G. Fedosejevs

Radiometric cross-calibration of the Landsat-7 ETM+ and Landsat-5 TM sensors based on tandem data sets

Abstract Early in its mission, the Landsat-7 spacecraft was temporarily placed in a “tandem” orbit very close to that of the Landsat-5 spacecraft in order to facilitate the establishment of sensor calibration continuity between the Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-5 Thematic Mapper (TM) sensors. The key period for the tandem configuration was June 1–4, 1999, during which hundreds of nearly coincident matching scenes were recorded by both the Landsat-7 ETM+ and, in cooperation with Space Imaging/EOSAT and international ground stations, the Landsat-5 TM as well. The paper presents a methodology for radiometric cross-calibration of the solar reflective spectral bands of the Landsat-7 ETM+ and Landsat-5 TM sensors and results based on analysis of two different tandem image pairs for which ground reference data are available. With the well-calibrated ETM+ as a reference, the tandem-based cross-calibrations for the two image pairs yield TM responsivities that are consistent to each other to within 2% or better depending on the spectral band. Comparisons with independent methods and results obtained by other groups indicate that the tandem-based cross-calibration is within 3% of the independent results on average in spectral bands 1–4 but compares less favorably in bands 5 and 7. The present study indicates that the tandem cross-calibration approach can provide a valuable “contemporary” calibration update for Landsat-5 TM in the visible and near-infrared spectral bands based on the excellent radiometric performance of Landsat-7 ETM+. The methodology also incorporates adjustments for spectral band differences between the two Landsat sensors. Spectral band difference effects are shown to be more dependent on the surface reflectance spectrum than on atmospheric and illumination conditions. A variety of terrestrial surfaces are assessed regarding their suitability for Landsat radiometric cross-calibration in the absence of surface reflectance spectra.

A generalized approach to the vicarious calibration of multiple Earth observation sensors using hyperspectral data

Abstract The paper describes a new methodology that uses spatially extensive hyperspectral imagery as reference data to carry out vicarious radiometric calibrations for multiple satellite sensors. The methodology has been validated using data from a campaign at the Railroad Valley playa test site in Nevada in June 1998. The proof of concept has been further tested based on data acquisition campaigns at the Newell County rangeland test site in Alberta in August and October 1998. The rangeland test site in the Newell County region of Alberta is tested for its suitability as a calibration test site for satellite sensor systems. All three campaigns included ground-based measurements, satellite imagery, and airborne hyperspectral data. The airborne hyperspectral sensor data were acquired using the Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) at Railroad Valley and the Compact Airborne Spectrographic Imager (casi) in all three campaigns. This paper describes the formulation and implementation of the new methodology, and radiometric calibration monitoring results obtained for five different sensors: NOAA-14 AVHRR, OrbView-2 SeaWiFS, SPOT-4 VGT, SPOT-1/2 HRV, and Landsat-5 TM. The results indicate that the nominal on-orbit radiometric calibrations of all the satellite sensors fit within their predicted uncertainties. The combination of both lower-reflectance and higher-reflectance test sites is shown to improve the quality of the calibration monitoring results. In particular, the combined QUASAR monitoring results obtained from the three airborne data acquisition days at the two test sites, encompassing five satellite sensors and a total of 40 spectral band cases, yield a correlation between QUASAR-based and nominal TOA radiances characterized by y=1.026x−1.26, and r2=0.990. Temporal extensions of QUASAR data sets to calibrate satellite sensors imaging the test site one or more days away from the airborne data acquisition day yield mixed results.

