surface. Problems studied were associated with sensor miscalibration, atmospheric and aerosol spatial variability, surface anisotropy of reflected radiation, narrow satellite band reflectance to broad solar band conversion, and ground emissivity correction. The middle-infrared reflectance was approximated with a visible AVHRR reflectance for improving solar albedo estimates. In addition, the spectral composition of solar irradiance (direct and diffuse radiation) between major spectral regions (i.e., ultraviolet, visible, near-infrared, and middle-infrared) was found to be insensitive to changes in the clear sky atmospheric optical depth in the narrow band to solar band conversion procedure. Solar albedo derived estimates for both sites were not found to change markedly with significant antecedent precipitation events or correspondingly from increases in green leaf vegetation density. The bright soil/substrate contributed to a high albedo for the dry related scenes, whereas the high internal leaf reflectance in green vegetation canopies in the near-infrared contributed to high solar albedo for the wet related scenes. The relationship between solar albedo and ground temperature was poor, indicating the solar albedo has little control of the ground temperature. The normalized difference vegetation index (NDVI) and the derived visible reflectance were more sensitive to antecedent rainfall amounts and green vegetation changes than were near-infrared changes. The information in the NDVI related to green leaf density changes primarily was from the visible reflectance. The contribution of the near-infrared reflectance to explaining green vegetation is largely reduced when there is a bright substrate

The absolute radiometric calibration of the advanced very high resolution radiometer

The early results of an absolute radiometric calibration of the NOAA-9 AVHRR sensor indicate significant degradations in the response of bands 1 and 2 compared to prelaunch values. The results are currently in the process of being verified and it may be that refinements of the methodology will be in order as additional data sets are analyzed. The LANDSAT TM calibration used in this approach is known to be very precise and the Herman radiative transfer code, supplemented by the 5-S code for gaseous transmission, is reliable as well. The extent to which other steps in the analysis procedure give rise to uncertainties in the results is currently under investigation. Particular attention is being given to the geometric matching of the AVHRR and TM imagery, as well as to the spectral redistribution procedure. By taking advantage of a reasonably precise calibration of TM imagery acquired on the same day as the AVHRR data at White Sands, a promising approach to the in-orbit calibration of AVHRR sensors is being developed. Current efforts involve primarily the examination of additional test cases and the investigation of possible simplifications in the procedure through judicious use of atmospheric models

Retrieving Near-Global Aerosol Loading over Land and Ocean from AVHRR

The spaceborne AVHRR sensors have provided a data record approaching 40 years, which is a crucial asset for studying the long-term trends of aerosol properties on both a global and regional basis. However, due to the limitations on its channels and information content, aerosol optical depth (AOD) data from AVHRR over land are still largely lacking. In this paper, we describe a new physics-based algorithm to retrieve global aerosol properties over both land and ocean from AVHRR for the first time. The over-land algorithm is an extension of our SeaWiFSMODIS Deep Blue algorithm, while a simplified version of the Satellite Ocean Aerosol Retrieval (SOAR) algorithm is used over ocean. We compare the retrieved AVHRR AOD values with those from MODIS collection 6 aerosol products on a daily and seasonal basis, and find in general good agreement between the two. For the satellites with equatorial crossing times within two hours of solar noon, the spatial coverage of the AVHRR aerosol product is comparable to that of MODIS, except over very bright arid regions (such as the Sahara and deserts in the Arabian Peninsula), where the underlying surface reflectance at 630 nm reaches the critical surface reflectance. Based upon comparisons of the AVHRR AOD against the AERONET data, the preliminary results indicate that the expected error is around +/-(0.03+15%) over ocean and +/-(0.05+25%) over land for this first version of the AVHRR aerosol products. Consequently, these new AVHRR aerosol products can contribute important building blocks for constructing a consistent long-term data record for climate studies

A method for an accurate in-flight calibration of AVHRR data for vegetation index calculation

International audienceA significant degradation in the Advanced Very High Resolution Radiometer (AVHRR) responsitivity, on the NOAA satellite series, has occurred since the prelaunch calibration and with time since launch. This affects the index vegetation (NDVI), which is an important source of information for monitoring vegetation conditions on regional and global scales. Many studies have been carried out which use the Viewing Earth calibration approach in order to provide accurate calibration correction coefficients for the computation of the vegetation index using the visible and near-infrared spectral channels 1 and 2 of AVHRR. In the work reported here, we present an accurate, simple and promising method using high reflective clouds as a target to calibrate the vegetation index derived from AVHRR data

