Terra and Aqua MODIS TEB Inter-Comparison Using Himawari-8/AHI as Reference

Intercomparison between the two MODIS instruments is very useful for both the instrument calibration and its uncertainty assessment. Terra and Aqua MODIS have almost identical relative spectral response, spatial resolution, and dynamic range for each band, so the site-dependent effect from spectral mismatch for their comparison is negligible. Major challenges in cross-sensor comparison of instruments on different satellites include differences in observation time and view angle over selected pseudoinvariant sites. The simultaneous nadir overpasses (SNO) between the two satellites are mostly applied for comparison and the scene under SNO varies. However, there is a dearth of SNO between the Terra and Aqua. This work focuses on an intercomparison method for MODIS thermal emissive bands using Himawari-8 Advanced Himawari Imager (AHI) as a reference. Eleven thermal emissive bands on MODIS are at least to some degree spectrally matched to the AHI bands. The sites selected for the comparison are an ocean area around the Himawari-8 suborbital point and the Strzelecki Desert located south of the Himawari-8 suborbital point. The time difference between the measurements from AHI and MODIS is <5 min. The comparison is performed using 2017 collection 6.1 L1B data for MODIS. The MODISAHI difference is corrected to remove the view angle dependence. The TerraAqua MODIS difference for the selected TEB is up to 0.6 K with the exception of band 30. Band 30 has the largest difference, which is site dependent, most likely due to a crosstalk effect. Over the ocean, the band 30 difference between the two MODIS instruments is around 1.75 K, while over the desert; the difference is around 0.68 K. The MODIS precision is also compared from the Gaussian regression of the double difference. Terra bands 27 to 30 have significant extra noise due to crosstalk effects on these bands. These TerraAqua comparison results are used for MODIS calibration assessments and are beneficial for future calibration algorithm improvement. The impact of daytime measurements and the scene dependence are also discussed

Noise Characterization and Performance of MODIS Thermal Emissive Bands

The MODerate-resolution Imaging Spectroradiometer (MODIS) is a premier Earth-observing sensor of the early 21st century, flying onboard the Terra (T) and Aqua (A) spacecraft. Both instruments far exceeded their six-year design life and continue to operate satisfactorily for more than 15 and 13 years, respectively. The MODIS instrument is designed to make observations at nearly a 100% duty cycle covering the entire Earth in less than two days. The MODIS sensor characteristics include a spectral coverage from 0.41micrometers to 14.4 micrometers, of which those wavelengths ranging from 3.7 micrometers to 14.4 micrometers cover the thermal infrared region which is interspaced in 16 thermal emissive bands (TEBs). Each of the TEB contains ten detectors which record samples at a spatial resolution of 1 km. In order to ensure a high level of accuracy for the TEB-measured top-of-atmosphere radiances, an onboard blackbody (BB) is used as the calibration source. This paper reports the noise characterization and performance of the TEB on various counts. First, the stability of the onboard BB is evaluated to understand the effectiveness of the calibration source. Next, key noise metrics such as the noise equivalent temperature difference and the noise equivalent dn difference (NEdN) for the various TEBs are determined from multiple temperature sources. These sources include the nominally controlled BB temperature of 290 K for T-MODIS and 285 K for A-MODIS, as well as a BB warm up-cool down cycle that is performed over a temperature range from roughly 270 to 315 K. The space-view port that measures the background signal serves as a viable cold temperature source for measuring noise. In addition, a well characterized Earth-view target, the Dome Concordia site located in the Antarctic plateau, is used for characterizing the stability of the sensor, indirectly providing a measure of the NEdN. Based on this rigorous characterization, a list of the noisy and inoperable detectors for the TEB for both instruments is reported to provide the science user communities quality control of the MODIS Level 1B calibrated product

Spatial and seasonal variations of aerosols over China from two decades of multi-satellite observations – Part 1: ATSR (1995–2011) and MODIS C6.1 (2000–2017)