An automated cloud detection method for daily NOAA-14 AVHRR data for Texas, USA

A variety of cloud types appears in each Advanced Very High Resolution Radiometer (AVHRR) image. Clouds may contaminate solar reflectance data to be used for vegetation studies. This may jeopardize the accuracy of any quantitative results from data analysis. Published cloud detection algorithms for AVHRR data to date have mainly used data over Europe received from the National Oceanic and Atmospheric Administration (NOAA)-12 or earlier satellites. This study examined the previously published cloud detection methods with the intent to develop an automated cloud detection algorithm for NOAA-14 AVHRR data for Texas. Through testing a whole year of AVHRR scenes, the Texas automated cloud detection algorithm was capable of correctly identifying most of the cloudcontaminated pixels except for cloud shadow pixels. The overall accuracy reached 89%. The developed algorithm includes three major steps, top-of-the-atmosphere reflectance of channel 1, temperature difference of channels 3 and 4, and a combination of ratio of channel 2 to channel 1 and temperature in channel 4.

Multisensor analysis of integrated atmospheric water vapor over Canada and Alaska

[1] Atmospheric water vapor is a key parameter for the analysis of climatic systems (greenhouse gas effect), in particular over high latitudes where water vapor displays an important seasonal variability. The sparse spatial and temporal sampling of atmospheric water vapor observations across Canada needs to be improved. A series of instruments and methods including a 940-nm solar absorption band radiometer (R) and radiosonde (S) analysis from a numerical weather prediction model and a ground-based bi-frequency Global Positioning System (GPS) were used to evaluate the integrated atmospheric water vapor (IWV) at various sites in Canada and Alaska from a multiyear database. The IWV-R measurements were collected within the framework of the North American Sun Radiometry network (AERONET/AEROCAN). Intercomparisons between [IWV-GPS and IWV-S], [IWV-R and IWV-GPS], and [IWV-R and IWV-S] show root mean square (RMS) differences of 1.8, 1.9, and 2.2 kg m � 2 , respectively. GPS meteorology appears to be the easiest approach to calibrate the solar radiometer water vapor band owing to its flexibility, and it allows us to overcome the Sun radiometry limitation in high-latitude areas like the Arctic. The sensitivity of the GPS retrieval to various parameters like GPS satellite constellation and meteorological data are discussed. The classical linear relationship between the surface temperature and the integrated weighted mean temperature profile needed for IWV-GPS retrieval may be significantly different for Arctic air masses compared with midlatitude air masses in the case of tropospheric temperature profile inversion. An ever-expanding multiyear (1994–2001) North American summer water vapor climatology, derived from AERONET/Canadian Sun Radiometer Network, is presented and analyzed, showing a mean value of 19.8 ± 6.1 kg m � 2 and variations from 17 kg m � 2 in Alaska to 23 kg m � 2 in southeastern Canada. The results in Bonanza Creek, Alaska, show significant interannual variations with a peak in 1997, which may

Evaluating different NDVI composite techniques using NOAA-14 AVHRR data

Normalized Difference Vegetation Index (NDVI) data derived from Advanced Very High Resolution Radiometer (AVHRR) data are influenced by cloud contamination, which is common in individual AVHRR scenes. Maximum value compositing (MVC) of NDVI data has been employed to minimize cloud contamination. Two types of weekly NDVI composites were built for crop seasons in summer: one from all available AVHRR data (named the traditional NDVI composite) and the other from solely cloud-free AVHRR data (named the conditional NDVI composite). The MVC method was applied to both composites. The main objective of this study was to compare the two types of NDVI composites using Texas data. The NDVI seasonal profiles produced from the conditional NDVI composites agreed with the field measured leaf area index (LAI) data, reaching maximum values at similar times. However, the traditional NDVI composites showed irregular patterns, primarily due to cloud contamination. These study results suggest that cloud detection for individual AVHRR scenes should be strongly recommended before producing weekly NDVI composites. Appropriate AVHRR data pre-processing is important for composite products to be used for short-term vegetation condition and biomass studies, where the traditional NDVI composite data do not eliminate cloud-contaminated pixels. In addition, this study showed that atmosphere composition affected near-infrared reflectance more than visible reflectance. The near-infrared reflectance was increasingly adjusted through atmospheric correction.

