Bryan Leavitt

Relationship between gross primary production and chlorophyll content in crops: Implications for the synoptic monitoring of vegetation productivity

CO2/m 2 s in maize (GPP ranged from 0 to 3.1 mg CO2/m 2 s) and less than 0.2 mg CO2/m 2 s in soybean (GPP ranged from 0 to 1.8 mg CO2/m 2 s). Validation using an independent data set for irrigated and rainfed maize showed robustness of the technique; RMSE of GPP prediction was less than 0.27 mg CO2/m 2 s.

NIR-red reflectance-based algorithms for chlorophyll-a estimation in mesotrophic inland and coastal waters: Lake Kinneret case study

A variety of models have been developed for estimating chlorophyll-a (Chl-a) concentration in turbid and productive waters. All are based on optical information in a few spectral bands in the red and near-infra-red regions of the electromagnetic spectrum. The wavelength locations in the models used were meticulously tuned to provide the highest sensitivity to the presence of Chl-a and minimal sensitivity to other constituents in water. But the caveat in these models is the need for recurrent parameterization and calibration due to changes in the biophysical characteristics of water based on the location and/or time of the year. In this study we tested the performance of NIR-red models in estimating Chl-a concentrations in an environment with a range of Chl-a concentrations that is typical for coastal and mesotrophic inland waters. The models with the same spectral bands as MERIS, calibrated for small lakes in the Midwest U.S., were used to estimate Chl-a concentration in the subtropical Lake Kinneret (Israel), where Chl-a concentrations ranged from 4 to 21 mg m(-3) during four field campaigns. A two-band model without re-parameterization was able to estimate Chl-a concentration with a root mean square error less than 1.5 mg m(-3). Our work thus indicates the potential of the model to be reliably applied without further need of parameterization and calibration based on geographical and/or seasonal regimes.

Proximal Sensing of Coral Features: Spectral Characterization of Siderastrea siderea

Remote sensing has been suggested as a potential tool for monitoring coral-reef ecosystems. However, before remote sensing can be viewed as a practical monitoring and diagnostic tool for entire coral communities, there is a need to understand the spectral responses from individual coral species. Toward that end, it seems important to not only establish the range of natural variability in spectral reflectance but also to develop procedures to enhance understanding of the spectra. This paper describes our data collection approach as well as a procedure for examining the spectral features associated with one species of coral (Siderastrea siderea). The paper also examines the spectral variability in reflectance within samples of the same species and suggests a procedure for highlighting the spectral positions of absorbers/pigments, thus yielding useful information about pigment composition in one coral species.

Compensating for Irradiance Fluxes When Measuring the Spectral Reflectance of Corals In Situ

This study employed an ensemble of instruments to monitor irradiance fluxes during measurement of the spectral reflectance of one coral in situ at Roatan Island, Honduras. The underwater light field (400-700 nm) was considerably more variable under sunny or partially cloudy sky conditions than the above-water light field. Measurements under overcast conditions had low variability both above and at the coral target. Correlations between simultaneously collected above water and underwater downwelling fluxes had R 2 values of 0.02%, 63.0%, and 96.3% under sunny, variable, and overcast sky conditions, respectively. Simultaneous measurements of both upwelling radiance and downwelling irradiance using a dual-fiber hyperspectral sensor significantly reduced the variance in the spectral reflectance of a coral target, regardless of sky conditions, especially as compared with a single-fiber configuration.