Aerosol optical depth (AOD) patterns and interannual and seasonal variations over China are discussed based on the AOD retrieved from the Along-Track Scanning Radiometer (ATSR-2, 1995–2002), the Advanced ATSR (AATSR, 2002–2012) (together ATSR) and the MODerate resolution Imaging Spectroradiometer (MODIS) aboard the Terra satellite (2000–2017). The AOD products used were the ATSR Dual View (ADV) v2.31 AOD and the MODIS/Terra Collection 6.1 (C6.1) merged dark target (DT) and deep blue (DB) AOD product. Together these datasets provide an AOD time series for 23 years, from 1995 to 2017. The difference between the AOD values retrieved from ATSR-2 and AATSR is small, as shown by pixel-by-pixel and monthly aggregate comparisons as well as validation results. This allows for the combination of the ATSR-2 and AATSR AOD time series into one dataset without offset correction.ADV and MODIS AOD validation results show similar high correlations with the Aerosol Robotic Network (AERONET) AOD (0.88 and 0.92, respectively), while the corresponding bias is positive for MODIS (0.06) and negative for ADV (−0.07). Validation of the AOD products in similar conditions, when ATSR and MODIS/Terra overpasses are within 90&thinsp;min of each other and when both ADV and MODIS retrieve AOD around AERONET locations, show that ADV performs better than MODIS in autumn, while MODIS performs slightly better in spring and summer. In winter, both ADV and MODIS underestimate the AERONET AOD.Similar AOD patterns are observed by ADV and MODIS in annual and seasonal aggregates as well as in time series. ADV–MODIS difference maps show that MODIS AOD is generally higher than that from ADV. Both ADV and MODIS show similar seasonal AOD behavior. The AOD maxima shift from spring in the south to summer along the eastern coast further north.The agreement between sensors regarding year-to-year AOD changes is quite good. During the period from 1995 to 2006 AOD increased in the southeast (SE) of China. Between 2006 and 2011 AOD did not change much, showing minor minima in 2008–2009. From 2011 onward AOD decreased in the SE of China. Similar patterns exist in year-to-year ADV and MODIS annual AOD tendencies in the overlapping period. However, regional differences between the ATSR and MODIS AODs are quite large. The consistency between ATSR and MODIS with regards to the AOD tendencies in the overlapping period is rather strong in summer, autumn and overall for the yearly average; however, in winter and spring, when there is a difference in coverage between the two instruments, the agreement between ATSR and MODIS is lower.AOD tendencies in China during the 1995–2017 period will be discussed in more detail in Part 2 (a following paper: Sogacheva et al., 2018), where a method to combine AOD time series from ADV and MODIS is introduced, and combined AOD time series are analyzed.</p

PACE Technical Report Series, Volume 6: Data Product Requirements and Error Budgets Consensus Document

This chapter summarizes ocean color science data product requirements for the Plankton, Aerosol, Cloud,ocean Ecosystem (PACE) mission's Ocean Color Instrument (OCI) and observatory. NASA HQ delivered Level-1 science data product requirements to the PACE Project, which encompass data products to be produced and their associated uncertainties. These products and uncertainties ultimately determine the spectral nature of OCI and the performance requirements assigned to OCI and the observatory. This chapter ultimately serves to provide context for the remainder of this volume, which describes tools developed that allocate these uncertainties into their components, including allowable OCI systematic and random uncertainties, observatory geo location uncertainties, and geophysical model uncertainties

Observations and Recommendations for the Calibration of Landsat 8 OLI and Sentinel 2 MSI for Improved Data Interoperability

