Parameters of a GPP Capacity Estimation Algorithm in Maize and Soybean Fields Using Chlorophyll Indices: Application to Sentinel-2/MSI Data

Abstract

An algorithm for estimating gross primary productivity (GPP) under low stress (GPP_cap) based on the green chlorophyll index (CI_G) was developed by Thanyapraneedkul et al. in 2012, and its parameters have been determined for several vegetation types, including paddy fields. Although it is not yet possible to correctly classify precise crop types globally by using satellite data, this algorithm can be easily applied for various types of crops when the same parameters are used. The Sentinel-2/MSI (MultiSpectral Instrument) has red-edge bands, and red-edge bands were reported to be more sensitive than a green band for determining amounts of chlorophyll. In this study, we sought to identify the optimal band of the chlorophyll index (CI) among green and red-edge bands at 705 nm and 740 nm for estimating the GPP_cap at 2000 μmolm⁻² s⁻¹, i.e., GPP_cap (2000) in maize and soybean for irrigation and no-irrigation conditions, by using flux data from the US-Ne1, US-Ne2, and US-Ne3 sites. We observed a higher correlation between the CI_RE at 705 nm and GPP_cap (2000) than the other pairs of parameters, and the differences of the parameters for the GPP_cap (2000) estimation were smallest for the CI_RE at 705 nm for maize and soybean. We also converted a prior study’s rice paddy CI_G result to that with a CI_RE at 705 nm. This result implies that the same parameters can be used among maize, soybean, and rice paddies in GPP_cap (2000) estimations.