Hyperspectral Imager For The Coastal Ocean Data Research Articles

Remote-Sensing Applications for Environmental Health Research

More than 1,000 manmade satellites currently orbit our planet.1 Some are near the edge of the Earth’s atmosphere just a few hundred kilometers up. Others are tens of thousands of kilometers above us.2 They aid in communication, navigation, defense, and science. A small number3,4 play a critical and quickly expanding role: monitoring the Earth’s surface and atmosphere to track environmental conditions that are intimately tied to human health. A number of new Earth-observing missions are planned for the next decade, including Sentinel-5 aboard the European Space Agency’s MetOp Second Generation satellites (pictured).48 In the meantime researchers are finding new uses for the satellite ... Researchers and government agencies worldwide already use satellite data to monitor air pollutants, infectious disease epidemics, harmful algal blooms (HABs), climate change, and more. But as current research indicates, that’s only the beginning of what we can do with the technology, broadly referred to as “remote sensing.” In the coming years, new satellites will offer higher-resolution imagery in conjunction with more robust and precise algorithms to process the data they deliver. As a result, researchers expect to dramatically expand their ability to view and understand Earth’s land, water, and air, from its remotest ocean waters to its largest cities. The National Aeronautics and Space Administration (NASA) launched its first satellite in 1958,5 and TIROS-1, the country’s first meteorological satellite, came 2 years later.6 Within a few decades members of the epidemiological and public health communities began actively looking at satellite data, says John Haynes, program manager of the NASA Applied Sciences Health and Air Quality Applications Program. In recent years interest in remote-sensing data has soared, with newer avenues being developed and fine-tuned, including air-quality measurements and vector-borne disease projections. “There’s really been a paradigm shift in the use of remote sensing for public health issues,” Haynes says. “Every year there seems to be more and more interest.” Indeed, by March 2015 NASA will have launched 6 Earth-observing missions in 12 months,7 more than in any year in at least a decade.8 New launches include a “global precipitation observatory” that will make frequent global measurements of rain and snowfall, plus one satellite designed to measure soil moisture and another that will measure how carbon moves through the Earth’s atmosphere, land, and oceans. In addition, the International Space Station will receive three new instruments, one that will observe how winds behave around the world, one that will measure clouds and aerosols (particles suspended in the atmosphere)—two variables that remain difficult to predict in climate-change models—and one that will take global, long-term measurements of key components of the Earth’s atmosphere, including aerosols and ozone.9 The momentum will carry through at least the next 8 or so years, with NASA and other space agencies in Europe and Asia planning to launch new satellites that will provide even higher-resolution snapshots of the Earth. Along with technological and scientific advances, a third development is leading to new and improved applications of satellite data: NASA and the National Oceanic and Atmospheric Administration (NOAA) have made their satellite data available free of charge, Haynes says, while the European Space Agency (ESA) has reduced prices and promised to provide free access to data from its next generation of instruments. “More people use the data, and you get more out of it than when you try to restrict it,” says Raphael Kudela, an oceanographer at the University of California, Santa Cruz, who uses satellite imagery to study HABs. This free sharing of data has been instrumental in his field, allowing researchers at institutions around the world to study HABs from above and to improve systems to track and predict them.

HICO-Based NIR–Red Models for Estimating Chlorophyll- $a$ Concentration in Productive Coastal Waters

We present here results that demonstrate the potential of near-infrared (NIR)-red models to estimate chlorophyll- a (chl- a) concentration in coastal waters using data from the spaceborne Hyperspectral Imager for the Coastal Ocean (HICO). Since the recent demise of the MEdium Resolution Imaging Spectrometer (MERIS), the use of sensors such as HICO has become critical for coastal ocean color research. Algorithms based on two- and three-band NIR-red models, which were previously used very successfully with MERIS data, were applied to HICO images. The two- and three-band NIR-red algorithms yielded accurate estimates of chl- a concentration, with mean absolute errors that were only 10.92% and 9.58%, respectively, of the total range of chl- a concentrations measured over a period of several months in 2012 and 2013 on the Taganrog Bay in Russia. Given the uncertainties in the radiometric calibration of HICO, the results illustrate the robustness of the NIR-red algorithms and validate the radiometric, spectral, and atmospheric corrections applied to HICO data as they relate to estimating chl- a concentration in productive coastal waters. Inherent limitations due to the characteristics of the sensor and its orbit prohibit HICO from providing anywhere near the level of frequent global coverage as provided by standard multispectral ocean color sensors. Nevertheless, the results demonstrate the utility of HICO as a tool for determining water quality in select coastal areas and the cross-sensor applicability of NIR-red models and provide an indication of what could be achieved with future spaceborne hyperspectral sensors in estimating coastal water quality.