Combining data from multiple sensors into a single seamless time series, also known as data interoperability, has the potential for unlocking new understanding of how the Earth functions as a system. However, our ability to produce these advanced data sets is hampered by the differences in design and function of the various optical remote-sensing satellite systems. A key factor is the impact that calibration of these instruments has on data interoperability. To address this issue, a workshop with a panel of experts was convened in conjunction with the Pecora 20 conference to focus on data interoperability between Landsat and the Sentinel 2 sensors. Four major areas of recommendation were the outcome of the workshop. The first was to improve communications between satellite agencies and the remote-sensing community. The second was to adopt a collections-based approach to processing the data. As expected, a third recommendation was to improve calibration methodologies in several specific areas. Lastly, and the most ambitious of the four, was to develop a comprehensive process for validating surface reflectance products produced from the data sets. Collectively, these recommendations have significant potential for improving satellite sensor calibration in a focused manner that can directly catalyze efforts to develop data that are closer to being seamlessly interoperable

Calibration and Validation of Thermal Infrared Remote Sensing Sensors and Land/Sea Surface Temperature algorithms over the Iberian Peninsula

La Temperatura de la Superficie Terrestre (TST) y la Temperatura de la Superficie del Mar (TSM) son parámetros clave en los procesos físicos de intercambio de energía entre la superficie y la atmósfera. La TST/TSM están directamente relacionadas con el espectro Infrarrojo Térmico (TIR) que constituye la principal fuente de emisión de radiación de la superficie terrestre. El control de los datos térmicos se puede realizar con la Calibración Vicarea (VC) para, de esta forma, garantizar la calidad de los datos una vez el sensor a bordo de satélite está en órbita. Normalmente, la validación directa de los algoritmos de TST y la VC del espectro térmico se realiza con datos in-situ en tierra, mientras que la TSM se puede validar con datos de boyas. En el marco del proyecto CEOS-Spain, la Unidad de Cambio Global (UCG) ha instalado seis estaciones fijas y automáticas en la península Ibérica, en tres sitios de validación (Barrax, Doñana y Cabo de gata) los cuales obtienen datos para la realización de las actividades de calibración y validación (cal/val) de sensores con una baja y media resolución espacial. La validación de la TSM ha sido realizada con datos de boyas disponibles en la página web de Puertos del Estado. Antes de la realización de la cal/val, un estudio completo de los sitios de validación ha sido realizado para obtener la máxima precisión de las medidas realizadas por las estaciones. Las fuentes de error más comunes asociadas a las medidas in-situ de la TST son, entre otras: la homogeneidad del terreno, la emisividad y la radiación descendente. Conociendo cada error y su contribución a la medida de la TST, se ha podido establecer la precisión de nuestras medidas in-situ. Para nuestras estaciones, se ha obtenido un error por debajo de 1 K. Teniendo en cuenta los errores de la medidas in-situ, la VC ha sido realizada la los sensores TIR sensor (TIRS), Enhanced Thematic Mapper Plus (ETM+) y MODIS, mostrando todos ellos valores precisos de las bandas del térmico. La validación de los algoritmos de TST también se ha realizado de forma directa e indirecta (con datos de sensor a bordo de avión). Los resultados de validación muestran valore por debajo de 2 K y, en los mejores casos y en las condiciones más favorables, valores por debajo de 1 K. Los algoritmos de estimación de la TSM (de tipo split-window) también han obtenido una precisión por debajo de 0.8 K y, en los mejores casos (sin radiación solar y con altas velocidades del viento), valores por debajo de 0.5 K. Finalmente, dos algoritmos de la TST (para TIRS y MODIS) y uno de la TSM (para MODIS) han sido propuestos para su inclusión en la cadena de procesado gestionada por la UCG.Land Surface Temperature (LST) and Sea Surface Temperature (SST) are a key parameters in physical processes of surface energy at local and global scales. LST/SST are directly related to Thermal Infrared (TIR) spectra, which constitute the main source of Earth emission. Control of satellite TIR data can be performed through Vicarious Calibration (VC), which is the more common way to guaranty data quality once sensor is on orbit. Usually, direct validation of LST algorithms and VC of TIR data is performed through in-situ measurements of LST while SST is controlled through anchor buoys or ship transect data. In the framework of CEOS-SPAIN project, Global Unit Change (GCU) group has installed six fixed and automatic stations in three test sites over the Iberian Peninsula (Barrax, Doñana and Cabo de Gata), which provides suitable data for calibration and validation (cal/val) activities of middle and low spatial resolution Earth Observation Sensors (EOS). Validation of SST has been performed with buoys web data available in the database of Puertos del Estado webpage. Before sensors cal/val, complete suitability study of land test sites was performed in order to obtain the maximal precision given by our fixed stations (in Kelvin). Uncertainties sources linked to in-situ LST retrievals were analyzed such as area inhomogeneity, emissivity or down-welling radiance among others. Finally, with each uncertainty source contribution it was possible to establish the precision of our in-situ measurements regarding the sensor’s spatial resolution. For our test sites, LST precision was set below 1 K. Keeping in mind the values of in-situ LST precision, VC was performed on Landsat TIR sensor (TIRS) and Enhanced Thematic Mapper Plus (ETM+) as well as Terra/Aqua MODerate-resolution Imaging Spectroradiometer (MODIS), showing no displacement in raw TIR data. Test of LST algorithms was also performed with direct and indirect (through airborne sensor data) validations. Results showed Root Mean Square Errors (RMSE) in LST estimations below 2 K and, in the best cases (with the most favorable external conditions), values of 1 K. SST algorithms (Split-Window type) demonstrated precisions below 0.8 K and, in the best case (no solar radiation and high wind velocity), values of 0.5 K. Finally, two LST algorithms (for TIRS and MODIS) and one SST algorithm (MODIS) have been proposed for its inclusion in the sensor images process chain managed by the GCU group

