GLORIA - A globally representative hyperspectral in situ dataset for optical sensing of water quality.

The development of algorithms for remote sensing of water quality (RSWQ) requires a large amount of in situ data to account for the bio-geo-optical diversity of inland and coastal waters. The GLObal Reflectance community dataset for Imaging and optical sensing of Aquatic environments (GLORIA) includes 7,572 curated hyperspectral remote sensing reflectance measurements at 1 nm intervals within the 350 to 900 nm wavelength range. In addition, at least one co-located water quality measurement of chlorophyll a, total suspended solids, absorption by dissolved substances, and Secchi depth, is provided. The data were contributed by researchers affiliated with 59 institutions worldwide and come from 450 different water bodies, making GLORIA the de-facto state of knowledge of in situ coastal and inland aquatic optical diversity. Each measurement is documented with comprehensive methodological details, allowing users to evaluate fitness-for-purpose, and providing a reference for practitioners planning similar measurements. We provide open and free access to this dataset with the goal of enabling scientific and technological advancement towards operational regional and global RSWQ monitoring.

Spectral separability and mapping potential of cassava leaf damage symptoms caused by whiteflies (Bemisia tabaci).

BACKGROUND: This study examines whether leaf spectra can be used to measure damage to cassava plants from whitefly (Bemisia tabaci), and the potential to translate measurements from leaf to landscape scale in eastern Africa. Symptoms of the cassava brown streak disease (CBSD) and cassava mosaic disease (CMD) viruses, and sooty mould (SM) blackening of lower leaves from whiteflies feeding on the upper leaves, were measured at the leaf scale with a high-resolution spectroradiometer and a single photon avalanche diode (SPAD) meter, which retrieves relative chlorophyll concentration. Spectral measurements were compared to the five-level visual scores used to assess the severity of each of the three damaging agents in the field, and also to leaf chemistry data. RESULTS: Leaves exhibiting severe CBSD and CMD were spectrally indistinguishable from leaves without any symptoms. Severe SM was spectrally distinctive but is likely to be difficult to map because of its occurrence in the lower crown. SPAD measurements were highly correlated with most foliar chemistry measurements and field scores of disease severity. Regression models between simulated Sentinel 2 bands, field scores and SPAD measurements were strongest using wavelengths with high importance weightings in random forest models. CONCLUSION: SPAD measurements are highly correlated to many foliar chemistry parameters, and should be considered for use in mapping disease severity over larger areas. Remaining challenges for mapping relate to the subtle expression of symptoms, the spatial distribution of disease severity within fields, and the small size and complex structure of the cassava fields themselves. © 2017 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.