GEO-LEO Reflective Band Inter-Comparison with BRDF and Atmospheric Scattering Corrections

The inter-comparison of the reflective solar bands (RSB) between the instruments onboard a geostationary orbit satellite and a low Earth orbit satellite is very helpful in assessing their calibration consistency. Himawari-8 was launched 7 October 2014 and GOES-R was launched on 19 November 2016. Unlike previous GOES instruments, the Advanced Himawari Imager (AHI) on Himawari-8 and the Advanced Baseline Imager (ABI) on GOES-R have onboard calibrators for the RSB. Independent assessment of calibration is nonetheless important to enhance their product quality. MODIS (Moderate Resolution Imaging Spectroradiometer) and VIIRS (Visible Infrared Imaging Radiometer Suite) can provide good references for sensor calibration. In this work, the inter-comparison between AHI and VIIRS is performed over a pseudo-invariant target. The use of stable and uniform calibration sites provides comparison with accurate adjustment for band spectral difference, reduction of impact from pixel mismatching, and consistency of BRDF (Bidirectional Reflectance Distribution Function) and atmospheric correction. The site used is the Strzelecki Desert in Australia. Due to the difference in solar and view angles, two corrections must be applied in order to compare the measurements. The first is the atmospheric scattering correction applied to the top of atmosphere reflectance measurements. The second correction is applied to correct the BRDF effect. The atmospheric correction is performed using a vector version of the Second Simulation of a Satellite Signal in the Solar Spectrum (6SV) model and the BRDF correction is performed using a semi-empirical model. Our results show that AHI band 1 (0.47 microns) has a good agreement with VIIRS band M3 within 0.15 percent. AHI band 5 (1.61 microns) shows the largest difference (5.09 percent) with VIIRS band M10, while AHI band 5 shows the least difference (1.87 percent) in comparison with VIIRS band I3. The methods developed in this work can also be directly applied to assess GOES-16/ABI (Geostationary Operational Environment Satellite16 / Advanced Baseline Imager) calibration consistency, a topic we will address in the future

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, stabilitymonitoring, 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

An Introduction to the Geostationary-NASA Earth Exchange (GeoNEX) Products: 1. Top-of-Atmosphere Reflectance and Brightness Temperature

GeoNEX is a collaborative project led by scientists from NASA, NOAA, and many other institutes around the world to generate Earth monitoring products using data streams from the latest Geostationary (GEO) sensors including the GOES-16/17 Advanced Baseline Imager (ABI), the Himawari-8/9 Advanced Himawari Imager (AHI), and more. An accurate and consistent product of the Top-Of-Atmosphere (TOA) reflectance and brightness temperature is the starting point in the scientific processing pipeline and has significant influences on the downstream products. This paper describes the main steps and the algorithms in generating the GeoNEX TOA products, starting from the conversion of digital numbers to physical quantities with the latest radiometric calibration information. We implement algorithms to detect and remove residual georegistration uncertainties automatically in both GOES and Himawari L1bdata, adjust the data for topographic relief, estimate the pixelwise data-acquisition time, and accurately calculate the solar illumination angles for each pixel in the domain at every time step. Finally, we reproject the TOA products to a globally tiled common grid in geographic coordinates in order to facilitate intercomparisons and/or synergies between the GeoNEX products and existing Earth observation datasets from polar-orbiting satellites

Seasonal comparisons of Himawari-8 AHI and MODIS vegetation indices over latitudinal australian grassland sites

© 2020 by the authors. The Advanced Himawari Imager (AHI) on board the Himawari-8 geostationary (GEO) satellite offers comparable spectral and spatial resolutions as low earth orbiting (LEO) sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) sensors, but with hypertemporal image acquisition capability. This raises the possibility of improved monitoring of highly dynamic ecosystems, such as grasslands, including fine-scale phenology retrievals from vegetation index (VI) time series. However, identifying and understanding how GEO VI temporal profiles would be different from traditional LEO VIs need to be evaluated, especially with the new generation of geostationary satellites, with unfamiliar observation geometries not experienced with MODIS, VIIRS, or Advanced Very High Resolution Radiometer (AVHRR) VI time series data. The objectives of this study were to investigate the variations in AHI reflectances and normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and two-band EVI (EVI2) in relation to diurnal phase angle variations, and to compare AHI VI seasonal datasets with MODIS VIs (standard and sun and view angle-adjusted VIs) over a functional range of dry grassland sites in eastern Australia. Strong NDVI diurnal variations and negative NDVI hotspot effects were found due to differential red and NIR band sensitivities to diurnal phase angle changes. In contrast, EVI and EVI2 were nearly insensitive to diurnal phase angle variations and displayed nearly flat diurnal profiles without noticeable hotspot influences. At seasonal time scales, AHI NDVI values were consistently lower than MODIS NDVI values, while AHI EVI and EVI2 values were significantly higher than MODIS EVI and EVI2 values, respectively. We attributed the cross-sensor differences in VI patterns to the year-round smaller phase angles and backscatter observations from AHI, in which the sunlit canopies induced a positive EVI/ EVI2 response and negative NDVI response. BRDF adjustments of MODIS VIs to solar noon and to the oblique view zenith angle of AHI resulted in strong cross-sensor convergence of VI values (R2 > 0.94, mean absolute difference <0.02). These results highlight the importance of accounting for cross-sensor observation geometries for generating compatible AHI and MODIS annual VI time series. The strong agreement found in this study shows promise in cross-sensor applications and suggests that a denser time series can be formed through combined GEO and LEO measurement synergies