Pre-Aerosol, Clouds, and Ocean Ecosystem (PACE) Mission Science Definition Team Report

We live in an era in which increasing climate variability is having measurable impact on marine ecosystems within our own lifespans. At the same time, an ever-growing human population requires increased access to and use of marine resources. To understand and be better prepared to respond to these challenges, we must expand our capabilities to investigate and monitor ecological and bio geo chemical processes in the oceans. In response to this imperative, the National Aeronautics and Space Administration (NASA) conceived the Pre-Aerosol, Clouds, and ocean Ecosystem (PACE) mission to provide new information for understanding the living ocean and for improving forecasts of Earth System variability. The PACE mission will achieve these objectives by making global ocean color measurements that are essential for understanding the carbon cycle and its inter-relationship with climate change, and by expanding our understanding about ocean ecology and biogeochemistry. PACE measurements will also extend ocean climate data records collected since the 1990s to document changes in the function of aquatic ecosystems as they respond to human activities and natural processes over short and long periods of time. These measurements are pivotal for differentiating natural variability from anthropogenic climate change effects and for understanding the interactions between these processes and various human uses of the ocean. PACE ocean science goals and measurement capabilities greatly exceed those of our heritage ocean color sensors, and are needed to address the many outstanding science questions developed by the oceanographic community over the past 40 years

PACE Technical Report Series, Volume 7: Ocean Color Instrument (OCI) Concept Design Studies

Extending OCI hyperspectral radiance measurements in the ultraviolet to 320 nm on the blue spectrograph enables quantitation of atmospheric total column ozone (O3) for use in ocean color atmospheric correction algorithms. The strong absorption by atmospheric ozone below 340 nm enables the quantification of total column ozone. Other applications are possible but were not investigated due to their exploratory nature and lower priority.The first step in the atmospheric correction processing, which converts top-of-the-atmosphere radiances to water-leaving radiances, is removal of the absorbance by atmospheric trace gases such as water vapor, oxygen, ozone and nitrogen dioxide. Details of the atmospheric correction process currently used by the Ocean Biology Processing Group (OBPG) and will be employed for PACE with appropriate modifications, are described by Mobley et al. [2016]. Atmospheric ozone absorbs within the visible to near-infrared spectrum between ~450 nm and 800nm and most appreciably between 530 nm and 650 nm, a spectral region critical for maintaining NASA's chlorophyll-a climate data record and for PACE algorithms planned to characterize phytoplankton community composition and other ocean color products.While satellite-based observations will likely be available during PACE's mission lifetime, the difference in acquisition time with PACE, the coarseness in their spatial resolution, and differences in viewing geometries will introduce significant levels of uncertainties in PACE ocean color data products

