Intercalibration Technique Research Articles

Microstructure evolution and densification behavior of ultrafast high-temperature sintered Li6.5La3Zr1.5Ta0.5O12 ceramics

Increasing the heating rate during the sintering process of Li6.5La3Zr1.5Ta0.5O12 (LLZTO) solid-state electrolyte is an effective method for suppressing lithium volatilization, tailoring grain growth, and achieving rapid densification. However, the ultrafast heating rate can lead to uneven surface temperature distribution of the electrolyte, subsequently affecting its microstructure and performance. Herein, we systematically investigate the microstructural evolution and densification behavior of LLZTO ceramics under rapid heating conditions using the ultrafast high-temperature sintering (UHS) method. An intercalibration technique combining finite element simulations and experimental data from infrared spectral measurements is used for prediction of the temperature distribution of the sample. Adjustment of the size ratio of the graphite felt to the sample diameter resulted in uniform temperature distribution, which aids in improving sintering quality. The densification mechanism of the samples during the UHS process is revealed by thermodynamics. Under sintering conditions with a current of 25 A applied for 30 s, the ultrafast heating rate (up to 104 °C/min) results in fine grains with a diameter of approximately 3.4 μm and a relative density of 93.2 %. The preferred samples finally exhibit good ionic conductivity stability, maintaining 3.09 × 10−4 S cm−1 at dynamic pressures of 5–50 MPa at room temperature.

Reliability of resilience estimation based on multi-instrument time series

Abstract. Many widely used observational data sets are comprised of several overlapping instrument records. While data inter-calibration techniques often yield continuous and reliable data for trend analysis, less attention is generally paid to maintaining higher-order statistics such as variance and autocorrelation. A growing body of work uses these metrics to quantify the stability or resilience of a system under study and potentially to anticipate an approaching critical transition in the system. Exploring the degree to which changes in resilience indicators such as the variance or autocorrelation can be attributed to non-stationary characteristics of the measurement process – rather than actual changes in the dynamical properties of the system – is important in this context. In this work we use both synthetic and empirical data to explore how changes in the noise structure of a data set are propagated into the commonly used resilience metrics lag-one autocorrelation and variance. We focus on examples from remotely sensed vegetation indicators such as vegetation optical depth and the normalized difference vegetation index from different satellite sources. We find that time series resulting from mixing signals from sensors with varied uncertainties and covering overlapping time spans can lead to biases in inferred resilience changes. These biases are typically more pronounced when resilience metrics are aggregated (for example, by land-cover type or region), whereas estimates for individual time series remain reliable at reasonable sensor signal-to-noise ratios. Our work provides guidelines for the treatment and aggregation of multi-instrument data in studies of critical transitions and resilience.

Inter-calibration of atmospheric Cherenkov telescopes with UAV-based airborne calibration system

The recent advances in the flight capability of remotely piloted aerial vehicles (here after referred to as UAVs) have afforded the astronomical community the possibility of a new telescope calibration technique: UAV-based calibration. Building upon a feasibility study which characterised the potential that a UAV-based calibration system has for the future Cherenkov Telescope Array, we created a first-generation UAV-calibration prototype and undertook a field-campaign of inter-calibrating the sensitivity of the H.E.S.S. telescope array with two successful calibration flights. In this paper we report the key results of our first test campaign: firstly, by comparing the intensity of the UAV-calibration events, as recorded by the individual HESS-I cameras, we find that a UAV-based inter-calibration is consistent with the standard muon inter-calibration technique at the level of 5.4% and 5.8% for the two individual UAV-calibration runs. Secondly, by comparing the position of the UAV-calibration signal on the camera focal plane, for a variety of telescope pointing models, we were able to constrain the pointing accuracy of the HESS-I telescopes at the tens of arc-second accuracy level. This is consistent with the pointing accuracy derived from other pointing calibration methods. Importantly both the inter-calibration and pointing accuracy results were achieved with a first-generation UAV-calibration prototype, which eludes to the potential of the technique and highlights that a UAV-based system is a viable calibration technique for current and future ground-based γ-ray telescope arrays.

Leveraging intercalibration techniques to support stray-light removal from Landsat 8 Thermal Infrared Sensor data

Since its launch in 2013, the Thermal Infrared Sensor (TIRS) onboard Landsat 8 has exhibited artifacts in its image data that can be attributed to stray-light. A 3-year effort was initiated to develop a stray-light correction algorithm to support TIRS calibration. A methodology was developed to predict the additional (stray-light) signal on each detector from an estimate of the stray-light source locations in the sensor’s out-of-field area. The initial version of the algorithm estimated the magnitude of out-of-field radiance sources through the use of geostationary wide-field thermal band imagers. However, this methodology necessitated a strong effort to cross calibrate the two sensors. Ultimately, a variation of the algorithm was implemented operationally into the United States Geological Survey ground system that utilizes image data from TIRS itself as an estimate of the out-of-field stray-light sources. This paper highlights the intercalibration techniques investigated while developing the stray-light correction algorithm. The impact of differing view-angles, spectral responses, and collection times on at-sensor radiance was considered to assess the feasibility of using data from Geostationary Operational Environmental Satellite geostationary instruments to estimate the out-of-field stray-light radiance incident on the TIRS detectors. Results of the studies presented here illustrate the complexities associated with intercalibration in the thermal and provide justification for the current form of the TIRS stray-light correction algorithm.