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, stabilitymonitoring, 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

Assessing the Calibration Differences in the Reflective Solar Bands of Terra MODIS and Landsat-7 Enhanced Thematic Mapper Plus

Long-term data records obtained from Earth observing sensors depend not only onthe calibration accuracy of individual sensors but also on the consistency across instruments andplatforms. Hence, sensor calibration intercomparison plays a vital role for a better understandingof various science products. The Moderate Resolution Imaging Spectroradiometer (MODIS)and enhanced thematic mapper plus (ETM+) on the Terra and Landsat 7 platforms have operatedsuccessfully since their launch, collecting measurements in the reflective solar and infrared partsof the spectrum. Terra MODIS has employed a reflectance-based calibration since beginning itsmission. In the case of ETM+, a radiance-based calibration was employed until recent years,when a reflectance-based calibration was introduced. Being in the AM constellation with lessthan 30 min difference in overpass times, near-simultaneous Earth scene measurements can beeffectively used to assess the calibration differences between the spectrally matching bands ofthese two instruments. The pseudoinvariant calibration sites (PICS) in the North African desertare widely used for on-orbit calibration and validation of satellite sensors. Four PICS from thisregion have been employed to assess the multitemporal reflectance differences. Correction forbidirectional reflectance, spectral response function mismatch, and impacts of atmosphericwater-vapor have been incorporated to provide an assessment of the long-term stability ofeach spectral band and reflectance differences amongst them. Results indicate that the spectralbands of both instruments show a long-term stability to within 2% from 2000 to 2017. Thetop-of-atmosphere reflectances between the two instruments postcorrection agree to within 4%.Also included in this paper is a detailed discussion of various parameters contributing to theuncertainties of this cross-calibration. The techniques presented in this paper can be furtherextended to perform similar intercomparison between Landsat 8 Operational Land Imager, AquaMODIS, and Suomi-NPP VIIRS

Sun-angle effects on remote-sensing phenology observed and modelled using himawari-8

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. Satellite remote sensing of vegetation at regional to global scales is undertaken at considerable variations in solar zenith angle (SZA) across space and time, yet the extent to which these SZA variations matter for the retrieval of phenology remains largely unknown. Here we examined the effect of seasonal and spatial variations in SZA on retrieving vegetation phenology from time series of the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) across a study area in southeastern Australia encompassing forest, woodland, and grassland sites. The vegetation indices (VI) data span two years and are from the Advanced Himawari Imager (AHI), which is onboard the Japanese Himawari-8 geostationary satellite. The semi-empirical RossThick-LiSparse-Reciprocal (RTLSR) bidirectional reflectance distribution function (BRDF) model was inverted for each spectral band on a daily basis using 10-minute reflectances acquired by H-8 AHI at different sun-view geometries for each site. The inverted RTLSR model was then used to forward calculate surface reflectance at three constant SZAs (20°, 40°, 60°) and one seasonally varying SZA (local solar noon), all normalised to nadir view. Time series of NDVI and EVI adjusted to different SZAs at nadir view were then computed, from which phenological metrics such as start and end of growing season were retrieved. Results showed that NDVI sensitivity to SZA was on average nearly five times greater than EVI sensitivity. VI sensitivity to SZA also varied among sites (biome types) and phenological stages, with NDVI sensitivity being higher during the minimum greenness period than during the peak greenness period. Seasonal SZA variations altered the temporal profiles of both NDVI and EVI, with more pronounced differences in magnitude among NDVI time series normalised to different SZAs. When using VI time series that allowed SZA to vary at local solar noon, the uncertainties in estimating start, peak, end, and length of growing season introduced by local solar noon varying SZA VI time series, were 7.5, 3.7, 6.5, and 11.3 days for NDVI, and 10.4, 11.9, 6.5, and 8.4 days for EVI respectively, compared to VI time series normalised to a constant SZA. Furthermore, the stronger SZA dependency of NDVI compared with EVI, resulted in up to two times higher uncertainty in estimating annual integrated VI, a commonly used remote-sensing proxy for vegetation productivity. Since commonly used satellite products are not generally normalised to a constant sun-angle across space and time, future studies to assess the sun-angle effects on satellite applications in agriculture, ecology, environment, and carbon science are urgently needed. Measurements taken by new-generation geostationary (GEO) satellites offer an important opportunity to refine this assessment at finer temporal scales. In addition, studies are needed to evaluate the suitability of different BRDF models for normalising sun-angle across a broad spectrum of vegetation structure, phenological stages and geographic locations. Only through continuous investigations on how sun-angle variations affect spatiotemporal vegetation dynamics and what is the best strategy to deal with it, can we achieve a more quantitative remote sensing of true signals of vegetation change across the entire globe and through time

