Assessment of the Visible Channel Calibrations of the TRMM VIRS and MODIS on Aqua and Terra

Several recent research satellites carry self-calibrating multispectral imagers that can be used for calibrating operational imagers lacking complete self-calibrating capabilities. In particular, the visible (VIS, 0.65 m) channels on operational meteorological satellites are generally calibrated before launch, but require vicarious calibration techniques to monitor the gains and offsets once they are in orbit. To ensure that the self-calibrating instruments are performing as expected, this paper examines the consistencies between the VIS channel (channel 1) reflectances of the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on the Terra and Aqua satellites and the Version 5a and 6 reflectances of the Visible Infrared Scanner (VIRS) on the Tropical Rainfall Measuring Mission using a variety of techniques. These include comparisons of Terra and Aqua VIS radiances with coincident broadband shortwave radiances from the well-calibrated Clouds and the Earth s Radiant Energy System (CERES), time series of deep convective cloud (DCC) albedos, and ray-matching intercalibrations between each of the three satellites. Time series of matched Terra and VIRS data, Aqua and VIRS data, and DCC reflected fluxes reveal that an older version (Version 5a, ending in early 2004) of the VIRS calibration produced a highly stable record, while the latest version (Version 6) appears to overestimate the sensor gain change by approx.1%/y as the result of a manually induced gain adjustment. Comparisons with the CERES shortwave radiances unearthed a sudden change in the Terra MODIS calibration that caused a 1.17% decrease in the gain on 19 November 2003 that can be easily reversed. After correction for these manual adjustments, the trends in the VIRS and Terra channels are no greater than 0.1%/y. Although the results were more ambiguous, no statistically significant trends were found in the Aqua MODIS channel-1 gain. The Aqua radiances are 1% greater, on average, than their Terra counterparts, and after normalization are 4.6% greater than VIRS radiances, in agreement with theoretical calculations. The discrepancy between the two MODIS instruments should be taken into account to ensure consistency between parameters derived from them. With the adjustments, any of the three instruments can serve as references for calibrating other satellites. Monitoring of the calibrations continues in near-real-time and the results are available via the world wide web

Joint Polar Satellite System

The Joint Polar Satellite System (JPSS) is a joint NOAA/NASA mission comprised of a series of polar orbiting weather and climate monitoring satellites which will fly in a sun-synchronous orbit, with a 1330 equatorial crossing time. JPSS resulted from the decision to reconstitute the National Polar-orbiting Operational Environmental Satellite System (NPOESS) into two separate programs, one to be run by the Department of Defense (DOD) and the other by NOAA. This decision was reached in early 2010, after numerous development issues caused a series of unacceptable delays in launching the NPOESS system

On-Orbit Calibration and Performance of Aqua MODIS Reflective Solar Bands

Aqua MODIS has successfully operated on-orbit for more than 6 years since its launch in May 2002, continuously making global observations and improving studies of changes in the Earth's climate and environment. 20 of the 36 MODIS spectral bands, covering wavelengths from 0.41 to 2.2 microns, are the reflective solar bands (RSB). They are calibrated on-orbit using an on-board solar diffuser (SD) and a solar diffuser stability monitor (SDSM). In addition, regularly scheduled lunar observations are made to track the RSB calibration stability. This paper presents Aqua MODIS RSB on-orbit calibration and characterization activities, methodologies, and performance. Included in this study are characterizations of detector signal-to-noise ratio (SNR), short-term stability, and long-term response change. Spectral wavelength dependent degradation of the SD bidirectional reflectance factor (BRF) and scan mirror reflectance, which also varies with angle of incidence (AOI), are examined. On-orbit results show that Aqua MODIS onboard calibrators have performed well, enabling accurate calibration coefficients to be derived and updated for the Level 1B (L1B) production and assuring high quality science data products to be continuously generated and distributed. Since launch, the short-term response, on a scan-by-scan basis, has remained extremely stable for most RSB detectors. With the exception of band 6, there have been no new RSB noisy or inoperable detectors. Like its predecessor, Terra MODIS, launched in December 1999, the Aqua MODIS visible (VIS) spectral bands have experienced relatively large changes, with an annual response decrease (mirror side 1) of 3.6% for band 8 at 0.412 microns, 2.3% for band 9 at 0.443 microns, 1.6% for band 3 at 0.469 microns, and 1.2% for band 10 at 0.488 microns. For other RSB bands with wavelengths greater than 0.5 microns, the annual response changes are typically less than 0.5%. In general, Aqua MODIS optics degradation is smaller than Terra MODIS and the mirror side differences are much smaller. Overall, Aqua MODIS RSB on-orbit performance is better than Terra MODIS

