The Dark Target aerosol algorithm was developed to exploit the information content available from the observations of Moderate-Resolution Imaging Spectroradiometers (MODIS), to better characterize the global aerosol system. The algorithm is based on measurements of the light scattered by aerosols toward a space-borne sensor against the backdrop of relatively dark Earth scenes, thus giving rise to the name “Dark Target”. Development required nearly a decade of research that included application of MODIS airborne simulators to provide test beds for proto-algorithms and analysis of existing data to form realistic assumptions to constrain surface reflectance and aerosol optical properties. This research in itself played a significant role in expanding our understanding of aerosol properties, even before Terra MODIS launch. Contributing to that understanding were the observations and retrievals of the growing Aerosol Robotic Network (AERONET) of sun-sky radiometers, which has walked hand-in-hand with MODIS and the development of other aerosol algorithms, providing validation of the satellite-retrieved products after launch. The MODIS Dark Target products prompted advances in Earth science and applications across subdisciplines such as climate, transport of aerosols, air quality, and data assimilation systems. Then, as the Terra and Aqua MODIS sensors aged, the challenge was to monitor the effects of calibration drifts on the aerosol products and to differentiate physical trends in the aerosol system from artefacts introduced by instrument characterization. Our intention is to continue to adapt and apply the well-vetted Dark Target algorithms to new instruments, including both polar-orbiting and geosynchronous sensors. The goal is to produce an uninterrupted time series of an aerosol climate data record that begins at the dawn of the 21st century and continues indefinitely into the future.

Thin cirrus clouds frequently contaminate images acquired with either Landsat 7 ETM+ or the earlier generation of Landsat series satellite instruments. The situation has changed since the launch of the Landsat 8 Operational Land Imager (OLI) into space in 2013. OLI implemented a cirrus detecting channel (Band 9) centered within a strong atmospheric water vapor absorption band near 1.375 μm with a width of 30 nm. The specifications for this channel were the same as those specified for the NASA Moderate Resolution Imaging Spectroradiometer (MODIS) in the early 1990s. The OLI Band 9 has been proven to be very effective in detecting and masking thin cirrus-contaminated pixels at the high spatial resolution of 30 m. However, this channel has not yet been routinely used for the correction of thin cirrus effects in other OLI band images. In this article, we describe an empirical technique for the removal of thin cirrus scattering effects in OLI visible near infrared (IR) and shortwave IR (SWIR) spectral regions. We present results from applications of the technique to three OLI data sets. We also discuss issues associated with parallax anomalies in OLI data.

Passive high spatial resolution hyperspectral and multispectral imaging systems in the solar spectral region from aircraft and satellite platforms are being increasingly used for remote sensing of coastal waters and inland lakes. However, the remotely sensed data are often contaminated by the specular reflection of solar radiation at the air/water interface. Thus, the effects of sunglint need to be corrected. The purpose of sunglint correction is to remove the undesired specular reflected portion of radiance that does not carry any information about the water body and its bottom surface properties and to obtain the water-leaving radiance from the measured total radiance. In general, adequate treatment of the spectral variation of the sunglint effect with due consideration of the physics and the spectroscopy principles has not been done in previously reported hyperspectral sunglint removing techniques. In this study, a practical sunglint removing technique using either a shortwave infrared (SWIR) band or a near-infrared (NIR) band has been developed, where the liquid water absorption is sufficiently large, and the water-leaving radiance (in units of reflectance) is negligible. In the proposed method, proper consideration has been taken for the spectral Fresnel reflection in the wavelength range of 0.35–2.5 µm. Four cases of sunglint removal from hyperspectral imaging data acquired from two airborne imaging spectrometers have been presented.

Through the analysis of hyperspectral imaging data collected over water surfaces covered by floating vegetation, such as Sargassum and algae, we observed that the spectra commonly contain a reflectance peak centered near 1.07 μm. This peak results from the competing effects between the well-known vegetation reflectance plateau in the 0.81–1.3 μm spectral range and the absorption effects above 0.75 μm by liquid water within the vegetation and in the surrounding water bodies. In this article, we propose a new index, namely the floating vegetation index (FVI), for the hyperspectral remote sensing of vegetation over surface layers of oceans and inland lakes. In the formulation of the FVI, one channel centered near 1.0 μm and another 1.24 μm are used to form a linear baseline. The reflectance value of the third channel centered at the 1.07-μm reflectance peak above the baseline is defined as the FVI. Hyperspectral imaging data acquired with the AVIRIS (Airborne Visible Infrared Imaging Spectrometer) instrument over the Gulf of Mexico and over salt ponds near Moffett Field in southern portions of the San Francisco Bay were used to demonstrate the success in detecting Sargassum and floating algae with this index. It is expected that the use of this index for the global detection of floating vegetation from hyperspectral imaging data to be acquired with future satellite sensors will result in improved detection and therefore enhanced capability in estimating primary production, a measure of how much carbon is fixed per unit area per day by oceans and inland lakes.

Since the October 2011 launch of the VIIRS (Visible Infrared Imaging Radiometer Suite) instrument, a number of inter-sensor comparisons between VIIRS and MODIS (Moderate Resolution Imaging Spectroradiometer) radiances have been reported. Most of these comparisons are between calibrated radiances and temperatures based on observations of the two sensors from simultaneous nadir overpasses (SNO). Few comparisons between the retrieved ocean color data products, such as chlorophyll concentration, from VIIRS and MODIS data have been reported. Retrievals from measured data at large solar zenith angles and large view zenith angles are excluded from these comparison studies. In this paper, we report the inter-sensor comparisons between VIIRS and MODIS data acquired over the Chesapeake Bay and nearby areas with relatively large differences in sensor view angles. The goal for this study is to check the consistency between MODIS and VIIRS ocean color data products in order to merge the products from the two sensors. We compare total radiances (Lt) at the top of atmosphere (TOA) and the ocean color (OC) data products derived with the automatic processing system (APS) from both VIIRS and MODIS data. APS was developed at the Naval Research Laboratory, Stennis Space Center (NRL/SSC). We have found that, although there are large differences between the measured radiances (Lt) of the two sensors when the sensor zenith angle differences are significant, the mean percent differences between the retrieved normalized water-leaving radiances are about 15%. The results show that the variation in satellite view zenith angles is not a main factor affecting the retrieval of ocean color data products, i.e., the atmospheric correction routine adequately removes the view-angle dependence.

The Geostationary Ocean Color Imager (GOCI) is the first geostationary ocean color sensor in orbit that provides bio-optical properties from coastal and open waters around the Korean Peninsula at unprecedented temporal resolution. In this study, we compare the normalized water-leaving radiance (nLw) products generated by the Naval Research Laboratory Automated Processing System (APS) with those produced by the stand-alone software package, the GOCI Data Processing System (GDPS), developed by the Korean Ocean Research & Development Institute (KORDI). Both results are then compared to the nLw measured by the above water radiometer at the Ieodo site. This above-water radiometer is part of the Aerosol Robotic NETwork (AeroNET). The results indicate that the APS and GDPS processed correlates well within the same image slot where the coefficient of determination (r²) is higher than 0.84 for all the bands from 412 nm to 745 nm. The agreement between APS and the AeroNET data is higher when compared to the GDPS results. The Root-Mean-Squared-Error (RMSE) between AeroNET and APS data ranges from 0.24 [mW/(cm²srμm)] at 555 nm to 0.52 [mW/(cm²srμm)] at 412 nm while RMSE between AeroNET and GDPS data ranges from 0.47 [mW/(cm²srμm)] at 443 nm to 0.69 [mW/(cm²srμm)] at 490 nm.