Modeling Carbon Uptake of Dryland Maize Using High Resolution Satellite Imagery

Dorothy Menefee,Song Cui,Sanaz Shafian,Nithya Rajan

doi:10.3389/frsen.2022.810030

Abstract

Quantifying carbon uptake or gross primary production (GPP) from agroecosystems is important for understanding the spatial and temporal dynamics of carbon fixation by crops. 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 the 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 and leaf area index by linking it with carbon uptake data from an eddy covariance flux tower. Four vegetation indices were investigated: the normalized difference vegetation index (NDVI), the soil adjusted vegetation index (SAVI), the weighted difference vegetation index (WDVI), and the two-band enhanced vegetation index (EVI2). This study was conducted over a three-year period from 2017 to 2019 in East-Central Texas. A total of 12 GPP prediction models were developed using individual yearly data and were used for predicting GPP of the other 2 years. Predicted maize GPP values were then compared against tower-based GPP. The NDVI models were the least successful in predicting GPP and had the highest root mean square error (average: 10.1 3 gC m−2; maximum: 26.3 gC m−2). Models based on SAVI performed especially well with 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 success of the SAVI-based GPP models for predicting dryland maize carbon uptake indicates that it was the least affected vegetation index by changing soil background condition in this row cropping system.

Highlights

Photosynthetic carbon dioxide (CO2) uptake and respiration are important mechanisms that determine the carbon source or sink status of agroecosystems (Mowrer et al, 2020)
We developed relatively simple regression-based models of Gross primary production (GPP) using vegetation index (VI) estimated from PlanetScope satellite data and compared it with eddy covariance-based GPP estimates from a conventional dryland maize (Zea mays L.) field in East-Central Texas
This late-season GPP and visible plant growth was largely influenced by the growth of warm-season weeds, Bermudagrass (Cynodon dactylon) and Palmer amaranth (Amaranthus palmeri) during the latter portion of the growing season, driven by high precipitation received at the site during the post-maturity period of maize

Summary

Introduction

Photosynthetic carbon dioxide (CO2) uptake and respiration are important mechanisms that determine the carbon source or sink status of agroecosystems (Mowrer et al, 2020). A portion of GPP is consumed during plant respiration and the remainder is stored as net primary production (NPP) or biomass (Suyker et al, 2004). Respiratory loses of carbon coupled with the removal of harvestable biomass can lead to steady decline in soil carbon stocks over time in agricultural lands. Because of the contributory effects of both environmental factors and management practices on carbon dynamics, accurately estimating GPP of agricultural systems is essential for understanding how cropping systems interact with atmospheric carbon pools, both as sink and source of CO2 emissions

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