Abstract
Reflectance, a crucial earth observation variable, is converted from hyperspectral to multispectral through convolution. This is done to combine time series, validate instruments, and apply retrieval algorithms. However, convolution is often done incorrectly, with reflectance itself convolved rather than the underlying (ir)radiances. Here, the resulting error is quantified for simulated and real multispectral instruments, using 18 radiometric data sets (N = 1799 spectra). Biases up to 5% are found, the exact value depending on the spectrum and band response. This significantly affects extended time series and instrument validation, and is similar in magnitude to errors seen in previous validation studies. Post-hoc correction is impossible, but correctly convolving (ir)radiances prevents this error entirely. This requires publication of original data alongside reflectance.
Highlights
Reflectance, the spectral fraction of light reflected by a surface, is an essential earth observation (EO) variable
Examples include the TriOS RAMSES, Seabird HyperOCR, and ASD FieldSpec field-going spectroradiometers, as well as the Ocean Color Instrument (OCI) due to fly on the Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission
Archival data sets containingradiance and reflectance data were used to test the principles described in Sect. 2 and quantify the errors resulting from working in R-space rather than L-space
Summary
Reflectance, the spectral fraction of light reflected by a surface, is an essential earth observation (EO) variable. It forms the basis for data products such as chlorophyll and suspended matter in water [1,2,3], and canopy cover and biomass on land [4,5]. Hyperspectral data have a finer spectral sampling and, typically, resolution and contain more information than multispectral ones, but depending on the instrument design, often collect less light in each band, giving a worse signal-to-noise ratio
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