The utilization of remotely sensed observations for light use efficiency (LUE) and tower-based gross primary production (GPP) estimates was studied in a USDA cornfield. Nadir hyperspectral reflectance measurements were acquired at canopy level during a collaborative field campaign conducted in four growing seasons. The Photochemical Reflectance Index (PRI) and solar induced chlorophyll fluorescence (SIF), were derived. SIF retrievals were accomplished in the two telluric atmospheric oxygen absorption features centered at 688 nm (O₂-B) and 760 nm (O₂-A). The PRI and SIF were examined in conjunction with GPP and LUE determined by flux tower-based measurements. All of these fluxes, environmental variables, and the PRI and SIF exhibited diurnal as well as day-to-day dynamics across the four growing seasons. Consistent with previous studies, the PRI was shown to be related to LUE (r² = 0.54 with a logarithm fit), but the relationship varied each year. By combining the PRI and SIF in a linear regression model, stronger performances for GPP estimation were obtained. The strongest relationship (r² = 0.80, RMSE = 0.186 mg CO₂/m²/s) was achieved when using the PRI and SIF retrievals at 688 nm. Cross-validation approaches were utilized to demonstrate the robustness and consistency of the performance. This study highlights a GPP retrieval method based entirely on hyperspectral remote sensing observations.

Remote sensing technologies provide a unique opportunity to identify ground surfaces that are more susceptible to dust emissions at a large scale. As part of the Salton Sea Air Quality Mitigation Program (SSAQMP) of the Imperial Irrigation District (IID), efforts have been made to improve our understanding of fugitive, wind-blown dust emissions around the Salton Sea region in Southern California, United States. Field campaigns were conducted for multiple years to evaluate surface conditions and measure the dust emissions potential in the area. Data collected during the field work were coupled with remote sensing imagery and data mining techniques to map surface characteristics that are important in identifying dust emissions potential. Around the playa domain, surface crust type, sand presence, and soil moisture were estimated. Geomorphic surface types were mapped in the desert domain. Overall accuracy ranged from 91.7% to 99.4% for the crust type mapping. Sand presence mapping showed consistent and slightly better accuracy, ranging from 96.2% to 99.7%. Soil moisture assessment agreed with precipitation records. Geomorphic mapping in the desert domain achieved accuracy above 93.5%, and the spatial pattern was consistent with previous studies. These land surface condition assessments provide important information to support dust emissions estimates in the region.

In situ directional reflectance observations were taken on trees of three species and were used to validate the Four-Scale Linear Model for AnIsotropic Reflectance (FLAIR). FLAIR-simulated spectra were satisfactory in general, especially for the shaded and shaded/sunlit mixed portions of the canopy but were species dependent. Good agreements with field observations were found when utilizing these simulations to derive band ratio indexes, Normalized Difference Vegetation Index (NDVI) and Photochemical Reflectance Index (PRI). Sensitivity analysis were performed to investigate the importance of three canopy biophysical parameters, and the leaf to shoot ratio showed the most impact on simulation results. The results in this study showed the potential to simulate directional reflectances with FLAIR on canopies with complicated structure.