Effect of environmental conditions on the relationship between solar induced fluorescence and gross primary productivity at an OzFlux grassland site

Recent studies have utilized coarse spatial and temporal resolution remotely sensed solar induced fluorescence (SIF) for modeling terrestrial gross primary productivity (GPP) at regional scales. Although these studies have demonstrated the potential of SIF, there have been concerns about the ecophysiological basis of the relationship between SIF and GPP in different environmental conditions. Launched in 2014, the Orbiting Carbon Observatory-2 (OCO-2) has enabled fine scale (1.3-by-2.5 km) retrievals of SIF that are comparable with measurements recorded at eddy covariance towers. In this study, we examine the effect of environmental conditions on the relationship of OCO-2 SIF with tower GPP over the course of a growing season at a well-characterized natural grassland site. Combining OCO-2 SIF and eddy covariance tower data with a canopy radiative transfer and an ecosystem model, we also assess the potential of OCO-2 SIF to constrain the estimates of V_(cmax), one of the most important parameters in ecosystem models. Based on the results, we suggest that although environmental conditions play a role in determining the nature of relationship between SIF and GPP, overall the linear relationship is more robust at ecosystem scale than the theory based on leaf-level processes might suggest. Our study also shows that the ability of SIF to constrain V_(cmax) is weak at the selected site

From Top to Bottom - the Multiwavelength Campaign of V824 Ara (HD 155555)

A great deal of progress has been made in recent years in decomposing the 2-D structure in the atmospheres of late-type stars. Doppler images of many photospheres single stars, T Tauri stars, Algols, RS CV(sub n) binaries to name a few - are regularly published (Strassmeier 1996; Richards and Albright 1996; Rice and Strassmeier 1996; Kuerster et al. 1994). Ultraviolet spectral images of chromospheres appear in the literature (e.g., Walter et al. 1987; Neff et al. 1989) but are less common owing to the difficult nature of obtaining complete phase coverage. Zeeman doppler images of magnetic fields are now feasible (e.g., Donati et al. 1992). Performing Doppler imaging of the same targets over many seasons has also been accomplished (e.g, Vogt et al. 1997). Even when a true image reconstruction is not possible due to poor spectral resolution, we can still infer a great deal about spatial structure if enough phases are observed. However, it is increasingly apparent that to make sense of recent results, many different spectral features spanning a range of formation temperature and density must be observed simultaneously for a coherent picture to emerge. Here we report on one such campaign. In 1996, we observed the southern hemisphere RS CV(sub n) binary V824 Ara (P=1(sup d).68, G5IV+K0V-IV-IV) over one complete stellar rotation with the Hubble Space Telescope and EUVE. In conjunction, radio and optical photometry and spectroscopy were obtained from the ground. Unique to this campaign is the complete phase coverage of a number of activity proxy indicators that cover source temperatures ranging from the photosphere to the corona

Assimilated Remote and Proximal Sensing Improved Crop Model Wheat Yield Prediction Accuracy

This project tests if assimilating remote and proximal sensing observations into the APSIM crop model can improve wheat yield prediction accuracy without calibrating against observed yield. This is achieved by using a sensitivity analysis to map the weighting from the errors between crop condition simulations and observations with the impact of individual model parameters on outputs and taking the mean of an ensemble of predictions. This thesis consists of chapters on: (1) a review on relevant literature, (2) remote sensing of field (wheat) experiments over the 2019 and 2020 seasons, (3) sensitivity analysis of the APSIM crop model; and, (4) Data Assimilation (DA) framework and performance. Results indicate that remote sensing has strong potential for sampling crop status with high spatial, spectral and temporal resolution, as seen with accurate protein concentration estimation (r2 of 0.88 in 2020) and strong yield prediction (r2 of 0.64 in 2019). There was limited consistency between seasons, however. ‘Sobol’ main effect indices were calculated for 23 parameters in APSIM and averaged across the season and then used in the crop modelling DA framework. In the DA, ensemble members covering 4 key areas of uncertainty were run over 15 iterations of the 2019 and 2020 seasons, with their parameters modified between iterations using the relative errors in their predictions scaled by the sensitivity indices. Error weighting was derived from comparing predicted height growth, water extraction and yield against height growth from RGB photogrammetry, water extraction from soil moisture probes and potential yield from a statistical model. The mean of each ensemble was tested against observed yield for the year as well as APSIM performance without DA. Results indicate there is strong potential for this approach, with average accuracy improvements between 7-12% of observed yield in all tests

