Even as more and better technologies emerge to help simplify water management, knowing when, where and how much to irrigate vineyards is a complex question. Commonly, farmers rely on point measures like leaf water potential to help guide management decisions. But it’s not that simple. These measures are destructive and time- and labor-intensive. Plus, spatial variation commonly found in vineyards can be missed with single point measurements. With support from my colleagues in the McElrone Lab at UC Davis, I intend to help find a better way.

Under water stress, the leaves and canopies of grapevines exhibit biological and physical changes that aren’t visible to the naked eye. Whether done via towers, drones, planes, or satellites, remote sensing can elucidate these changes. Remote sensing-aided technologies for irrigation management currently use light reflectance and energy balance modeling to estimate water use and stress. However, these approaches still require work to better understand how detected changes relate to meaningful physiological events.

Taking a biophysical perspective on this problem, I’m working from the leaf level first to understand physical and biological changes under drought stress before applying this knowledge to the large scale. I’m tracking physiological responses to drought stress and mapping how these relate to changes seen using ground-based and remote sensing systems. By answering the question of “how well do stress measures via remote sensing match actual physiology?” I hope to improve irrigation management and water use efficiency in vineyards.

To better understand leaf-level changes under water stress, I’ll be performing two sets of experiments. The first will be a series of short-term dry downs to evaluate spectral and structural effects of quick water loss, and the second will be longer term drought experiments to evaluate the biochemical component of water loss and its effect on spectral data. Thus far, in the rapid dry down studies, by making pressure-volume curves to identify turgor loss points (an indicator of stomatal closure and general drought tolerance of the tissue), we’ve found that the turgor loss point becomes more negative over the course of a season. This finding suggests that grapevines become more drought-resistant over time and confirms that methods for monitoring water stress need to be calibrated with the time of the season, leaf age and by integrating the canopy as a whole. During these pressure-volume curve experiments, we were also collecting spectral data, which we hope to correlate with canopy-level spectral measurements.

How will this help? Spectral data is a measure of how light interacts with a surface. As sunlight hits leaves, some of it is reflected back, some is absorbed and some is transmitted. What’s measured with spectrometers and hyperspectral cameras is the reflected values. The reason spectral data works as an indicator of water stress is because, when the structure and chemical content of leaves change, it affects the amount of reflected light the sensor receives. Certain wavelengths are more sensitive to water content changes than others. More than 100 spectral indicators for water stress exist today, in the form of ratios or the normalized difference of two wavelengths. My goal is to find the best indicators of tissue dehydration by using current indices and looking for new ones, as well.

My next steps will be to compare the pressure-volume curve data to a long-term drought study to see how my water stress indicators perform, since the biochemical interactions occurring under longer drought conditions may affect spectral readings. One way of learning what kind of changes happen may include looking at chlorophyll fluorescence in these drought-stressed leaves to see how photosynthesis is affected by long-term water stress.

By building a full picture of what is happening physiologically, we can better inform models used for satellite and other remote sensing imagery. To test this theory, I will use the large-scale, long-term GRAPEX project field sites and existing datasets to perform experiments under field conditions that could cause similar rapid changes in water content, as in the short-term dry downs. The GRAPEX project will also help to integrate broad-scale measurements, such as drone, plane and satellite data.

While my project is mostly focused on water stress, remote sensing has other useful applications like disease management and monitoring for nutrient deficiencies. The goal of applying remote sensing to vineyard management is to ultimately make the output of any one plot as uniform as possible, and to maximize total yield without sacrificing quality. I hope my project will lead to improvements to make this tool more accessible and accurate for growers.

Abby Hammermeister is the inaugural NGRA Fellow. She is a Ph.D. student at UC Davis, working in the McElrone Lab. Her research leverages the USDA-ARS GRAPEX project, in which Abby’s academic advisor, Dr. Andrew McElrone, is a primary investigator. Additional authors on her project are Nico Bambach, Mimar Alsina, Bill Kustas, Nick Daman, Robin Nguyen, Ian Wright, Peter Tolentino, Troy Williams, Mina Momayyezi, Morgan Furze and Sebastian Castro Bustamante and Dr. McElrone.