Supraglacial dust and debris characterization via in situ and optical remote sensing methods

Supraglacial dust and debris affects many glaciologic variables, including radiative absorption, ablation, generation of supraglacial melt as well as mass flux. Earth observing satellite technology has advanced greatly in recent decades and allows for unprecedented spatial, temporal and spectral imaging of Earth’s glaciers. While remote sensing of ‘clean’ glacier ice can be done quite successfully, strategies for satellite mapping of supraglacial debris remain in development. This work provides the first visible to thermal infrared full optical spectrum satellite data analysis of supraglacial dust and debris characterization and differentiation. Dust and debris covered glaciers in the following six contrasting study regions were targeted: Iceland, Nepal, New Zealand, southern Norway, Svalbard and Switzerland. A combination of field spectrometry and surface samples of snow, ice and debris were utilized to investigate supraglacial dust and debris diversity. This in situ data served as ground truth for evaluating spaceborne supraglacial debris mapping capabilities. Glacier snow, ice and debris samples were analyzed for mineral composition and inorganic elemental abundances via the following analytical geochemical techniques: X-ray diffraction, X-ray fluorescence spectroscopy and inductively coupled plasma mass spectrometry. A synoptic data set from four contrasting alpine glacier regions – Svalbard, southern Norway, Nepal and New Zealand – and 70 surface snow, ice and debris samples was presented, comparing supraglacial composition variability. Distinct supraglacial geochemical abundances were found in major, trace and rare earth elemental concentrations between the four study regions. Elemental variations were attributed to both natural and anthropogenic processes. Over 8800 glacier surface spectra were collected in Nepal, Svalbard and Switzerland, as well as from Nepal, New Zealand and Switzerland debris samples. Surface glacier debris mineralogy and moisture content were assessed from field spectra. Spaceborne supraglacial dust and debris mineral mapping techniques using visible to shortwave reflective and thermal emissive data were evaluated. Successful methods for mineral identification allowed mapping of volcanic vs. continental supraglacial debris, as well as different mineral classes within one glacier’s supraglacial debris. Granite- vs. schist-dominant debris was mapped on Khumbu glacier in Nepal. Iron-rich vs. iron-poor serpentine debris was mapped on Zmutt glacier in the Swiss Alps. Satellite emissivity derived silica mapping suggested potential use of silica thresholds for delineation of debris covered glacier extent or sediment transport and weathering processes. Satellite derived surface temperatures were compared in Iceland, Nepal, Switzerland and New Zealand glacier study regions, with results demonstrating variations in supraglacial temperatures coincident with changing mineral abundances. Consistently higher surface temperatures with increasing dust and debris cover were mapped at all four glacier study regions. Repeat supraglacial debris imagery was used to estimate ablation area velocities and particulate transport times at debris covered glaciers. Velocity derivations used in conjunction with supraglacial composition variation analysis from shortwave and thermal infrared false color composites, allowed for estimation of glacial mass flux in the Khumbu Himalayas. In short, the visible to thermal infrared satellite spectral analysis, combined with in situ spectral and geochemical ground truth data, proved that glacier dust and debris characterization is possible via satellite spectral data. Furthermore, this supraglacial dust and debris satellite characterization can be applied to a range of glaciologic studies, including thermal, mass balance and surface process interpretations on large spatial and temporal scales