Data intercalibration technique for infrared channels of the Elektro-L/MSU-GS imager with the AIRS infrared sounder data

A new intercalibration technique for infrared channels of the MSU-GS imager on the Elektro-L no. 1 Russian geostationary meteorological satellites is suggested using the Aqua/AIRS sounder data. Intercalibration is carried out over the ocean surface in the region of the Elektro-L subsatellite point. The proposed technique accounts for possible FOV contamination by the land surface or clouds. According to the intercalibration, the average biases for the MSU-GS channels are as following: 4.8 K for channel 7, 2.0 K for channel 8, and 2.7 K for the channels 9 and 10. The technique was validated using the same approach for two infrared channels of the European Meteosat-10/SEVIRI imager.

An integrated approach to monitoring the calibration stability of operational dual-polarization radars

Abstract. The stability of weather radar calibration is a mandatory aspect for quantitative applications, such as rainfall estimation, short-term weather prediction and initialization of numerical atmospheric and hydrological models. Over the years, calibration monitoring techniques based on external sources have been developed, specifically calibration using the Sun and calibration based on ground clutter returns. In this paper, these two techniques are integrated and complemented with a self-consistency procedure and an intercalibration technique. The aim of the integrated approach is to implement a robust method for online monitoring, able to detect significant changes in the radar calibration. The physical consistency of polarimetric radar observables is exploited using the self-consistency approach, based on the expected correspondence between dual-polarization power and phase measurements in rain. This technique allows a reference absolute value to be provided for the radar calibration, from which eventual deviations may be detected using the other procedures. In particular, the ground clutter calibration is implemented on both polarization channels (horizontal and vertical) for each radar scan, allowing the polarimetric variables to be monitored and hardware failures to promptly be recognized. The Sun calibration allows monitoring the calibration and sensitivity of the radar receiver, in addition to the antenna pointing accuracy. It is applied using observations collected during the standard operational scans but requires long integration times (several days) in order to accumulate a sufficient amount of useful data. Finally, an intercalibration technique is developed and performed to compare colocated measurements collected in rain by two radars in overlapping regions. The integrated approach is performed on the C-band weather radar network in northwestern Italy, during July–October 2014. The set of methods considered appears suitable to establish an online tool to monitor the stability of the radar calibration with an accuracy of about 2 dB. This is considered adequate to automatically detect any unexpected change in the radar system requiring further data analysis or on-site measurements.

Overview of Intercalibration of Satellite Instruments

Intercalibration of satellite instruments is critical for detection and quantification of changes in the Earth's environment, weather forecasting, understanding climate processes, and monitoring climate and land cover change. These applications use data from many satellites; for the data to be interoperable, the instruments must be cross-calibrated. To meet the stringent needs of such applications, instruments must provide reliable, accurate, and consistent measurements over time. Robust techniques are required to ensure that observations from different instruments can be normalized to a common scale that the community agrees on. The long-term reliability of this process needs to be sustained in accordance with established reference standards and best practices. Furthermore, establishing physical meaning to the information through robust Système International d'unités traceable calibration and validation (Cal/Val) is essential to fully understand the parameters under observation. The processes of calibration, correction, stability monitoring, and quality assurance need to be underpinned and evidenced by comparison with “peer instruments” and, ideally, highly calibrated in-orbit reference instruments. Intercalibration between instruments is a central pillar of the Cal/Val strategies of many national and international satellite remote sensing organizations. Intercalibration techniques as outlined in this paper not only provide a practical means of identifying and correcting relative biases in radiometric calibration between instruments but also enable potential data gaps between measurement records in a critical time series to be bridged. Use of a robust set of internationally agreed upon and coordinated intercalibration techniques will lead to significant improvement in the consistency between satellite instruments and facilitate accurate monitoring of the Earth's climate at uncertainty levels needed to detect and attribute the mechanisms of change. This paper summarizes the state-of-the-art of postlaunch radiometric calibration of remote sensing satellite instruments through intercalibration.

Toward an Intercalibrated Fundamental Climate Data Record of the SSM/I Sensors

Multiple independent intercalibration techniques are used to derive calibration adjustments for the development of a fundamental climate data record of physically consistent brightness temperature data from the series of six special sensor microwave/imagers (SSM/Is). The techniques include direct polar matchups, double differencing against model simulations from reanalysis profile data, double differencing against matchups with the Tropical Rainfall Measuring Mission Microwave Imager, vicarious cold calibration, and an Amazon warm calibration. Multiple realizations of three of the five techniques have been applied using different reanalysis data and retrieval techniques to account for Earth incidence angle-dependent differences between sensors. Excellent agreement has been achieved between each of the techniques with typical spread within 0.5 K at the cold end, with slightly higher spread when the warm end estimate is included. A strategy for estimating mean intercalibration values is described with justification for the use of a simple offset based on error characteristics. Intercalibration offsets are smaller for the more recent SSM/I (<; 1 K for F14 and F15 compared with F13) and slightly larger for the older satellites (<; 2 K for F08, F10, and F11 when compared to F13).

