Terra MODIS Band 27 Electronic Crosstalk Effect and Its Removal

The MODerate-resolution Imaging Spectroradiometer (MODIS) is one of the primary instruments in the NASA Earth Observing System (EOS). The first MODIS instrument was launched in December, 1999 on-board the Terra spacecraft. MODIS has 36 bands, covering a wavelength range from 0.4 micron to 14.4 micron. MODIS band 27 (6.72 micron) is a water vapor band, which is designed to be insensitive to Earth surface features. In recent Earth View (EV) images of Terra band 27, surface feature contamination is clearly seen and striping has become very pronounced. In this paper, it is shown that band 27 is impacted by electronic crosstalk from bands 28-30. An algorithm using a linear approximation is developed to correct the crosstalk effect. The crosstalk coefficients are derived from Terra MODIS lunar observations. They show that the crosstalk is strongly detector dependent and the crosstalk pattern has changed dramatically since launch. The crosstalk contributions are positive to the instrument response of band 27 early in the mission but became negative and much larger in magnitude at later stages of the mission for most detectors of the band. The algorithm is applied to both Black Body (BB) calibration and MODIS L1B products. With the crosstalk effect removed, the calibration coefficients of Terra MODIS band 27 derived from the BB show that the detector differences become smaller. With the algorithm applied to MODIS L1B products, the Earth surface features are significantly removed and the striping is substantially reduced in the images of the band. The approach developed in this report for removal of the electronic crosstalk effect can be applied to other MODIS bands if similar crosstalk behaviors occur

Multitemporal Cross-Calibration of the Terra MODIS and Landsat 7 ETM+ Reflective Solar Bands

In recent years, there has been a significant increase in the use of remotely sensed data to address global issues. With the open data policy, the data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Enhanced Thematic Mapper Plus (ETM+) sensors have become a critical component of numerous applications. These two sensors have been operational for more than a decade, providing a rich archive of multispectral imagery for analysis of mutitemporal remote sensing data. This paper focuses on evaluating the radiometric calibration agreement between MODIS and ETM+ using the near-simultaneous and cloud-free image pairs over an African pseudo-invariant calibration site, Libya 4. To account for the combined uncertainties in the top-of-atmosphere (TOA) reflectance due to surface and atmospheric bidirectional reflectance distribution function (BRDF), a semiempirical BRDF model was adopted to normalize the TOA reflectance to the same illumination and viewing geometry. In addition, the spectra from the Earth Observing-1 (EO-1) Hyperion were used to compute spectral corrections between the corresponding MODIS and ETM+ spectral bands. As EO-1 Hyperion scenes were not available for all MODIS and ETM+ data pairs, MODerate resolution atmospheric TRANsmission (MODTRAN) 5.0 simulations were also used to adjust for differences due to the presence or lack of absorption features in some of the bands. A MODIS split-window algorithm provides the atmospheric water vapor column abundance during the overpasses for the MODTRAN simulations. Additionally, the column atmospheric water vapor content during the overpass was retrieved using the MODIS precipitable water vapor product. After performing these adjustments, the radiometric cross-calibration of the two sensors was consistent to within 7%. Some drifts in the response of the bands are evident, with MODIS band 3 being the largest of about 6% over 10 years, a change that will be corrected in Collection 6 MODIS processing

Implementation and validation of the snow grain size retrieval SGSP from spectral reflectances of the satellite sensor MODIS

Snow is part of the cryosphere in the climate system of the Earth. It has a high albedo in the visible, decreasing towards the near-infrared. Snow on ground is a porous medium of ice, air, and possibly impurities like dust or soot. After deposition, it undergoes snow metamorphism changing the grain size, grain shape, and density. In the visible, the reflection characteristics of snow are mainly determined by the amount of impurities, and in the near-infrared by the size of the snow grains. Satellite sensors allow observing the snow in remote areas like the polar regions on a regular basis and on a global scale. A method to compute the snow grain size and impurity amount from optical satellite observations is the Snow Grain Size and Pollution amount (SGSP) retrieval. It uses data of three reflectance channels (here: at 0.47 µm, 0.86 µm, and 1.24 µm), has a reduced dependency on the snow grain shape, and is applicable at solar zenith angles up to 75°. In this work, the SGSP retrieval is implemented in a near-real time processing chain using data from the Moderate Resolution Imaging Spectrometer (MODIS) operating on the satellites Terra and Aqua. A sensitivity analysis reveals that currently only the snow grain size can be determined reliably by the SGSP retrieval, as the uncertainties of the MODIS instrument are too high for the amount of impurities typically occurring in polar regions. Sensitivity studies on the influence of vertically inhomogeneous snow, wet snow, and cirrus clouds show that the SGSP retrieval typically underestimates the grain size by 15% to 25% for those three cases. The SGSP-retrieved snow grain size is validated using six different ground truth data sets from the Arctic, the Antarctic, Greenland, and Japan from the years 2001 to 2009, and various subsurfaces (land, land ice, sea ice, lake ice). In general, the retrieved and ground-measured grain size are in good agreement. 17 cases have small differences (1 14%), 16 cases intermediate differences (18 53%), and four cases large differences (72 178%). The SGSP retrieval tends to underestimate the grain size for wet snow cases (by 18% 31%) and cirrus cloud cases (by 14% 31%), and overestimates it for surface hoar cases (by 30% 53%) and wind crust cases (by 23% 77%). A comparison of the SGSP retrieval with a previous retrieval using ground measurements from the Himalayan basin shows that the SGSP-retrieved grain size tends to be smaller (by 5 48 µm) and that vertically inhomogeneous snow influences the retrieval. A comparison of SGSP-retrieved snow grain size time series on the Ross ice shelf, Antarctica, at three Automatic Weather Stations (AWS) with snow depth change data from those three stations shows that a snow fall event of 6 cm is detected by the sudden decrease of the retrieved grain size from 200 µm to 50 µm. A comparison of the spectral snow albedo for the MODIS Channels 1 to 5 over 16 days on a large-scale area in Greenland between the SGSP-derived albedo and the spectral MODIS albedo product MOD43 shows a correlation of 0.82 for Channel 5,which is most sensitive to the snow grain size

Requirements for an Advanced Ocean Radiometer

This document suggests requirements for an advanced ocean radiometer, such as e.g. the ACE (Aerosol/Cloud/Ecosystem) ocean radiometer. The ACE ocean biology mission objectives have been defined in the ACE Ocean Biology white paper. The general requirements presented therein were chosen as the basis for the requirements provided in this document, which have been transformed into specific, testable requirements. The overall accuracy goal for the advanced ocean radiometer is that the total radiometric uncertainties are 0.5% or smaller for all bands. Specific mission requirements of SeaWiFS, MODIS, and VIIRS were often used as a model for the requirements presented here, which are in most cases more demanding than the heritage requirements. Experience with on-orbit performance and calibration (from SeaWiFS and MODIS) and prelaunch testing (from SeaWiFS, MODIS, and VIIRS) were important considerations when formulating the requirements. This document describes requirements in terms of the science data products, with a focus on qualities that can be verified by prelaunch radiometric characterization. It is expected that a more comprehensive requirements document will be developed during mission formulatio

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