iSPEX 2: A universal smartphone add-on for portable spectroscopy and polarimetry
O. Burggraff, A. Perdujin, R. van Hek, N. Schmidt, C. Keller and F. Snik (2020). "A universal smartphone add-on for portable spectroscopy and polarimetry: iSPEX 2"
Abstract: Spectropolarimetry is a powerful technique for remote sensing of the environment. It enables the retrieval of particle shape and size distributions in air and water to an extent that traditional spectroscopy cannot. SPEX is an instrument concept for spectropolarimetry through spectral modulation, providing snapshot, and hence accurate, hyperspectral intensity and degree and angle of linear polarization. Successful SPEX instruments have included groundSPEX and SPEX airborne, which both measure aerosol optical thickness with high precision, and soon SPEXone, which will fly on PACE. Here, we present a low-cost variant for consumer cameras, iSPEX 2, with universal smartphone support. Smartphones enable citizen science measurements which are significantly more scaleable, in space and time, than professional instruments. Universal smartphone support is achieved through a modular hardware design and SPECTACLE data processing. iSPEX 2 will be manufactured through injection molding and 3D printing. A smartphone app for data acquisition and processing is in active development. Production, calibration, and validation will commence in the summer of 2020. Scientific applications will include citizen science measurements of aerosol optical thickness and surface water reflectance, as well as low-cost laboratory and portable spectroscopy.
Biases from incorrect reflectance convolution
O.Burggraaff (2020). "Biases from incorrect reflectance convolution" Optics Express, Vol. 28, Issue 9, pp 13801-13816. doi.org/10.1364/OE.391470
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.
O. Burggraaff, N. Schmidt, J. Zamorano, K. Pauly, S. Pascual, C. Tapia, E. Spyrakos, and F. Snik, (2019). "Standardized spectral and radiometric calibration of consumer cameras," Opt. Express 27, 19075-19101.
Abstract: Consumer cameras, particularly onboard smartphones and UAVs, are now commonly used as scientific instruments. However, their data processing pipelines are not optimized for quantitative radiometry and their calibration is more complex than that of scientific cameras. The lack of a standardized calibration methodology limits the interoperability between devices and, in the ever-changing market, ultimately the lifespan of projects using them. We present a standardized methodology and database (SPECTACLE) for spectral and radiometric calibrations of consumer cameras, including linearity, bias variations, read-out noise, dark current, ISO speed and gain, flat-field, and RGB spectral response. This includes golden standard ground-truth methods and do-it-yourself methods suitable for non-experts. Applying this methodology to seven popular cameras, we found high linearity in RAW but not JPEG data, inter-pixel gain variations >400% correlated with large-scale bias and read-out noise patterns, non-trivial ISO speed normalization functions, flat-field correction factors varying by up to 2.79 over the field of view, and both similarities and differences in spectral response. Moreover, these results differed wildly between camera models, highlighting the importance of standardization and a centralized database.
Ceccaroni, L., & Piera, J. (2018). Stakeholder engagement in water quality research: A case study based on the Citclops and MONOCLE projects. In Hecker S., Haklay M., Bowser A., Makuch Z., Vogel J., & Bonn A. (Eds.), Citizen Science: Innovation in Open Science, Society and Policy (pp. 201-209). London: UCL Press.