Spatial and Spectral Dependencies of Maize Yield Estimation Using Remote Sensing.

Corn (Zea mays L.) is the most abundant food/feed crop, making accurate yield estimation a critical data point for monitoring global food production. Sensors with varying spatial/spectral configurations have been used to develop corn yield models from intra-field (0.1 m ground sample distance (GSD)) to regional scales (>250 m GSD). Understanding the spatial and spectral dependencies of these models is imperative to result interpretation, scaling, and deploying models. We leveraged high spatial resolution hyperspectral data collected with an unmanned aerial system mounted sensor (272 spectral bands from 0.4-1 µm at 0.063 m GSD) to estimate silage yield. We subjected our imagery to three band selection algorithms to quantitatively assess spectral reflectance features applicability to yield estimation. We then derived 11 spectral configurations, which were spatially resampled to multiple GSDs, and applied to a support vector regression (SVR) yield estimation model. Results indicate that accuracy degrades above 4 m GSD across all configurations, and a seven-band multispectral sensor which samples the red edge and multiple near-infrared bands resulted in higher accuracy in 90% of regression trials. These results bode well for our quest toward a definitive sensor definition for global corn yield modeling, with only temporal dependencies requiring additional investigation.

Tracking diurnal variation in photosynthetic down-regulation using low cost spectroscopic instrumentation.

Photosynthetic light-use efficiency (LUE) has gained wide interest as an input to modeling forest gross primary productivity (GPP). The photochemical reflectance index (PRI) has been identified as a principle means to inform LUE-based models, using airborne and satellite-based observations of canopy reflectance. More recently, low-cost electronics have become available with the potential to provide for dense in situ time-series measurements of PRI. A recent design makes use of interference filters to record light transmission within narrow wavebands. Uncertainty remains as to the dynamic range of these sensors and performance under low light conditions, the placement of the reference band, and methodology for reflectance calibration. This paper presents a low-cost sensor design and is tested in a laboratory set-up, as well in the field. The results demonstrate an excellent performance against a calibration standard (R2 = 0.9999) and at low light conditions. Radiance measurements over vegetation demonstrate a reversible reduction in green reflectance that was, however, seen in both the reference and signal wavebands. Time-series field measurements of PRI in a Douglas-fir canopy showed a weak correlation with eddy-covariance-derived LUE and a significant decline in PRI over the season. Effects of light quality, bidirectional scattering effects, and possible sensor artifacts on PRI are discussed.

A Compact Laboratory Spectro-Goniometer (CLabSpeG) to Assess the BRDF of Materials. Presentation, Calibration and Implementation on Fagus sylvatica L. Leaves.

The design and calibration of a new hyperspectral Compact Laboratory Spectro-Goniometer (CLabSpeG) is presented. CLabSpeG effectively measures the bidirectionalreflectance Factor (BRF) of a sample, using a halogen light source and an AnalyticalSpectral Devices (ASD) spectroradiometer. The apparatus collects 4356 reflectance datareadings covering the spectrum from 350 nm to 2500 nm by independent positioning of thesensor, sample holder, and light source. It has an azimuth and zenith resolution of 30 and15 degrees, respectively. CLabSpeG is used to collect BRF data and extract BidirectionalReflectance Distribution Function (BRDF) data of non-isotropic vegetation elements suchas bark, soil, and leaves. Accurate calibration has ensured robust geometric accuracy of theapparatus, correction for the conicality of the light source, while sufficient radiometricstability and repeatability between measurements are obtained. The bidirectionalreflectance data collection is automated and remotely controlled and takes approximatelytwo and half hours for a BRF measurement cycle over a full hemisphere with 125 cmradius and 2.4 minutes for a single BRF acquisition. A specific protocol for vegetative leafcollection and measurement was established in order to investigate the possibility to extractBRDF values from Fagus sylvatica L. leaves under laboratory conditions. Drying leafeffects induce a reflectance change during the BRF measurements due to the laboratorySensors 2007, 7 1847 illumination source. Therefore, the full hemisphere could not be covered with one leaf. Instead 12 BRF measurements per leaf were acquired covering all azimuth positions for a single light source zenith position. Data are collected in radiance format and reflectance is calculated by dividing the leaf cycle measurement with a radiance cycle of a Spectralon reference panel, multiplied by a Spectralon reflectance correction factor and a factor to correct for the conical effect of the light source. BRF results of measured leaves are presented.