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ARS Home » Plains Area » El Reno, Oklahoma » Oklahoma and Central Plains Agricultural Research Center » Livestock, Forage and Pasture Management Research Unit » Research » Publications at this Location » Publication #405400

Research Project: Integrated Agroecosystem Research to Enhance Forage and Food Production in the Southern Great Plains

Location: Livestock, Forage and Pasture Management Research Unit

Title: A comparison of moderate and high spatial resolution satellite data for modeling gross primary production and transpiration of native prairie, alfalfa, and winter wheat

Author
item CELIS, JORGE - University Of Oklahoma
item XIAO, XIANGMING - University Of Oklahoma
item Wagle, Pradeep
item BASARA, JEFFREY - University Of Oklahoma
item MCCARTHY, HEATHER - University Of Oklahoma
item SOUZA, LARA - University Of Oklahoma

Submitted to: Agricultural and Forest Meteorology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/2/2023
Publication Date: 11/9/2023
Citation: Celis, J., Xiao, X., Wagle, P., Basara, J., Mccarthy, H., Souza, L. 2023. A comparison of moderate and high spatial resolution satellite data for modeling gross primary production and transpiration of native prairie, alfalfa, and winter wheat. Agricultural and Forest Meteorology. 344:Article 109797. https://doi.org/10.1016/j.agrformet.2023.109797.
DOI: https://doi.org/10.1016/j.agrformet.2023.109797

Interpretive Summary: This study used the vegetation photosynthesis model (VPM) and vegetation transpiration model (VTM) to estimate field-level daily gross primary production (GPP) and transpiration (T), respectively, in native prairie, alfalfa, and winter wheat in central Oklahoma, USA. We also evaluated the reliability and advantages of vegetation indices (enhanced vegetation index, EVI and land surface water index, LSWI) in monitoring the land surface phenology and GPP using moderate spatial resolution (MSR) data from Moderate Resolution Imaging Spectroradiometer (MODIS) and high spatial resolution (HSR) data from Landsat and Sentinel-2 at these study sites. The comparison of eddy covariance-derived GPP (GPPEC) and modeled GPP using VPM (GPPVPM) with in-situ climate data and remote sensing data (HSR and MSR) show the capacity of the VPM to capture the patterns of GPPEC. The GPPVPM-LS2 (VPM using Landsat and Sentinel-2 data) agreed more strongly with GPPEC than did GPPVPM-MOD (VPM using MODIS data), mostly due to the coarse spatial resolution (~500 m) of the MODIS pixels. The results show that the T estimates from VTM also followed the seasonal dynamics of ET in native prairie, alfalfa, and winter wheat. The results demonstrate the necessity and potential of Landsat and Sentinel-2 images for the study of phenology, GPP, and T of tallgrass prairie, alfalfa pasture, and winter wheat, particularly in capturing the crop phenology in smaller areas with conservation measures or disturbances. Further assessment of VPM and VTM at other C3 agroecosystems and C4-dominated native prairie fields with EC flux tower sites is needed.

Technical Abstract: Despite having a significant capacity for offsetting carbon dioxide (CO2), agroecosystems exhibit considerable uncertainty in CO2 capture under different management practices. Accurate estimates of gross primary production (GPP) and transpiration (T) of agroecosystems at field scale are essential to improve water use efficiency (WUE) and reduce production costs. The precision of determining carbon and water fluxes in commercial agroecosystems at the field scale is restricted by the spatial and temporal resolution of GPP and T data products. This study used the vegetation photosynthesis model (VPM) and vegetation transpiration model (VTM) to estimate field-level daily GPP and T, respectively, in native prairie, alfalfa, and winter wheat in central Oklahoma, USA. We evaluated the reliability and advantages of vegetation indices (enhanced vegetation index, EVI and land surface water index, LSWI) in monitoring the land surface phenology using moderate spatial resolution (MSR) data from Moderate Resolution Imaging Spectroradiometer (MODIS) and high spatial resolution (HSR) data from Landsat and Sentinel-2. The accuracy of GPPVPM and TVTM estimates at different spatial scales was evaluated using eddy covariance tower data. Results demonstrate the capacity of VPM and VTM models to estimate the field-level carbon and water flux dynamics and their responses to weather conditions. The utilization of HSR vegetation indices helped address certain issues encountered with MSR indices, particularly in capturing the crop phenology in smaller areas with conservation measures. The findings highlight the significance of employing HSR GPP estimates in minimizing uncertainties in quantifying CO2 fluxes in crops, as well as the models' ability to estimate the field-level vegetation phenology, carbon uptake, and water use in agroecosystems under different management practices.