Atmospheric correction of visible to middle-infrared EOS-MODIS data over land surfaces: Background, operational algorithm and validation

The NASA moderate resolution imaging spectroradiometer (MODIS) instrument will provide a global and improved source of information for the study of land surfaces with a spatial resolution of up to 250 m

Remote sensing of surface albedo and cloud properties in the Arctic from AVHRR measurements

Thesis (Ph.D.) University of Alaska Fairbanks, 1996Based on a comprehensive radiative transfer model, algorithms suitable for arctic conditions are developed to retrieve broadband surface albedo and water cloud properties from National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) narrowband measurements. Reflectance anisotropy of snow surfaces is first simulated by an discrete ordinates radiative transfer formulation, and is then included in the comprehensive model for the retrieval. Ground-based irradiance measurements made by NOAA Climate Monitoring and Diagnostics Laboratory (CMDL) in Barrow, Alaska are compared with retrieved albedo and downwelling irradiances computed from retrieved cloud optical depth and effective radius. Good agreement is found between satellite estimates and ground-based measurements, which indicate that the retrieval algorithms proposed in this thesis are suitable for arctic conditions. It is found that the effects of snow bidirectional reflectance on the retrieval of the broadband albedo are significant, and that the Lambertian approximation could lead to a 30% underestimate of the surface albedo. It is also found that cloud effective radius in the Arctic is generally smaller as compared with mid- and low-latitudes

Study of atmospheric and bidirectional effects on surface reflectance and vegetation index time series: Application to NOAA AVHRR and preparation for future space missions

The objectives of the investigation, namely 'to characterize the atmospheric and directional effects on surface reflectance and vegetation index using the First International Satellite Cloud Climatology Project (ISLCSP) Field Experiment (FIFE) data set, develop new algorithms to obtain better Advanced Very High Resolution Radiometer (AVHRR) indices, and define possible improvements for future satellite missions', were addressed in three separate, yet complementary studies. First, it was shown, from theoretical calculations, that visible and near infrared reflectances combined linearly at optimum (one or two) viewing angles relate linearly to the fraction of photosynthetically available radiation absorbed by plants, f(sub par), can be used independently of the type of foliage and substrate, eliminate the effects of sub-pixel spatial heterogeneity, and improve the accuracy of the f(sub par) estimates when compared to the Normalized Difference Vegetation Index, NDVI. Second, it was demonstrated that NDVI, even though it is not a linear combination of radiances or reflectances, can be spatially integrated without significant loss of information from scales of 300 to 1000 m. Third, AVHRR visible and near-infrared reflectances over the FIFE site, separating temporal and bidirectional components and determining the model parameters through an original iterative scheme was successfully modeled. It appears that NDVI generated from the top-of-atmosphere reflectances normalized by the bidirectional effects (as determined in the scheme) is a better vegetation index than maximum NDVI. Details about the three studies are presented

High Performance Computing Applications in Remote Sensing Studies for Land Cover Dynamics

Global and regional land cover studies require the ability to apply complex models on selected subsets of large amounts of multi-sensor and multi-temporal data sets that have been derived from raw instrument measurements using widely accepted pre-processing algorithms. The computational and storage requirements of most such studies far exceed what is possible on a single workstation environment. We have been pursuing a new approach that couples scalable and open distributed heterogeneous hardware with the development of high performance software for processing, indexing, and organizing remotely sensed data. Hierarchical data management tools are used to ingest raw data, create metadata, and organize the archived data so as to automatically achieve computational load balancing among the available nodes and minimize I/O overheads. We illustrate our approach with four specific examples. The first is the development of the first fast operational scheme for the atmospheric correction of Landsat TM scenes, while the second example focuses on image segmentation using a novel hierarchical connected components algorithm. Retrieval of global BRDF (Bidirectional Reflectance Distribution Function) in the red and near infrared wavelengths using four years (1983 to 1986) of Pathfinder AVHRR Land (PAL) data set is the focus of our third example. The fourth example is the development of a hierarchical data organization scheme that allows on-demand processing and retrieval of regional and global AVHRR data sets. Our results show that substantial improvements in computational times can be achieved by using the high performance computing technology