Abstract
We propose a method for classifying radiometric oceanic color data measured by hyperspectral satellite sensors into known spectral classes, irrespective of the downwelling irradiance of the particular day, i.e., the illumination conditions. The focus is not on retrieving the inherent optical properties but to classify the pixels according to the known spectral classes of the reflectances from the ocean. The method compensates for the unknown downwelling irradiance by white balancing the radiometric data at the ocean pixels using the radiometric data of bright pixels (typically from clouds). The white-balanced data is compared with the entries in a pre-calibrated lookup table in which each entry represents the spectral properties of one class. The proposed approach is tested on two datasets of in situ measurements and 26 different daylight illumination spectra for medium resolution imaging spectrometer (MERIS), moderate-resolution imaging spectroradiometer (MODIS), sea-viewing wide field-of-view sensor (SeaWiFS), coastal zone color scanner (CZCS), ocean and land colour instrument (OLCI), and visible infrared imaging radiometer suite (VIIRS) sensors. Results are also shown for CIMEL’s SeaPRISM sun photometer sensor used on-board field trips. Accuracy of more than 92% is observed on the validation dataset and more than 86% is observed on the other dataset for all satellite sensors. The potential of applying the algorithms to non-satellite and non-multi-spectral sensors mountable on airborne systems is demonstrated by showing classification results for two consumer cameras. Classification on actual MERIS data is also shown. Additional results comparing the spectra of remote sensing reflectance with level 2 MERIS data and chlorophyll concentration estimates of the data are included.
Highlights
Satellite hyperspectral sensors are used for remote sensing of terrestrial, oceanic, and atmospheric features
We further show that our approach may enable non-satellite sensors such as the multi-band SeaPRISM sensor and extend the use of the trichromatic consumer cameras for environmental sensing
We observe that overall recall for all the satellite and field sensors is more than 90%, and the recall is more than 95% for moderate-resolution imaging spectroradiometer (MODIS), medium resolution imaging spectrometer (MERIS), ocean and land colour instrument (OLCI), and visible infrared imaging radiometer suite (VIIRS)
Summary
Satellite hyperspectral sensors are used for remote sensing of terrestrial, oceanic, and atmospheric features. The radiometric data measured by a remote sensing satellite sensor, such as MODIS or OLCI is the top-of-atmosphere (TOA) upwelling radiance measured over multiple narrow band channels of the sensor. Our proposed method alleviates the need for estimating or interpolating different components of upwelling radiances as measured by satellite It instead uses the concept of white balancing where the radiometric data of clouds is used to compensate indirectly for the downwelling irradiance at the time of measurement of the ocean color data. It stores a lookup table for the radiometric projections of the spectral distributions representing the classes. The utility of our method is being able to derive direct correspondence between sensor’s actual measurements and the classes of remote sensing reflectance which indicate some useful characteristics of remote sensing reflectance
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