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Quantifying the variability of surface reflectance and estimating canopy chlorophyll content and green leaf biomass using hyperspectral close-range data and airborne imagery
Advances in agricultural studies have benefited from the use of remote sensing in generating and analyzing datasets, efficiently. Remotely sensed images facilitate a diverse array of non-intrusive agricultural investigations including new approaches such as high-throughput phenotyping. This research examines the variability of surface reflectance and estimates two biophysical parameters associated with crops. The first goal of the project was to provide an estimation of reflectance variability within low-resolution satellite imagery. The quantified variability of intra-pixel spectral reflectance can then be used to determine the level of uncertainty in estimating biophysical characteristics of plants. The study revealed how the variability in a composite spectral signal emanating from a large pixel was influenced by crop type, phenological stage, and irrigation method. A second goal of this study was to examine algorithms developed using multi-temporal airborne hyperspectal imagery for estimation and mapping of canopy Chl content in irrigated and rainfed maize and soybean fields. The optimal spectral range for two conceptual models, Chlorophyll Index and Normalized Difference, were determined and calibrated for the spectral bands of AISA, Sentinel-2 MSI and Sentinel-3 OLCI sensors. The results showed that CI red edge model derived solely from airborne imagery was capable of accurately estimating canopy Chl in fields with different crop management practices, field history and climatic conditions. The spatial and temporal dynamics of canopy Chl content were elucidated for maize and soybean fields at different phenological stages and rainfall regimes. The final goal of this study was to evaluate the performance of several vegetation indices for estimating green leaf biomass (GLB) in maize and soybean fields using canopy reflectance collected at close-range and airborne imagery. It was determined that models containing red edge and near-infrared bands were capable of accurately predicting GLB in maize and soybean where estimations could be done across a wide range of GLB including moderate-to-high biomass. The red edge WDRVI 0.2, at Sentinel-2 MSI band 6, appeared to be non-species-specific and the most accurate index for estimation of GLB in both crops. The findings pertaining to the estimation of crop biophysical properties can be used to support the advancement of plant phenotyping.
Plant sciences|Agricultural engineering|Remote sensing
Razzaghi, Tarlan, "Quantifying the variability of surface reflectance and estimating canopy chlorophyll content and green leaf biomass using hyperspectral close-range data and airborne imagery" (2016). ETD collection for University of Nebraska - Lincoln. AAI10246529.