A study of the time evolution of GERB shortwave calibration by comparison with CERES Edition-3A data

This study examines the evolution of the GERB-2 and GERB-1 Edition 1 shortwave radiance calibration between 2004-2007 and 2007-2012 respectively, through comparison with CERES instrument FM1 Edition 3A SSF instantaneous radiances. Two periods when simultaneous observations from both GERB-2 and GERB-1 were available, January 13th to February 11th 2007 and May 1st to May 10th 2007, are also compared. For these two overlap periods respectively, averaged over all CERES ‘unfiltered-to-filtered radiance ratio’ subsets, the GERB-1/CERES unfiltered radiance ratio is on average found to be 1.6% and 1.9% lower than the associated GERB-2/CERES unfiltered radiance ratio. Over the two longer time series the GERB/CERES unfiltered radiance ratio shows a general decrease with time for both GERB-2 and GERB-1. The rate of decrease varies through time but no significant seasonal dependence is seen. Averaged over all subsets the GERB-2/CERES unfiltered radiance ratio showed a decrease of 1.9% between June 2004 and June 2006. Between June 2007 and June 2012, the corresponding decrease in the GERB-1/CERES unfiltered radiance ratio was 6.5%. The evolution of the GERB/CERES unfiltered radiance ratio for both GERB-2 and GERB-1 shows a strong dependence on the CERES unfiltered-to-filtered radiance ratio, indicating that it is spectrally dependent. Further time-series analysis and theoretical work using simulated spectral radiance curves suggests that for GERB-1 the evolution is consistent with a darkening in the GERB shortwave spectral response function which is most pronounced at the shortest wavelengths. For GERB-2, no single spectral cause can be identified, suggesting that the evolution is likely due to a combination of several different effects

Method for Ground-to-Space Laser Calibration System

The present invention comprises an approach for calibrating the sensitivity to polarization, optics degradation, spectral and stray light response functions of instruments on orbit. The concept is based on using an accurate ground-based laser system, Ground-to-Space Laser Calibration (GSLC), transmitting laser light to instrument on orbit during nighttime substantially clear-sky conditions. To minimize atmospheric contribution to the calibration uncertainty the calibration cycles should be performed in short time intervals, and all required measurements are designed to be relative. The calibration cycles involve ground operations with laser beam polarization and wavelength changes

Efficient Detection of Cloud Scenes by a Space-Orbiting Argus 1000 Micro-Spectrometer

The description, interpretation and imagery of clouds using remote sensing datasets collected by Earth-orbiting satellites have become a great debate spanning several decades. Presently, many models for cloud detection and classification have been reported in the modern literature. However, none of the existing models can efficiently detect clouds within the shortwave upwelling radiative wavelength flux (SWupRF) band. Therefore, in order to detect clouds more efficiently, a method known as radiance enhancement (RE) can be implemented. A satellite remote sensing database is one of the most essential parts of research for monitoring different atmospheric changes. This study proposes an innovative approach using RE and SWupRF to distinguish cloud and non-cloud scenes by using a space-orbiting Argus 1000 spectrometer utilizing the GENSPECT line-by-line radiative transfer simulation tool for space data retrieval and analysis. We apply this approach within the selected wavelength band of the Argus 1000 spectrometer in the range from 1100 nm to 1700 nm to calculate the integrated SWupRF synthetic spectral datasets. We used the collected Argus observations starting from 2009 to investigate radiative flux and its correlation with cloud and non-cloud scenes. Our results show that the RE and SWupRF model can identify most of the cloudy scenes except for some thin clouds that cannot be identified reasonably with high confidence due to complexity of the atmospheric system. Based on our analysis, we suggest that the relative correlation between SWupRF and RE within a small wavelength band can be a promising technique for the efficient detection of cloudy and non-cloudy scenes

Method for Ground-to-Satellite Laser Calibration System

The present invention comprises an approach for calibrating the sensitivity to polarization, optics degradation, spectral and stray light response functions of instruments on orbit. The concept is based on using an accurate ground-based laser system, Ground-to-Space Laser Calibration (GSLC), transmitting laser light to instrument on orbit during nighttime substantially clear-sky conditions. To minimize atmospheric contribution to the calibration uncertainty the calibration cycles should be performed in short time intervals, and all required measurements are designed to be relative. The calibration cycles involve ground operations with laser beam polarization and wavelength changes

Band to Band Calibration and Relative Gain Analysis of Satellite Sensors Using Deep Convective Clouds

