Recipient Organization
UNIVERSITY OF CALIFORNIA, BERKELEY
(N/A)
BERKELEY,CA 94720
Performing Department
Ecosystem Sciences
Non Technical Summary
Improving crop yields is essential for improving food security and meeting the demands for food and supplies from a growing population. One potential approach to improving crop yield that is sustainable and cost-efficient is to improve primary productivity by optimizing growing conditions around the seasonality of photosynthetic activity. The timing and intensity of photosynthetic activity can vary in response to variation in temperature, precipitation, solar radiation, and topography, meaning that plant productivity can show fine-scale variation between nearby parcels of land. Hence, new tools that evaluate the factors and conditions contributing to spatial variation in photosynthetic activity and increases in plant biomass have the potential to guide strategies for improving agricultural outputs.This project will use statellite-derived data on sun-induced chlorophyll fluorescence, temperature, precipitation, solar radiation, and topography to identify the factors contributing to fine-scale variation in photosynthesis for natural- and agro-ecosystems. The outcomes will include a map of the seasonality and peak timing of plant photosynthetic activityacross the state of California and an improved understanding of the factors contributing to plant growth and biomass. These products will inform and improve agricultural strategies statewide for a wide variety of natural and agricultural systems.
Animal Health Component
60%
Research Effort Categories
Basic
10%
Applied
60%
Developmental
30%
Goals / Objectives
This research will be conducted with two major goals: (1) to evaluate the peak timing of photosynthetic activity, using SIF data, at fine-spatial scales (<100m) and to statistically model its relationship with topoclimate variation, and (2) toquantify asynchrony in photosynthetic activity and primary productivity across the state of California at multiple spatial scales.Under these goals, we have a set of six major objectives:1. Assemble a dataset including data layers for temperature, precipitation, solar radiation and topography, and remote-sensing measurements of chlorophyll fluorescence.2. Calculate the peak timing of photosynthetic activity from the chlorophyll fluorescence data.3. Evaluate the relationships between photosynthetic activity and temperature, precipitation, solar radiation, and topography.4. Construct a digital map (raster layer) of peak photosynthetic activity across the state of California.5. Calculate and construct a digital map of asynchrony in photosynthetic activity across the state of California.6. Communicate results to target audiences.
Project Methods
We will download recently available, high-resolution SIF data from Orbiting Carbon Observatory-2 (OCO-2) NASA satellite mission (Sun et al. 2017). We will then subset these data to the 3-year time series for North America and perform topographic correction. We will then validate these fine-scale data against coarser-scale, longer-term SIF data from the Global Ozone Monitoring Experiment-2 (GOME-2; Joiner et al. 2011). These are important initial steps in filtering and validating the data we will use in all of our analyses.Next, we will normalize each pixel's SIF values to that pixel's observed range to enable inter-ecosystem comparison of phenological signal. To make these data compatible with other earth systems data, we will average all SIF layers to the monthly temporal resolution of the WorldClim and EarthEnv Cloud Cover climate data that we will use in downstream analyses (Hijmans et al. 2005; Wilson and Jetz 2016). We will also download high resolution digital elevation model (DEM) data layers and calculate an index of topographic ruggedness from these elevation layers.To calculate the peak of photosynthetic activity, we will use fast Fourier transforms (FFT), an algorithm that computes the discrete Fourier transform (DFT) of a time series. These will be applied to each grid cell in our study area and will detect the cyclical timing of variation in photosynthetic activity from the three-year time series of SIF data. These analyses will be performed using the Google Earth Engine platform.Following Martin et al. (2009), we will generate maps of asynchrony in photosynthetic activity from the SIF data, mean monthly precipitation, mean monthly temperature, and mean monthly cloud-cover for our study region (the state of California and adjacent areas in Oregon, Nevada, and Arizona). We will then perform a spatial regression of the grid cells of the SIF asynchrony map against the climate asynchrony maps, latitude, the interaction of latitude and the climate asynchrony variables, and DEM-derived elevation and topographic ruggedness. This will allow us to statistically model the relationships between these key variables, quantifying their contributions to primary productivity and the peak timing of photosynthetic activity.