Assessment of Spectral Band Impact on Intercalibration Over Desert Sites Using Simulation Based on EO-1 Hyperion Data

Since the beginning of the 1990s, stable desert sites have been used for the calibration monitoring of many different sensors. Many attempts at sensor intercalibration have been also conducted using these stable desert sites. As a result, site characterization techniques and the quality of intercalibration techniques have gradually improved over the years. More recently, the Committee on Earth Observation Satellites has recommended a list of reference pseudo-invariant calibration sites for frequent image acquisition by multiple agencies. In general, intercalibration should use well-known or spectrally flat reference. The reflectance profile of desert sites, however, might not be flat or well characterized (from a fine spectral point of view). The aim of this paper is to assess the expected accuracy that can be reached when using desert sites for intercalibration. In order to have a well-mastered estimation of different errors or error sources, this study is performed with simulated data from a hyperspectral sensor. Earth Observing-1 Hyperion images are chosen to provide the simulation input data. Two different cases of intercalibration are considered, namely, Landsat 7 Enhanced Thematic Mapper Plus with Terra Moderate Resolution Imaging Spectroradiometer (MODIS) and Environmental Satellite MEdium Resolution Imaging Spectrometer (MERIS) with Aqua MODIS. The simulation results have confirmed that intercalibration accuracy of 1% to 2% can be achieved between sensors, provided there are a sufficient number of available measurements. The simulated intercalibrations allow explaining results obtained during real intercalibration exercises and to establish some recommendations for the use of desert sites for intercalibration.

Intercalibration and Evaluation of ResourceSat-1 and Landsat-5 NDVI

ResourceSat-1 is a designated alternative to Landsat should the existing TM (Thematic Mapper) and ETM+ (Enhanced Thematic Mapper Plus) sensors fail prior to the successful launch of Landsat 8 in late 2012. However, to enable integration of ResourceSat-1 into the many existing long-term Landsat projects around the world, practicable similarity must be demonstrated. To quantify the potential for ResourceSat-1 to satisfy some of the needs of the remote sensing community, Normalized Difference Vegetation Index (NDVI) values derived from Landsat-5 were compared to NDVI values derived from ResourceSat-1. An intercalibration equation that converts ResourceSat-1 NDVI values to equivalent Landsat-5 NDVI values was derived thereby enabling direct comparison between the two sensors. Comparisons were made using imagery spanning a 3-year time period. Prior to intercalibration, NDVI values were highly correlated (mean R 2 > 0.73) but statistically different (P < 0.001). Following intercalibration, the resulting indices were statistically inseparable (P ≥ 0.56). The intercalibration technique described in this paper represents an easily repeatable process that demonstrates practicable similarity between ResourceSat-1 and Landsat-5 imagery.

Satellite intercalibration of IR window radiance observations

The paper provides an outline of a satellite intercalibration technique applied to IR window measurements by Meteosat-6 and NOAA-14 AVHRR (channels 4 and 5) and channel 4 of the GOES-8 imager. The goal is to arrive at an intercalibration of the IR window channels within about 1 K which is the typical error claimed for current operational satellite calibration. This can only be achieved if differences in observed target areas, viewing geometry, spectral response, and collocation in time and space are properly considered and corrected for. The intercalibration of the different IR window observations provides an agreement of the order 1–2 %, corresponding to about 1 – 2 K for typical surface temperatures.

Accurate determination of magnetic field gradients from four-point vector measurements. II. Use of natural constraints on vector data obtained from four spinning spacecraft

For part I see ibid., vol.32, p.377 (1996). The errors that result from an inadequate knowledge of the orientations, zero levels, and scale factors of the magnetometer sensor assemblies become particularly troublesome when the objective is to measure spatial gradients using several spacecraft. In an accompanying paper, we introduced an efficient approach to the intracalibration of a sensor assembly onboard a spinning spacecraft which provides accurate values for 8 of the 12 calibration parameters. The approach relies on the concept that errors in these parameters generate coherent monochromatic signals at the first and second harmonics of the spin frequency. In this work, we show how a least squares technique may be used to intercalibrate magnetometers on a group of four spacecraft to reduce the effects of the errors in the remaining calibration parameters to negligible levels. The intercalibration technique relies on the concept that /spl nabla//spl middot/B is zero everywhere and /spl nabla//spl times/B is vanishingly small in certain regions of the magnetosphere. If the data have not been properly intercalibrated, they yield nonzero averages for /spl nabla//spl middot/B and /spl nabla//spl times/B in those regions. Correct calibration parameters are determined by requiring that the final data set must yield values of /spl nabla//spl middot/B and /spl nabla//spl times/B close to zero. The technique is extremely efficient provided appropriate input data are available, and should prove useful for the Cluster mission.

Spectrophotometric equations: An intercalibration technique 1

A set of equations is presented for the interconversion of chlorophyll concentrations calculated by any of five sets of trichromatic equations.