Two calibration techniques were developed in this research. First, a calibration technique, in which calibration was transferred to cirrus band and coastal aerosol band from well calibrated reflective bands of Landsat 8 using SCIAMACHY Deep Convective Cloud (DCC) spectra. Second, a novel method to derive relative gains using DCCs and improve the image quality of cirrus band scenes was developed. DCCs are very cold, bright clouds located in the tropopause layer. At small sun elevation and sensor viewing angles, they act as near Lambertian solar reflectors. They have very high signal to noise ratio and can easily be detected using simple IR threshold. Thus, DCCs are an ideal calibration target. Cirrus band in Landsat 8 has band center at 1375nm. Due to high water vapor absorption at this wavelength it is difficult to calibrate the cirrus band using other standard vicarious calibration methods. Similarly, the coastal aerosol band has short wavelength (443nm). At this wavelength maximum scattering can be observed in the atmosphere, due to which it is difficult to calibrate this band. Thus DCCs are investigated to calibrate these two channels. DCC spectra measured by the SCIAMACHY hyperspectral sensor were used to transfer calibration. The gain estimates after band to band calibration using DCC for the coastal aerosol band was 0.986 ±0.0031 and that for cirrus band was 0.982±0.0398. The primarily target was to estimate gains with uncertainty of less than 5%. The results are within required precision levels and the primarily goal of the research was successfully accomplished. The non-uniformity in detector response can cause visible streaks in the image. To remove these visible streaks, modified histogram equalization method was used in the second algorithm. A large number of DCC scenes were binned and relative gains were derived. Results were validated qualitatively by visual analysis and quantitatively by the streaking metric. The streaking metric was below 0.2 for most of the detector which was the required goal. Visible streaks were removed by applying DCC derived gains and in most of the cases DCC gains outperforms the default gains

REMOTE SENSING DATA ANALYSIS FOR ENVIRONMENTAL AND HUMANITARIAN PURPOSES. The automation of information extraction from free satellite data.

This work is aimed at investigating technical possibilities to provide information on environmental parameters that can be used for risk management. The World food Program (WFP) is the United Nations Agency which is involved in risk management for fighting hunger in least-developed and low-income countries, where victims of natural and manmade disasters, refugees, displaced people and the hungry poor suffer from severe food shortages. Risk management includes three different phases (pre-disaster, response and post disaster) to be managed through different activities and actions. Pre disaster activities are meant to develop and deliver risk assessment, establish prevention actions and prepare the operative structures for managing an eventual emergency or disaster. In response and post disaster phase actions planned in the pre-disaster phase are executed focusing on saving lives and secondly, on social economic recovery. In order to optimally manage its operations in the response and post disaster phases, WFP needs to know, in order to estimate the impact an event will have on future food security as soon as possible, the areas affected by the natural disaster, the number of affected people, and the effects that the event can cause to vegetation. For this, providing easy-to-consult thematic maps about the affected areas and population, with adequate spatial resolution, time frequency and regular updating can result determining. Satellite remote sensed data have increasingly been used in the last decades in order to provide updated information about land surface with an acceptable time frequency. Furthermore, satellite images can be managed by automatic procedures in order to extract synthetic information about the ground condition in a very short time and can be easily shared in the web. The work of thesis, focused on the analysis and processing of satellite data, was carried out in cooperation with the association ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action), a center of research which works in cooperation with the WFP in order to provide IT products and tools for the management of food emergencies caused by natural disasters. These products should be able to facilitate the forecasting of the effects of catastrophic events, the estimation of the extension and location of the areas hit by the event, of the affected population and thereby the planning of interventions on the area that could be affected by food insecurity. The requested features of the instruments are: • Regular updating • Spatial resolution suitable for a synoptic analysis • Low cost • Easy consultation Ithaca is developing different activities to provide georeferenced thematic data to WFP users, such a spatial data infrastructure for storing, querying and manipulating large amounts of global geographic information, and for sharing it between a large and differentiated community; a system of early warning for floods, a drought monitoring tool, procedures for rapid mapping in the response phase in a case of natural disaster, web GIS tools to distribute and share georeferenced information, that can be consulted only by means of a web browser. The work of thesis is aimed at providing applications for the automatic production of base georeferenced thematic data, by using free global satellite data, which have characteristics suitable for analysis at a regional scale. In particular the main themes of the applications are water bodies and vegetation phenology. The first application aims at providing procedures for the automatic extraction of water bodies and will lead to the creation and update of an historical archive, which can be analyzed in order to catch the seasonality of water bodies and delineate scenarios of historical flooded areas. The automatic extraction of phenological parameters from satellite data will allow to integrate the existing drought monitoring system with information on vegetation seasonality and to provide further information for the evaluation of food insecurity in the post disaster phase. In the thesis are described the activities carried on for the development of procedures for the automatic processing of free satellite data in order to produce customized layers according to the exigencies in format and distribution of the final users. The main activities, which focused on the development of an automated procedure for the extraction of flooded areas, include the research of an algorithm for the classification of water bodies from satellite data, an important theme in the field of management of the emergencies due to flood events. Two main technologies are generally used: active sensors (radar) and passive sensors (optical data). Advantages for active sensors include the ability to obtain measurements anytime, regardless of the time of day or season, while passive sensors can only be used in the daytime cloud free conditions. Even if with radar technologies is possible to get information on the ground in all weather conditions, it is not possible to use radar data to obtain a continuous archive of flooded areas, because of the lack of a predetermined frequency in the acquisition of the images. For this reason the choice of the dataset went in favor of MODIS (Moderate Resolution Imaging Spectroradiometer), optical data with a daily frequency, a spatial resolution of 250 meters and an historical archive of 10 years. The presence of cloud coverage prevents from the acquisition of the earth surface, and the shadows due to clouds can be wrongly classified as water bodies because of the spectral response very similar to the one of water. After an analysis of the state of the art of the algorithms of automated classification of water bodies in images derived from optical sensors, the author developed an algorithm that allows to classify the data of reflectivity and to temporally composite them in order to obtain flooded areas scenarios for each event. This procedure was tested in the Bangladesh areas, providing encouraging classification accuracies. For the vegetation theme, the main activities performed, here described, include the review of the existing methodologies for phenological studies and the automation of the data flow between inputs and outputs with the use of different global free satellite datasets. In literature, many studies demonstrated the utility of the NDVI (Normalized Difference Vegetation Index) indices for the monitoring of vegetation dynamics, in the study of cultivations, and for the survey of the vegetation water stress. The author developed a procedure for creating layers of phenological parameters which integrates the TIMESAT software, produced by Lars Eklundh and Per Jönsson, for processing NDVI indices derived from different satellite sensors: MODIS (Moderate Resolution Imaging Spectroradiometer), AVHRR (Advanced Very High Resolution Radiometer) AND SPOT (Système Pour l'Observation de la Terre) VEGETATION. The automated procedure starts from data downloading, calls in a batch mode the software and provides customized layers of phenological parameters such as the starting of the season or length of the season and many others