Copernicus Cal/Val Solution - D3.2 - Recommendations for R&D on Cal/Val Methods

This document presents a gap analysis of the methods used in the calibration and validation of Earth Observation satellites relevant to the Copernicus programme and suggests recommendations for the research and developments required to fulfil this gap when/where possible. The document identifies the gaps and limitations of the CalVal methods, used for calibration and validation (CalVal) activities for the current Copernicus missions. It will also address the development needs for future Copernicus missions. Four types of missions are covered based on the division used in the rest of the CCVS project: optical, altimetry, radar and microwave and atmospheric composition. Finally, it will give a prioritized list of recommendations for R&D activities on the CalVal methods. The information included is mainly collected from the deliverables of work packages 1 and 2 in the CCVS project and from the consortium experts in CalVal activities

Simulation and Measurement of Multispectral Space Debris Light Curves

The accumulation of space debris has become one of the greatest threats facing the space industry to date. Through an increasing amount of objects deposited in Earth's orbit, such as rocket bodies, defunct satellites and general debris fragments, space missions are exposed to a growing risk of collisions. Moreover, the recent surge in commercial space applications is expected to further contribute to the problem. At the Institute of Technical Physics of Deutsches Zentrum für Luft- und Raumfahrt (DLR) in Stuttgart, resident space objects are monitored using a number of telescopes through active laser and passive sunlight illumination. Due to the high altitude and relatively small size of the objects they generally appear as unresolved points in photometric images. An object's temporal variation in brightness is referred to as a light curve and implies key information concerning the object's shape, material composition and rotation. Recovering these parameters from light signals is not trivial and it is anticipated that additional information provided by multispectral observations will contribute to a more reliable characterization of space debris. This research covers the development of a physically based simulation to model multispectral light reflections from space debris. The software is targeted towards ground-based observations and is expected to form an integral part in facilitating future strategies for comprehensive collision avoidance and space debris removal. Both passive light curves and laser ranging measurements are simulated using three-dimensional satellite models. To improve the accuracy of simulations, spectral lab measurements of common space materials are incorporated into the render. Further, the process of gathering reference measurements using the DLR's 43 cm telescope at the Uhlandshöhe Forschungsobservatorium is presented. For the comparison between synthetic and empirical light curves, a detailed calibration of the optical system is performed. The validity of the light curve simulator is confirmed the on the basis of recordings obtained from radar calibration targets. Finally, simulated data is used to study benefits of multispectral observations for characterization and parameter estimation from space debris

An Overview of Approaches and Challenges for Retrieving Marine Inherent Optical Properties from Ocean Color Remote Sensing

Ocean color measured from satellites provides daily global, synoptic views of spectral water-leaving reflectances that can be used to generate estimates of marine inherent optical properties (IOPs). These reflectances, namely the ratio of spectral upwelled radiances to spectral downwelled irradiances, describe the light exiting a water mass that defines its color. IOPs are the spectral absorption and scattering characteristics of ocean water and its dissolved and particulate constituents. Because of their dependence on the concentration and composition of marine constituents, IOPs can be used to describe the contents of the upper ocean mixed layer. This information is critical to further our scientific understanding of biogeochemical oceanic processes, such as organic carbon production and export, phytoplankton dynamics, and responses to climatic disturbances. Given their importance, the international ocean color community has invested significant effort in improving the quality of satellite-derived IOP products, both regionally and globally. Recognizing the current influx of data products into the community and the need to improve current algorithms in anticipation of new satellite instruments (e.g., the global, hyperspectral spectroradiometer of the NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission), we present a synopsis of the current state of the art in the retrieval of these core optical properties. Contemporary approaches for obtaining IOPs from satellite ocean color are reviewed and, for clarity, separated based their inversion methodology or the type of IOPs sought. Summaries of known uncertainties associated with each approach are provided, as well as common performance metrics used to evaluate them. We discuss current knowledge gaps and make recommendations for future investment for upcoming missions whose instrument characteristics diverge sufficiently from heritage and existing sensors to warrant reassessing current approaches