Potential of Getis statistics to characterize the radiometric uniformity and stability of test sites used for the calibration of Earth observation sensors

The calibration of airborne and satellite remote sensing sensors is a fundamental step for the rigorous validation of products derived from satellite data. Because of the inaccessibility of Earth Observation Satellites on orbit, the direct calibration method based on a test site with ground reference data is often considered necessary. However, the problem of radiometric spatial uniformity and temporal stability of test sites constitutes an important issue in the accuracy achieved in calibration operations and the long-term characterization of satellite sensor radiometry. Generally, the coefficient of variation and semivariograms are the most widely used tools for evaluating the radiometric uniformity and stability of a calibration site. In this study, we analyze for the first time the potential of Getis statistics compared to the coefficient of variation for the study of the radiometric spatial uniformity and temporal stability of the Lunar Lake Playa, Nevada (LLPN) test site. The results obtained show the potential and the importance of the synergy generated by these two methods for analyzing the radiometric temporal stability of the LLPN site. Getis statistics provide an excellent spatial analysis of the site while the coefficient of variation provides complementary information on the temporal evolution of the site.

Investigation of continental aerosols with high-spectral-resolution solar-extinction measurements

A modified implementation of the Langley method has been used to measure the atmospheric optical-depth spectrum at 5-nm intervals from 0.36 to 1.10 microm. Extensive measurements of the aerosol optical depth at 550 nm and the Junge exponent showed that there was a distinct separation of atmospheric conditions into clear and hazy conditions. A study of the sensitivity of the retrieval of the 550-nm surface reflectance factor from spaceborne observations was carried out, using the above characterization of typical atmospheric conditions in terms of mean and standard-deviation values for the aerosol optical depth and Junge exponent.

Towards integrated Earth sensing: Advanced technologies for in situ sensing in the context of Earth observation

Significant advancements in Earth observation are expected to come about by developing more systematic capabilities for assimilating remote sensing observations and in situ measurements for use in models, at relevant scales, to generate geophysical and biospheric information products. This paper provides an overview of the role of in situ sensing in the context of integrated Earth sensing. It also defines a framework for taking advantage of intelligent sensorwebs based on the converging technologies of microsensors, computers, and wireless telecommunications in support of critical activities such as the monitoring of remote environments, risk assessment and hazard mapping, and renewable resource information management. The knowledge gleaned from integrated Earth sensing has the potential to empower managers and decision makers to act on critical climate, sustainable development, natural resource, and environmental issues.

Sensitivity of surface reflectance retrieval to uncertainties in aerosol optical properties.

We formulate a procedure to investigate the sensitivity of surface reflectances retrieved from satellite sensor data to uncertainties in aerosol optical properties. Aerosol optical characteristics encompassed in the study include the aerosol optical depth, the Junge parameter (i.e., spectral dependence), and the imaginary part of the refractive index (i.e., aerosol absorption). The study includes both clear and hazy atmospheric conditions, wavelengths of 0.550 and 0.870 µm, three solar zenith angles, and five viewing geometries. Key results are presented graphically in terms of accuracy requirements on the aerosol property under consideration for a 5% uncertainty in predicted surface reflectance.

Multisensor and multiscale survey and characterization for radiometric spatial uniformity and temporal stability of Railroad Valley Playa (Nevada) test site used for optical sensor calibration

In this study, we analyzed for the first time the potential of Getis statistics compared to the coefficient of variation for the study of the radiometric spatial uniformity and temporal stability of the Railroad Valley Playa, Nevada (RVPN) test site. We evaluated multi-sensor and multi-scale image data acquired for the RVPN, including four SPOT HRV images acquired in 1997 and 1998, five NOAA AVHRR images acquired in 1999, and one Landsat TM image acquired in 1998. The results show the potential and the importance of the synergy generated by these two methods for analyzing the radiometric spatial uniformity and temporal stability of the RVPN site. Getis statistics provide an excellent spatial analysis of the site while the coefficient of variation provides complementary information on the temporal evolution of the site.