Hideyuki Tonooka

Absolute Radiometric In-Flight Validation of Mid Infrared and Thermal Infrared Data From ASTER and MODIS on the Terra Spacecraft Using the Lake Tahoe, CA/NV, USA, Automated Validation Site

In December 1999, the first Moderate Resolution Imaging Spectroradiometer (MODIS) instrument and an Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) instrument were launched into polar orbit on the Terra spacecraft. Both instruments measure surface radiance, which requires that they are calibrated and validated in flight. In-flight validation is essential to independently verify that instrument calibration correctly compensates for any changes in instrument response over time. In order to meet this requirement, an automated validation site was established at Lake Tahoe on the California/Nevada border in 1999 to validate the ASTER and MODIS thermal infrared (TIR, 7-13 mum) and MODIS mid infrared (MIR, 3-5 mum) land-monitoring channels. Daytime and nighttime data were used to validate the TIR channels, and only nighttime data were used to validate the MIR channels to avoid any reflected solar contribution. Sixty-nine ASTER scenes and 155 MODIS-Terra scenes acquired between years 2000 and 2005 with near-nadir views were validated. The percent differences between the predicted and instrument at-sensor radiances for ASTER channels 10-14 were 0.165plusmn0.776, 0.103plusmn0.613, -0.305plusmn0.613, -0.252plusmn0.464, and -0.118plusmn0.489, respectively. The percent differences for MODIS-Terra channels 20, 22, 23, 29, 31, and 32 were -1.375plusmn0.973, -1.743plusmn1.027, -0.898plusmn0.970, 0.082plusmn0.631, 0.044plusmn0.541, and 0.151plusmn0.563, respectively. The results indicate that the TIR at-sensor radiances from ASTER and MODIS-Terra have met the preflight radiometric calibration accuracy specification and provide well-calibrated data sets that are suitable for measuring absolute change. The results also show that the at-sensor radiances from the MODIS-Terra MIR channels have greater bias than expected based on the preflight radiometric calibration accuracy specification

Validation of a crosstalk correction algorithm for ASTER/SWIR

The mechanism of crosstalk phenomena in the shortwave infrared (SWIR) subsystem of the Advanced Spaceborne Thermal Emission and Reflection Radiometer, which has six bands in the wavelength region of 1.6-2.43 /spl mu/m, is investigated. It is found that light incident to band 4 is reflected at the detector and the filter boundary, and then transported to other bands by multiple reflections in the focal plane area. A crosstalk correction algorithm is developed to improve the spectral separation performance of SWIR. Parameters of the crosstalk model, i.e., the amount of stray light and its area of influence, are determined by image analysis. By careful investigation of SWIR images around peninsulas, lakes, and islands, the crosstalk model is validated. Therefore, the correction algorithm is implemented in the preprocessing of higher level data products.

Vicarious calibration of ASTER thermal infrared bands

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) on the Terra satellite has five bands in the thermal infrared (TIR) spectral region between 8-12 /spl mu/m. The TIR bands have been regularly validated in-flight using ground validation targets. Validation results are presented from 79 experiments conducted under clear sky conditions. Validation involved predicting the at-sensor radiance for each band using a radiative transfer model, driven by surface and atmospheric measurements from each experiment, and then comparing the predicted radiance with the ASTER measured radiance. The results indicate the average difference between the predicted and the ASTER measured radiances was no more than 0.5% or 0.4 K in any TIR band, demonstrating that the TIR bands have exceeded the preflight design accuracy of <1 K for an at-sensor brightness temperature range of 270-340 K. The predicted and the ASTER measured radiances were then used to assess how well the onboard calibration accounted for any changes in both the instrument gain and offset over time. The results indicate that the gain and offset were correctly determined using the onboard blackbody, and indicate a responsivity decline over the first 1400 days of the Terra mission.

