Satellite-based modeling of gross primary production of C3 fields with time series of Landsat and Sentinel-2 images

Authors: CELIS, JORGE - University Of Oklahoma; XIAO, XIANGMING - University Of Oklahoma; Wagle, Pradeep; BASARA, JEFFREY - University Of Oklahoma; SOUZA, LARA - University Of Oklahoma

Submitted to: American Geophysical Union Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 10/4/2022
Publication Date: N/A
Citation: N/A

Interpretive Summary: Abstract only.

Technical Abstract: Croplands have large CO2 offset capacity and yet there is high uncertainty in the CO2 stabilization under different management practices. Gross primary production (GPP), the largest carbon flux in terrestrial ecosystems, represents the vegetation carbon uptake used to build biomass and support plant processes. GPP is used as an indicator of crop production making it important for agricultural resiliency and food security. However, most data are only available at moderate spatial resolution (MSR), limiting the accurate representation of the carbon changes at the field level. Furthermore, severe weather events pose a threat to crop productivity, food security, and the offset of carbon emissions. For these reasons, it is important to reduce the uncertainty on the CO2 sink capacity of crops under different management and their response to changes in environmental conditions. This study focused on winter wheat, alfalfa, and tallgrass prairie in central Oklahoma in a research facility at El Reno. These crops have an important role in food security and carbon sequestration. Wheat is one of the most widely grown grain crops in the world, alfalfa is a high-quality perennial legume used worldwide as a forage crop, and prairie is used for livestock. The vegetation photosynthesis model (VPM) was used to produce daily GPP field-level estimates for the three sites. We evaluated the consistency of vegetation indices (EVI and LSWI) from MSR (MODIS) and HSR (Landsat and Sentinel-2) in tracking the land surface phenology in alfalfa, winter-what, and prairie. In addition, we used the GPP estimates from one eddy covariance tower at each crop type to evaluate the accuracy of the MSR GPPVPM and HSR GPPVPM . HSR vegetation indices contributed to overcoming some of the challenges of MSR indices, representing the crop phenology in sites with conservation practices (tillage) that allows soil cover of ~50% that can be used for forage. Furthermore, the results of the study support the potential and capacity of the VPM model to represent the field-level carbon dynamics and its responses to weather conditions. The study highlights the importance of HSR GPP estimates to reduce the uncertainty in quantifying the CO2 flux in crops and the capabilities of the VPM model to represent the field-level vegetation carbon uptake in agroecosystems under different management practices.