Machine Learning Optimised Hyperspectral Remote Sensing Retrieves Cotton Nitrogen Status

Hyperspectral imaging spectrometers mounted on unmanned aerial vehicle (UAV) can capture high spatial and spectral resolution to provide cotton crop nitrogen status for precision agriculture. The aim of this research was to explore machine learning use with hyperspectral datacubes over agricultural fields. Hyperspectral imagery was collected over a mature cotton crop, which had high spatial (~5.2 cm) and spectral (5 nm) resolution over the spectral range 475–925 nm that allowed discrimination of individual crop rows and field features as well as a continuous spectral range for calculating derivative spectra. The nominal reflectance and its derivatives clearly highlighted the different treatment blocks and were strongly related to N concentration in leaf and petiole samples, both in traditional vegetation indices (e.g., Vogelman 1, R2 = 0.8) and novel combinations of spectra (R2 = 0.85). The key hyperspectral bands identified were at the red-edge inflection point (695–715 nm). Satellite multispectral was compared against the UAV hyperspectral remote sensing’s performance by testing the ability of Sentinel MSI to predict N concentration using the bands in VIS-NIR spectral region. The Sentinel 2A Green band (B3; mid-point 559.8 nm) explained the same amount of variation in N as the hyperspectral data and more than the Sentinel Red Edge Point 1 (B5; mid-point 704.9 nm) with the lower 10 m resolution Green band reporting an R2 = 0.85, compared with the R2 = 0.78 of downscaled Sentinel Red Edge Point 1 at 5 m. The remaining Sentinel bands explained much lower variation (maximum was NIR at R2 = 0.48). Investigation of the red edge peak region in the first derivative showed strong promise with RIDAmid (R2 = 0.81) being the best index. The machine learning approach narrowed the range of bands required to investigate plant condition over this trial site, greatly improved processing time and reduced processing complexity. While Sentinel performed well in this comparison and would be useful in a broadacre crop production context, the impact of pixel boundaries relative to a region of interest and coarse spatial and temporal resolution impacts its utility in a research capacity

Sex-Specific Effects of Stress on Oxytocin Neurons Correspond With Responses to Intranasal Oxytocin

Background Oxytocin (OT) is considered to be a stress-buffering hormone, dampening the physiologic effects of stress. However, OT can also be anxiogenic. We examined acute and long-lasting effects of social defeat on OT neurons in male and female California mice. Methods We used immunohistochemistry for OT and c-fos cells to examine OT neuron activity immediately after defeat (n = 6–9) and 2 weeks (n = 6–9) and 10 weeks (n = 4–5) later. We quantified Oxt messenger RNA with quantitative polymerase chain reaction (n = 5–9). Intranasal OT was administered to naïve and stressed mice tested in social interaction and resident-intruder tests (n = 8–14). Results Acute exposure to a third episode of defeat increased OT/c-fos colocalizations in the paraventricular nucleus of both sexes. In the medioventral bed nucleus of the stria terminalis, defeat increased Oxt messenger RNA, total OT neurons, and OT/c-fos colocalizations in female mice but not male mice. Intranasal OT failed to reverse stress-induced social withdrawal in female mice and reduced social interaction behavior in female mice naïve to defeat. In contrast, intranasal OT increased social interaction in stressed male mice and reduced freezing in the resident-intruder test. Conclusions Social defeat induces long-lasting increases in OT production and OT/c-fos cells in the medioventral bed nucleus of the stria terminalis of female mice but not male mice. Intranasal OT largely reversed the effects of stress on behavior in male mice, but effects were mixed in female mice. These results suggest that changes in OT-sensitive networks contribute to sex differences in behavioral responses to stress