Accurate atmospheric correction of ASTER thermal infrared imagery using the WVS method

The water vapor scaling (WVS) method involves an atmospheric correction algorithm for thermal infrared (TIR) multispectral data, designed mainly for the five TIR spectral bands of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) on the Terra satellite. First, this method is improved for better applicability to ASTER/TIR imagery. The major improvement is the determination of a water vapor scaling factor on a band-by-band basis, which can reduce most of the errors induced by various factors such as algorithm assumptions. Next, the WVS method is validated by assessing the surface temperature and emissivity retrieved for a global-based simulation model (416 448 conditions), 183 ASTER scenes selected globally, and ASTER scenes from two test sites, Hawaii Island and Tokyo Bay. In situ lake surface temperatures measured in 13 vicarious calibration experiments, Moderate Resolution Imaging Spectroradiometer sea surface temperature products, and a climatic lake temperature are also used in validation. All the results indicate that although the ASTER/TIR standard atmospheric correction algorithm performs less well in humid conditions, the WVS method will provide more accurate retrieval of surface temperature and emissivity in most conditions including notably humid conditions. The expected root mean square error is about 0.6 K in temperature. Since the WVS method will be degraded by errors in gray pixel selection and cloud detection, these processing steps should be applied accurately.

ASTER preflight and inflight calibration and the validation of Level 2 products

Describes the preflight and inflight calibration approaches used for the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). The system is a multispectral, high-spatial resolution sensor on the Earth Observing Systems EOS-AM1 platform. Preflight calibration of ASTER uses well-characterized sources to provide calibration and preflight round-robin exercises to understand biases between the calibration sources of ASTER and other EOS sensors. These round-robins rely on well-characterized, ultra-stable radiometers. An experiment field in Yokohama, Japan, showed that the output from the source used for the visible and near-infrared (VNIR) subsystem of ASTER may be underestimated by 1.5%, but this is still within the 4% specification for the absolute, radiometric calibration of these bands. Inflight calibration will rely on vicarious techniques and onboard blackbodies and lamps. Vicarious techniques include ground-reference methods using desert and water sites. A recent joint field campaign gives confidence that these methods currently provide absolute calibration to better than 5%, and indications are that uncertainties less than the required 4% should be achievable at launch. The EOS-AM1 platform will also provide a spacecraft maneuver that will allow ASTER to see the Moon, allowing further characterization of the sensor. A method for combining the results of these independent calibration results is presented. The paper also describes the plans for validating the Level 2 data products from ASTER. These plans rely heavily upon field campaigns using methods similar to those used for the ground-reference, vicarious calibration methods.

An atmospheric correction algorithm for thermal infrared multispectral data over land-a water-vapor scaling method

Thermal infrared (TIR) multispectral data over land can be atmospherically corrected by radiative transfer calculations combined with global assimilated data from a weather forecast system. This approach is advantageous to operational processing but is not accurate. A new atmospheric correction algorithm with global assimilated data, a water vapor scaling (WVS) method, has improved results. In this algorithm, the accuracy of global assimilated data is markedly improved on a pixel-by-pixel basis as follows: (1) selecting gray pixels from an image; (2) estimating the scaling factors for the water-vapor profiles of gray pixels by an improved multichannel algorithm; (3) estimating the scaling factors for the water-vapor profiles of nongray pixels by horizontal interpolation; and (4) improving the water-vapor profiles of all pixels with the scaling factors. The proposed method can be applied if the image has one or more gray pixels. The simulation results for the advanced spaceborne thermal emission and reflection radiometer (ASTER) TIR subsystem show that the proposed method reduces errors on air temperature profiles as well as on water-vapor profiles and is as accurate as atmospheric correction with radiosonde measurements.

