|RAJAN, NITHYA - Texas A&M University|
|SHAFIAN, SANAZ - Virginia Tech|
|CUI, SONG - Middle Tennessee State University|
Submitted to: Frontiers in Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/14/2022
Publication Date: 3/14/2022
Citation: Menefee, D.S., Rajan, N., Shafian, S., Cui, S. 2022. Modeling carbon uptake of dryland maize using high resolution satellite imagery. Frontiers in Remote Sensing. 3. Article 810030. https://doi.org/10.3389/frsen.2022.810030.
Interpretive Summary: Gross primary productivity was modeled using high resolution satellite images. Modeled GPP was compared to GPP measured using an eddy covariance system. A comparison of models found that the models using the Soil Adjusted Vegetation Index was most effective at simulating GPP.
Technical Abstract: Quantifying gross primary production (GPP) from agroecosystems is important for understanding the spatial and temporal dynamics of carbon fixation by crop production. The availability of high-resolution remote sensing data can significantly improve GPP estimation of small-scale agricultural fields. Multispectral satellite data with 3-m spatial resolution and frequent global coverage are available from the PlanetScope network of satellites. However, this data remains largely unexplored for studying carbon dynamics of agroecosystems. The overarching goal of this study was to develop a simple empirical method for quantifying the GPP of dryland maize (Zea mays L.) using remotely sensed vegetation indices along with in situ measurements of photosynthetically active radiation (PAR) and leaf area index (LAI) by linking it with carbon uptake data from an eddy covariance flux tower. Four vegetation indices that were investigated: the normalized difference vegetation index (NDVI), the soil adjusted vegetation index (SAVI), the weighted difference vegetation index (WDVI), and the enhanced vegetation index 2 (EVI2). The study was conducted over a three-year period from 2017 to 2019 in East Central Texas. The models using SAVI performed especially well with a standard error ranging from 0.05 to 0.94 gC m-2. The slope of the regression between SAVI-based estimated GPP and measured GPP was not different from 1.0 in all combinations of years. The models using EVI and WDVI were successful for two of six model/validation combinations, the 2019 validation of the 2018 model, and the 2018 validation of the 2019 model, however they had higher RMSE than the SAVI alternative. The NDVI models was the least successful with only one successful validation and the highest RMSE at 26.3 gC m-2. The variable success of different indices indicates the need to carefully select model inputs.