Assessing the Application of Cloud–Shadow Atmospheric Correction Algorithm on HICO

Several ocean color earth observation satellite sensors are presently collecting daily imagery, including the Hyperspectral Imager for the Coastal Ocean (HICO). HICO has been operating aboard the International Space Station since its installation on September 24, 2009. It provides high spatial resolution hyperspectral imagery optimized for the coastal ocean. Atmospheric correction, however, still remains a challenge for this sensor, particularly in optically complex coastal waters. In this paper, we assess the application of the cloud-shadow atmospheric correction approach on HICO data and validate the results with the in situ data. We also use multiple sets of cloud, shadow, and sunlit pixels to correct a single image multiple times and intercompare the results to assess variability in the retrieved reflectance spectra. Retrieved chlorophyll values from this intercomparison are similar and also agree well with the in situ chlorophyll measurements.

Performance evaluation of normalized difference chlorophyll index in northern Gulf of Mexico estuaries using the Hyperspectral Imager for the Coastal Ocean

The Hyperspectral Imager for the Coastal Ocean (HICO) was used to derive chlorophyll-a (chl-a) based on the normalized difference chlorophyll index (NDCI) in two Gulf of Mexico coastal estuaries. Chl-a data were acquired from discrete in situ water sample analysis and above-water hyperspectral surface acquisition system (HyperSAS) remote sensing reflectance in Pensacola Bay (PB) and Choctawhatchee Bay (CB). NDCI algorithm calibrations and validations were completed on HICO data. Linear and best-fit (polynomial) calibrations performed strongly with R2 of 0.90 and 0.96, respectively. The best validation of NDCI resulted with an R2 of 0.74 and root-mean-square error (RMSE) of 1.64 µg/L. A strong spatial correspondence was observed between NDCI and chl-a, with higher NDCI associated with higher chl-a and these areas were primarily located in the northern PB and eastern CB at the river mouths. NDCI could be effectively used as a qualitative chl-a monitoring tool with a reduced need for site-specific calibration.

Removal of Thin Cirrus Scattering Effects for Remote Sensing of Ocean Color From Space

Thin cirrus clouds frequently contaminate satellite images. These clouds are traditionally treated as aerosols in atmospheric correction processes for satellite remote sensing of ocean color. Through analysis of spectral imaging data covering a solar spectral region between approximately 0.4 and 0.9 μm and acquired with the Hyperspectral Imager for the Coastal Ocean (HICO) instrument currently on board the International Space Station, we have developed an empirical but very effective technique to remove thin cirrus effects from HICO data acquired over fairly clear ocean waters where the water leaving reflectances above 0.8 μm are close to zero. The development of the technique is based on this property and on the fact that cirrus reflectances in the 0.4-1.0-μm region are spectrally constant. The empirical technique is described, and sample results are presented. The same technique is applicable to thin cirrus corrections over clear water surfaces for other hyperspectral or multichannel imaging instruments covering similar spectral range.

Vicarious calibrations of HICO data acquired from the International Space Station

The Hyperspectral Imager for the Coastal Ocean (HICO) presently onboard the International Space Station (ISS) is an imaging spectrometer designed for remote sensing of coastal waters. The instrument is not equipped with any onboard spectral and radiometric calibration devices. Here we describe vicarious calibration techniques that have been used in converting the HICO raw digital numbers to calibrated radiances. The spectral calibration is based on matching atmospheric water vapor and oxygen absorption bands and extraterrestrial solar lines. The radiometric calibration is based on comparisons between HICO and the EOS/MODIS data measured over homogeneous desert areas and on spectral reflectance properties of coral reefs and water clouds. Improvements to the present vicarious calibration techniques are possible as we gain more in-depth understanding of the HICO laboratory calibration data and the ISS HICO data in the future.

A Technique For Removing Second-Order Light Effects From Hyperspectral Imaging Data

The Hyperspectral Imager for the Coastal Ocean (HICO) instrument currently on board the International Space Station is a new sensor designed specifically for the studies of turbid coastal waters and large inland lakes and rivers. It covers the wavelength range between 0.4 and 0.9 μm with a spectral resolution of 5.7 nm and a spatial resolution of approximately 90 m. The HICO sensor is not equipped with a second-order blocking filter in front of the focal plane array. As a result, the second-order light from the shorter visible spectral region falls onto the detectors covering the near-IR spectral region above 0.8 μm. In order to have accurate radiometric calibration of the near-IR channels, the second-order light contribution needs to be removed. The water-leaving radiances of these near-IR channels over clear ocean waters are close to zero because of strong liquid water absorption above 0.8 μm. Through analysis of HICO imaging data containing features of shallow underwater objects, such as coral reefs, we have developed an empirical technique to correct for the second-order light effects in near-IR channels. HICO data acquired over Midway Island in the Pacific Ocean and the Bahamas Banks in the Atlantic Ocean are used to demonstrate the effectiveness of the new technique.