Analysis of the precipitation characteristics on the Tibetan Plateau using Remote Sensing, Ground-Based Instruments and Cloud models

In this Thesis work, carried out in the frame of CEOP-AEGIS, an EU FP7 funded project, the problem of the precipitation monitoring over the Tibetan Plateau has been addressed. Despite the Plateau key role in water cycle of South East Asia (and in the life of 1.5 billions of people), there is a critical lack of knowledge, because the current estimates of relevant geophysical parameters are based on sparse and scarce observations than can not provide the required accuracy for quantitative studies and reliable monitoring, especially on a climate change perspective. This is particularly true for precipitation, the geophysical parameter with highest spatial and temporal variability. The constantly increasing availability of Earth system observation from spaceborne sensors makes the remote sensing an effective option for precipitation monitoring and the main focus of the present work is the implementation and applications for three years of data (2008, 2009 and 2010) of an array of satellite precipitation techniques, based on different methodological approaches and data sources. First, a sensitivity study on the capability of the most used satellite sensors to detect precipitation at the ground, assessed with respect to raingauges data for selected case studies, has been carried out. Then, two physically based techniques have been implemented based on satelliteborne active (for snow-rate) and passive (for rain-rate) microwave sensor data and the output used for calibrate geostationary IR-based techniques. Finally, two well established global multisensor precipitation products have been considered for reference and intercomparison. All the techniques have been implemented for the 3 years and the results compared at different spatial and temporal scales. The analysis of daily rain amount has shown that in general global algorithms are able to estimate rain amount larger than the ones estimated by other techniques during the monsoon season. In cold months global techniques underestimate precipitation amount and areas, resulting in a dry bias with respect to IR calibrated techniques. Case studies compared with ground radar precipitation data on convective episodes shown that global products tend to underestimate precipitation areas, while IR calibrated techniques provides reliable rainrate patterns, as compared with radar data. Unfortunately, the number of radar case studies was not large enough to allow significant validation studies, and also non data were available for cold months. Annual precipitation cumulated maps show marked differences among the techniques: IR calibrated techniques generally overestimate precipitation amount by a factor of 2 with respect of global products. Reasons for discrepancies are investigated and discussed, pointing out the uncertainties that will probably be solved only with the exploitation of new satellite missions