Validation of ASTER/TIR standard atmospheric correction using water surfaces

The standard atmospheric correction algorithm for the five thermal infrared (TIR) bands of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) is based on radiative transfer calculation using the MODTRAN code. Atmospheric profiles input to MODTRAN are extracted from either the Global Data Assimilation System (GDAS) product or the Naval Research Laboratory (NRL) climatology model. The present study provides validation results of this algorithm. First, in situ lake surface temperatures measured in 13 vicarious calibration (VC) experiments were compared with surface temperatures retrieved from ASTER data. As the results, the mean bias was 0.8 and 1.8 K for GDAS and NRL, respectively. The NRL model performed worse than GDAS for four experiments at Salton Sea, CA, probably because the model was not suitable for this site, which has typically higher surface temperature and humidity than other VC sites. Next, the algorithm was validated based on the max-min difference (MMD) of water surface emissivity retrieved from each of 163 scenes acquired globally. As a result, the algorithm error increased quadratically with the precipitable water vapor (PWV) content of the atmosphere, and the expected MMD error was 0.049 and 0.067 for GDAS and NRL, respectively, with a PWV of 3 cm, where 0.05 on MMD is roughly corresponding to -0.8 or +2.3 K on the retrieved surface temperature error. The algorithm performance degraded markedly when the surface temperature exceeded about 25/spl deg/C, particularly for NRL. Consequently, GDAS performs better than NRL as expected, while both will perform less well for humid conditions.

Radiometric performance evaluation of ASTER VNIR, SWIR, and TIR

Radiometric performance of the Advanced Spectrometer for Thermal Emission and Reflection Radiometer (ASTER) is characterized by using acquired imagery data. Noise-equivalent reflectance and temperature, sensitivity (gain), bias (offset), and modulation transfer function (MTF) are determined for the visible and near-infrared (VNIR), the shortwave infrared (SWIR), and the thermal infrared (TIR) radiometers that constitute ASTER. The responsivity evaluated from onboard calibration (OBC) and from instrumented scenes show similar trends for the VNIR: the OBC data yield 2.7% to 5.5% a year for the VNIR. The SWIR response changed less than 2% in the 3.5 years following launch. The zero-radiance offsets of most VNIR and SWIR bands have increased about 1/2 digital number per year. The in-orbit noise levels, calculated by the standard deviation of dark (VNIR and SWIR) or ocean (TIR) scenes, show that all bands are within specification. The MTF at Nyquist and 1/2 Nyquist frequencies was determined for all bands using the Moon (VNIR and SWIR) or terrestrial scenes with lines of sharp thermal contrast. In-orbit performance along-track and cross-track is better than prelaunch for the VNIR and SWIR bands in nearly all cases; the TIR effectively meets specification in-orbit.

Image sharpening method to recover stream temperatures from ASTER images

Linear unmixing of spectra from daytime Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images can be used to improve the spatial resolution of temperatures calculated for streams that are not fully resolved in the 90-m thermal infrared (TIR) data. We first examine ASTER 15-m Visible-Near Infrared (VNIR) data to select three endmembers using a simple automated technique. These endmembers correspond to vegetation, shade/water, and other scene components (e.g. urban/soil/non-photosynthetic vegetation). Then the 15-m VNIR data are unmixed into the three corresponding fraction images. Threshold and adjacency tests are used to separate the shade and water fractions creating a total of four fraction images that together are used to specify the amount of the scene components in each 90-m TIR pixel. The emitted thermal radiance (ETR) from each of the scene components can be estimated if we assume that it is the same as for a

Improvement of ASTER/SWIR crosstalk correction

Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), one of five sensors on Terra, has bands 4 to 9 in the short-wave infrared (SWIR) region. These bands, particularly bands 5 and 9, are affected by band-to-band crosstalk. A crosstalk correction algorithm already developed is practically used for reducing a leaked ghost image, but does not satisfactorily work for all scenes. We therefore analyze crosstalk effects in more detail for improving this algorithm. As the results, it is shown that crosstalk includes several band-to-band/intra-band components, and the cause of each component is estimated to be reflection, scattering, and/or refraction in a CCD chip and/or interference filters. Based on these facts, a new crosstalk correction algorithm is developed by improving the original algorithm. In the new algorithm, all known crosstalk components are included, kernel functions for convolution with a source image are updated, and sensitivity correction applied for keeping consistency with radiometric calibration is improved. Comparison results indicate that the new algorithm reduces ghost images more correctly than the original algorithm.